Journal of South China University of Technology (Natural Science Edition) ›› 2021, Vol. 49 ›› Issue (7): 51-58.doi: 10.12141/j.issn.1000-565X.200769

Special Issue: 2021年计算机科学与技术

• Computer Science & Technology • Previous Articles     Next Articles

Path Planning of Mobile Robots Based on Dual-Tree Quick-RRT* Algorithm

WEI Wu HAN Jin LI Yanjie GAO Tianxiao   

  1. School of Automation Science and Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China
  • Received:2020-12-17 Revised:2021-03-01 Online:2021-07-25 Published:2021-07-01
  • Contact: 李艳杰 ( 1991-) ，女，博士生，主要从事机器人路径规划及机器人控制研究。 E-mail:1073889317@qq.com
  • About author:魏武 ( 1970-) ，男，教授，博士生导师，主要从事机器人控制技术、智能控制技术、模式识别与人工智能研 究。E-mail:weiwu@scut.edu.cn
  • Supported by:
    Supported by the Science and Technology Planning Project of Guangdong Province ( 2019A050520001)

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Abstract: The optimal rapid expansion randomized tree ( RRT* ) is an asymptotically optimal path planning method for mobile robots． Quick-RRT* reduces the initial path length of RRT* and increases the path convergence speed． In order to further improve the convergence speed of Quick-RRT* ，this paper proposed a dual-tree Quick-RRT* ( Quick-RRT* -Connect) algorithm． Firstly，two random trees were generated at the start and end points respectively based on the Quick-RRT* algorithm． Two trees grew in turn and they were connected with greedy method． Then， the probability completeness and asymptotic optimality of the proposed algorithm were analyzed and testified． Finally，based on the Matlab platform，Quick-RRT* -Connect was compared with RRT* ，Quick-RRT* and RRT* - Connect in three environments． The results show that the improved algorithm can not only find initial path and suboptimal path in a shorter time，but also reduce the initial path length．

Key words: path planning, mobile robot, Quick-RRT* , initial path, convergence speed

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