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Greedy Heuristics for Sampling-Based Motion Planning in High-Dimensional State Spaces
Informed sampling techniques accelerate the convergence of sampling-based motion planners by biasing sampling toward regions of the state space that are most likely to yield better solutions. However, when the current solution path contains redundant or tortuous segments, the resulting informed subs
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