基于强化学习的多无人机避碰计算制导方法

Aiming at the problem of cooperative collision avoidance for a large number of fixed wing UAVs in limited airspace, a computational guidance method based on multi-agent deep reinforcement learning is proposed. Firstly, the process of collision avoidance and guidance is formulated as a sequential decision problem, which is mathematically described by Markov game theory. Then, a decision-making method of autonomous collision avoidance guidance based on multilayer neural network technology is proposed. The network is trained by the improved Actor-Critic model. Furthermore, the machine learning architecture is designed to implement the method. The relevant neural network structure and coordination mechanisms among UAVs are given. Finally, a flight simulator with variable number of entities is established, in which centralized training and distributed execution are performed. In order to verify the performance of the algorithm, several simulation experiments are carried out in the scene of high traffic density. The simulation results show that the proposed onboard computational guidance method can effectively reduce the collision probability of multiple UAVs in flight process have a good adaptability to the scene of high route density.