FDIT* uses repulsive forces from invalid samples and an elliptical k-nearest-neighbor search to guide sampling-based planning, reporting up to 34.65% lower initial path cost than EIT* on R4-R16 benchmarks.
Benchmarking motion planning algorithms: An extensible infrastructure for analysis and visualization,
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Elliptical K-Nearest Neighbors -- Path Optimization via Coulomb's Law and Invalid Vertices in C-space Obstacles
FDIT* uses repulsive forces from invalid samples and an elliptical k-nearest-neighbor search to guide sampling-based planning, reporting up to 34.65% lower initial path cost than EIT* on R4-R16 benchmarks.