Abstract
When the multi-UAVs cooperatively attack multi-tasks, the dynamic changes of environments can lead to a failure of the tasks. So a novel path re-planning algorithm of multiple Q-learning based on cooperative fuzzy C means clustering is proposed. Our approach first reflects the dynamic changes of re-planning space by updating the fuzzy cooperative matrix. Then, the key way-points on the current global paths are used as the initial clustering centers for the cooperative fuzzy C means clustering, which generates the classifications of space points for multi-tasks. Furthermore, we use the classifications as the state space of each task and the fuzzy cooperative matrix as the reward function of the Q-learning. So a multi Q-learning algorithm is presented to synchronously re-plan the paths for multi-UAVs at every step. The simulation results show that the method subtracts the re-planning space of the tasks and improves the search efficiency of the learning algorithm.
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Su, Xh., Zhao, M., Zhao, Ll., Zhang, Yh. (2016). A Novel Multi Stage Cooperative Path Re-planning Method for Multi UAV. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_40
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DOI: https://doi.org/10.1007/978-3-319-42911-3_40
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