aiqbench analysis
This research addresses a critical reliability challenge in space robotics by developing a motion planning framework that explicitly models actuator failures using Markov chains and RRT* algorithms. The approach leverages the inherent drift of free-flying spacecraft to maximize mission success probability even when thrusters degrade, validated through physical experiments with injected faults. While the application is robotic, the core contribution lies in advanced probabilistic control theory and resilience strategies applicable to autonomous systems operating in constrained environments.
Resilient Motion Planning for Free-Flying Space Robots under Actuator Failures
arXiv:2609.20407v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2609.20407 [cs.RO]
(or arXiv:2609.20407v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.20407 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 233 words
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