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Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

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 dedic...

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Autores principales: Ashith Shyam, R. B., Hao, Zhou, Montanaro, Umberto, Dixit, Shilp, Rathinam, Arunkumar, Gao, Yang, Neumann, Gerhard, Fallah, Saber
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130759/
https://www.ncbi.nlm.nih.gov/pubmed/34017860
http://dx.doi.org/10.3389/frobt.2021.638849
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author Ashith Shyam, R. B.
Hao, Zhou
Montanaro, Umberto
Dixit, Shilp
Rathinam, Arunkumar
Gao, Yang
Neumann, Gerhard
Fallah, Saber
author_facet Ashith Shyam, R. B.
Hao, Zhou
Montanaro, Umberto
Dixit, Shilp
Rathinam, Arunkumar
Gao, Yang
Neumann, Gerhard
Fallah, Saber
author_sort Ashith Shyam, R. B.
collection PubMed
description 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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spelling pubmed-81307592021-05-19 Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation Ashith Shyam, R. B. Hao, Zhou Montanaro, Umberto Dixit, Shilp Rathinam, Arunkumar Gao, Yang Neumann, Gerhard Fallah, Saber Front Robot AI Robotics and AI 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. Frontiers Media S.A. 2021-05-04 /pmc/articles/PMC8130759/ /pubmed/34017860 http://dx.doi.org/10.3389/frobt.2021.638849 Text en Copyright © 2021 Ashith Shyam, Hao, Montanaro, Dixit, Rathinam, Gao, Neumann and Fallah. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Ashith Shyam, R. B.
Hao, Zhou
Montanaro, Umberto
Dixit, Shilp
Rathinam, Arunkumar
Gao, Yang
Neumann, Gerhard
Fallah, Saber
Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation
title Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation
title_full Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation
title_fullStr Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation
title_full_unstemmed Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation
title_short Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation
title_sort autonomous robots for space: trajectory learning and adaptation using imitation
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130759/
https://www.ncbi.nlm.nih.gov/pubmed/34017860
http://dx.doi.org/10.3389/frobt.2021.638849
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