Collective Navigation

Collective navigation studies how groups of agents, whether robots or simulated entities, coordinate their movements to achieve shared goals, such as efficient foraging or coordinated exploration. Current research emphasizes developing robust algorithms for decentralized control, particularly in communication-constrained environments, often drawing inspiration from biological swarms and employing techniques like graph-based representations, reinforcement learning (including federated approaches), and optimization methods to achieve optimal trajectories and efficient task completion. These advancements have implications for diverse applications, including autonomous robotics, environmental monitoring, and a deeper understanding of collective behavior in biological systems.