A Survey of Multi-UAV Cooperative Encirclement and Capture: Task Allocation, Motion Planning, and Control

Multi-UAV swarms offer significant potential in military and civilian applications due to their high efficiency, robustness, and flexibility. Cooperative envelopment, a key application for target interception, area blockade, and surveillance, faces technical challenges from non-cooperative targets, dynamic environments, and limited communication capabilities. This paper systematically reviews Multi-UAV cooperative envelopment, focusing on three core modules. First, Task Allocation methods are analyzed, including heuristic intelligent algorithms (e.g., Genetic Algorithms (GA), Particle Swarm Optimization (PSO)), distributed market mechanisms (e.g., auction algorithms, contract net protocol), and clustering algorithms. Second, Motion Planning and Cooperative Control techniques are examined, covering geometry/topology-based methods (e.g., Voronoi diagrams, Apollonian circles), Reinforcement Learning (RL), and consensus theory. Finally, the paper summarizes the field's trend from classical control toward AI integration. It also highlights future challenges in scalability and robustness, noting the broad application prospects for this technology.