Scholarlink

This paper addresses the limitations of the bidirectional Rapidly-exploring Random Tree algorithm (RRT-Connect) in mobile robot path planning, particularly its slow convergence speed and high memory consumption. We propose an improved algorithm that integrates the Artificial Potential Field (APF) method with RRT-Connect. The enhanced algorithm employs three key innovations: (1) variable step-size sampling strategy, (2) APF-guided intelligent tree expansion toward target regions, and (3) a node pruning strategy to reduce memory usage. Comprehensive experiments conducted in various typical scenarios demonstrate that the proposed algorithm significantly outperforms conventional RRT and RRT-Connect methods in terms of path quality, convergence speed, and computational efficiency.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Improved Path Planning Algorithm for Mobile Robots Based on RRT-Connect

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Pengtao Ji,
  • Tao Chao,
  • Ping Ma,
  • Qing Guo

摘要

This paper addresses the limitations of the bidirectional Rapidly-exploring Random Tree algorithm (RRT-Connect) in mobile robot path planning, particularly its slow convergence speed and high memory consumption. We propose an improved algorithm that integrates the Artificial Potential Field (APF) method with RRT-Connect. The enhanced algorithm employs three key innovations: (1) variable step-size sampling strategy, (2) APF-guided intelligent tree expansion toward target regions, and (3) a node pruning strategy to reduce memory usage. Comprehensive experiments conducted in various typical scenarios demonstrate that the proposed algorithm significantly outperforms conventional RRT and RRT-Connect methods in terms of path quality, convergence speed, and computational efficiency.