A review of space robotics technologies for on-orbit servicing Angel Flores-Abad ,

A review of space robotics technologies for on-orbit servicing Angel Flores-Abad ,
Progress in Aerospace Sciences 68 (2014) 1–26
Contents lists available at ScienceDirect
Progress in Aerospace Sciences
journal homepage: www.elsevier.com/locate/paerosci
A review of space robotics technologies for on-orbit servicing
Angel Flores-Abad a,n, Ou Ma a, Khanh Pham b, Steve Ulrich c
a
b
c
New Mexico State University, Mechanical and Aerospace Department, Las Cruces, NM 88003, USA
U.S. Air Force Research Laboratory, Kirtland Air Force Base, NM 87117-5776, USA
Carleton University, Spacecraft Robotics and Control Laboratory, Ottawa, Canada K1S 5B6
art ic l e i nf o
a b s t r a c t
Article history:
Received 10 September 2013
Received in revised form
7 March 2014
Accepted 8 March 2014
Available online 29 March 2014
Space robotics is considered one of the most promising approaches for on-orbit servicing (OOS) missions
such as docking, berthing, refueling, repairing, upgrading, transporting, rescuing, and orbital debris
removal. Many enabling techniques have been developed in the past two decades and several
technology demonstration missions have been completed. A number of manned on-orbit servicing
missions were successfully accomplished but unmanned, fully autonomous, servicing missions have not
been done yet. Furthermore, all previous unmanned technology demonstration missions were designed
to service cooperative targets only. Robotic servicing of a non-cooperative satellite is still an open
research area facing many technical challenges. One of the greatest challenges is to ensure the servicing
spacecraft safely and reliably docks with the target spacecraft or capture the target to stabilize it for
subsequent servicing. This is especially important if the target has an unknown motion and kinematics/
dynamics properties. Obviously, further research and development of the enabling technologies are
needed. To motivate and facilitate such research and development, this paper provides a literature
review of the recently developed technologies related to the kinematics, dynamics, control and
verification of space robotic systems for manned and unmanned on-orbit servicing missions.
& 2014 Elsevier Ltd. All rights reserved.
Keywords:
Space robotics
On-orbit servicing
Spacecraft dynamics and control
Contents
1.
2.
3.
4.
5.
6.
7.
n
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Kinematics and dynamics of space manipulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1.
Kinemo-dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1.1.
Kinemo-dynamics of single-arm space manipulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1.2.
Kinemo-dynamics of multiple-arm space manipulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2.
Contact dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Observation and planning phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.1.
Target motion prediction and parameter identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.2.
Proximity rendezvous for autonomous capturing and docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Final approaching phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.1.
Path planning and control of a single-arm free-floating system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.1.1.
Nonholonomic path planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.1.2.
Nonholonomic control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.2.
Path planning and control of single-arm free-flying systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.3.
Path planning and control of multiple-arm systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Capturing and post-capturing phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.1.
Free-floating case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.2.
Free-flying case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Flexibility and vibration suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Corresponding author.
E-mail address: [email protected] (A. Flores-Abad).
http://dx.doi.org/10.1016/j.paerosci.2014.03.002
0376-0421/& 2014 Elsevier Ltd. All rights reserved.
2
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
8. Ground verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
9. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1. Introduction
Statistical data reveal that, on average, 100 satellites (from 78 to
130) were launched every year in the past decade. Most of them
performed their missions without any major problems. However, a
small number of them experienced anomalies and even failures of
various degrees of severity [1]. In the past, launcher failure was the
most common cause of failure. However, on-orbit failures have
exceeded launch failures in recent years for the first time [2] and
cumulatively account for losses of billions of dollars [3]. Besides, every
launched satellite eventually runs out of fuel and thus, must be
decommissioned even if the satellite may still be functional [4]. A
number of studies [5–7] have demonstrated potential savings in terms
of cost effectiveness of in-flight repair of damaged spacecraft, and a
model that also includes risk and uncertainties analysis was presented
by Sale et al. [8,9]. For these reasons, the National Aeronautics and
n -DOFmanipulator
Capturing interfaces
Servicing system
Target or client
satellite
Chaser or base satellite
Fig. 1. Components of a spacecraft servicing system for on-orbit servicing.
I-Observing and Planning
III-Impact and Capture
Space Administration (NASA) realized the importance of robotics onorbit servicing operations to protect their assets in space as early as the
1980s [10,11]. The term on-orbit servicing (OOS) refers to the maintenance of space systems in orbit, including repair, assembly, refueling
and/or upgrade of spacecraft, after their deployment. It is notable that
such complex space missions have motivated the development of new
space robotics technologies and several experimental demonstration
missions including both manned and unmanned missions [12].
A space robotic system (also referred to as space manipulator
or space robot) for an OOS mission typically consists of three major
components: the base spacecraft or servicing satellite, an ndegree-of-freedom (n-DOF) robot manipulator attached to the
servicing satellite, and the target spacecraft to be serviced. A
spacecraft-manipulator servicing vehicle (illustrated in Fig. 1) is
sometimes termed the servicing system.
The capturing process includes a series of operations. After having
completed the far and close-range rendezvous maneuvers [13] with
the target satellite, the servicing spacecraft remains at a safe, stationkeeping, distance from the tumbling target satellite. Then, the capture
operation mode starts, which may be divided into four phases. The
first phase corresponds to the observing and planning phase for
acquiring motion and physical properties information about the target
satellite, to plan how the robot manipulator should grasp the target.
The second phase is to control the robot to move toward the planned
grasping location, such that the robotic arm is ready to capture the
target. The third phase consists in the actual capture (physical
interception) phase in which the manipulator physically grasps the
capturing device of the target satellite. The fourth phase is the postcapture phase, where the captured target is stabilized along with the
servicing system. Fig. 2 shows the four phases of the capturing
maneuver.
II-Final Approaching
IV-Post-capturing stabilization
Fig. 2. The four phases of a satellite capturing operation.
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
This paper provides a survey of the literature on different topics
related to space robotic systems for manned and unmanned onorbit servicing missions. The remainder of this paper is organized
as follows. Section 2 presents background information on the
existing space robotics manipulators and demonstration OOS
missions. Then, the basic approaches for the kinematics and
dynamics modelling of space manipulators are presented in
Section 3. It is followed with a description of the schemes for
accomplishing an approaching maneuver in Section 4. In Section 5,
path planning and control techniques for approaching a target
satellite are discussed. Section 6 outlines the methods for capturing the target satellite with the less reaction on the base and
the proposed stabilization approaches. Section 7 discusses the
flexibility problem inherent to lightweight space robotic manipulators. In Section 8, the existing test facilities employed for
ground-based verification of spacecraft servicing systems are
described. Finally, a conclusion is provided in Section 9.
2. Background
Ever since the first deployment of the Shuttle Remote Manipulator System (SRMS) from the cargo bay of the Space Shuttle
Colombia in 1981, robotic systems have been used on many space
missions, and are also employed on the International Space Station
(ISS). Developed by the Canadian Space Agency (CSA), SRMS is a
15.2 m long, 6-DOF robotic manipulator known as Canadarm
which has conducted numerous on-orbit service missions [14].
Nowadays, with the advances in robotics technologies, some of the
current space manipulators can move with high dexterity to
support or even replace astronauts for some precise, complex or
risky tasks. For example, with the addition of Dextre (also known
as the Special Purpose Dexterous Manipulator or SPDM for short)
to the Space Station Remote Manipulator System (SSRMS), many
delicate assembly and maintenance tasks that were previously
performed by astronauts during spacewalks can now be carried
out by the robot [15]. The SSRMS, nicknamed Canadarm2, is a
3
7-DOF, 17-meter long robotic arm with a symmetric structure
capable of walking around the ISS. The manipulator has a modular
design for easy maintenance and force–moment sensors for
advanced robotic control [16].
In addition to the SRMS and the ISS's Mobile Servicing System (the
main ISS robotic system comprising the SSRMS, the SPDM and the
Mobile Remote Servicer Base System which acts as a movable platform
for the SSRMS and the SPDM), other ISS-servicing robotic systems
have been designed by some international space agencies. The
Japanese Experiment Module Remote Manipulator System (JEMRMS),
built by the Japan Aerospace Exploration Agency (JAXA), is a robotic
manipulator system intended for supporting experiments conducted
on the Exposed Facility (EF) of the Japanese Experiment Module (JEM)
[17]. This robot includes a 6-DOF, 10-meter long main arm, and a 6DOF, 2-meter long small fine arm (SFA) designed to perform dexterous
tasks [18]. The European Robotic Arm (ERA), developed by the
European Space Agency (ESA), is a 11-meter manipulator with
7-DOF, two booms and a reallocable base to be attached to the
Russian segment of the ISS [19].
In additional to the aforementioned ISS servicing robotic
manipulators, a number of other experimental space manipulators
have been developed and successfully flown in space. DLR developed the Robot Technology Experiment (ROTEX) to study and
experimentally demonstrate robotics technologies aboard the
Space Shuttle [20]. A variety of teleoperation modes were verified
despite several seconds of delay, such as on-board teleoperation,
teleoperation from the ground, and sensor-based offline programming. Later, DLR developed the Robotics Component Verification
on the ISS (ROKVISS) robotics experiment [21]. Long term DLR's
space robotics projects were presented in [22]. DLR is also
developing Space Justin, a humanoid robot capable of performing
complex repair tasks in orbit. This humanoid has a head, torso, and
arms, but no wheels or legs, as it is expected to be mounted on a
spacecraft. While the long-term objective is to have Justin operate
autonomously, this robot is expected to be teleoperated from
the ground [23]. Similarly, NASA developed Robonaut 1 (R1), a
dexterous robot to assist astronauts during Extra Vehicular
Fig. 3. Examples of robots which have served in current or past space missions: (a) ROTEX of DLR [20], (b) JEMRMS of JAXA [17], (c) Canadarm of CSA [14], (d) Curiosity
robotic arm of JPL (http://www.jpl.nasa.gov/spaceimages/), (e) SSRMS and SPDM of MDA (http://www.mdacorporation.com) and (f) Robonaut 2 of NASA-GM [25].
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A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
Activities (EVA) tasks. R1 has the ability to work with existing EVA
tools and interfaces in high-fidelity ground-based test facilities
[24]. Further, NASA and General Motors jointly developed a second
generation of R1, the Robonaut 2 or R2 for flight testing on ISS. This
is a state-of-the-art, dexterous, anthropomorphic robot that has
significant technical improvements over its predecessor making it
a far more valuable tool for astronauts. Upgrades include increased
force sensing, greater range of motion, higher bandwidth, and
improved dexterity [25]. R2 arrived on the ISS in February 2011
[26] becoming the first humanoid robot in space and is currently
undergoing testing. While working side-by-side with human
astronauts, Robonaut 2 will actuate switches, use standard tools,
and manipulate space station interfaces, soft objects and cables.
Some of the space robots are shown in Fig. 3.
Robotic manipulators are well suited to execute highly repetitive tasks that would be too time-consuming, risky and expensive
if performed by astronauts. Besides, space robots require much
less infrastructure than humans (e.g. life support systems), which
makes them preferable in space [27]. Exhaustive lists of possible
applications of robotic on-orbit servicing are found in [28,29],
which can summarized as follows:
Assembly, maintenance and repair. Highly advanced robotic
systems are commonly used on the ISS and play a key role in
station assembly and maintenance: moving equipment and supplies around the station and servicing instruments and other
payloads attached to the space station (such as batteries and
electronic components). Typically, those manipulators are selfrelocatable and can move from one location to another on the ISS
by translating along a special truss of the station or inchworm-like
locomotion over a network of grapple fixtures on the station.
Spacecraft deployment, release and retrieve. For nearly three
decades, robotic arms have also been used to deploy, release, and
retrieve spacecraft of all sizes. Not only they can retrieve satellites,
but they can also assist with berthing/de-berthing of a spacecraft
to a station. This has been done for module redocking on the MIR
orbital complex [30,31].
Extravehicular activity support. Robotic manipulators have
proved to be useful in supporting astronauts during EVA tasks.
One of the best examples of such an operation occurred in 1994,
for the on-orbit fixing of the Hubble space telescope (HST) [32],
which is widely known for advancing astronomy and scientific
understanding of the universe. During long and meticulous
repairs, a robotic arm was used to hold the telescope still while
the astronauts were replacing the solar arrays and fixing the
attitude control system and the main computer of the spacecraft.
The complete operation lasted 35 h and 28 min and it was a
success. Robotic arms assisted astronauts for several more repairs
of the HST in 1997, 1999, 2002 and 2009. More frequently, during
spacewalks on ISS, astronauts also anchor themselves to robotic
arms in order to reach specified spots and stay anchored with
respect to the ISS.
Inspection. CSA developed an extension to the Space Shuttle's
robotic arm to perform on-orbit inspections of the Shuttle's
thermal protection system, known as the Inspection Boom Assembly (IBA), whose main role was to inspect the thermo protection
titles around the body areas of the Shuttle where the SRMS could
not reach by itself [33]. Weighing 211 kg (excluding sensors), and
nearly 15 m long, the IBA had roughly the same dimensions as
SRMS. Once in orbit, the SRMS would pick up the IBA and move it
around the necessary positions to permit a complete inspection of
the shuttle tiles and other critical surfaces to ensure a safe return
to the Earth [34].
Refueling. Extending a satellite's operational life might gain
considerable economical savings [6]. Recently, NASA used the ISS's
Canadarm2 and the Dextre robots to accomplish an experimental
demo of robotic refueling mission. In this mission, Dextre used
Fig. 4. Candarm2 and Dextre performing a demo of a robotic refueling task [35].
four unique tools to demonstrate a refueling tasks, including
cutting and manipulating protective blankets and wires, unscrewing tiny caps and accessing valves, transferring fluid, and putting a
new cap in place for future refueling activities [35] (see Fig. 4.)
Multi-arms cooperation. Two arms can also work together
towards accomplishment of difficult tasks that would be otherwise
impossible to achieve by just a single arm. This occurred on April
28, 2001 when SSRMS transferred a piece of equipment over to
SRMS. This event is now referred to as a handshake in space of the
two Canadian robotic arms. SSRMS and SPDM have also performed
cooperative work on ISS, such as the SSRMS is often used as a
mobile platform for the SPDM to perform some work (see Fig. 4).
It is expected that future space robots such as Robonaut2 will
perform more multi-arm cooperation tasks.
Several space agencies have developed robotic OSS missions.
The Experimental Test Satellite VII (ETS-VII) of the JAXA is
considered the first robotic OOS demonstration mission, which
included a 2-meter long, 6-DOF robotic arm mounted on an
unmanned spacecraft. The experimental system was launched in
November 1997, with the objective to verify technologies for
autonomous rendezvous and docking (AR&D), and robotic servicing in space [36]. The experiment included a variety of tasks such
as teleoperation from the ground with a time-delay, robotic
servicing tasks such as orbital replacement units (ORU) exchange,
deployment of a space structure, and capture and berthing of a
target satellite. To avoid flying away due to a possible failed
capture, the robotic capture task was performed while the two
satellites were still physically tied using a latching mechanism
[37]. A study on the challenges introduced by teleoperation with
time delays for ETS-VII was reported in [38].
Defense Advanced Research Projects Agency (DARPA) in conjunction with Boeing successfully launched and accomplished the
Orbital Express mission in 2007. As an advanced OOS technology
demonstration mission, it demonstrated the technologies of one
spacecraft servicing another one such as autonomous rendezvous
and docking, in-orbit refueling, and robotic ORU replacements.
During the mission, a robotic arm autonomously transferred a
supplemental battery and backup computer to a target spacecraft
designed to be serviced [39,40]. Another DARPA OOS program was
the Spacecraft for the Universal Modification of Orbits (SUMO),
which was executed by the Naval Research Laboratory. Initiated in
2002, the program aimed at combining a detailed stereo photogrammetric imaging with robotic manipulators to grapple space
objects of an existing spacecraft for servicing [41]. A laboratory
demonstration provided realistic test and evaluation of critical
technologies associated with unaided target approach and capture
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
5
Fig. 5. Different concepts of on-orbit servicing missions: (a) ETS-VII of JAXA [36], (b) orbital express of DARPA [39], (c) TECSAS of DLR/CSA/RKA [52] and (d) DEOS of DLR [53].
[42]. The program was renamed to Front-end Robotics Enabling
Near-term Demonstration (FREND) with the objective of performing AR&D with satellites that have not been built to enable robotic
servicing [43]. The FREND program used a 7-DOF flight robotic
arm system with its associated avionics to accomplish full-scale
laboratory demonstration of autonomous rendezvous and grapple
of a variety of spacecraft interfaces [44,45]. The FREND robotic arm
is being currently utilized in a new DARPA OSS program, called
PHOENIX, which is aimed at removal and reuse of some existing
parts of decommissioned satellites in GEO orbit [46]. The program
started in July 2012 and its first keystone mission in 2015 plans to
demonstrate harvesting an existing, cooperative, retired satellite
aperture, by physically separating it from the host non-working
satellite using on-orbit grappling tools controlled remotely
from the earth. The aperture will then be reconfigured into a
new free-flying space system and operated independently to
demonstrate the concept of re-using space asset [47,48].
In 2002, the United States Air Force Research Laboratory (US
AFRL) demonstrated accurate detection, tracking and pose estimation of on-orbit targets to enable satellite rendezvous and docking
operations with the Experimental Satellite System 11 (XSS-11)
mission [49]. Similarly, NASA sponsored the Demonstration for
Autonomous Rendezvous Technology (DART) project which was
launched in 2005 [50]. The objective was to validate innovative
hardware and software systems enabling autonomous rendezvous
maneuvers. The integration of an advanced video guidance sensor
and autonomous rendezvous and proximity operations algorithms
was also intended. The proximity rendezvous operation was not
completed before the fuel was used up during rendezvous maneuvering [51].
Another recently planned OOS mission known as Technology
Satellites for Demonstration and Verification of Space Systems
(TECSAS) was jointly developed by DLR, CSA, and RKA (Russian
Space Agency). TECSAS consisted of a servicer satellite equipped
with a robotic arm and a target microsatellite to be captured and
serviced in orbit [52]. The mission comprised different phases in
which numerous features were to be demonstrated, such as far
rendezvous, close approach, flying-around inspection, formation
flight, capture, stabilization and calibration of the coupled system,
flight maneuvers with the coupled system, manipulation on
the target satellite, active ground control via tele-presence, and
passive ground control during autonomous operations. While the
multi-nation effort of this mission was discontinued due to the
priority shift of individual participating agencies, Germany nevertheless continued their development work under the Deutsche
Orbital Servicing Mission (DEOS) mission. The main purpose of
this revamped mission is to find and evaluate procedures and
techniques for rendezvous, capture and deorbiting of a noncooperative spacecraft from its operational orbit [53,54]. The DEOS
mission objectives are divided into primary and secondary mission
goals. The primary mission goal comprises capturing of a slowtumbling (4 degrees per second) and non-cooperative satellite by a
manipulator and controlled re-entry of the rigidly coupled satellites within a given re-entry corridor [55]. Fig. 5 shows some of the
above-mentioned mission concepts.
A few commercial OSS programs are also currently being developed. One is the ConeXpress Orbital Life Extension Vehicle (called CXOLEV) which is being developed by Orbital Recovery Limited (ORL) to
extend the operating life of large geostationary satellites [56]. The
other is being developed by Space Exploration Technologies (SpaceX),
the first private company to launch a servicing mission to the ISS. The
goal is to demonstrate reliable crew and cargo transportation services
for the ISS, thereby replacing the Space Shuttle's capability. In May
2012, as shown in Fig. 6, the SpaceX vehicle (Dragon) successfully
accomplished its first docking with the ISS [57], delivered about
1200 lbs of water, food, and other supplies for the astronauts stationed
in ISS. Thus far, the vehicles of both commercial programs have not yet
been equipped with a robotic arm.
3. Kinematics and dynamics of space manipulators
The main differences between space robots and ground-based
manipulators are that the base of a space manipulator is not fixed
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to the ground and the gravity forces exerted on them are reduced.
The robot is instead flying or freely floating (including rotating) in
an orbital environment. Also, the dynamics of the manipulator and
its base are coupled, because movements of the manipulator
disturb the attitude of the spacecraft, complicating the kinematics
and dynamics analysis of the system [58–62].
Regarding the base spacecraft, three types of operation are
considered. The first type corresponds to the free-flying case, where
the base is actively controlled and hence, the entire servicing system is
capable of being transferred and orientated arbitrarily in space. The
utilization of such a system may be limited because the manipulator
motion can both saturate the reaction jet system and consume large
amounts of fuel [63]. In the second type, the base attitude is controlled
by using reaction wheels, leaving the spacecraft only in free translation. These two categories are split because in some cases only the
control of the attitude change is enough to reach the target position
and to avoid loss of communication with ground stations and
disorientation of solar panels. The third type is the free-floating case,
where the attitude control of the base is inactive and thus, the base is
completely free to translate and rotate in reaction to the manipulator
motion. Similar to some other authors, for instance [64,65], we will
merge the first two categories (fully actuated and partially actuated)
into the free-flying case.
3.1. Kinemo-dynamics
Kinematics of a manipulator describes the relationship between
the motion variables of the end-effector (EE) in a Cartesian space and
those of the joints of the manipulator in the joint space. In a groundfixed manipulator case, the position of the EE in an inertial frame
Fig. 6. The SpaceX Dragon capsule captured by the ISS's robotic arm. The
commercial craft brought food and other supplies to the station [57].
depends only on the current joint positions (in joint space) and the
geometrical parameters of the manipulator. However, for a freefloating manipulator, any EE position change in an inertial frame is a
function of not only a change of the joint positions but also the change
of the inertia distribution of the manipulator which is configuration
dependent. Thus, in general, the inverse and forward kinematics
problem for a space manipulator is also considered a dynamic
problem because it involves the manipulator's inertia properties.
3.1.1. Kinemo-dynamics of single-arm space manipulators
One of the seminal works analyzing the kinemo-dynamics of
space manipulators was presented by Longman [61], where the
author demonstrated that given the history of the robot joint
angles as a function of time, the final joint angles can be used as in
the standard fixed-base manipulators problem to obtain the
robot's end-effector position relative to the base spacecraft. Then,
by the principle of angular momentum conservation, it is possible
to get the inertial position of the satellite as well as its orientation.
Following such a method, the authors were able to find a feasible
inverse kinematics solution that achieves not only the desired
end-effector position but also the desired spacecraft attitude. The
workspace was also analyzed and found to be a perfect sphere
whose radius is a monotonically decreasing function of the
manipulator's mass. Umetani and Yoshida [66] developed an
inverse kinematics solution by defining the Generalized Jacobian
Matrix (GJM), which is a function not only of the joint angles but
also of the inertia parameters. They further showed that the GJM is
very close to the conventional Jacobian matrix of the same
manipulator as if it was fixed to the ground when the mass of
the manipulator is much smaller than the mass of the base
spacecraft. Nevertheless, it was demonstrated in [64] that
the rank of the GJM is deficient at some configurations in
the manipulator's joint space, which makes the manipulator
unable to move its end-effector in some directions of the inertial
space. These singular configurations cannot be determined solely
by the kinematics of the system, instead, they also depend on the
system's inertia properties. Hence, they are called dynamic singularities. The authors also showed that the end-effector's linear and
angular velocities in inertial space can be expressed solely as a
function of the manipulator joint angles and rates, and that they
do not depend upon the uncontrolled linear and angular velocities
of the base spacecraft. A Moore–Penrose pseudo-inverse version of
the GJM was used to overcome the dynamic singularities problem
by using the redundant DOFs of the space manipulator system [67].
However, this formulation is even more complex than the generalized
Jacobian technique.
Another effort to provide tools that aid at understanding and
solving the kinematics and dynamics problem of space manipulators was done by Vafa and Dubowsky. They introduced the Virtual
Manipulator (VM) concept [59]. The VM is a massless kinematic
Fig. 7. Virtual manipulator and dynamically equivalent manipulator representation of a space servicing system: (a) a space manipulator and its corresponding VM [58] and
(b) a space manipulator and its corresponding DEM with its first joint being spherical [68].
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
chain whose base is fixed in the inertial space at a point called the
virtual ground (VG) and whose tip is at an arbitrary point on the
real manipulator tip. The VG is located at the center of mass of
the manipulator-spacecraft system. This point does not move in
the inertial space when there are no external forces applied on the
system. Once the VM is constructed, it moves with the real
manipulator. The endpoint of the VM is always coincident with
the endpoint of the real manipulator. These properties enable both
kinematics and dynamics of a space manipulator system to be
modeled in the same way as its corresponding VM which is like a
ground robot because its base is always fixed in the inertial frame.
In the above-mentioned approaches to find kinematics model, no
generalized expression for the total momentum is obtained, which
may lead to some numerical inefficiencies. Therefore, Saha [68]
proposed to derive the total momentum of a space manipulator in
terms of an arbitrary body, which is called primary body, and it was
shown that when the end-effector is chosen as the primary body, it
will lead to a kinematics model that results in the most efficient
algorithms.
Note that a VM is an idealized massless kinematic chain and thus,
it can only be simulated in a computer but cannot be physically built.
This means that the concept of VM cannot be used as an experimental
method for space manipulators. Liang et al. [69] first proposed the
concept of Dynamically Equivalent Manipulator (DEM). The DEM goes
beyond the VM concept because it represents a space manipulator
both kinematically and dynamically and thus, it can be physically built
for experimental study of the dynamic behavior of a space manipulator. The DEM is a fixed-base manipulator whose first joint is a
passive spherical joint and whose kinematics and dynamics models
are identical to those of the corresponding space manipulator system.
The first joint is fixed in the same point where the VG of the VM is
located. The lengths of the links are also the same as those of the VM.
The dynamics of the DEM maps identically the dynamics of the space
manipulator under the action of a control law. This equivalence is valid
not only for the free-floating case where the base attitude is
uncontrolled but also for the case where the base attitude is actively
controlled. The DEM concept was employed to test some control
methods which were originally developed for fixed-base manipulators
but would be used for space manipulators. For example, different
adaptive controllers have been verified using the DEM concept in
[70–73], and recently, a singularity free formulation of the DEM was
presented in [74]. The concepts of VM and DEM for a simple space
manipulator are shown in Fig. 7.
Authors in [75] presented a solution of the inverse kinematics
problem for space manipulators using optimization criteria rather than
applying conventional schemes based on pseudo-inverse matrix
methods. To handle the dynamic singularities, in [76] a method called
Singularity Separation Plus Damped Reciprocal (SSPDR) was proposed.
The approach separates the singularity parameters from the inverse
Jacobian matrix, replaces their reciprocals using the damped reciprocals, and combines that information with the measured angular
velocity of the base. Then, the dynamic singularity problem is
transformed into a kinematic singularity problem which can be
handled by many existing techniques.
3.1.2. Kinemo-dynamics of multiple-arm space manipulators
The use of multiple-arm systems instead of a single-arm
manipulator offers some advantages from the OOS point of view.
In this case one of the arms can be used to follow the planned
trajectory while the other is used to compensate the reaction on
the base satellite, as demonstrated by Yoshida et al. [78], where
dynamics and kinematics analysis of multiple-arm space robots
was discussed. The authors obtained the GJM for a dual-arm case,
so that a proper motion control of the multiple manipulators can
be implemented. Advantages in coordinated control between the
7
Fig. 8. ATLAS robotic servicer concept [77].
manipulator and its base spacecraft were also discussed. Fig. 8 shows
the artistic representation of ATLAS (Advanced Telerobotic Actuation
System), which is a dual-arm space manipulator system proposed by
Ellery [79] as a potential space robot dedicated to OSS. Kinematics and
dynamics modelling of ATLAS can be found in [77].
Moosavian and Papadopoulos [80] developed and compared two
kinematics models of multiple-arm space manipulators, called the
barycentric vector approach and the direct path method. It was found
that the latter requires significantly less computations for position and
velocity analysis, as it results in equations with simpler terms. Later,
the same authors presented an explicit dynamics model of a multiplearm manipulator system based on a direct path kinematics approach
[81]. Derivation of the equations of motion resulted in an explicit
formulation of the system's mass matrix and the generalized nonlinear
inertia forces. The obtained explicit dynamics model of a multiple-arm
manipulator can be implemented either numerically or symbolically.
A method based on the treatment of structural changes which is
suitable to analyze the dynamics of space manipulators with multiple
arms was presented in [82,83]. The computational efficiency of the
algorithm was studied to conclude that the method is simple to
implement and can be easily parallelized.
3.2. Contact dynamics
A critical portion of the dynamics modeling for the OOS
applications is contact dynamics (including low-speed impact
dynamics). Contact dynamics is one of the most difficult areas in
multibody dynamics and is still an active research subject. An early
application of contact dynamics in space robots was reported in
[84], where, assuming a point contact scenario, the contact force is
modeled as an impulse function. However, the main modeling
difficulties arise from the complicated geometries of the contact
interfaces such as the ones shown in Fig. 9. Thus, Ma et al. [85,86]
developed a generic contact dynamics modeling and simulation
system to support the development and operations of the ISS
robotic systems SSRMS and SPDM. They further developed a
model reduction technique to improve the efficiency of highfidelity but usually very time-consuming contact dynamics simulations [87]. Model reduction of contact dynamics is a very difficult
problem and it is still an active research topic because of the
highly nonlinear nature of the involved impact-contact mechanics.
8
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
Capture latch
CBM Docking Interface
(Details on arrow “A”)
International Space Station
Alignment
Guide
CBM Passive Part CBM Active Part
(on HTV)
(on US Lab)
Node
2
JEM
US
Lab
A
SSRMS
HTV
CBM Contact Dynamics Model
(When two parts are fully mated)
Fig. 9. Contact interfaces for robotic berthing the H-II Transfer Vehicle (HTV) to the ISS [85].
An approach that considers the generalized constraint forces
between a space robot's end-effector and the target satellite as internal
forces rather than as external forces was presented by Shibili et al.
[88], where the initial conditions for the post-impact motion were
obtained from the impact model. Nenchev and Yoshida [89] also
addressed the contact dynamics modeling and control issues for space
manipulators. Ma et al. [90] developed a control strategy for achieving
high fidelity contact dynamics simulation of a new, robotics-based,
hardware-in-the-loop (HIL) rendezvous and docking simulation. They
used the EPOS (European Proximity Operations System) facility as a
HIL simulation platform to test and validate OOS tasks developed by
DLR and ESA. Abiko et al. [91] introduced a contact dynamics
simulation for capturing a floating target by a long reach space
manipulator with a snaring-wire type end-effector. The authors used
this kind of end-effector because current space manipulators such as
the SSRMS are equipped with a latching end-effector with three snare
wires inside. An experimental evaluation of the contact/impact
dynamics between a space robot and a tumbling object was introduced by Uyama et al. [92]. To study the contact phenomenon and
avoid hard impact, the authors employed a stiff manipulator with
compliant wrist. The coefficient of restitution and the contact duration
were used as evaluating parameters. Sawada et al. [93] focused on the
contact dynamics of the manipulator's end-effector and the grapple
fixture of a target satellite. To validate the method, they set up a hybrid
simulation using a numerical model and a 6-DOF robot with a 6-axis
force–torque sensor. The authors also employed a three-wired endeffector mechanism. A survey of general contact dynamics modeling
techniques can be found in [94].
4. Observation and planning phase
The tasks of the observation and planning phase may include to
acquire the 6-DOF motion information including the position,
attitude, linear and angular velocities of the target body; identify
physical properties such as the inertia parameters; determine
when and where to grasp the target, and plan the motion
trajectory for a final approaching and capturing. A malfunctioning
satellite or an orbital object may have a tumbling motion and
poses unknown kinematics and dynamics properties. Therefore,
it becomes essential to predict its motion pattern and identify
its kinematics and dynamics properties. Another necessary task for
a robotics-based OOS mission may consist on guiding a space
manipulator to perform proximity rendezvous to a target satellite.
The term “proximity” means that the servicing spacecraft has
completed its orbit transferring and it has been in a very short
distance to the target satellite [13].
4.1. Target motion prediction and parameter identification
In order to estimate the position and attitude of a target
satellite, Nagamatsu et al. [95] proposed a 12th order extended
Kalman filter method. To verify the validity and applicability of the
method, experiments were performed to capture a free-flying
object by using a 3D hardware simulator with a 5-DOF manipulator. Huang et al. [96] used the estimation scheme introduced in
[95] to develop an algorithm for tracking trajectory planning. The
authors assumed that the target has a symmetric geometric shape
and the target's size, shape and mass were all known. Besides, the
handling location was determined by human inspection. It was
also assumed that the target is equipped with visual markers,
signal reflector, and GPS device for simplification. Thienel et al.
[97] took advantage of the fact that the HST is equipped with
vision-based sensors to develop a method for estimating the
angular rates of the HST. The method was used in the estimation
part of a tracking control scheme in [98]. Assuming that an object
is not acted upon by any external force and moment, the motion of
the target satellite was predicted in [99]. Litcher and Dubowsky,
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
using 3D vision sensors, proposed an architecture for estimation of
dynamic state, geometric shape, and model parameters of an
object in orbit, with potential application to satellite capturing
[100]. To allow a faster prediction, range data as measured by
stereo vision or a laser range sensor was used to estimate the
motion and the parameters of the target in [101]. Inaba et al. [102]
introduced the design concept of a visual servoing system for a
space robot and presented the experimental results using the
Japanese ETS-VII test bed. They analyzed the system requirements
such as computing power, frequency and range of measurements
as well as accuracy. Assistance from the ground was considered to
choose a time line to maintain acceptable light conditions. From
the visual information of a stereo-vision system, reference [103]
proposed an iterative recursive least-square pose refinement to
perform the relative pose estimation of a floating object. Xu et al.
also introduced an autonomous path planning method for target
capturing in [104,105]. The target features were extracted based
on the visually measured information via the hand-eye camera
and the target pose (position and orientation) and velocities were
estimated using a Kalman filter scheme. Disturbance on the base
due to manipulator's motion was also estimated and reduced. The
authors validated the method using both computer simulations
and experiments. Along the same line, from noisy measurements
of a vision system, a Kalman filter was used to estimate the motion
state and some dynamics parameters for the capture of a tumbling
satellite in [106]. A 3D-image generated by a PMD (photonic mixer
device) camera was used for determination of the relative distance
and orientation, as well as the motion identification of a satellite
[107]. A markerless visual 3D model-based servoing using a
monocular camera mounted on the chaser was presented in
[108]. The system also included a robotic arm, and a chaser
satellite mockup as shown in Fig. 10.
An extension of this work was presented in [109], where the
authors used a GPU (Graphic Process Units) acceleration and 3D
rendering to track targets of complex geometrical shapes. A fault
tolerant pose estimation algorithm for a free-floating space object
using large range sensor was presented in [110], where the 3D
vision data was integrated using a Kalman filter. To perform the
experiments, a Neptec's Laser Camera System was used for real
time scanning of a satellite model attached to the manipulator
arm, which was driven by a simulator according to orbital and
attitude dynamics, as depicted in Fig. 11. English et al. presented
analysis and lessons learned from the real-time vision systems for
dynamic pose estimation used by NASA in the assembly of the ISS
and for AR&D operations [111]. Visual servoing using CCD cameras
for simulation of space robot capturing was presented in [112,113].
Fig. 10. Experimental setup for pose estimation with a monocular camera [108].
9
Fig. 11. Experimental setup for satellite's position/attitude estimation using
Neptec's laser rangefinder scanner [110].
Issues such as lighting, computer power and time delays were
taken into account.
Based on stereo camera hardware, the Massachusetts Institute
of Technology (MIT) Space Systems Laboratory is currently developing relative vision-based navigation and control techniques for
autonomous inspection and 3D mapping of an unknown, uncooperative spacecraft that is spinning and tumbling at different
rates [114–116]. The vision-based system has been successfully
demonstrated onboard the ISS and several tests have been conducted to analyze its performance. An image-based visual servoing
considering the vibration induced by the manipulator's links
motion was presented by Sabatini et al. [117,118]. In order to
increase the system robustness and to reduce the possibility of
failure, an extended Kalman filter for the estimation of the feature
motions was developed.
The inertia parameters of a target satellite may not be the only
unknowns. Due to fuel consumption, hardware reconfiguration,
payload deployment, or capturing of a flyer, the inertia properties
of the servicing satellite may also change. Therefore, dynamics
parameters identification methods have been proposed to handle
this problem [119]. The first work in this sense was proposed by
Murotsu et al. [120,121], where two parameter identification
methods for the captured payload were introduced. One method
was based on the linear and angular momentum conservation law,
and the other on Newton–Euler equations of Motion. The latter
required measuring velocities and angular accelerations. The ETSVII robot was used to test a number of inertia parameter identification methods, such as the case of an algorithm developed by
Yoshida and Abiko [122] to identify the masses, moments of
inertia and products of inertia, of a free-flying space robot, without
requiring torque or acceleration measurements. The method made
use of measurements of the joint velocities, reaction wheels and
the base attitude, and was complemented with the use of the
gravity gradient effect. A method for identification of the base
spacecraft's inertia parameters of a free-flying robot was introduced in [123,124]. This work was part of the GETEX Dynamic
Motion experiments carried out by DLR in collaboration with
NASDA (now JAXA) on the ETS-VII satellite. Using measuring data,
the parameters identification method refined the inertia parameters as an optimization problem where the cost function is the
sum of the differences between the simulated and the measured
velocity. Inclusion of noise was also considered. Ma et al. [125]
proposed an on-orbit inertia identification method which uses an
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A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
onboard robotic arm to excite the angular velocity changes of the
base spacecraft and then using the measured angular velocity
changes to identify the unknown inertia parameters of the base
spacecraft. Since the robotic arm is powered by solar energy, the
approach does not require the use of fuel. Further, as the method
was derived from the momentum equation of the system, it
requires the measurement of velocities only, and does not need
any information of the acceleration and energy-dissipating internal forces. The latter is very difficult to accurately measure.
4.2. Proximity rendezvous for autonomous capturing and docking
Most of the actual rendezvous studies deal with cooperative
targets. However, a space robot for OOS may demand the proximity or close-range rendezvous with a noncooperative target
spacecraft. Therefore, this section focuses on research studies
applicable to proximity rendezvous operations with noncooperative target objects. A two-phase navigation solution for rendezvous with a tumbling satellite in a 2D space was studied by FitzCoy and Liu [126]. The target vehicle was assumed uncontrolled
but having constant linear and angular velocities. It was shown
that this kind of maneuvers requires two phases. In the first phase,
the LOS (line-of-sight) rotation is driven to zero while aligning the
capturing mechanisms of the two vehicles. During the second
phase, the chase vehicle maintains the angular velocity of the
target and simultaneously reduces the range-to-go rate to zero. In
both phases, the berthing mechanism is aligned with the LOS and
the angular velocity of the vehicle relative to the LEO is kept in
a small value. In [127] a method for matching angular velocities
between the servicer and the target by changing the target's
moments of inertia was presented. Similarly, Tsuda and Nakasuka
[128] proposed a strategy that assumes that the chaser spacecraft
is equipped with long adjustable booms. After estimating the
moments of inertia of the target, the chaser spacecraft adjusts the
length and orientation of its booms, to synchronize its motion
with that of the target. Once the motion is synchronized, the same
rotation pattern as the target can be maintained, with no control
torque required.
However, there are several technological challenges that
remain to be solved to make this approach feasible in practice.
A more recent work by Matsumoto et al. [129] proposed the use of
the natural dynamics to complete a passive fly-by approach and an
optimal trajectory for close-range rendezvous with a rotating
satellite, considering issues such as collision avoidance between
the manipulator and the target satellite. Ma et al. [130] designed
an optimal trajectory for a spacecraft to approach a tumbling
satellite by minimizing time and fuel. They obtained the required
thrust force profiles that would guide the chasing spacecraft to
approach a tumbling object such that the two vehicles would
eventually have no relative rotation and thus, a subsequent
capture operation can be safely performed with a normal docking
or capture mechanism. A guidance methodology to generate fueloptimal trajectories using a mixed-integer linear programming
(MILP) solver was developed by Breger and How [131]. The work
by Boyarko et al. [132] expanded the scope of Ma et al. [130] by
taking into account the proximity motion dynamics, and considering the full 6-DOF model, and by determining both the minimumtime and the minimum-control (energy) solution to the rendezvous problem. Another paper by the same authors [133] considered three different performance indexes adding further
constraints to match terminal attitude and angular rate, along
with position and velocity. In both studies, the optimal trajectory
planning was analytically formulated through the use of the
Pontryagin minimum principle and solved numerically with a
Gauss pseudospectral approach. A simpler algorithm for trajectory
planning in real time that can be handled more easily by an
onboard computer is the widely used glideslope algorithm
[134,135]. A glideslope is defined as a straight path from the
current location of the chaser spacecraft to its intended destination, which may be a target spacecraft's center of mass, a docking
port, or a location of interest near the target. The guidance
equations are based on the closed-form solution of the linear
Clohessy–Wiltshire equations. To mitigate the fuel-optimality
problem inherent to the glideslope approach, cubic spline-based
analytical guidance laws can be employed. Recently, building upon
the work of Sultan et al. [136] for spacecraft formation flying,
Fejzic [137] developed a collision-avoidance cubic spline-based
planning algorithm for spacecraft docking by defining way-points
to avoid an obstacle. Once the position and the attitude were
measured based on stereo vision, autonomous rendezvous and
robotic capturing of a non-cooperative target was proposed by Xu
et al. [138]. 3D simulation results were performed to verify the
algorithm. Xin and Pan [139] developed an optimal control of
spacecraft approaching a tumbling target. They minimized the
flexible motion induced by large angular maneuvers using a
nonlinear optimal control technique.
5. Final approaching phase
In order to perform on-orbit servicing, the servicing satellite,
which is assumed to carry a manipulator, has to first approach
following a desired trajectory to the target satellite. Here, approach
implies the approaching motion of a manipulator or manipulators
to the target satellite.
5.1. Path planning and control of a single-arm free-floating system
In the absence of external forces, the system's linear and
angular momentums should be conserved. Although both of them
are represented by velocities, the linear momentum is exhibited by
the motion of the center of mass of the whole system and can
therefore be integrated into the equations of positions instead of
velocities. This implies that the linear momentum equations are
integrable. On the other hand, the angular momentum equations
cannot be represented by their integrated form, which means that
they are nonholonomic [140]. Some researchers have used these
nonholonomic and redundant characteristics to develop interesting solutions for robot path planning and control algorithms [77].
5.1.1. Nonholonomic path planning
Inspired by the astronauts' motion allowing them to reorient
their body by just moving the limbs, Vafa and Dubowsky [62]
proposed a special cyclic motion trajectory of a manipulator's
joints to change the base spacecraft's orientation. They called the
method Self-Correcting Motions. In the method, a nominal trajectory was selected for the end-effector and base orientations. Then,
the selected joint motions are executed assuming that the base
remains stationary. If at any point the base orientation deviates
from its desired path by more than a specific amount, then a
selected series of small cyclic motions were added to the joint
motions to correct the vehicle orientation. The same authors
introduced a technique called Disturbance Map (DM) [141], which
can aid at selecting paths that reduce the disturbances of the
spacecraft by identifying the direction of the joint movements,
which results in minimum or maximum disturbances. The method
ignores the effort of the attitude control system and assumes that
the system has zero initial angular momentum. The DM can be
constructed by dividing the space manipulator's joint space into a
grid of points. At every point the directions of minimum and
maximum spacecraft movements are plotted. Later, Dubowsky and
Torres [142] presented an improved version of the DM called
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
Enhanced DM (EDM) and showed how it could be effectively used
to plan the manipulator motion for reducing the disturbances to
the base spacecraft. An optimal approaching motion was obtained
in [143] which assumed unknown target's inertial property. The
EDM was used in manipulators with more than two degrees of
freedom while the original DM was used only for a 2-link planar
case. The same authors showed in [144] that the EDM can be used
with the objective of minimizing the fuel usage for attitude
control. Another extension of the DM concept to the case of planar
polar manipulators was presented in [145]. The method of selfcorrection motion assumes small cyclic movements to neglect the
nonlinearities of orders greater than two and it requires many
cycles to make even a small change in the vehicle orientation. To
solve this problem, Nakamura and Mukherjee [146] proposed a
path planning scheme that deals with the total nonlinearity of the
satellite/manipulator system. The method is based on a Lyapunov
function to control both the base orientation and the manipulator's joints by actuating the manipulator's joints only. The scheme
was called Bi-Directional Approach. Two desired paths were
planned, one starting from the initial configuration and going
forward, and the other starting from the initial configuration and
going backward. A drawback of this technique is that it is affected
by singularities. Papadopoulos [147] proposed a path planning
technique in the Cartesian space that not only reduces the
disturbance but also avoids the dynamical singularities. The author
found that a workspace point may or may not induce a dynamic
singularity, depending on the joint space path. To solve this
ambiguity, he defined the Path Dependent Workspace (PDW) to
contain all workspace locations that may induce a dynamic
singularity. If the PDW is subtracted from the reachable workspace, the Path Independent Workspace (PIW) is obtained. All
the points in the PIW are guaranteed not to have dynamic
singularities.
Control strategies using an underactuated space manipulator to
reduce the load and power-usage have also been proposed [148].
This study revealed that it is possible to make all the manipulator's
joints converge to desired values by controlling only the actuated
joints. DeSilva utilized a local optimization approach to generate
trajectories that minimize the reaction torques and forces transferred to the base spacecraft [149].
Pandey and Agrawal [150] proposed a method called Mode
Summation for planning a Cartesian path of a free-floating system
with prismatic joints. Their method avoids inversion of the
Jacobian matrix and it also results in a singularity-free path for
the end-effector. However, the requirement for the desired final
attitude was not taken into account. Lampariello and Deutrich
[151] applied a similar method, but to a system with rotational
joints only.
Nechev et al. introduced the Reaction Null Space (RNS) to find
the manipulator's motion that yields no spacecraft attitude disturbance when following a predefined path [152,153]. A reactionless trajectory generation strategy based on the RNS to find
manipulator's paths without affecting the attitude of the base
was also proposed by Piersigilli et al. [154]. Similarly, using the
RNS idea, a model and control law for the JEMRMS with the SFA
attached on it was proposed by Fukazu et al. [155].
To overcome the dynamic singularity problem in the Cartesian
space path planning, Xu et al. [156] used the direct kinematic
equations instead. In their method, the joint trajectories were
parameterized by polynomial or sinusoidal functions first. Then,
the joint functions were normalized and the system of equations
about the parameters was established by integrating the differential kinematics equations. Finally, the parameters were solved
by an iterative Newtonian method. The drawback of this technique
is that the convergence time may be long because of the required
numerical iterations. Furthermore, there exist different paths to
11
reach the desired pose because of the nonholonomic nature of the
free-floating system.
Fernandes et al. [157] showed that the falling cat problem is
equivalent to the nonholonomic motion problem of a free-floating
space robot and they used this analogy to develop a near-optimal
motion planning method. The authors applied the method directly
to a space manipulator platform in [158], using a 3-DOF Puma
robot as a manipulator attached to a space platform. One drawback of the approach is that it needs symbolic manipulation
software to obtain the Jacobian matrices which are required by
the algorithm.
The path planning problem of a free-floating target with a
manipulator having angular momentum was addressed by Yamada
et al. in [159,160]. To find a closed-loop path, the authors proposed
a variational optimization approach in joint space for trajectory
planning. In that way, the required change of the satellite orientation can be obtained using joint control only. Suzuki and
Nakamura [161] demonstrated that a free-floating space robot
having 6-DOF cannot follow an arbitrarily desired trajectory in
the 9-D generalized coordinate space (three coordinates for the
base spacecraft and six for the manipulator) with only the joint
controls. Then, they proposed a method to approximate the
desired 9-D path by introducing a perturbation around the path,
resulting in a Spiral Motion trajectory. With the objective of
reducing the disturbances on the base, Yoshida et al. proposed
the Zero Reaction Maneuver (ZRM) in [162]. The ZRM is obtained by
making the angular velocity of the base zero in the angular
momentum equation. The existence of the ZRM is limited to 6DOF manipulators. Most of the above-mentioned methods are
time consuming. A computationally inexpensive method developed for terrestrial mobile manipulator systems was extended for
potential use in space robotics [163], but it has not been validated
for a space application.
The bi-directional approach requires that the joints stop at the
switching point and the self-correcting method is based on small
cyclical motions. These techniques yield non-smooth trajectories.
Papadopoulos et al. [164] proposed a smooth planning methodology in joint space for planar free-floating space manipulators that
allow an endpoint Cartesian location control and a simultaneous
control of the base attitude. In this method, smooth and continuous functions such as polynomials were employed. Further work
showed that the final configuration accessibility is improved
drastically when high order polynomials were used for the joint
angle solution. The planning problem was reduced to solving a set
of nonlinear equations representing the integral of motion. Based
on the same idea, the authors of [165] developed a numerical path
planning approach for the general case of an n-DOF manipulator.
Taking into account of the dynamics of large space manipulators,
Belousov et al. [166] proposed a two-stage iterative algorithm that
can generate collision-free robot motion paths.
Recently, Franch et al. [167] have employed flatness theory to
plan trajectories for free-floating systems. Their method requires
the selection of robot parameters so that the system is made
controllable and linearizable by prolongations. Agrawal et al.
extended this method to a three-link spatial space robot in
[168]. Using genetic algorithms, a nonholonomic path planning
approach was introduced by Xu et al. [169]. The method's
advantages are the motion of the manipulator and the disturbance
to the base are practically constrained; the planned motion path is
smooth; and the convergence of the algorithm is not affected by
the singularities. A nonholonomic path planning technique was
proposed based on a particle swarm optimization in [170]. The
method was applied to the target berthing and base re-orientation
after the capture of a target. Assuming that the path was
predefined, Nanos and Papadopoulous [171] developed a Cartesian
space path planning method that provides the initial configuration
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of the system and avoids dynamic singularities. Therefore, it
allows effective use of the entire workspace. Two recent algorithms aiming at minimizing the reaction torque transferred to the
spacecraft during manipulator maneuvers were presented by
Cocuzza et al. [172]. One of the solutions is based on a weight
Jacobian pseudoinverse and the other is formulated using constrained least squares [173].
5.1.2. Nonholonomic control
Depending on the control objectives, different schemes for
controlling a free-floating system have been introduced. By using
the GJM, Umetani and Yoshida [174] proposed a resolved motion
rate control for a space manipulator. Later, a similar control
approach was applied to redundant [175] systems. Nenchev et al.
[67,176] developed a specific joint decomposition technique,
called Fixed-Attitude-Restricted (FAR) motion, which allows a
manipulator arm to move without inducing any reaction moments
to the base spacecraft. Along with the solution of a conventional
end-effector trajectory tracking, a solution of base motion control
was also obtained. The Coupling Factor as a measurement of the
degrees of dynamic coupling between the manipulator and its
base was defined in [177,178]. This measurement can be considered as a performance index in the planning of the robot motion
control. A differentially flat open-chain-based controller for a
space robot equipped with two momentum wheels at the base
was presented by Agrawal et al. [168]. The main advantage of the
method is that it avoids the use of nonlinear programming (NLP)
to solve the nonintegrable rate equations, which can provide only
approximate solutions. During operation, small amounts of angular momentum tend to accumulate. Therefore, the ability to work
on orbit under this condition was studied in [179–182].
Adaptive controllers have been widely proposed as a feasible
solution to overcome the problem of uncertainties and parameters
variation. Nevertheless, since the dynamics of a free-floating space
manipulator cannot be expressed linearly with respect to a group
of physical parameters, the design of the adaptive control law is
quite complicated. An early adaptive control for space manipulators was developed by Walker and Wee [183], where uncertainties
in the inertia parameters were considered. Since dynamics of
space robots cannot be linearly parameterized, i.e., cannot be
linearly expressed in terms of parameters, such as the mass and
the inertia of the robot, adaptive control schemes based on a linear
parametrization model cannot be applied. In order to overcome
this problem, Gu and Xu [184] proposed an extended manipulator
model, which is composed of a pseudo-arm representing the base
motion and a real arm. The extended model can be linearly
parameterized, thus the authors were able to design an adaptive
control scheme for a space manipulator in the Cartesian space.
The method was termed the normal form augmentation approach.
A drawback of this approach is that it requires measuring the base
spacecraft acceleration. Parlaktuna and Ozkan [185] also used an
augmentation method but with a prediction-error-based adaptation approach, so that base acceleration is not needed. Wee et al.
[186] proposed an adaptive control method with parameter
identification, based on the principle of conservation of momentum. However, their method did not solve explicitly the nonlinear
parametrization problem associated with the computed torque
control. Neural network-based methods that do not require either
the acceleration measurement or the linear parametrization of
dynamic uncertainties were presented in [187,188]. On the other
hand, McCourt and de Silva used a model predictive control in the
space manipulator to deal with the problem of the unknown
dynamics of a target satellite [189]. Abiko and Hirzinger [190]
proposed an adaptive controller using the inverted chain
approach. They focused on the uncertainty of kinematic mapping,
which included the dynamic parameters of the system. Later, the
same authors in [191] developed an adaptive controller considering kinematics and dynamics uncertainties. One advantage of the
approach is that it has no need of measuring the angular acceleration, which was otherwise difficult. Wang [192] proposed an
adaptive scheme using the adaptive inverse dynamics and the
generalized dynamic regressor of a space manipulator. An adaptive
controller based on a robust fuzzy compensator, capable of dealing
with joint friction, disturbance and variation of payload, was
presented in [193]. Wang and Xie [194] developed a passivitybased adaptive Jacobian tracking controller, where, by defining a
new reference velocity termed spacecraft reference velocity, the
proposed method does not involve the acceleration measurement.
An adaptive controller that considers the presence of external
forces was developed in [195]. Pazelli et al. used the DEM concept
to perform theoretical research on adaptive robust controllers for
free-floating systems [71,72] and later they performed an experimental investigation of adaptive procedures based on linear
parametrization, neural networks and fuzzy systems [73]. On the
other hand, Wang [192] introduced the Generalized Dynamic
Regressor (GDR) to overcome the nonlinear parametric problem
of the inertia matrix. Then, using the GDR, the authors developed
an adaptive inverse dynamics control law. A recent progress in the
development of adaptation schemes was presented in [196],
where the authors extended the prediction error based adaptive
Jacobian control of fixed-base robots to space manipulators. The
introduced method is capable of dealing with uncertainties in
kinematics as well as in dynamics.
Robust controllers represent another applicable solution to deal
with parameters variations and unknown dynamics. Based on
the second method of Lyapunov, Xu et al. [197] proposed a robust
control to overcome the difficulty in controlling the internal
dynamics subject to parameter uncertainties. Li [198] proposed a
robust and adaptive composite control of coordinated motion for a
space robot with prismatic joints. Later, the method was applied to
a dual-arm robot [199]. A robust control for a dual-arm space
manipulator with uncertain inertial parameters was introduced in
[200] where the authors demonstrated that the dynamic equations of the system can be linearly dependent on a group of inertial
parameters with augmented inputs and outputs. Huang et al. [201]
proposed to transform a robust control problem into an optimal
control problem by including the uncertainties in the objective
function. The above-mentioned robust controllers do not compensate external disturbances such as sensor noise. Thus, authors in
[202] developed a robust controller that addressed this need.
Another advantage of the method is that it does not require
measurement of the position, linear velocity and acceleration of
the base with respect to the orbit. Pathak et al. [203] presented a
method for robust trajectory tracking. The idea is based on the
overwhelming robust trajectory control of a ground robot. A backstepping robust control for a dual-arm space robot was presented in
[204]. And a robust controller, which can cope with model uncertainties and disturbances, was presented in [205].
Optimal controllers have been demonstrated to be a suitable
option to reduce or nullify the undesirable attitude disturbance of
the base satellite generated by the manipulator's motion during
the final approaching phase. Oki et al. [206] extended the timeoptimal control with specified paths from fixed-ground robots to
free-floating robots. However, instead of assigning joint torque
constraints, the authors constrained the reaction torque generated
by the manipulator's motion. Later, Flores-Abad and Ma [207]
developed an optimal controller based on Pontryagin's maximum
principle to not only constrain but also minimize the reaction
torque on the base. Besides, this approach features some other
advantages such as it does not require zero relative velocity
between the end-effector and the grasping handle of the target
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
13
Fig. 12. Paths generated by the optimal motion planning algorithm [65]: (a) maneuver in the local workspace – free floating solution, (b) maneuver in the global workspace –
free flying case and (c) maneuver in a global workspace – attitude fixed case.
satellite at the capturing moment, uncertainties were introduced in
the manipulator's motion control scheme, minimization of servicing
satellite's attitude disturbances was considered in the final robot
approaching phase and at the capturing moment as well.
Some of the efforts being performed in nonholonomic path
planning and control were discussed in [208,209]. Major advances
in the kinematics and dynamics modeling and path planning as
well as control of free floating space robots in the early nineties
were reported in [210]. Those seminal works have laid a good
theoretical foundation for some of the newer developments.
5.2. Path planning and control of single-arm free-flying systems
To increase the mobility and perform larger tip displacements,
free-flying space robotic systems in which the associated manipulator is mounted on a thruster-equipped spacecraft have been
proposed by some robotics scientist [211,212]. Furthermore, as
described in [213], the use of a dedicated attitude controller will
enhance the computational efficiency of the path planning and
control algorithms. However, these types of robotic systems
require a coordinated controller of the base spacecraft and the
manipulator. Based on augmenting the control requirements to
include the location and attitude of the spacecraft, Papadopoulos
and Dubowsky [214] presented a coordinated control of both the
base spacecraft and the manipulator. They used a transpose
Jacobian-type controller. Xu et al. [215] proposed an adaptive
control with an attitude control on the base. Such a method avoids
the use of joint acceleration measurements, inversion of inertial
matrix, and high gain feedback. Oda [216,217] also addressed the
problem of coordinated control, where the robot control system
estimates the angular momentum that the robotic arm produces
and then the satellite attitude control system compensates the
arm's reaction. Instead of performing a single inverse kinematics
calculation at the beginning of a movement, multiple inverse
kinematics updates based on an optimal algorithm were performed in [218]. Coordinated attitude control experiments using
the ETS-VII were reported in [219], where the attitude of the
spacecraft was stabilized. Taking the kinematic and dynamic
constraints into account, an optimal motion for a free-flying
system was formulated in [65]. The solutions were found for local
and global motions. For the latter, the unnecessary spacecraft
actuation was shown to be efficiently avoided. However, the final
attained spacecraft attitude is known beforehand, and it is
obtained only after an optimal solution is implemented. Fig. 12
shows the paths generated when using this optimal algorithm in a
6-DOF space manipulator. Based on the Pontryagin maximum
principle, an optimal controller for a free-flying system was
developed in [106,220]. A multivariable cost function was
proposed to minimize the operation time and the relative velocity
between the robot tip and the target.
5.3. Path planning and control of multiple-arm systems
The use of multiple arms to capture a tumbling object offers
some advantages in the control of a space robot because one of the
arms can be used to follow the trajectory and the other or others to
compensate for the reactions, such as the case of the method
presented in [221]. Yoshida et al. [78] designed a coordination
controller for a dual-arm space robot and showed that the torque
required to follow a determined path is smaller when using two
arms, thus saving total energy. Agrawal and Shirumalla [221]
presented a planning motion strategy using a dual-arm manipulator where one arm was commanded to perform desired tasks
while the other provided compensating motions to keep the base
inertially fixed. To ensure stability in the control of a dual-arm
system, Yale and Agrawal [222] presented a Lyapunov-based
controller, where the disturbance torque transmitted to the spacecraft by the motion of the manipulator was reduced by altering the
order of the reference trajectory polynomial and coefficients.
Another motion control was developed based on the general
three-dimensional equations of motion. An efficient algorithm
for computing the GJM and the resolved acceleration control for
multi-arm space robots was presented in [223]. Dynamics modeling of multiple-arm systems and motion control of the endeffectors coordinated with the base spacecraft to chase a moving
object was proposed in [224,225]. The authors improved their
control algorithm by using the Modified Transpose Jacobian (MTJ),
which allowed storing data from control command at the previous
time step [226]. Based on the dynamics coupling and measuring
method, Huang et al. [227,228] proposed the Dynamic Balance
Control concept to justify the use of one arm to compensate for the
disturbance caused by the other arm. Chen and Guo [229]
introduced an adaptive coordinated control of the base satellite
and a dual-arm manipulator. The asymptotic stability of the
system was proven by Lyapunov's method. The proposed control
method has the advantage that it can eliminate the effect of
uncertain parameters of the robot. A numerically more efficient
on-line coordinate control of a dual-arm space robot was presented by Xu et al. [230]. The key point of the method is the
separate analysis of the linear and angular momentums. However,
the method requires the two arms to be fully identical and
mounted centrosymmetrically with respect to the centroid of the
base. Thus, the authors extended their work to a more general case
by introducing the concept of system centroid equivalent manipulator and avoided singularities because such an approach does
not need to resolve the differential kinematics.
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6. Capturing and post-capturing phases
The capturing phase involves physical interception and thus is
highly risky. The main goal is to capture the moving and possibly
tumbling target without destabilizing the attitude of the base
spacecraft. Once the target is successfully captured, the combined
system must be stabilized as soon as possible to avoid damaging
and to start the corresponding service of the captured target.
6.1. Free-floating case
An early effort to study the effect of physical contact between a
space manipulator and a tumbling object was reported in [231].
Their system aimed at simulating the catching and handling of a
free-flying target with the manipulator installed on a light structure emulating a space satellite (see Fig. 13). The relative motion
between the space robot and the target was simulated by servo
mechanisms. While the manipulator was in contact with the
target, the momentum was derived by integrating the force
measured by force/torque sensors.
The effect of impacts upon a flexible-link free-floating space
robot was discussed by Cyril et al. [232]. The method also
determines the initial conditions for post-impact simulation. Since
it is difficult to sense the impact force precisely because impact is a
short-time phenomenon and force sensor signal is very noisy,
Yoshida et al. [233] modeled the collision dynamics, using the
Fig. 13. Photograph of the system to perform catching and handling of a free-flying
target. It consists of an up-down table with a manipulator and two CCD cameras
(the space robot), and a 2-axis translational and 3-axis rotational table (the target)
[231].
Extended Generalized Inertia Tensor (Ex-GIT) without sensing the
impact force. Ex-GIT is an extension of the conventional GIT for
ground-based chains. They formulated the collision problem
focusing on the velocity relationship just before and after the
impact considering the momentum conservation law. The authors
also proposed the concepts of Impulse Ellipsoid and Impulse Index
to conveniently express impulse characteristics. In addition, in
order to count for the joint behavior with resistance during the
impact, the theory was improved by introducing the concept of
Virtual Rotor Inertia [234]. However, the analysis mainly focused on
the moments just before and after the collision. Yoshikawa and
Yamada [235] followed this concept and provided mathematical
proof in the frequency domain and the method was experimentally verified [236]. Wee and Walker [84] studied the dynamics of
contact between space robots and developed an algorithm to
achieve both trajectory tracking and impulse minimization. Their
study revealed that the impulse at contact moment could be
minimized by the optimization of a scalar cost function based on
the gradient projection technique. Impact experiments for estimating the impact effect were reported in [237,238], the experimental platform consisted of a rigid manipulator supported by a
flexible deployable structure. In [239], Yoshida and Nenchev [89]
utilized the concept of RNS (Reaction-Null Space), which corresponds to the null-space of the coupling inertia matrix to find out
proper manipulator configurations, to achieve a safe capture and
minimize the impact. The authors extended the study to investigate the joint reaction and the base reaction due to the impulsive
force. They used the RNS and the FAR (Fixed-Attitude-Restricted)
technique to analyze the pre-impact phase and develop a postimpact control law keeping the base reaction in a minimum value.
One drawback of this method is that since it is based on the
angular momentum conservation, after the impact, the momentum is exchanged between the base and the manipulator, thus
action of additional base actuators to stop the system is required.
However, this approach allows having the momentum with the
lowest velocity in the manipulator and effectively stops the
angular momentum of the base in a relatively short period of time.
Cyril et al. [240] studied the dynamics associated with the
capture of a spinning satellite. Nevertheless, it was assumed that at
the time of capture there is zero relative velocity between the
payload and the end-effector of the manipulator, which is very
unlikely the case in reality as it requires that the robot tip must
move as fast as the grasping point of the tumbling object. This is
very difficult or even impossible if the target object has a fast
tumbling motion because the tip speed of a manipulator is always
limited not only by the joint rate and torque limits, but also by the
attitude tolerance of the servicing satellite. Cyril and Jaar [241]
analyzed the behavior of a space manipulator capturing a flexible
payload during the impact and the post-capture phase. Papadopoulos and Paraskevas [242] proposed a methodology based on
the well-known Percussion Point of Bodies to minimize the forces
instead of the momentum transmitted to the base of the manipulator when grasping an object. The authors proposed some
guidelines for the best configuration of mechanism at time of
impact. Huang et al. [243] also found that the configuration of the
space manipulator at the contact moment is an important factor
to consider in order to reduce the impact effect. Then, authors
in [244] proposed an optimal approach trajectory planning
method for minimizing the impact. In the same direction, a genetic
algorithm to search optimal configuration of a space manipulator
at the capturing instant to reduce or eliminate the impact effect
was proposed in [245]. To control the system after grasping the
object, an adaptive approach was employed considering the
flexibility of the transported object [246].
During the impact phase, the interaction with the environment
should be considered. Hybrid position/force control has been a
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
basic strategy adopted. However, control mode switching is
required at many points during the task [247]. To handle that
problem, in [248] an impedance-based scheme has been proposed
for controlling the dynamic interaction of a manipulator with the
environment. Yoshida and Nakanishi [249,250] used impedance
matching to model the contact motion between a space manipulator and a non-cooperative satellite. Based on this model, the
authors proposed a criteria to decide if the contact is maintained
with the target or if the target is pushed away. Experiments were
carried out using two manipulators as a motion simulator of the
servicer and the target. Later, Nakanishi et al. [251] introduced the
virtual mass concept to represent the influence of the hand
impedance on the target motion, so that it is possible to prevent
the pushing of the target after contact.
Most of the previous works modeled the contact dynamics only
as an impulse force acting on the tip of the manipulator. Nevertheless, in reality, the contact model is more complicated. Thus, a
more realistic contact model should be included for a more
truthful study. To this end, a generic contact dynamics modeling
and simulation software package called Contact Dynamics Toolkit
(CDT) was developed to support the development and operations
of the SRMS, SSRMS and SPDM robots [252]. The software is
capable of modeling multi-point frictional contact between objects
with complex geometries. It has been experimentally validated for
simulating various contact behaviors such as impact, bouncing,
sliding, rolling spinning, sticking and jamming. The CDT software
was later integrated into a satellite docking simulator to support
the development of satellite docking systems [253]. A robotics
based simulator for verifying microgravity contact dynamics was
developed in [254]. Liu et al. [255] studied the effect of payload
collision on the dynamics and control of a flexible dual-arm space
robot when capturing an object. An impact model to study the
contact during a grasping operation was studied in [256,257] and
an active damping controller was designed to reduce the impact
effect. The contact forces were calculated using the Hertz model.
Impact dynamics was studied using the impulse principle in [258],
where the collision detection was performed with analytic geometry. Ma and Flores-Abad proposed a methodology to reduce the
impact effect during a capturing process [259]. Their approach is
first to predict the best capturing time and configuration such that
the contact force resulting from the first physical contact will or
nearly pass through the center of mass of the whole servicing
system (including both the servicing satellite and the robot). Then
an optimal trajectory of the manipulator was computed for the tip
of the robot to reach the best capturing configuration at the best
time. In this way, the attitude disturbance caused by the contact is
zero or minimal. A contact dynamics analysis for space robotics
applications was presented in [260]. The contact force direction
was estimated based on the known geometries and motion states
of the end-effector and the grapple fixture on the target. Using the
estimated contact force and the observed target motion, an
optimal capturing time and location were determined such that
the resulting physical contact for capturing will cause minimal
attitude impact to the base spacecraft. A recent adaptive reactionless control scheme to overcome the change in the dynamic
parameters of the space manipulator system due to the capture
of an unknown target was presented by Nguyen–Huynh and Sharf
[261,262]. The algorithm is intended to provide minimum disturbances on the base satellite from the capturing phase until the
unknown parameters are identified. The algorithm has the property of producing arm motions with minimum disturbance to the
base after capture of an unknown tumbling target.
Multiple-arm systems. The use of multiple-arms manipulators
(see for example, Fig. 14) also offers advantages in the capturing
and post-capturing phases [263]. Because it allows a firmer
grasping of the target. Thus, as early as 1989, JPL conducted lab
15
Fig. 14. MIT Space Laboratory's double-arm space manipulator [263].
experiments to perform dual-arm satellite grappling [264]. With
the characteristic of reducing the effect of system's angular
momentum change caused by the impact force during a capture
operation and the burden of post impact control, a pre-impact
configuration of a dual-arm space manipulator was introduced by
Cong and Zhang [265,266]. The approach is based on the concept
of generalized straight-arm capture, which was proposed initially
for a single-arm system in [267].
6.2. Free-flying case
To allow larger displacements, free flying systems have also
been proposed in the capturing and the post-capture phase. In this
sense, impedance-based schemes are among the most preferred
methods to handle the physical contact between the robot's endeffector and the target. An impedance control strategy has been
developed for several cooperating manipulators [268] and also
applied to a space robot with multiple arms [269,270], both the
manipulator's end-effector and the capturing object are controlled
to behave with the designated impedance in reaction to any
disturbing external force on the object. Hence, a coordinated
motion of the manipulator and the payload is achieved. With the
objective of reducing the disturbances on the base spacecraft
during a contact with the target, another control structure with
force compensation for multi-arm cooperating manipulator was
proposed in [271]. Methods in [226,246,268,269,271,272] assumed
that the input force to the base from the robots can be controlled
perfectly. However, the thrusters cannot provide accurate position
control because the output forces of the thrusters are constant,
and only the total impulse may be controlled by varying the
thrusters durations [273]. Therefore, Nakanishi and Yoshida
[274,275] presented a control method that does not require
precision in the base control. The end-effector of the manipulator
was controlled like a mass–damper–spring system fixed at a point
in space regardless of the reactive motion of the base. An
impedance controller based on the interaction torque between
the robot tip and the target was analyzed in [276], where the
impedance controller was achieved by introducing passive DOFs in
the controller. The changes in orientation of the spacecraft were
restored back using reaction wheels. Uyama et al. [277,278]
proposed an impedance-based contact control utilizing a stiff
manipulator with a compliant wrist. Their approach is based on
the achievement of a desired coefficient of restitution and
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damping ratio between the manipulator hand and a contact point
of the target.
Another possible solution for the capturing problem is from
the viewpoint of angular momentum. Grasping a target without
considering its momentum imposes difficulties for the postimpact control, and most likely the capturing operation will fail.
One method utilizes a device with controllable momentum
wheels (space leech), which has to be attached to the target and
absorbs the angular momentum [279]. In [280] the idea of
rotational motion-damper was proposed. Using a contact/push
based method, the angular momentum from the target is partially
transferred to the servicing satellite. However, this could result in
separation from the target after each contact and therefore the
usage of gas-jet thrusters for linear motion is unavoidable. This
idea might be useful if the amount of angular momentum in the
target is very large and direct capture is impossible. A similar
method using Impulsive Control was proposed by Yoshikawa et al.
[281]. An experimental verification of the strategy was reported in
[282]. Nakamura et al. [283] utilized a “tethered retrieve” which
was guided to the target through the tension force in the tether
and thrusters positioned on the retriever. During the post-impact
phase, the angular momentum of the target is absorbed in attitude
devices positioned on the retriever. In [284] the service satellite
makes a fly-around maneuver such that the capturing operation
can be conducted with small relative motion between the two
systems. The authors proposed a free-motion path method which
enabled to completely ignore the nonlinearity effect in the
dynamics by taking advantage of the conservative quantities of
the system.
A capture strategy to minimize the base attitude disturbance from
the viewpoint of angular momentum management was discussed in
[285]. The technique is called Bias Momentum. Moreover, a method to
control the transfer of the angular momentum from the target to the
robot base by controlling the arm motion was proposed in [286].
A control method which can realize the consecutive contact so that
the target is not pushed away and the suppression of the undesirable
base rotation is required. Such a strategy was discussed in [287]. In
order to guarantee conservation of contact, impedance control was
utilized and the Distributed Momentum Control (DMC) was used for
zero base rotation. Yoshida et al. [288] proposed a possible control
sequence for the successful completion of a capturing operation. The
authors used the bias momentum approach during the approaching
phase, impedance control during the impact phase and DMC during
the pos-impact phase. Inaba et al. [289] presented some design
requirements when using a space robot to capture a satellite. Special
attention was paid on the image processing for the visual servoing. In
order to reduce and even eliminate the base reactions during a
contact, control-moment reaction gyroscopes (CMGs) were proposed
as actuators for space manipulators [290]. It was shown that the
power consumption was the same as that of a robotic system driven
by conventional joint motors.
In reality a time delay may occur when using an impedance
control approach, which can cause the impulse of the contact to be
very large. Therefore, in [291], experiments and numerical simulations were carried out to verify the effect of the time delay of
impedance control. References [292,293] addressed an optimal
control of a space manipulator in the post-capture phase to bring
the tumbling non-cooperative satellite to rest in minimum time
while ensuring that the magnitude of the interaction torque
between the manipulator and the target remains below a prescribed threshold. A space robotic system that includes components to capture malfunctioning satellites in GEO (Geostationary
Orbit) was proposed by Xu et al. [294]. In the work, 2-DOF docking
and latching mechanisms similar to those used by the SMARTOLEV [295,296] were utilized for capture and docking of the target
satellite.
Once a manipulator has captured a target satellite, the manipulator and the target become a single system with combined mass
properties and dynamics characteristics. In order for the controller
to handle these changes, an adaptation law may be designed. For
this, Liang and Ma [297] introduced an adaptive control approach,
which can be used to assist the control of a servicing satellite
to rendezvous and dock with or capture a tumbling satellite.
A Lyapunov-based tracking law and an adaptation law were proposed
to guarantee the success of the nonlinear control for the post-capture
stabilization of the combined two-satellite system.
Space structures assembly. Due to the need of handling large
structures in space, using free-flying space manipulators has attracted
the attention of some researchers. An example is the work presented
by Senda and Matsumoto, where they conducted experiments for
autonomous truss assembly by a space robot [298]. Different tasks
were tested during this development, such as collision avoidance,
manipulator berthing, components manipulation, visual servoing, task
error recovering and structure construction. According to Whittaker
et al. [299], the team members will be heterogeneous because the
structural assembly tasks are too complex to be done by a single robot
type (Fig. 15). The teams might include remote free-flying robots (with
thrusters and manipulators), simple observation robots for sensing,
and worker robots that can walk across structures and perform fine
manipulation for the assembly and maintenance jobs [300].
Dubowsky and Boning [263] suggested employing a team of
space robots for manipulation and assembly of large flexible
structures (see Fig. 16). The approach uses linear quadratic optimal
control methods to determine the forces needed to position the
structures while minimizing the vibration. In this method, the
vibration damping contribution should come primarily from the
manipulators instead of from the thrusters. Then, the actuation
effort for combined thrust and manipulation is calculated from the
integral of the net forces applied to the structure by the robot.
A reconfigurable brachiating space robot using handrails to
inspect, repair and construct structures in orbit was proposed by
Sawada and Matunga [301]. A method for micro-satellite assembly
tasks in orbit using a two-arm robotic platform was presented in
[302]. For that purpose, control strategies for fitting parts with
almost no clearance and also dealing with flexible objects using
visual and force feedback were developed. Tanaka et al. [303]
proposed the use of a group of orbital servicing robots to provide
assistance in different tasks, such as satellite assembly, disassembly, refueling, and reconfiguration. In [304] a strategy that used
several robots was presented. Later [305], the authors calculated
an optimal trajectory for reducing the fuel consumption. HEROS
[306] (Heterogeneous Expert Robots for On-Orbit Servicing) is
another concept that used a group of robots cooperatively performing servicing missions. The work reported in [305] addressed
this type of application as well. The authors proposed a point-topoint trajectory tracking control of the passive object, while
keeping limited trust firing. Later, the authors calculated an optimal
trajectory to lower the fuel consumption. Rutkovsky et al. [307] also
proposed the use of free-flying manipulators for on-orbit assembly. In
this work a safety payload installation method was introduced. On the
other hand, robots required for constructions of large space structures
need to be precisely controlled. Inaccuracy in joint/actuator friction
and spacecraft attitude control thrusters' inaccuracies can substantially
degrade control system performance. Sensor-based control algorithms
can be used to mitigate the effects of actuator error, but sensors can
add substantially to a space system's weight, complexity, and cost, and
reduce its reliability. Thus, it is desired having a reduced number of
sensors. Boning and Dubowsky [308] presented a method called space
base sensor control, which based on the kinematics configuration of the
system uses the minimum number of sensors that can simultaneously
compensate for errors and disturbance in aspace robot's joint actuators, spacecraft thrusters, and reaction wheels.
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
17
Fig. 15. Concept of heterogeneous robotic teams constructing large flexible space structures on-orbit [263].
Fig. 16. Parallel assembly maneuver [263].
7. Flexibility and vibration suppression
It has been an increasing demand that space manipulators are
lightweight and also capable of accurately performing autonomous manipulation tasks within an acceptable execution time.
As a result, both link and joint flexibility effects become the main
limitation to achieving a satisfactory performance of trajectory
tracking. Thus, the study of this kind of flexibility effects become
important [86,309].
To develop dynamics model of such flexible systems, various
approaches have been used, including the Lagrange method, the
Hamilton principle, and Newton–Euler equations. Torres and
Dubowsky presented the Coupling Map to plan motions of elastically constrained space manipulator systems for lower vibration to
robot's supporting elastic base [310]. A study on the dynamics and
control of large flexible space structures was presented in [311],
where the authors demonstrated their method using LQG/LTR and
H1 controllers. Meirovitch and Lim [312] introduced a maneuvering and control method for flexible space robots. The robot was
assumed to consist of a rigid base, two flexible arms and a rigid
end-effector holding a payload. The rigid body maneuvers were
addressed in an open loop configuration. However, the elastic
motions were controlled using a closed loop LQR method. A study
in the dynamics of space manipulators with an arbitrary number
of slewing and deployable flexible links was performed in [313].
An order-N algorithm, based on the Lagrangian approach and
velocities transformations, was used so that a parametric analysis
of the system dynamics could be carried out to investigate the
effects of initial disturbances, variation of system parameters and
maneuver profiles. The study suggested a significant coupling
between the rigid body motion and structural vibrations. Based
on a nonlinear inversion technique, De Rivals-Mazres et al. [314]
described the position and orientation of the base and the joint
angles of a flexible manipulator by deriving a control law that controls
the output variables. The concept of Virtual Rigid Manipulator was
introduced in [315] to design a feed-back position controller for a
space robot with flexible links. The stability of the controller was
shown by Lyapunov's method. A command input shaping technique
and a sliding mode control method with smooth joint friction
compensation were applied in the control of flexible manipulators in
[316]. Such a controller proved to be very efficient for tracking of
reference trajectories in joint space. Zohoor and Khorsandijou [317]
developed a nonlinear dynamic model of a flying manipulator with
two revolute joints and two highly flexible links. Tension, compression,
twisting and spatial deflections of each link are coupled to each other
by some nonlinear terms. Another work dealing with links flexibility
was reported in [318] where they approximated the bending due to
the flexibility using the static bending curve of Shimoya. An optimal
trajectory planning of a flexible dual-arm space robot with vibration
reduction capability was presented by Wu et al. [319]. The authors
used the Particle Swarm Optimization algorithm to describe the
motion trajectory by the use of a fourth order B-spline with control
points as the parameters to be optimized. For vibration reduction, the
vibrations induced by the links flexibility are included in the performance index. With an objective of suppressing the vibrations due to
the link flexibility in JEMRMS, Abiko and Yoshida [320] introduced an
adaptive controller. Later the authors extended their work by using the
reaction dynamics concept considering inaccuracy in kinematics and
dynamics parameters [321]. Ma and Wang [87] presented a model
reduction for impact-contact dynamics simulations of flexible manipulators. They first linearized the nonlinear contact force model and
then applied the traditional modal analysis and reduction techniques
to reduce the order of the resulting dynamics equations for more
stable and efficient simulation process.
The latest generation of advanced space robots specifically
designed for OOS operations is equipped with extremely lightweight
joint mechanisms, including harmonic drives. These gear mechanisms
18
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
have received increasing attention in robotic applications due to their
attractive properties such as very high reduction ratio, compact size,
low weight, and coaxial assembly. However, with harmonic drives,
elastic vibrations of the flexspline becomes the main issue that
significantly challenges control system development. As explained by
Sweet and Good [322], joint stiffness coupled with damping at the
joints can lead to strongly resonant behaviors when using rigid control
schemes, unless the control bandwidth is severely restricted. In
addition, when handling large payloads, joint or structural flexibility
effect become even more important and can result in payload-attitude
controller fuel-replenishing dynamic interactions. Although both joint
and link flexibilities are influential to the performance of space
manipulators, joint flexibility is often considered more influential than
link flexibility. In [323] Sabatini et al. modeled the vibrations generated
due to the flexibility in the links as well as in the joints of a
manipulator and designed active damping strategies and devices that
could be used to reduce the structural vibrations. Zarafshan and
Moosavian studied the dynamics and control of space robotic systems
with flexible members such as solar panels, appendages and flexible
joints [324]. For a better analysis and control, the authors classified the
flexible members into passive and active categories. The same authors
proposed a fuzzy tuning manipulation control algorithm for the space
robot with flexible members [325]. Masoudi and Mahzoon [326]
presented a LQR regulator to suppress the vibration on a freefloating space robot with flexible arms. The governing equations were
derived using Kane's method and the resulting nonlinear problem was
separated into two sets of equations by a perturbation approach. One
equation is for rigid-body maneuvering of the robot and the other for
elastic vibrations suppression and rigid-body perturbation control.
Ulrich and Sasiadek [327] developed an end-point tracking trajectory
of a space robot considering the elastic vibrations occurring in the
joints. Simulation results showed that in this scenario a better tracking
performance can be achieved by an adaptive control scheme. Thus, the
authors presented a direct adaptive controller [328] and a fuzzy logic
adaptive controller [329] to maintain adequate performance regardless
of parametric uncertainties and modeling errors in the plant (arising
mainly from soft-windup and time-varying joint stiffness effects).
Recently, Kumar et al. [330] developed a trajectory tracking control of a
2-DOF flexible space robot using the Virtual Space Vehicle concept. The
flexible links were modeled as Euler–Bernoulli beams. Another
important contribution of this work is that the authors used Bondgraphs to model the dynamics of the system and to devise the control
strategy.
8. Ground verification
Just as any other space systems, a space manipulator and its
associated control systems must pass all the verification tests on
the ground before it can be launched to the space. For the dynamic
test of control functions and performance, the test facility should
be able to simulate reduced conditions and allow 6-DOF motions
of the robotic system. Surveyed below are the different approaches
employed to emulate zero-G conditions for the development and
verification of space robotics for OOS missions.
Air-bearing supported floating. The most commonly used technology for emulating zero-G is to use an air-bearing based floating
test facility. Such a system usually includes one or two mobile
platforms floating on a flat floor through air bearing pads. It allows
us to test a space manipulator or manipulators to operate in a
simulated 0-G floating condition in a 2D space, which includes one
rotational and two translational DOFs. More DOFs of maneuvering
may be added by suspending the tested object with a multi-DOF
mechanism but more massive support hardware has also to be
added and thus the dynamics properties of the test system would
also be altered. Such a method has been applied for testing the
Fig. 17. Neutral buoyancy facility at University of Maryland [340].
control algorithms of Japanese free-floating systems [331,332], and
the free-flying experiments at Stanford University [333]. All the
major space companies and some research groups in academia
own this kind of facilities such as the ones described in [334–339].
Neutral buoyancy. Another technology for simulating reduced
gravity is to use a water pool to achieve neutral buoyancy, so that
the submerged body has an equal tendency to float as it would in
space. This method has the advantages that an experiment can be
carried out in a 6-DOF space without time constraints. MIT
performed several tests of a teleoperated manipulator using
NASA's neutral buoyancy lab (NBL) at NASA Johnson Space Center
[340]. University of Maryland also owns such a facility and it has
been used to explore the arm–base interaction for a multi-arm
free-flying robot called Ranger [341] as depicted in Fig. 17.
Researchers of the University of Padova have developed a freeflying robot prototype with one extended arm suitable for underwater conditions [342]. The neutral buoyancy technology suffers
from the drag force induced by the water which does not exist in
the space and it is also very expensive to operate. Moreover, all the
tested hardware units must be made water proof or sealed, which
means that the real space hardware cannot be tested as is.
Parabolic flight. An airplane flying in a parabolic trajectory can
also achieve reduced gravity condition. In [343] a 4-DOF robotic
arm was tested in a parabolic flight, under 0.02 g for a 20 s
generated by an MU-300 aircraft. Menon et al. [344] carried out
two flights of 30 parabolas each. Parabolic flight tests were
performed to evaluate an attitude controller for a tethered robot
in [345]. This reduced gravity simulation technology suffers
several obvious drawbacks such as short time duration ð10–30 sÞ
of microgravity or reduced-gravity condition, limited work space
due to the small volume inside an aircraft, and non-smooth or
jittering working environment due to the aircraft dynamic motion
[346]. It is also an expensive technology for operation.
Free fall. A microgravity environment can be generated by freefalling experiments as well. Iwata et al. [347] achieved 3 10 3 g
for 10 s from a distance of 710 m, and Watanabe et al. [348]
obtained 1 10 3 g for 10 s by a free falling experiment from a
vertical distance of 490 m. Both experiments were performed at
the Japan Microgravity Center (JAMIC). The time interval of zero-G
condition using this method is even less than the parabolic flight
and, if it is not performed very carefully, the robot can be easily
damaged. Therefore, this technology is not very suitable for testing
robotic operations.
A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
Force compensation. The gravity force may also be compensated
by a suspension system or a balancing mechanism for zero or
reduced gravity testing of a space manipulator [349]. Such a
system generates compensating forces of the same amplitude
but in the opposite direction as the gravity force of the tested
robot. Sato et al. [350] developed an experiment to test a freefloating space manipulators using such a method. Brown and
Dolan [351] suspended a robot with a cable from an electromechanical system that passively generates mechanical counterbalance forces. White and Xu [352] also developed an active gravity
compensation system. Their testbed utilizes lightweight cables
passing through several pulleys before terminating in the counterweight that has the same effective mass as the simulated robot.
Menon et al. [338] suspended the base of a robot employing an
inextensible cable fixed in the vertical projection of its center of
mass, while the arm links are suspended by springs. The main
drawback of this technology is obviously a static balancing of the
gravity force and thus the system cannot preserve the true
microgravity dynamics of a space manipulator as it would experience in the space. Further, all the suspension cables, as long as
they are not perfectly vertical during a test, apply extra tensions to
the tested robot in non-vertical directions which can significantly
alter the multi-DOF dynamic behavior of the tested space robot.
This problem may be solved by the method of using a multi-DOF
statically balanced mechanism as the one proposed by Ma et al.
[353]. The mechanism employs springs to achieve multi-DOF
gravity balancing at each configuration within the workspace of
the mechanism.
Hardware-in-the-loop systems. The combination of hardware
test and computer simulation is an attractive approach for verification of space robots performing complicated contact tasks.
Since the technology involves both hardware test and software
simulation, it is called hardware-in-the-loop (HIL) simulation or
hybrid simulation. With this technology, a high-bandwidth hardware robotic system is employed to mimic the dynamic behavior
of the simulated space manipulator by strictly following the 3D
motion commands generated by software that simulates the
dynamics of the space manipulator working in the space environment [354]. Such a system helps to simulate not only the
approaching phase but also the capturing/docking phase. The first
19
hybrid simulator reported was developed by Shimoshi et al. [231].
They combined numerical simulation and servo mechanisms. The
system consisted of a facility robot, a 5-DOF translational target,
and software simulating the dynamics of the space robot. Agrawal
et al. [355] proposed two possible laboratory setups to achieve the
relative motion of a free-floating robot with respect to the space
target. In both cases, the target is mounted on the end-effector of
the facility robot. CSA developed a sophisticated HIL simulation
system called STVF (SPDM Task Verification Facility), shown in
Fig. 18, to simulate the dynamic behavior of the space robot SPDM
performing maintenance on the ISS [356]. The simulation facility
has been accepted as the formal verification tool for the SPDM. The
space robot has been successfully launched to the ISS and is
currently performing its regular services there.
Dubowsky et al. implemented a dual-robot HIL simulation
system named Vehicle Emulation System (VES) and VES II, which
consists of a PUMA manipulator mounted on a Stewart platform to
simulate the base spacecraft motion. The emulation was done
using admittance control, so that the Stewart platform moved
according to the desired admittance model [357]. A dualmanipulator-based system for capturing operations was built by
Matunaga et al. [358]. Emulation of zero-G was obtained at CSA
using a controlled manipulator by Aghili [359]. Another HIL facility
developed at CSA for testing autonomous capturing of a tumbling
satellite was presented in [360]. The facility consists of a dual
manipulator system that simulates the tracking and capture
scenario; the manipulator on the left is equipped with a hand,
and the manipulator on the right holds a mock-up satellite to
simulate its tumbling motion. DLR recently developed a dual-robot
HIL simulation system called European Proximity Operations
Simulator (EPOS) [361]. In the system, two KUKA industrial robots
are employed, one of which behaves as a servicing satellite and the
other as a target satellite. The system is capable of simulating
proximity rendezvous and docking of two spacecraft. Such a test
bed is currently being used to support the development of the
DEOS and other future OOS missions [55]. Takahashi et al. [362]
also developed a hybrid system by using a 14-DOF dual-arm robot
and a 9-DOF motion table (including a 6-DOF parallel robot) to
verify orbital operations. Some other test facilities that are also
based on the idea of HIL were presented in [43,363–367], and a
Fig. 18. SPDM Task Verification Facility (STVF) [354].
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A. Flores-Abad et al. / Progress in Aerospace Sciences 68 (2014) 1–26
Fig. 19. Facilities for testing a space robotic system to capture a free-flying satellite or object in space using the HIL concept: (a) NRL's facility for autonomous docking
experiments [43], (b) MIT's VES system [357], (c) CSA's Experimental facility for autonomous capturing [360], (d) DLR's EPOS for proximity operations and docking/capturing
experiments [361], (e) Shenzhen Space Technology Center's ground experimental system [365] and (f) Tohuku University's hybrid simulator for orbital operations
experiments [370].
system that combines air bearing table with HIL concept was
developed by Tsinghua University [368]. HIL simulation is powerful for testing complex space systems for complicated robotic tasks
but it also suffers some drawbacks. First, since the system is driven
by simulation, the mathematical model of the spacecraft or space
robot has to be accurate and the simulation must be performed in
real time for interacting with hardware. Second, the hardware part
of the system must have sufficient bandwidth and proper impedance, so that the active hardware system can produce dynamic
behavior close enough to the real space robot. Finally, the system
has to be able to deal with the inevitable time delay from a
hardware contact to the corresponding simulation-driven reaction
at the tip of the facility robot (not the immediate and passive
reaction of the facility robot). Nevertheless, some researchers have
proposed techniques to handle the HIL time delay problem
[369,370]. Fig. 19 shows some of the above-described HIL simulation facilities.
9. Conclusions
It has been shown that on-orbit services (OOS) such as docking,
berthing, refueling, repairing, upgrading, transporting, rescuing,
and orbit cleanup are of increasing interest to the space industry
because of their high economical potential and also the strategic
benefits. As a result, many enabling techniques for OOS missions
have been developed by the academia and space industry across
the world in the past two decades. These development works
reported in 370 publications have been reviewed with an emphasis on the key areas of kinematics, dynamics, trajectory planning,
control, and ground-based task verification. In addition, several
technology demonstration missions have been successfully accomplished. A review of these accomplished missions revealed that all
of them were designed to service perfectly known and cooperative
targets only. Servicing a non-cooperative satellite or space object
in orbit such as a tumbling satellite or a piece of space debris with
unknown properties by a space robot or multiple robots is still an
untested mission facing many technical challenges. This review
also found that there are still many challenges in the development
and operation of space robots for OOS missions which requires
further development, such as motion estimation and prediction of
the target object, kinematics and dynamics models uncertainties,
on-orbit model parameters identification, safe and reliable capture
mechanisms and strategies, contact dynamics modeling and
simulation of capturing operations, reduction of disturbances to
the base spacecraft caused by robot motion, smart grasping of
natural objects (not designed for grasping), dexterous and efficient
manipulation, time delay and sensor errors in feedback controls,
compliance and intelligent controls, multi-arm coordinated controls and operations, safety when operated near human, physical
simulation of 6-DOF microgravity operation for system level test
and verification, among others. Therefore, further research and
development of these robotics technologies and other related
technologies such as the sensing, actuation, and communication
technologies definitely need to be further advanced.
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