Path Planning for Autonomous Articulated Vehicle Based on Improved Goal-Directed Rapid-Exploring Random Tree

Tong Xu, Yang Xu, Dong Wang, Siwei Chen, Weigong Zhang and Lihang Feng

Mathematical Problems in Engineering, 2020, vol. 2020, 1-14

Abstract:

The special steering characteristics and task complexity of autonomous articulated vehicle (AAV) make it often require multiple forward and backward movements during autonomous driving. In this paper, we present a simple yet effective method, named head correction with fixed wheel position (HC-FWP), for the demand of multiple forward and backward movements. The goal-directed rapid-exploring random tree (GDRRT) algorithm is first used to search for a feasible path in the obstacle map, and then, the farthest node search (FNS) algorithm is applied to obtain a series of key nodes, on which HC-FWP is used to correct AAV heading angles. Simulation experiments with Dynapac CC6200 articulated road roller parameters show that the proposed improved goal-directed rapid-exploring random tree (IGDRRT), consisting of GDRRT, FNS, and HC-FWP, can search a feasible path on maps that require the AAV to move back and forth.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7123164

DOI: 10.1155/2020/7123164

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