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
Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?
Yongchao Chen, Jacob Arkin, Yang Zhang, Nicholas Roy, Chuchu Fan
Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty
Bo Fu, Tribhi Kathuria, Denise Rizzo, Matthew Castanier, X. Jessie Yang, Maani Ghaffari, Kira Barton
Analysis on Multi-robot Relative 6-DOF Pose Estimation Error Based on UWB Range
Xinran Li, Shuaikang Zheng, Pengcheng Zheng, Haifeng Zhang, Zhitian Li, Xudong Zou