Optimal Trajectory Planning and Compliant Spacecraft Capture Using a Space Robot

Crain, Alexander

doi:10.22215/etd/2018-12971

Cited by 4 publications

(3 citation statements)

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“…The related research [4][5][6][7][8][9] shows that the space robot is in a free-floating state during the process of capturing the target, and the collision from the target contact may lead to a large pulse momentum. The strong dynamic coupling between the spacecraft base and the manipulator may lead to instability of the attitude of the space robot base, which in turn may cause problems such as space robot rollover.…”

Section: Introductionmentioning

confidence: 99%

Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot

Dong

Hong

2019

Electronics

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With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the robot base posture control. Traditional Reaction Null Space (RNS) motion planning and control methods require the construction of precise dynamic models, which is impossible in reality. In order to solve this problem, this paper proposes a new Adaptive Reaction Null Space (ARNS) path planning and control strategy for the contact of free-floating space robots with unknown targets. The ARNS path planning strategy is constructed by the Variable Forgetting Factor Recursive Least Squares (VFF–RLS) algorithm. At the same time, a robust adaptive control strategy based on the Strategy Self-Adaption Differential Evolution–Extreme Learning Machine (SSADE–ELM) algorithm is proposed to track the dynamic changes of the planned path. The algorithm enables us to intelligently learn and compensate for the unknown disturbance. Then, this paper constructs a robust controller to compensate model uncertainty. A striking feature of the proposed strategy is that it does not require an accurate system model or any information about unknown attributes. This design can dynamically implement RNS path tracking performance. Finally, through simulation and experiment, the proposed algorithm is compared with the existing methods to prove its effectiveness and superiority.

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Section: Introductionmentioning

confidence: 99%

Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot

Dong

Hong

2019

Electronics

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“…The sum of the three measurements is taken as the total mass of the platform. The centre of mass location was also obtained as the ratio of the different measurements, as described in [141].…”

Section: Conference Proceedingsmentioning

confidence: 99%

“…B.1b. The centre of mass location of each link is obtained using the ratio of the two readings, as outlined in [141]. This chapter presents theory and preliminary results of using the Deep Deterministic Policy Gradient (DDPG) algorithm [90], a deep reinforcement learning algorithm, to guide and control a simulated robotic manipulator to place its end-effector on a desired location.…”

Section: Conference Proceedingsmentioning

confidence: 99%

Deep Reinforcement Learning as Guidance for Aerospace Robotics

Hovell¹

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The ability for a manipulator-equipped chaser spacecraft to autonomously capture a target spacecraft is an unsolved prerequisite for space debris removal and on-orbit servicing. This thesis investigates using deep reinforcement learning (DRL) to improve the capabilities of a manipulator-equipped chaser at this task. DRL allows for behaviour to be learned, rather than designed, according to a simple reward function.DRL uses trial-and-error to learn the behaviour, which is not feasible to perform on-board a spacecraft. Training must therefore be performed in simulation with the resulting behaviour transferred to the spacecraft. Transferring the learned-insimulation behaviour to a real robot, however, is difficult due to dynamics differences between the simulator and the real world, i.e., the simulation-to-reality gap. This thesis develops, over the course of four increasingly-difficult applications, a solution to the simulation-to-reality gap by restricting DRL to exclusively learn the guidance portion of the guidance, navigation, and control system needed for autonomous spacecraft operations. The first application is spacecraft proximity operations (without capture), where a DRL-based guidance strategy issuing desired velocity signals is designed, trained, and evaluated in simulation and experiment. Next, the DRL-based guidance strategy is improved upon and applied to a quadrotor proximity operations scenario. Here, it is demonstrated in simulation and experiment that desired acceleration signals lead to better performance compared to desired velocity signals. These two proof-of-concept results show the proposed DRL-based guidance strategy is viable for bringing DRL to real aerospace vehicles. Next, the DRL-based guidance strategy is applied to a more difficult scenario: a multi-agent cooperative quadrotor runway inspection task, where fault-tolerant behaviour is successfully learned and demonstrated in both simulation and a real, outdoor, GPS-driven quadrotor facility. Finally, with the now-developed DRL-based guidance strategy, the author returns to the central motivator for this research: autonomous manipulator-based capture of a iii spinning spacecraft. The DRL-based guidance strategy learns this task in simulation and is successfully transferred to an experimental facility where similar results are obtained. Additionally, capture is successful in experiment despite large perturbations and initial conditions not seen during training. Improvements to the experimental facility were performed to enable this research. iv PrefaceThis is an 'integrated thesis' that contains chapters which have already been published or have been prepared for publication as journal articles or conference proceedings.Chapter 2 has been peer-reviewed and was published in the Journal of Spacecraft and Rockets, with the authors retaining copyright. The paper is included in this thesis with minor formatting changes and variable renaming (for consistency between chapters), along with improvements to the theory in Sec. 2.3. This paper was c...

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Compliant Spacecraft Capture via a Nonlinear Disturbance Observer-based Impedance Controller

Crain

Ulrich

Flores-Abad

2020

AIAA Scitech 2020 Forum

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Optimal Trajectory Planning and Compliant Spacecraft Capture Using a Space Robot

Cited by 4 publications

References 50 publications

Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot

Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot

Deep Reinforcement Learning as Guidance for Aerospace Robotics

Compliant Spacecraft Capture via a Nonlinear Disturbance Observer-based Impedance Controller

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