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
Multi-Robot Cooperative Socially-Aware Navigation Using Multi-Agent Reinforcement Learning
Weizheng Wang, Le Mao, Ruiqi Wang, Byung-Cheol Min
When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applications
Enrico Saccon, Ahmet Tikna, Davide De Martini, Edoardo Lamon, Marco Roveri, Luigi Palopoli
RL-based Variable Horizon Model Predictive Control of Multi-Robot Systems using Versatile On-Demand Collision Avoidance
Shreyash Gupta, Abhinav Kumar, Niladri S. Tripathy, Suril V. Shah
RobotKube: Orchestrating Large-Scale Cooperative Multi-Robot Systems with Kubernetes and ROS
Bastian Lampe, Lennart Reiher, Lukas Zanger, Timo Woopen, Raphael van Kempen, Lutz Eckstein
FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity
Kaiyuan Chen, Ryan Hoque, Karthik Dharmarajan, Edith LLontop, Simeon Adebola, Jeffrey Ichnowski, John Kubiatowicz, Ken Goldberg
TacMMs: Tactile Mobile Manipulators for Warehouse Automation
Zhuochao He, Xuyang Zhang, Simon Jones, Sabine Hauert, Dandan Zhang, Nathan F. Lepora