凸障害物周りの滑らかな最短時間軌道のための双凸最適化
Biconvex Optimization for Smooth Minimum-Time Trajectories around Convex Obstacles
凸障害物を回避する最短時間軌道計画を双凸最適化問題として定式化し、分離平面の更新と軌道最適化を交互に行うことで、局所解に陥らず高品質な軌道を高速に生成する手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Peter Werner, Tobia Marcucci, Daniela Rus
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
We present a biconvex approach for minimum-time motion planning around convex obstacles that is guaranteed to converge, is anytime, and supports derivative constraints to arbitrary order. We jointly convexify the minimum-time objective and all derivative constraints through a change of variables, and handle collision avoidance via time-varying separating planes, reducing the problem to a biconvex program. This program is solved by alternating between computing maximum-margin separating planes and optimizing the trajectory. By only adding planes for obstacles that the current iterate collides with, the trajectory can jump around obstacles and escape local minima. The method is guaranteed to converge starting from a simple collision-free polygonal curve. In our experiments on drone navigation and dual-arm bin unloading, we find that the proposed method reliably produces high-quality trajectories with computation times comparable to state-of-the-art decomposition-based motion planners, while handling a larger class of problems and being substantially more robust to bad initialization. Project page:https://wernerpe.github.io/bmtp-website/
