Global optimal path planning for mobile robot based on improved Dijkstra algorithm and ant system algorithm
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A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
Key wordsmobile robot global optimal path planning improved Dijkstra algorithm ant system algorithm MAKLINK graph free MAKLINK line
CLC numberTP242 TP306.1
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- LI Lei, YE Tao, TAN Min. Present state and future development of mobile robot technology research [J]. Robot, 2002, 24(5): 475–480. (in Chinese)Google Scholar
- WANG Xing-ce, ZHANG Ru-bo, GU Guo-chang. Potential grids based on path planning for robots[J]. Journal of Harbin Engineering University, 2003, 24(2): 170–174. (in Chinese)Google Scholar
- ZHUANG Hui-zhong, DU Shu-xin, WU Tie-jun. Research on path planning and related algorithms for robots[J]. Bulletin of Science and Technology, 2004, 20(3): 210–215. (in Chinese)Google Scholar
- LIU Cai-hong, HU Ji-quan, QI Xiao-ning. Path design of robot with continuous space based on hybrid genetic algorithm[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2003, 27(6): 819–821. (in Chinese)Google Scholar
- QIN Yuan-qing, SUN De-bao, LI Ning, et al. Path planning for mobile robot based on particle swarm optimization[J]. Robot, 2004, 26(3): 222–225. (in Chinese)Google Scholar
- Habib M K, Asama H. Efficient method to generate collision free paths for autonomous mobile robot based on new free space structuring approach [C]. IEEE/RSJ International Workshop on Intelligent Robots and Systems. Osaka: 1991. 563–567.Google Scholar
- YAN Wei-min, WU Wei-min. Data Structure (C version) [M]. Beijing: Tsinghua University Press, 1997. (in Chinese)Google Scholar