DS-MPEPC: 混雑動的環境における安全でデッドロックを回避するロボットナビゲーション
DS-MPEPC: Safe and Deadlock-Avoiding Robot Navigation in Cluttered Dynamic Scenes
動的環境でのロボットナビゲーションにおいて、衝突確率と終端状態コストを新たに定式化したモデル予測平衡点制御の変種を提案し、デッドロックを減らし滑らかな軌道を生成する。
著者: Senthil Hariharan Arul, Jong Jin Park, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing over a trajectory cost function at each timestep. We present a novel trajectory cost formulation that significantly reduces the conservative and deadlock behaviors and generates smooth trajectories. In particular, we propose a new collision probability function that effectively captures the risk associated with a given configuration and the time to avoid collisions based on the velocity direction. Moreover, we propose a terminal state cost based on the expected time-to-goal and time-to-collision values that helps in avoiding trajectories that could result in deadlock. We evaluate our cost formulation in multiple simulated and real-world scenarios, including narrow corridors with dynamic obstacles, and observe significantly improved navigation behavior and reduced deadlocks as compared to prior methods.
