Academic Journal of Computing & Information Science, 2024, 7(2); doi: 10.25236/AJCIS.2024.070212.
Biao Chen1, Shan Hu1,2, Tianzhi Zhang1
1School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China
2Tianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin, China
Path planning is one of the key problems of welding robots. In the face of increasing accuracy and efficiency requirements in the manufacturing industry, the importance of robot path planning has also increased. For welding robots, short welding time and stable welding process are generally required. In this paper, genetic algorithm is used to optimize BP neural network algorithm, so as to obtain better weights and thresholds, so as to obtain better solutions in path planning. After simulation and verification in MATLAB, the results show that, the optimized BP neural network algorithm effectively improves the efficiency of the algorithm and optimizes the ability of finding the global optimal path.
Improved BP neural network algorithm; Welding robot; Path planning; Genetic algorithm
Biao Chen, Shan Hu, Tianzhi Zhang. Optimization of robot path planning based on improved BP algorithm. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 2: 85-90. https://doi.org/10.25236/AJCIS.2024.070212.
[1] Guo Yanbao, Wang Bin, Wang Deutd, Yu Haide, Zhou Xuanli, Yin Tie, Zhou Lun. Research progress and Development Trend of Welding Robot [J]. Modern Manufacturing Engineering, 2021(05): 53-63.
[2] FANG Jianwen, Chao Yongsheng, Yuan Yiping. Joint Trajectory Optimization of Welding Robot with Improved B-Spline Interpolation [J]. Machine Design & Manufacture, 2021(10):1-4+10.
[3] Wang Xuewu, Yan Yixin, Gu Xingsheng. Path planning of Welding Robot based on Levi Flight Particle Swarm Optimization [J]. Control and Decision, 2017, 32(02):373-377.
[4] Yang Heng, Li Yue, Sun Hanting, Li Zhuo. Research on Path Planning for Mobile Robot with Path Optimization [J]. Journal of Machine Design, 2022, 39(08):58-67.
[5] Feudal Hu, Zhang Tingyu, Feng Shuo, Zheng Baojuan. Improved Particle Swarm Optimization for Robot Path Planning [J]. Machine Design & Manufacture, 2021(09):291-294+298.
[6] Xu Li, Liu Yunhua, Wang Qifu. Application of Adaptive Genetic Algorithm in Robot Path Planning [J]. Computer Engineering and Applications, 2019, 56(18):36-41.
[7] Shi Zhigang, Mei Song, Shao Yifan, Wan Ru, Song Zhiyu, Xie Minglu, Li Yan. Research status and prospect of mobile robot path Planning based on Artificial Potential Field method [J]. Chinese Journal of Agricultural Mechanization, 2021, 42(12):182-188.