NeHMO approximates a safety value function via neural Hamilton-Jacobi reachability learning and integrates it into decentralized trajectory optimization for scalable safe multi-arm robotic motion planning.
A review of path-planning approaches for multiple mobile robots
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Introduces probabilistically complete and asymptotically optimal sampling-based planners for multi-modal multi-robot multi-goal path planning by adapting standard methods to the composite space of all robots.
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NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
NeHMO approximates a safety value function via neural Hamilton-Jacobi reachability learning and integrates it into decentralized trajectory optimization for scalable safe multi-arm robotic motion planning.
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Sampling-Based Multi-Modal Multi-Robot Multi-Goal Path Planning
Introduces probabilistically complete and asymptotically optimal sampling-based planners for multi-modal multi-robot multi-goal path planning by adapting standard methods to the composite space of all robots.