iPolicy: フィードバック運動計画のための漸進的方策アルゴリズム
iPolicy: Incremental Policy Algorithms for Feedback Motion Planning
サンプリングと集合値最適制御を組み合わせ、ロボットの運動計画とフィードバック制御器設計を同時に解く漸進的方策アルゴリズムを提案。
著者: Guoxiang Zhao, Devesh K. Jha, Yebin Wang, Minghui Zhu
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper presents policy-based motion planning for robotic systems. The motion planning literature has been mostly focused on open-loop trajectory planning which is followed by tracking online. In contrast, we solve the problem of path planning and controller synthesis simultaneously by solving the related feedback control problem. We present a novel incremental policy (iPolicy) algorithm for motion planning, which integrates sampling-based methods and set-valued optimal control methods to compute feedback controllers for the robotic system. In particular, we use sampling to incrementally construct the state space of the system. Asynchronous value iterations are performed on the sampled state space to synthesize the incremental policy feedback controller. We show the convergence of the estimates to the optimal value function in continuous state space. Numerical results with various different dynamical systems (including nonholonomic systems) verify the optimality and effectiveness of iPolicy.
