RRTガイド付きモデル予測パス積分法
RRT Guided Model Predictive Path Integral Method
RRTでMPPIの制御分布の平均を決めることで、パラメータ調整なしに静的・動的環境での実時間運動計画を可能にした手法を提案。
著者: Chuyuan Tao, Hunmin Kim, Naira Hovakimyan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This work presents an optimal sampling-based method to solve the real-time motion planning problem in static and dynamic environments, exploiting the Rapid-exploring Random Trees (RRT) algorithm and the Model Predictive Path Integral (MPPI) algorithm. The RRT algorithm provides a nominal mean value of the random control distribution in the MPPI algorithm, resulting in satisfactory control performance in static and dynamic environments without a need for fine parameter tuning. We also discuss the importance of choosing the right mean of the MPPI algorithm, which balances exploration and optimality gap, given a fixed sample size. In particular, a sufficiently large mean is required to explore the state space enough, and a sufficiently small mean is required to guarantee that the samples reconstruct the optimal controls. The proposed methodology automates the procedure of choosing the right mean by incorporating the RRT algorithm. The simulations demonstrate that the proposed algorithm can solve the motion planning problem in real-time for static or dynamic environments.
