Abstract
Unmanned aerial vehicles (UAVs) have been found significantly important in the air combats, where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics. The key to empower the UAVs with such capability is the autonomous maneuver decision making. In this paper, an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed. First, based on the process of air combat and the constraints of the swarm, the motion model of UAV and the multi-to-one air combat model are established. Second, a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation. Then, a swarm air combat algorithm based on deep deterministic policy gradient strategy (DDPG) is proposed for online strategy training. Finally, the effectiveness of the proposed algorithm is validated by multi-scene simulations. The results show that the algorithm is suitable for UAV swarms of different scales.
Highlights
Unmanned aerial vehicle (UAV) with the characteristics of low cost, strong mobility, high concealment and no need of pilot control, have been more and more widely used to replace manned aircraft to perform military tasks such as detection, monitoring and target strike, and is a typical representative of “non-contact” combat equipment [1]
Swarms beyond visual range air combat refers to the situation assessment [3,4,5], environment awareness [6, 7], and maneuver strategy [8] of UAVs through sensing or detection equipment, and maneuver strategy is the basis of the above tasks
5 Conclusion A maneuver strategy based on deep deterministic policy gradient strategy (DDPG) algorithm is proposed to realize UAV swarm combat
Summary
Unmanned aerial vehicle (UAV) with the characteristics of low cost, strong mobility, high concealment and no need of pilot control, have been more and more widely used to replace manned aircraft to perform military tasks such as detection, monitoring and target strike, and is a typical representative of “non-contact” combat equipment [1]. An autonomous maneuver strategy of swarm combat in beyond visual range air combat based on reinforcement learning is proposed. In this strategy, the autonomous maneuver decision and cooperative operation of UAV in swarm are realized, and the scalability of the algorithm is improved. Forth, based on the basic principle of reinforcement learning and the requirements of swarms control, the Actor-Critic network framework is designed, the state space and action strategy are given, and the reward function is designed based on the distance to realize the rapid convergence of the algorithm. Fifth, based on memory bank and target network, an algorithm is designed and A-C network is trained to obtain an autonomous cooperative maneuvering strategy method for UAV swarm.
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