发表机构
ETH Zürich; KTH Royal Institute of Technology; Digital Futures（苏黎世联邦理工学院; 瑞典皇家理工学院; Digital Futures）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自由飞行空间机器人推进器故障，提出基于马尔可夫链和可达集的概率运动规划框架，最大化目标到达概率，并在物理平台上验证了其弹性。
AI 中文摘要
自由飞行机器人依赖多个推进器在太空中机动。如果一个或多个推进器发生故障，机器人可能失去控制权限并面临任务失败的风险。同时，它们的自由飞行特性意味着，即使在无驱动的情况下，它们也会继续沿（局部）直线轨迹运动。在这项工作中，我们提出了一种概率性、主动式的运动规划框架，该框架明确考虑了太空中的执行器故障。我们将执行器故障模式建模为马尔可夫链，并在规划时域内传播成功到达目标的概率。预计算的可达集评估了机器人在潜在故障下到达航路点的能力，基于RRT$^*$的规划器将这些航路点串联起来。所提出的算法最大化总体目标到达概率，利用自由飞行特性提供最大弹性的运动计划。我们在物理自由飞行平台上通过注入执行器故障进行了实验验证。
英文摘要
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.
Comments7 pages, 7 figures
