Online Path Planning and Compliance Control of Space Robot for Capturing Tumbling Large Object

Hirano, Daichi; Kato, Hideki; Saito, Tatsuhiko

doi:10.1109/iros.2018.8594099

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Given an initial and final positions, xi and xf, respectively of the manipulator, and a set of desired constraints, find a continuous path such that the path starts from xi at initial time t0 and reaches xf at a given time tf while satisfying all constrains at all time during the operation. The considered constrains are: (1) obstacle avoidance (static or dynamic relative to the service robot), (2) collision avoidance, (3) singularity avoidance (kinematic and dynamic), and (4) dynamic coupling effect minimization or (5) attitude control [3,5,7]. Fast and on-line solution of this problem is also a vital feature for the space robots, and for this analysis.…”

Section: Considered Path Planning Problemmentioning

confidence: 99%

“…Additionally, relevant features stand out, for example: collaborative use of the base and manipulator for planning [3,5], effective handling of multiple constraints via dynamic adjusting of optimization penalty factors [5], as well as by constraint prioritization depending on the task [6]. On-line path planning was allowed by reducing the number of constraints and thus computation power [7], and by training machine learning models (ML) to derive optimal path planning policies off-line [8]. ML was also used to provide an initial guess for the optimization procedure [9], contrasting to the methods which do not require a priori knowledge [3].…”

Section: B Observationsmentioning

confidence: 99%

“…ML was also used to provide an initial guess for the optimization procedure [9], contrasting to the methods which do not require a priori knowledge [3]. For detailed discussion, readers are referred to papers [3][4][5][6][7][8][9].…”

Section: B Observationsmentioning

confidence: 99%

See 2 more Smart Citations

A Short Survey on Recent State-of-the-Art Methods for Optimal Path Planning for Small On-Orbit Space Robots

Arreola¹,

Upadhyay²

2022

UK-RAS Conference for PhD and Early Career Researchers Proceedings

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Optimal path planning in the presence of multiple kinodynamic constraints and limited onboard computation power is a key challenge for small on-orbit space robots. This work surveys parameterized function and machine-learning based path planning approaches based on number of constraints solved and computation complexity. Limitations of the existing approaches and potential directions are discussed.

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Section: Considered Path Planning Problemmentioning

confidence: 99%

Section: B Observationsmentioning

confidence: 99%

See 1 more Smart Citation

A Short Survey on Recent State-of-the-Art Methods for Optimal Path Planning for Small On-Orbit Space Robots

Arreola¹,

Upadhyay²

2022

UK-RAS Conference for PhD and Early Career Researchers Proceedings

View full text Add to dashboard Cite

show abstract

“…A coordinated control method for a single manipulator capturing of a tumbling target, implementing a fast, on-line updating manipulator path planner and end-effector compliance control, was proposed ( Gangapersaud et al, 2019 ). Coordinated detumbling of a non-cooperative captured target, with simultaneous servicing vehicle attitude PD control, was developed in Hirano et al (2018) . However, both abovementioned methods do not consider singularity avoidance or manipulator workspace constraints.…”

Section: Feedback Controlmentioning

confidence: 99%

Robotic Manipulation and Capture in Space: A Survey

Papadopoulos

Aghili

et al. 2021

Front. Robot. AI

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Space exploration and exploitation depend on the development of on-orbit robotic capabilities for tasks such as servicing of satellites, removing of orbital debris, or construction and maintenance of orbital assets. Manipulation and capture of objects on-orbit are key enablers for these capabilities. This survey addresses fundamental aspects of manipulation and capture, such as the dynamics of space manipulator systems (SMS), i.e., satellites equipped with manipulators, the contact dynamics between manipulator grippers/payloads and targets, and the methods for identifying properties of SMSs and their targets. Also, it presents recent work of sensing pose and system states, of motion planning for capturing a target, and of feedback control methods for SMS during motion or interaction tasks. Finally, the paper reviews major ground testing testbeds for capture operations, and several notable missions and technologies developed for capture of targets on-orbit.

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“…Exploiting the non-holonomic behavior of the orbital manipulator system for spacecraft attitude and end-effector trajectory control have been studied extensively. It usually involves joint space techniques to control both the motion of the arm and sometimes the spacecraft attitude Yoshida and Nakanishi, 2003;Hirano et al, 2018. Early research used mapping methods to correlate the end-effector position with the induced disturbances on the spacecraft to minimize the attitude disturbances Torres and Dubowsky, 1992;Vafa and Dubowsky, 1993. However, the mapping methods are computationally inefficient and furthermore, higher DoF manipulators will significantly increase the mapping difficulty and are challenging to find optimised paths.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

et al. 2021

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This paper adds on to the on-going efforts to provide more autonomy to space robots and introduces the concept of programming by demonstration or imitation learning for trajectory planning of manipulators on free-floating spacecraft. A redundant 7-DoF robotic arm is mounted on small spacecraft dedicated for debris removal, on-orbit servicing and assembly, autonomous and rendezvous docking. The motion of robot (or manipulator) arm induces reaction forces on the spacecraft and hence its attitude changes prompting the Attitude Determination and Control System (ADCS) to take large corrective action. The method introduced here is capable of finding the trajectory that minimizes the attitudinal changes thereby reducing the load on ADCS. One of the critical elements in spacecraft trajectory planning and control is the power consumption. The approach introduced in this work carry out trajectory learning offline by collecting data from demonstrations and encoding it as a probabilistic distribution of trajectories. The learned trajectory distribution can be used for planning in previously unseen situations by conditioning the probabilistic distribution. Hence almost no power is required for computations after deployment. Sampling from a conditioned distribution provides several possible trajectories from the same start to goal state. To determine the trajectory that minimizes attitudinal changes, a cost term is defined and the trajectory which minimizes this cost is considered the optimal one.

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Online Path Planning and Compliance Control of Space Robot for Capturing Tumbling Large Object

Cited by 7 publications

References 13 publications

A Short Survey on Recent State-of-the-Art Methods for Optimal Path Planning for Small On-Orbit Space Robots

A Short Survey on Recent State-of-the-Art Methods for Optimal Path Planning for Small On-Orbit Space Robots

Robotic Manipulation and Capture in Space: A Survey

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

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