Multi Robot
Multi-robot systems research focuses on coordinating multiple robots to achieve complex tasks more efficiently than single robots could. Current research emphasizes developing robust algorithms for tasks like collaborative mapping, target tracking, and exploration, often employing techniques like distributed optimization, reinforcement learning, and neural networks (including diffusion models and transformers) to handle challenges such as communication constraints, environmental uncertainties, and adversarial conditions. These advancements are significant for improving efficiency and reliability in various applications, including logistics, search and rescue, and environmental monitoring.
Papers
WiSER-X: Wireless Signals-based Efficient Decentralized Multi-Robot Exploration without Explicit Information Exchange
Ninad Jadhav, Meghna Behari, Robert J. Wood, Stephanie Gil
Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC
Xinglong Zhang, Wei Pan, Cong Li, Xin Xu, Xiangke Wang, Ronghua Zhang, Dewen Hu