Provably Safe Motion Planning Under Unknown
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Provably Safe Motion Planning Under Unknown Disturbances
Announce Type: replace Abstract: We present a provably safe sampling-based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We consider systems with linear or linearizable dynamics evolving in workspace with arbitrary-shaped obstacles subject to state and control constraints. Safety requirements are formulated as chance-constraints.