An Air-Ground Unmanned Swarm Collaborative Area Search Strategy Based on the Learning Wolf Pack Algorithm

Collaborative search by air-ground unmanned swarm, as a pivotal and efficient approach for intelligence gathering and disaster relief, highlights the critical role of search path planning in enhancing overall performance. Addressing the inefficiency resulting from insufficient collaboration between air and ground unmanned platforms in current research, this paper delves into the fundamental characteristics and challenges of collaborative search by air-ground unmanned swarm. This paper clarifies the objectives and constraints of path planning and introduces a method for collaborative search path planning based on the Learning Wolf Pack Algorithm (LWPA). This method initially constructs an optimization model that comprehensively considers area coverage, target detection probability, and search uncertainty. It integrates Distributed Model Predictive Control (DMPC) with the Distributed Constraint Optimization Problem (DCOP) framework, forming an architecture for real-time search path planning. To overcome the limitation of existing DCOP solution methods, which tend to get stuck in local optimal solutions, the LWPA employs a Q-learning mechanism for hierarchical learning and dynamically adjusts parameters to balance local refinement and global exploration. Experimental results demonstrate that this method offers significant advantages in improving search efficiency, coverage, and target detection rates, with an average area coverage of 99.28% and uncertainty as low as 0.86%. These results fully validate its effectiveness and superiority in search tasks in complex urban environments. Furthermore, tests on dynamic adaptability and scalability further verify the potential and value of this method in practical applications. Note to Practitioners—This study is stimulated by the complexity of executing area search missions by air-ground unmanned swarms in unknown environments and the pressing need for efficient path planning algorithms. In practical applications such as disaster response, environmental monitoring, and search and rescue, air-ground unmanned swarms can significantly broaden the search scope and enhance efficiency. However, the complex and unpredictable nature of unknown environments makes it challenging for existing path planning methods to balance comprehensiveness and real-time performance, often leading to local optima and reducing search efficiency. To address this challenge, the present work integrates DMPC and DCOP to construct an overall search objective function within a finite time horizon, and designs the LWPA to solve it. Experimental validation demonstrates that this method is both effective and practical.