A novel multi stage cooperative path re-planning method for multi UAV

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 multitasks. 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.