D-ALF: Deadlock-Recovery Asymmetric Lévy Flight for Swarm Coverage

Abstract

High-efficiency area coverage using Unmanned Aerial Vehicle swarms is a fundamental capability for large-scale search-and-rescue missions. In disaster scenarios lacking prior knowledge, where agents rely on local sensing and decentralized decision-making, stochastic exploration strategies provide a practical solution. Asymmetric Lévy flight has recently emerged as a promising theoretical framework for scalable autonomous coverage in complex environments. However, existing studies primarily focus on kinematic optimization in particle-level simulations and rarely consider practicable strategies tailored for real-world swarm deployment. This limitation becomes particularly critical in dense swarms, where execution-level deadlocks frequently arise as agents mutually obstruct access to their intended waypoints, undermining coverage continuity and reliability. When deployed on physical platforms with standard obstacle-avoidance mechanisms, these deadlocks can severely degrade operational performance. To address this challenge, this paper proposes D-ALF, a stochastic swarm coverage planner augmented with a deadlock-aware exploration policy. By integrating an asymmetric collaborative consensus mechanism, the framework enables agents to actively resolve local congestion, maintain continuous and coordinated stochastic exploration, and translate theoretical coverage efficiency into physically realizable swarm operation. Extensive simulations and real-world experiments demonstrate that D-ALF actively accounts for execution-level deadlocks and consistently enhances coverage efficiency, operational reliability, and scalability compared with classical asymmetric Lévy flight strategies.