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Research questionHow can sampling-based motion planners maintain asymptotic near-optimality when cheaper local paths crowd out near-optimal trajectory segments?Such guarantees may rely on retaining trajectory segments that approximate an optimal solution. Locally cheaper alternatives can instead prevent those segments from entering the planning tree, undermining the usual proof argument.
Robotics
Latest papersRecent research connected to this question, newest first.Achieving Asymptotic Near-Optimality Without $δ$-SimilarityThe source analyzes sampling-based motion planning in complex, high-dimensional environments with forward-dynamics propagation for kinodynamic constraints. It identifies crowding-out cases, provides an environment and system where inductive sampling of a δ-similar solution trajectory is impossible, and argues that near-optimality guarantees can still be obtained when crowding out is accounted for.research paper · Sep 3, 2026
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