Abstract
With the development of technology, robots are gradually being used more and more widely in various fields. Industrial robots need to perform path planning in the course of their tasks, but there is still a lack of a simple and effective method to implement path planning in complex industrial scenarios. In this paper, an improved whale optimization algorithm is proposed to solve the robot path planning problem. The algorithm initially uses a logistic chaotic mapping approach for population initialization to enhance the initial population diversity, and proposes a jumping mechanism to help the population jump out of the local optimum and enhance the global search capability of the population. The proposed algorithm is tested on 12 complex test functions and the experimental results show that the improved algorithm achieves the best results in several test functions. The algorithm is then applied to a path planning problem and the results show that the algorithm can help the robot to perform correct and efficient path planning.
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Acknowledgements
This work is supported by the fund of Fujian Province Digital Economy Alliance, the National Natural Science Foundation of China (No. U1905211), and the Natural Science Foundation of Fujian Province (No. 2020J01500).
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Huang, P., Dong, C., Chen, Z., Zhen, Z., Jiang, L. (2024). An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm. In: Jin, H., Yu, Z., Yu, C., Zhou, X., Lu, Z., Song, X. (eds) Green, Pervasive, and Cloud Computing. GPC 2023. Lecture Notes in Computer Science, vol 14503. Springer, Singapore. https://doi.org/10.1007/978-981-99-9893-7_16
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