車両型ロボットの軌道平滑化のための凸最適化手法
A Convex Optimization Approach to Smooth Trajectories for Motion Planning with Car-Like Robots
サンプリングベースの経路計画で生じるギザギザな軌道を、形状と速度の最適化を凸計画問題として反復的に解くことで高速に平滑化するCESアルゴリズムを提案した。
著者: Zhijie Zhu, Edward Schmerling, Marco Pavone
分類: cs.RO
原文アブストラクト
In the recent past, several sampling-based algorithms have been proposed to compute trajectories that are collision-free and dynamically-feasible. However, the outputs of such algorithms are notoriously jagged. In this paper, by focusing on robots with car-like dynamics, we present a fast and simple heuristic algorithm, named Convex Elastic Smoothing (CES) algorithm, for trajectory smoothing and speed optimization. The CES algorithm is inspired by earlier work on elastic band planning and iteratively performs shape and speed optimization. The key feature of the algorithm is that both optimization problems can be solved via convex programming, making CES particularly fast. A range of numerical experiments show that the CES algorithm returns high-quality solutions in a matter of a few hundreds of milliseconds and hence appears amenable to a real-time implementation.
