Affiliation:
1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
2. National Innovation Institute of Defense Technology, Academy of Military Science, Beijing 100071, China
Abstract
This paper provides an in-depth analysis of the current research landscape in the field of UAV (Unmanned Aerial Vehicle) swarm formation control. This review examines both conventional control methods, including leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field, and advanced AI-based (Artificial Intelligence) methods, such as artificial neural networks and deep reinforcement learning. It highlights the distinct advantages and limitations of each approach, showcasing how conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. This review underscores the critical need for innovative solutions and interdisciplinary approaches combining conventional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications.
Funder
Support Program of Young Talents of Huxiang
Chinese Postdoctoral Science Foundation
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