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

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Listed:
  • Tong Xu
  • Yang Xu
  • Dong Wang
  • Siwei Chen
  • Weigong Zhang
  • Lihang Feng

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.

Suggested Citation

  • Tong Xu & Yang Xu & Dong Wang & Siwei Chen & Weigong Zhang & Lihang Feng, 2020. "Path Planning for Autonomous Articulated Vehicle Based on Improved Goal-Directed Rapid-Exploring Random Tree," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, May.
    • Handle: RePEc:hin:jnlmpe:7123164
    DOI: 10.1155/2020/7123164
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    Cited by:

    1. Ai Zhang, 2021. "Influence of data mining technology in information analysis of human resource management on macroscopic economic management," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-12, May.

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