Author(s)

Wencheng Wang ^1,2, Pengcheng Zhang ^1,2, Hongrun Wang ³

Affiliation(s)

¹ Department of Mechanical Engineering, Hebei University of Water Resources and Electric Engineering, Changzhou, 061000, China
² Industrial Manipulator Control and Reliability Technology Innovation Center of Hebei, Cangzhou, Hebei, 061001, China; Industrial Manipulator Control and Reliability Technology Innovation Center of Cangzhou, Cangzhou, Hebei, 061001, China
³ State Grid Corporation of China Hebei Electric Power Co., Ltd. Nanpi County Power Supply Subsidiary, Cangzhou, Hebei, 061001, China

Corresponding Author

Wencheng Wang

ABSTRACT

Taking the path planning of inspection robots as the research object, a shortest path planning method based on simulated annealing algorithm was proposed. By analyzing the conditions of the shortest path generation, the mathematical solution model of the problem was established, and the global search strategy was formulated. Finally, the shortest path of the robot was solved through MATLAB software programming. The correctness and effectiveness of the algorithm are verified by a large number of examples. In addition, in view of the low efficiency of the traditional simulated annealing algorithm to solve the large-scale shortest path problem, the output result is unstable, and the path is easy to cross, a new solution is constructed in the form of random coordinate exchange. The results show that the shortest path output of this method not only has less crossover, but also the operation efficiency is obviously improved, and the result is more stable.

KEYWORDS

Simulated annealing algorithm, path planning, MATLAB

CITE THIS PAPER

Wencheng Wang, Pengcheng Zhang, Hongrun Wang, Research on Robot Path Planning Based on Simulated Annealing Algorithm. Journal of Artificial Intelligence Practice (2023) Vol. 6: 29-36. DOI: http://dx.doi.org/10.23977/jaip.2023.060705.

REFERENCES

[1] Fan Qi, Cai Yuqiang, Jia Sinan (2022). Multi-method Industrial Robot Trajectory Planning and Simulation [J]. Journal of North China University of Science and Technology(Natur-al Science Edition), 44(04):60-67. 
[2] Zhou Qijun (2022). Research on industrial robot trajectory planning and control system[D]. Tianjin:Tianjin University of Technology. 
[3] Kang Wenxuan (2022). City delivery route planning for shared bikes based on simulated annealing algorithm[J]. Science and Innovation, 13:104-106+109. 
[4] Yuan Jiaquan, Guo Jian (2019). Robot path planning method based on simulated annealing ant colony algorithm[J]. Path Planning, 36(10):329-333. 
[5] Chen Yao (2015). Design and implementation of global path planning for intelligent inspection robots in substations[D]. Jinan: Shandong University. 
[6] Vadim Indelman (2018). Cooperative multi-robot belief space planning for autonomous navigation in unknown environments. [J]. Auton. Robots, 42(2):353-373.

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