Problem
Research questions and friction points this paper is trying to address.
Actuator Failures
Free-Flying Space Robots
Motion Planning
Resilient
Resilient Motion Planning for Free-Flying Space Robots under Actuator Failures
🤖 AI Summary
该研究针对太空机器人在推进器故障下的运动规划问题，通过建立马尔可夫链模型预测故障，并采用RRT*算法规划路径以最大化目标到达概率。
📝 Abstract
Free-flying robots rely on multiple thrusters to maneuver in space. If one or more of these thrusters fail, the robot may lose control authority and risk mission failure. At the same time, their free-flying nature implies that, even in the absence of actuation, they continue along (locally) straight-line trajectories. In this work we present a probabilistic, proactive, motion planning framework that explicitly accounts for actuator failures in space. We model actuator failure modes as a Markov chain and propagate the probability of successfully reaching the goal along the planning horizon. Precomputed reachable sets evaluate the robot's capabilities of reaching waypoints under potential failures and an RRT$^*$-based planner concatenates these waypoints. The resulting algorithm maximizes the overall target-reaching probability, providing maximally resilient motion plans utilizing free-flying properties. We validate our approach experimentally on a physical free-flyer platform with injected actuator failures.
Innovation
Methods, ideas, or system contributions that make the work stand out.
probabilistic motion planning
actuator failures
Markov chain
RRT*
resilient motion plans
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