歪みを補正したパラメータ化配置空間による凸集合グラフ上の短い経路計画
Planning Shorter Paths in Graphs of Convex Sets by Undistorting Parametrized Configuration Spaces
凸集合グラフ(GCS)を用いた軌道最適化において、非線形パラメータ化による距離の歪みを補正し、非凸目的関数を扱えるようにすることで、実行可能性を保証しつつ経路長と軌道時間を大幅に改善する手法を提案した。
著者: Shruti Garg, Thomas Cohn, Russ Tedrake
分類: cs.RO
原文アブストラクト
Optimization based motion planning provides a useful modeling framework through various costs and constraints. Using Graph of Convex Sets (GCS) for trajectory optimization gives guarantees of feasibility and optimality by representing configuration space as the finite union of convex sets. Nonlinear parametrizations can be used to extend this technique to handle cases such as kinematic loops, but this distorts distances, such that solving with convex objectives will yield paths that are suboptimal in the original space. We present a method to extend GCS to nonconvex objectives, allowing us to "undistort" the optimization landscape while maintaining feasibility guarantees. We demonstrate our method's efficacy on three different robotic planning domains: a bimanual robot moving an object with both arms, the set of 3D rotations using Euler angles, and a rational parametrization of kinematics that enables certifying regions as collision free. Across the board, our method significantly improves path length and trajectory duration with only a minimal increase in runtime. Website: https://shrutigarg914.github.io/pgd-gcs-results/
