Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform
This work addresses the challenge of efficient fleet management for autonomous robots in industrial environments, offering practical guidance for students and engineers, though it is incremental in comparing existing frameworks.
The paper tackles the problem of managing a fleet of autonomous mobile robots in factory or warehouse settings by comparing three implementation approaches, including two using ROS and one using the Rapyuta Cloud Robotics framework, analyzing their performance through simulation and real-world experiments to provide insights into cloud robotics platforms.
In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.