静的環境と群衆環境における運動力学的局所計画のための長期動的ウィンドウ法
Long-Term Dynamic Window Approach for Kinodynamic Local Planning in Static and Crowd Environments
長期動的ウィンドウ法とグラフ最適化を組み合わせ、差動車輪ロボットの運動力学的制約を満たしつつ、静的環境と群衆環境の両方で安全で滑らかな局所計画をリアルタイムに生成する手法を提案した。
著者: Zhiqiang Jian, Songyi Zhang, Lingfeng Sun, Wei Zhan, Nanning Zheng, Masayoshi Tomizuka
分類: cs.RO
原文アブストラクト
Local planning for a differential wheeled robot is designed to generate kinodynamic feasible actions that guide the robot to a goal position along the navigation path while avoiding obstacles. Reactive, predictive, and learning-based methods are widely used in local planning. However, few of them can fit static and crowd environments while satisfying kinodynamic constraints simultaneously. To solve this problem, we propose a novel local planning method. The method applies a long-term dynamic window approach to generate an initial trajectory and then optimizes it with graph optimization. The method can plan actions under the robot's kinodynamic constraints in real time while allowing the generated actions to be safer and more jitterless. Experimental results show that the proposed method adapts well to crowd and static environments and outperforms most SOTA approaches.
