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.
Exact statistical mechanics of a one-dimensional system with coulomb forces,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.