Multi-Robot Systems
Overview
The field studies how robot teams can divide work, coordinate motion, and combine information to accomplish tasks that are difficult for one robot alone. Common approaches include multi-agent path finding and task allocation for warehouses and exploration, distributed control and reinforcement learning for swarms, and cooperative localization and SLAM that fuse cameras, ranging, and inertial sensors. Researchers also design communication protocols, simulators, and operator interfaces to support heterogeneous robots while managing limited bandwidth, computation, and human attention. A central challenge is making coordination safe and reliable as teams grow, robots fail, environments change, and methods move from simulation to real hardware.
Latest papers
205 papers30 papers in the last four weeks, up 76% on the four weeks before. 0.2% of all new papers.