Multi Robot System
Multi-robot systems (MRS) research focuses on designing and controlling groups of robots to collaboratively achieve tasks beyond the capabilities of individual robots. Current research emphasizes developing robust and efficient algorithms for coordination, communication, and task allocation, often employing techniques like graph neural networks, reinforcement learning (including Deep Q-Networks and Actor-Critic methods), and optimization methods such as the Alternating Direction Method of Multipliers. These advancements are crucial for addressing challenges in diverse applications, including warehouse automation, search and rescue, environmental monitoring, and space exploration, improving efficiency, scalability, and resilience in complex and dynamic environments.
Papers
Relative ultra-wideband based localization of multi-robot systems with kinematic extended Kalman filter
Salma Ichekhlef, Étienne Villemure, Shokoufeh Naderi, François Ferland, Maude Blondin
Distributed Timed Elastic Band (DTEB) Planner: Trajectory Sharing and Collision Prediction for Multi-Robot Systems
Yiu Ming Chung, Hazem Youssef, Moritz Roidl