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
Adaptive Robot Coordination: A Subproblem-based Approach for Hybrid Multi-Robot Motion Planning
Irving Solis, James Motes, Mike Qin, Marco Morales, Nancy M. Amato
Trust and Acceptance of Multi-Robot Systems "in the Wild". A Roadmap exemplified within the EU-Project BugWright2
Pete Schroepfer, Nathalie Schauffel, Jan Gründling, Thomas Ellwart, Benjamin Weyers, Cédric Pradalier
From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue
Gautam Siddharth Kashyap, Deepkashi Mahajan, Orchid Chetia Phukan, Ankit Kumar, Alexander E. I. Brownlee, Jiechao Gao
Stein Variational Belief Propagation for Multi-Robot Coordination
Jana Pavlasek, Joshua Jing Zhi Mah, Ruihan Xu, Odest Chadwicke Jenkins, Fabio Ramos