制御バリア関数を用いた動的障害物回避のための安全臨界計画と制御
Safety-Critical Planning and Control for Dynamic Obstacle Avoidance Using Control Barrier Functions
凸ポリトープから離散時間高次制御バリア関数を生成し、モデル予測制御と組み合わせることで、障害物の境界方程式を必要とせずに動的環境での衝突回避軌道を生成するフレームワークを提案した。
著者: Shuo Liu, Yihui Mao, Calin A. Belta
分類: cs.RO, math.OC
原文アブストラクト
Dynamic obstacle avoidance is a challenging topic for optimal control and optimization-based trajectory planning problems. Many existing works use Control Barrier Functions (CBFs) to enforce safety constraints for control systems. CBFs are typically formulated based on the distance to obstacles, or integrated with path planning algorithms as a safety enhancement tool. However, these approaches usually require knowledge of the obstacle boundary equations or have very slow computational efficiency. In this paper, we propose a framework based on model predictive control (MPC) with discrete-time high-order CBFs (DHOCBFs) to generate a collision-free trajectory. The DHOCBFs are first obtained from convex polytopes generated through grid mapping, without the need to know the boundary equations of obstacles. Additionally, a path planning algorithm is incorporated into this framework to ensure the global optimality of the generated trajectory. We demonstrate through numerical examples that our framework allows a unicycle robot to safely and efficiently navigate tight, dynamically changing environments with both convex and nonconvex obstacles. By comparing our method to established CBF-based benchmarks, we demonstrate superior computing efficiency, length optimality, and feasibility in trajectory generation and obstacle avoidance.
