RRT Guided Model Predictive Path Integral Method

TL;DR

This paper introduces an RRT-guided MPPI method for real-time motion planning that combines sampling efficiency with adaptive control, effectively handling static and dynamic environments without extensive parameter tuning.

Contribution

It presents a novel integration of RRT with MPPI to automatically select the control mean, enhancing real-time planning in complex environments.

Findings

Successfully solves motion planning in real-time

Balances exploration and optimality automatically

Effective in static and dynamic environments

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.