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.
Also a slightly related work is done by in which they have made a system to detect position of robots through a camera placed on ceiling and control their motion so that they don't collide. They have exploited the large computation power provided by the cloud. @cite_8 . A collision avoidance and path planning system which works on individual robots also exist @cite_14 . They have used common ROS topic for inter robot communication and AMCL for localization.
@cite_14 @cite_8
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This paper describes a multi-robot collision avoidance system based on the velocity obstacle paradigm. In contrast to previous approaches, we alleviate the strong requirement for perfect sensing (i.e. global positioning) using Adaptive Monte-Carlo Localization on a per-agent level. While such methods as Optimal Reciprocal Collision Avoidance guarantee local collision-free motion for a large number of robots, given perfect knowledge of positions and speeds, a realistic implementation requires further extensions to deal with inaccurate localization and message passing delays. The presented algorithm bounds the error introduced by localization and combines the computation for collision-free motion with localization uncertainty. We provide an open source implementation using the Robot Operating System (ROS). The system is tested and evaluated with up to eight robots in simulation and on four differential drive robots in a real-world situation. The pathway for the concept of cloud robotics is continually unfolding and revealing new opportunities in science. With this, the focus of the research paper is aimed at identifying the progress completed towards the development of a full scale cloud infrastructure to implement formation control on a multi robot system. A small scale cloud infrastructure was developed utilizing a single virtual machine operating with the boundaries of a hypervisor's resource pool. A robot with minimal hardware was constructed to work within the control of the cloud. Once the proof of concept on a lower tier has been completed, more advance robotics concepts, such as Null-Spaced-base behavior control and advanced neural network control, will be tested by offloading the computational load to the cloud infrastructure. The goal is to demonstrate the ability to simplify the robot hardware and implement control on a global scale utilizing the cloud infrastructure.