制御バリア関数で誘導するサンプリングベース動作計画
Sampling-based Motion Planning via Control Barrier Functions
動的障害物のある環境で非線形システムの制御を生成するため、制御バリア関数の制約を課した二次計画とRRTを組み合わせたCBF-RRTを提案し、近傍探索や衝突判定を不要にして計算負荷を削減した。
著者: Guang Yang, Bee Vang, Zachary Serlin, Calin Belta, Roberto Tron
分類: cs.RO
原文アブストラクト
Robot motion planning is central to real-world autonomous applications, such as self-driving cars, persistence surveillance, and robotic arm manipulation. One challenge in motion planning is generating control signals for nonlinear systems that result in obstacle free paths through dynamic environments. In this paper, we propose Control Barrier Function guided Rapidly-exploring Random Trees (CBF-RRT), a sampling-based motion planning algorithm for continuous-time nonlinear systems in dynamic environments. The algorithm focuses on two objectives: efficiently generating feasible controls that steer the system toward a goal region, and handling environments with dynamical obstacles in continuous time. We formulate the control synthesis problem as a Quadratic Program (QP) that enforces Control Barrier Function (CBF) constraints to achieve obstacle avoidance. Additionally, CBF-RRT does not require nearest neighbor or collision checks when sampling, which greatly reduce the run-time overhead when compared to standard RRT variants.
