An Improved RRT Algorithm for Multi-Robot Formation Path Planning

WANG Le-le; SUI Ze-zhi; PU Zhi-qiang; LIU Zhen; YI Jian-qiang

doi:10.3969/j.issn.0372-2112.2020.11.007

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An Improved RRT Algorithm for Multi-Robot Formation Path Planning

更新时间：2025-12-08

- An Improved RRT Algorithm for Multi-Robot Formation Path Planning
- Acta Electronica Sinica Vol. 48, Issue 11, Pages: 2138-2145(2020)
- 作者机构：
  
  1. 中国科学院大学人工智能学院,北京,100049
  2. 中国科学院自动化研究所,北京,100190
  3. 中国科学院大学人工智能学院,北京,100049
  4. 中国科学院自动化研究所,北京,100190
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.11.007
  CLC： TP242
- Published Online：25 November 2020，
  
  Published：2020
- 稿件说明：
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WANG Le-le, SUI Ze-zhi, PU Zhi-qiang, et al. An Improved RRT Algorithm for Multi-Robot Formation Path Planning[J]. Acta Electronica Sinica, 2020, 48(11): 2138-2145.
DOI：

WANG Le-le, SUI Ze-zhi, PU Zhi-qiang, et al. An Improved RRT Algorithm for Multi-Robot Formation Path Planning[J]. Acta Electronica Sinica, 2020, 48(11): 2138-2145. DOI： 10.3969/j.issn.0372-2112.2020.11.007.

摘要

多机器人路径规划是机器人领域的一个热点问题，相比于单机器人路径规划，其算法难度和复杂度都有所增加，在规划时需要兼顾多机避障、相互协作等难点问题.本文提出一种改进快速扩展随机树的多机器人编队路径规划算法，用于解决多机器人在复杂环境下的编队路径规划问题.针对多机器人在编队规划中的位置约束问题，定义机器人之间的领航-跟随结构，并对机器人队形建模.针对规划过程中编队朝向变化问题，建立搜索树扩展方向与队形方向之间的联系，通过调整队形方向改变规划时的编队朝向.针对具有质点模型和非完整约束动力学模型两种不同模型的多机器人系统，分别进行了仿真实验.仿真结果表明该算法在处理多机器人编队路径规划问题时可以取得良好的效果.

Abstract

Multi-robot path planning is one of the most attractive issues in the field of multiple robots. Compared with the algorithm for the single-robot path planning

the problem of multi-robot path planning

which takes obstacle avoidance and cooperation into account simultaneously

is more difficult and complex. Hence

a novel improved rapidly-exploring random tree algorithm for multi-robot formation path planning in this paper is proposed to address these obstacle avoidance and cooperation problems. The constraint condition of positional relationship is defined by modeling the multi-robot formation shape. Besides

the heading of formation is adjusted with the direction the exploring tree expands. Additionally

simulations are conducted for two different models of robots: the particle model and the non-holonomic constraint dynamic model. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

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