Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles

TL;DR

This paper presents a novel online planning method for autonomous robots navigating among moving vehicles in uncertain, dynamic environments, using probabilistic prediction and reachable space analysis to improve decision-making.

Contribution

It introduces a dynamic-obstacle-aware reachable space construction combined with nonlinear Gaussian filtering for efficient, reliable planning in uncertain, time-varying conditions.

Findings

Reduced computation time compared to existing methods

Improved decision accuracy in dynamic scenarios

Enhanced planning reliability under environmental disturbances

Abstract

We investigate the autonomous navigation of a mobile robot in the presence of other moving vehicles under time-varying uncertain environmental disturbances. We first predict the future state distributions of other vehicles to account for their uncertain behaviors affected by the time-varying disturbances. We then construct a dynamic-obstacle-aware reachable space that contains states with high probabilities to be reached by the robot, within which the optimal policy is searched. Since, in general, the dynamics of both the vehicle and the environmental disturbances are nonlinear, we utilize a nonlinear Gaussian filter -- the unscented transform -- to approximate the future state distributions. Finally, the forward reachable space computation and backward policy search are iterated until convergence. Extensive simulation evaluations have revealed significant advantages of this proposed method in terms of computation time, decision accuracy, and planning reliability.