Semantic communication aware reinforcement learning for UAVs intelligent following

In order to solve the problem of mission success degradation caused by the obstructed communication of UAVs in the communication uncertain environment, we propose a semantic communication reinforcement learning for UAVs intelligent following method for maintaining UAV communication in the scenario with high interferences to keep following the leader UAV. This method employs a semantic communication model and a leader behavior learning model as the follower intelligence so that the follower can resist communication interference without deploying additional communication anti-jamming devices. The follower using this method continuously follows the leader to improve the probability to complete the task in a communication-restricted environment. In experimental studies, the proposed method effectively improves the task success rate in the event of communication interruptions, which significantly outperforms benchmark methods in complex environments.