Volume 50 Issue 7
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(7): 107-114
ZHU Hongbo, HUA Rong. Path planning method for coal mine inspection robot[J]. Journal of Mine Automation，2024，50（7）：107-114.  doi: 10.13272/j.issn.1671-251x.2024040033
Citation: ZHU Hongbo, HUA Rong. Path planning method for coal mine inspection robot[J]. Journal of Mine Automation，2024，50（7）：107-114.  doi: 10.13272/j.issn.1671-251x.2024040033
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Path planning method for coal mine inspection robot

doi: 10.13272/j.issn.1671-251x.2024040033

  • College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

  • Received Date: 2024-04-11
  • Rev Recd Date: 2024-07-10
    • Available Online: 2024-07-30
    Abstract
  • Path planning is a key technology for autonomous movement of inspection robot. The coal mine inspection robot has problems such as slow convergence speed and low search efficiency when planning paths using the rapidly-expanding random tree (RRT) algorithm. In order to solve the above problems, a combined force potential field guided RRT algorithm is proposed. The algorithm uses the repulsive force field in the combined force potential field to construct a dynamic step size. The coal mine inspection robot can adjust the step size near obstacles to improve the convergence speed of the algorithm. By utilizing the combined force field formed by the gravitational field in both the target node and random node directions, as well as the repulsive field generated by the nearest obstacle on the coal mine inspection robot, the generation direction of new nodes can be improved. It reduces the randomness of tree expansion and enhances the search efficiency of the algorithm. A pruning operation is performed on the paths planned based on the combined potential field guided RRT algorithm and smoothed using third-order Bessel curve. A simulation experiment is conducted in Matlab software on the path planning method of the coal mine inspection robot guided by the combined force potential field RRT algorithm. The results show that compared with the RRT algorithm and RRT* algorithm, the average path planning time of the combined potential field guided RRT algorithm in simple environments is reduced by 33.84% and 44.27%. The average path length is reduced by 15.29% and 4.42%, respectively. In complex environments, the average path planning time is reduced by 34.93% and 47.12%, and the average path length is reduced by 13.64% and 9.44%, respectively. In simulated coal mine environments, the average path planning time is reduced by 28.06% and 42.67%, and the average path length is reduced by 12.22% and 10.18%, respectively. After pruning and smoothing the path planned by the combined force potential field guided RRT algorithm, the number of turning points and angle changes in the path decrease, making the path smoother.

     

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