Three-dimensional path planning method for robot in underground local complex space

被引:0
|
作者
Tan Y.-X. [1 ]
Yang W. [1 ]
Xu Z.-R. [1 ]
机构
[1] School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing
来源
Yang, Wei (wyang@bjtu.edu.cn) | 1634年 / China Coal Society卷 / 42期
关键词
Ant colony algorithm; Artificial bee colony algorithm; Coal mine; Path planning; Robot;
D O I
10.13225/j.cnki.jccs.2016.1047
中图分类号
学科分类号
摘要
The automatic robots are gradually applied in the process of unmanned coal mining, hazard detection and disaster relief. According to the characteristics of small confined space, complex obstacle distribution and uneven floor in underground coal mine, a three-dimensional (3D) path planning method for robot in underground local complex space is proposed based on hybrid ant colony and artificial bee colony algorithm. The proposed method can generate the initial paths simply and extend the search range of new feasible path, which effectively solves the problem that ant colony algorithm may trap into the local optimal solution and the iteration times of the artificial bee colony algorithm may be numerous. The B-spline interpolation is applied to the 3D broken path line to generate the optimal continuous smooth path, which is beneficial for robot to travel smoothly. Simulation results show that the robot can plan its path effectively in underground local complex space with the proposed path planning method based on hybrid ant colony and artificial bee colony algorithm. © 2017, Editorial Office of Journal of China Coal Society. All right reserved.
引用
收藏
页码:1634 / 1642
页数:8
相关论文
共 20 条
  • [1] Wang J., Yu B., Kang H., Et al., Key technologies and equipment for a fully mechanized top-coal caving operation with a large mining height at ultra-thick coal seams, International Journal of Coal Science & Technology, 2, 2, pp. 97-161, (2015)
  • [2] Wang J., Development and prospect on fully mechanized mining in Chinese coal mines, International Journal of Coal Science & Technology, 1, 3, pp. 253-260, (2014)
  • [3] Deng G., Zhang X., Liu Y., Ant colony optimization and particle swarm optimization for robot-path planning in obstacle environment, Control Theory & Applications, 26, 8, pp. 879-883, (2009)
  • [4] Liu J., Yan Q., Ma Y., Et al., Global path planning based on improved ant colony optimization algorithm for geometry, Journal of Northeastern University(Natural Science), 36, 7, pp. 923-928, (2015)
  • [5] Wang F., Li K., Yuan M., Ant colony algorithm based on optimization of potential field method for path planning, Computer Science, 41, 11, pp. 47-50, (2014)
  • [6] Zeng M., Xu X., Liu L., Et al., Improved ant colony optimization with potential field heuristic for robot path planning, Computer Engineering and Applications, 51, 22, pp. 33-37, (2015)
  • [7] Liu Q., Chen H., Zhang Y., Et al., An ant colony optimization algorithm based on dynamic evaporation rate and amended heuristic, Journal of Computer Research and Development, 49, 3, pp. 620-627, (2012)
  • [8] Contreras-Cruz M.A., Ramirez V.A., Hernandez-Belmonte U.H., Mobile robot path planning using artificial bee colony and evolutionary programming, Applied Soft Computing, 30, pp. 319-328, (2015)
  • [9] Sood M., Kaur M., Shortest path finding in country using hybrid approach of BBO and BCO, International Journal of Computer Applications, 40, 6, pp. 9-13, (2012)
  • [10] Liu H., Gao L., Kong X., An improved artificial bee colony algorithm, 25th Chinese Control and Decision Conference, pp. 401-404, (2013)