勾配場ベースの動的ウィンドウ法による複雑環境での衝突回避
Gradient Field-Based Dynamic Window Approach for Collision Avoidance in Complex Environments
障害物距離の勾配情報を新たなコスト項として動的ウィンドウ法に組み込み、非凸形状の障害物を含む複雑環境での予測的な衝突回避を実現した。
著者: Ze Zhang, Yifan Xue, Nadia Figueroa, Knut Åkesson
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
For safe and flexible navigation in multi-robot systems, this paper presents an enhanced and predictive sampling-based trajectory planning approach in complex environments, the Gradient Field-based Dynamic Window Approach (GF-DWA). Building upon the dynamic window approach, the proposed method utilizes gradient information of obstacle distances as a new cost term to anticipate potential collisions. This enhancement enables the robot to improve awareness of obstacles, including those with non-convex shapes. The gradient field is derived from the Gaussian process distance field, which generates both the distance field and gradient field by leveraging Gaussian process regression to model the spatial structure of the environment. Through several obstacle avoidance and fleet collision avoidance scenarios, the proposed GF-DWA is shown to outperform other popular trajectory planning and control methods in terms of safety and flexibility, especially in complex environments with non-convex obstacles.
