Artificial Swarm

Artificial swarm research focuses on designing and controlling groups of robots to achieve collective goals through decentralized coordination, mimicking natural swarms. Current research emphasizes developing efficient algorithms for task allocation, navigation in complex environments (including obstacle avoidance and target tracking), and human-swarm interaction, often utilizing models like centroidal Voronoi tessellation for path planning and deep reinforcement learning for autonomous navigation. This field is significant for its potential applications in diverse areas such as search and rescue, environmental monitoring, and military operations, driving advancements in distributed control, multi-agent systems, and human-machine teaming.