A sampling-based planner approximates Riemannian geodesic distances via midpoints with third-order accuracy and uses retractions plus natural gradients for local planning, producing lower-cost trajectories than Euclidean baselines on robotic arms and SE(2) systems.
Journal of the ACM (JACM)40(5), 1048–1066 (1993)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Geometry-Aware Sampling-Based Motion Planning on Riemannian Manifolds
A sampling-based planner approximates Riemannian geodesic distances via midpoints with third-order accuracy and uses retractions plus natural gradients for local planning, producing lower-cost trajectories than Euclidean baselines on robotic arms and SE(2) systems.