3 accepted papers
Unmanned aerial vehicle (UAV) swarms find extensive applications in diverse fields, including search and rescue, logistics delivery, and environmental surveillance, necessitating meticulous task and temporal scheduling to meet intricate spatiotemporal requirements. A market-based strategy emerges as
Distributed task allocation in the UAV swarm is sensitive to excessive communication overhead and frequent transmissions. Combining reinforcement learning and task allocation demonstrates great potential in enhancing algorithm performance and optimizing communication. However, existing studies rely