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用于在轨服务的空间机器人操纵器综合调查。

A comprehensive survey of space robotic manipulators for on-orbit servicing.

作者信息

Alizadeh Mohammad, Zhu Zheng H

机构信息

Department of Mechanical Engineering, York University, Toronto, ON, Canada.

出版信息

Front Robot AI. 2024 Oct 9;11:1470950. doi: 10.3389/frobt.2024.1470950. eCollection 2024.

DOI:10.3389/frobt.2024.1470950
PMID:39445150
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11496037/
Abstract

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.

摘要

在轨服务（OOS）机器人正在通过直接在太空中对航天器进行重要的维护和修理来改变太空探索。然而，在微重力环境下实现精确且安全的操作需要克服重大挑战。本综述深入探讨了成功进行在轨服务操作至关重要的四个关键领域：物体状态估计、运动规划和反馈控制。从传统视觉技术到先进的X射线和神经网络方法，都被用于探索物体状态估计。在运动规划中，研究了燃料优化轨迹、对接操作和碰撞避免的策略。本综述还探讨了反馈控制中各种场景的控制方法，包括协同操作和处理不确定性。此外，本综述研究了机器学习技术如何能进一步推动在轨服务机器人在太空中执行更复杂、更精细的任务。

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