Abstract
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.
Abstract (translated)
这项工作提出了一种基于最佳采样方法来解决静态和动态环境下实时运动规划问题的最佳方法。该方法利用快速探索随机树(RRT)算法和模型预测路径积分(MPPI)算法。RRT算法在MPPI算法中提供了随机控制分布的名义上的平均值,因此在静态和动态环境下具有满意的控制性能,不需要进行精细参数调整。我们还讨论了选择MPPI算法中的适当均值的重要性,该均值平衡探索和最优性差距,给定固定的样本大小。特别是,需要足够大的均值来充分探索状态空间,需要足够小的均值以确保样本重建最优控制。 proposed Methodology 自动选择了正确的均值,通过集成RRT算法。模拟表明,该算法可以在静态或动态环境下实时解决运动规划问题。
URL
https://arxiv.org/abs/2301.13143
