The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (3): 69-80.doi: 10.19682/j.cnki.1005-8885.2022.1007

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HQD-RRT*: a high-quality path planner for mobile robot in dynamic environment


  1. 1. School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
    2. Jinan Engineering Laboratory of Human-Machine Intelligent Cooperation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
    3. School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
  • Received:2021-04-22 Revised:2021-09-26 Online:2022-06-30 Published:2022-06-30
  • Contact: Feng Chao
  • Supported by:
    This work was supported by the Program for Youth Innovative Research Team in the University of Shandong Province in China (2019KJN010).


Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree ( RRT* ) for planning path in dynamic environment, a high-quality dynamic rapidly-exploring random tree ( HQD-RRT* ) algorithm is proposed in this paper, which generates a high-quality solution with optimal path length in dynamic environment. This method proceeds in two stages: initial path generation and path re-planning. Firstly, the initial path is generated by an improved smart rapidly-exploring random tree ( RRT* -SMART) algorithm, and the state tree information is stored as prior knowledge. During the process of path execution, a strategy of obstacle avoidance is proposed to avoid moving obstacles. The cost and smoothness of path are considered to re-plan the initial path to improve the path quality in this strategy. Compared with related work, a higher-quality path in dynamic

environment can be achieved in this paper. HQD-RRT* algorithm can obtain an optimal path with better stability. Simulations on the static and dynamic environment are conducted to clarify the efficiency of HQD-RRT* in avoiding unknown obstacles.

Key words: path planning, dynamic environment, smart rapidly-exploring random tree (RRT* -SMART), re-planning