Cooperative path planning for multiple unmanned aerial vehicles system in a game-theoretic environment

In this paper, the cooperative path planning problem in games for the unmanned aerial vehicles system is addressed under conditions of unknown dynamics and input constraints. By planning their routes and avoiding collisions and prohibited areas, friendly and enemy unmanned aerial vehicles must catch up to each other in the game. The trajectory of the opposing unmanned aerial vehicles is predicted to assist path planning by a long short term memory (LSTM) model with an attention mechanism. By creating the value function, the cooperative path planning issue is transformed into an optimum control problem with input restrictions. A method based on integral reinforcement learning is designed to achieve optimal control using the historical data, without the knowledge of inertial parameters. The results of the simulation confirm the efficacy of the proposed method.