Abstract
This paper studies a path planning method for multiple robots in unknown environment. Multiple robots adopt the leader-following formation method. For the Q-learning algorithm used by the leader robot, the Q-table is initialized by prior information of environment and the idea of filling concave obstacles is proposed. Then the strategy of choosing actions is improved by simulated annealing algorithm, which changes the greedy factor in real time according to the Q-learning. The follower robot uses an improved gravitational potential field method to follow the leader robot. The simulation results show that the improved algorithm is effective and multiple robots can plan an optimum path to reach the destination with this method.
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Acknowledgements
The work is supported by the National Natural Science Foundation of China (61673200, 61903172), the Major Basic Research Project of Natural Science Foundation of Shandong Province of China (ZR2018ZC0438) and the Key Research and Development Program of Yantai City of China (2019XDHZ085).
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Fan, Z., Yang, H., Han, Y., Ning, X. (2022). A Path Planning Method for Multi-robot Formation Based on Improved Q-Learning. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 805. Springer, Singapore. https://doi.org/10.1007/978-981-16-6320-8_87
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DOI: https://doi.org/10.1007/978-981-16-6320-8_87
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