A Three-Phase Framework for Global Path Planning for Nonholonomic Autonomous Vehicles on 3D Terrains

This paper presents the development and evaluation of a novel three-phase global path planning framework that combines and modifies D* Lite, Rapidly Exploring Random Tree Star (RRT*), and local path optimization for nonholonomic autonomous off-road navigation on 3D terrains. This hierarchical algorithm inherits the advantage of D* Lite + RRT* algorithm proposed by Brunner in terms of sampling efficiency and incorporates the local path optimization used by Kriisi to further improve the path's quality. To demonstrate the necessity for all three phases, a High Mobility Multipurpose Wheeled Vehicle (HMMWV) is simulated on 3D terrain and under various obstacle configurations, and the tracking performance of the proposed three-phase algorithm is compared with that of RRT* and D* Lite + RRT* as one-phase and two-phase benchmarks. The results show that the new framework offers a better path quality and higher success rate. Although the improvement comes at the expense of longer computation time, real-time performance is still ensured in the implementation. Copyright (C) 2021 The Authors.

Automated summary

This paper introduces a novel three-phase global path planning framework that combines D* Lite, RRT*, and local path optimization algorithms for nonholonomic autonomous off-road navigation on 3D terrains. Through simulation experiments and performance comparisons, the study finds that the new framework offers higher path quality and success rate.