UAV Swarm Scheduling for Large-Scale Coordination: Applications, Challenges, Opportunities

Abstract

The rapid advancement of unmanned aerial vehicle (UAV) swarm systems has enabled their deployment in large-scale applications such as disaster response, environmental monitoring, logistics, and communication networks. In these scenarios, effective scheduling and coordination of UAV swarms are critical for mission success, particularly under complex spatiotemporal constraints. This paper provides a comprehensive survey of scheduling and planning algorithms for large-scale UAV swarm coordination under spatiotemporal constraints. The unique challenges posed by these constraints are analyzed, and state-of-the-art algorithms, including sampling-based, graph-based, mathematical optimization-based, and learning-based methods, are systematically evaluated. In addition, two representative application domains—sensing and communication—are investigated to demonstrate how spatiotemporal constraints shape algorithm design and performance. Furthermore, this paper also explores the key challenges faced by UAV swarms and proposes corresponding future research directions, which are crucial for advancing the development of this field. This survey aims to serve as a foundational reference for researchers and practitioners developing scalable coordination and planning solutions for UAV swarms.