グラフ探索と軌道最適化を統合した高自由度ロボット向け遅延軌道最適化
LTO: Lazy Trajectory Optimization with Graph-Search Planning for High DOF Robots in Cluttered Environments
局所的な軌道最適化とグラフ探索計画を組み合わせ、混雑環境で高自由度ロボットの長期的に最適な軌道を高速かつ高信頼に生成する手法を提案した論文。
著者: Yuki Shirai, Xuan Lin, Ankur Mehta, Dennis Hong
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Although Trajectory Optimization (TO) is one of the most powerful motion planning tools, it suffers from expensive computational complexity as a time horizon increases in cluttered environments. It can also fail to converge to a globally optimal solution. In this paper, we present Lazy Trajectory Optimization (LTO) that unifies local short-horizon TO and global Graph-Search Planning (GSP) to generate a long-horizon global optimal trajectory. LTO solves TO with the same constraints as the original long-horizon TO with improved time complexity. We also propose a TO-aware cost function that can balance both solution cost and planning time. Since LTO solves many nearly identical TO in a roadmap, it can provide an informed warm-start for TO to accelerate the planning process. We also present proofs of the computational complexity and optimality of LTO. Finally, we demonstrate LTO's performance on motion planning problems for a 2 DOF free-flying robot and a 21 DOF legged robot, showing that LTO outperforms existing algorithms in terms of its runtime and reliability.
