不確実環境における高自由度ロボットのチャンス制約付き軌道最適化
Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments
環境と自己位置の不確実性を考慮し、リスクを制限した軌道を逐次凸最適化で生成するSCORAを提案し、安全性と計算速度で既存手法を上回ることを示した。
著者: Charles Dawson, Ashkan Jasour, Andreas Hofmann, Brian Williams
分類: cs.RO
原文アブストラクト
Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly important when planning safe robot trajectories. The first is environmental uncertainty -- uncertainty in the locations of nearby obstacles, stemming from sensor noise or (in the case of obstacles' future locations) prediction error. The second class of uncertainty is uncertainty in the robots own state, typically caused by tracking or estimation error. To achieve high levels of safety, it is necessary for robots to consider both of these sources of uncertainty. In this paper, we propose a risk-bounded trajectory optimization algorithm, known as Sequential Convex Optimization with Risk Optimization (SCORA), to solve chance-constrained motion planning problems despite both environmental uncertainty and tracking error. Through experiments in simulation, we demonstrate that SCORA significantly outperforms state-of-the-art risk-aware motion planners both in planning time and in the safety of the resulting trajectories.
