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
Multi-Robot On-site Shared Analytics Information and Computing
Joshua Vander Hook, Federico Rossi, Tiago Vaquero, Martina Troesch, Marc Sanchez Net, Joshua Schoolcraft, Jean-Pierre de la Croix, Steve Chien
Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots
Zichen He, Lu Dong, Chunwei Song, Changyin Sun