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REVIEW article
Front. Robot. AI
Sec. Space Robotics
Volume 11 - 2024 |
doi: 10.3389/frobt.2024.1470950
This article is part of the Research Topic Multi-Rover Exploration and Moon Orbital Spacecraft Servicing View all articles
A Comprehensive Survey of Space Robotic Manipulators for On-Orbit Servicing
Provisionally accepted- York University, Toronto, Ontario, Canada
On-Orbit Servicing (OOS) robots are transforming space exploration by enabling vital maintenance and repair of spacecraft directly in space. However, achieving precise and safe manipulation in microgravity necessitates overcoming significant challenges. This survey delves into four crucial areas essential for successful OOS manipulation: object state estimation, motion planning, and feedback control. Techniques from traditional vision to advanced X-ray and neural network methods are explored for object state estimation. Strategies for fuel-optimized trajectories, docking maneuvers, and collision avoidance are examined in motion planning. The survey also explores control methods for various scenarios, including cooperative manipulation and handling uncertainties, in feedback control. Additionally, this survey examines how Machine learning techniques can further propel OOS robots towards more complex and delicate tasks in space.
Keywords: On-orbit servicing, Space robots, Robotic Manipulator, motion planning, machine learning, Pose estimation, Control
Received: 26 Jul 2024; Accepted: 06 Sep 2024.
Copyright: © 2024 Alizadeh and Zhu. 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) or licensor 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.
* Correspondence:
Zheng H. Zhu, York University, Toronto, M3J 1P3, Ontario, Canada
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