Cooperative reconnaissance coverage for heterogeneous unmanned aerial vehicle swarm in unknown communication interference environments

In complex environments with uncertain communication interference, heterogeneous unmanned aerial vehicle (HUAV) swarm often encounter the challenge of communication disruptions while collaboratively executing reconnaissance and coverage missions, hindering efficient information sharing and subsequently leading to substantial issues of redundant coverage. Given that existing research overlooks the impact of communication interference, this paper proposes a Coverage-Oriented Multi-Agent Cooperative Artificial Potential Field (MACAPF) algorithm. Firstly, a communication model for HUAV swarm under communication interference is considered to accurately reflect real-time communication status. Secondly, an autonomous collaborative distributed-concentrated architecture is devised, which dynamically adjusts the distributed-concentrated state of the swarm based on varying communication conditions, providing support for resolving communication disruptions faced by the HUAV swarm. Lastly, addressing the limitations of traditional artificial potential field (APF) algorithm in unknown communication interference environments, individualized definitions of the HUAV potential field are introduced, and the MACAPF algorithm is designed based on the autonomous collaborative distributed-concentrated architecture. This algorithm effectively guides HUAVs experiencing communication disruptions to restore communication, enhancing the communication efficiency and cooperative operation capabilities of the HUAV swarm. Simulation results demonstrate that the proposed MACAPF algorithm exhibits significant advantages over other state of the art (SOTA) algorithms across multiple dimensions under various signal interference intensities.