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
Collective Anomaly Perception During Multi-Robot Patrol: Constrained Interactions Can Promote Accurate Consensus
Zachary R. Madin, Jonathan Lawry, Edmund R. Hunt
IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multi-Robot Systems Through Communication Eavesdropping
Sanjay Oruganti, Ramviyas Parasuraman, Ramana Pidaparti
Simultaneous Time Synchronization and Mutual Localization for Multi-robot System
Xiangyong Wen, Yingjian Wang, Xi Zheng, Kaiwei Wang, Chao Xu, Fei Gao
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance
Sizhao Li, Yuming Xiang, Rongpeng Li, Zhifeng Zhao, Honggang Zhang
Do We Run Large-scale Multi-Robot Systems on the Edge? More Evidence for Two-Phase Performance in System Size Scaling
Jonas Kuckling, Robin Luckey, Viktor Avrutin, Andrew Vardy, Andreagiovanni Reina, Heiko Hamann
Forward Kinematics of Object Transporting by a Multi-Robot System with a Deformable Sheet
Jiawei Hu, Wenhang Liu, Jingang Yi, Zhenhua Xiong