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.
IEEE Robotics & Automation Magazine19(4), 72–82 (2012)
3 Pith papers cite this work. Polarity classification is still indexing.
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Formulates optimal knock-pick planning for grid-placed uniform blocks and solves it in polynomial time via maximum-weight perfect matching on a graphical abstraction of minimal constraining gadgets.
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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.
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Optimal Knock-Pick Planning for Tightly Packed Tabletop Blocks With Parallel Grippers
Formulates optimal knock-pick planning for grid-placed uniform blocks and solves it in polynomial time via maximum-weight perfect matching on a graphical abstraction of minimal constraining gadgets.
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