RRT-CBFに基づく動作計画
RRT-CBF Based Motion Planning
RRTとモデル予測制御を組み合わせ、動的に更新される制御バリア関数で安全制約を課すことで、静的・動的障害物や他の移動ロボットとの衝突を避ける軌道計画手法を提案し、ロボットアームへの適用を実現した。
著者: Leonas Liu, Yingfan Zhang, Larry Zhang, Mehbi Kermanshabi
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Control barrier functions (CBF) are widely explored to enforce the safety-critical constraints on nonlinear systems recently. There are many researchers incorporating the control barrier functions into path planning algorithms to find a safe path, but these methods involve huge computational complexity or unidirectional randomness, resulting in arising of run-time. When safety constraints are satisfied, searching efficiency, and searching space are sacrificed. This paper combines the novel motion planning approach using rapid exploring random trees (RRT) algorithm with model predictive control (MPC) to enforce the CBF with dynamically updating constraints to get the safety-critical resolution of trajectory which will enable the robots not to collide with both static and dynamic circle obstacles as well as other moving robots while considering the model uncertainty in process. Besides, this paper first realizes application of CBF-RRT in robot arm model for nonlinear system.