Robot Team
Robot team research focuses on enabling groups of robots to collaboratively achieve complex tasks, often in challenging or unpredictable environments. Current efforts concentrate on developing efficient algorithms for task allocation, path planning (including hazard avoidance and connectivity maintenance), and information sharing under communication constraints, frequently employing techniques like reinforcement learning, optimization (including Riemannian optimization and mixed-integer programming), and large language models for decision-making. These advancements are crucial for improving the robustness, efficiency, and adaptability of multi-robot systems across diverse applications, such as environmental monitoring, search and rescue, and industrial automation.
Papers
Hierarchical LLMs In-the-loop Optimization for Real-time Multi-Robot Target Tracking under Unknown Hazards
Yuwei Wu, Yuezhan Tao, Peihan Li, Guangyao Shi, Gaurav S. Sukhatmem, Vijay Kumar, Lifeng Zhou
Bi-objective trail-planning for a robot team orienteering in a hazardous environment
Cory M. Simon, Jeffrey Richley, Lucas Overbey, Darleen Perez-Lavin