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Abstract-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.

This paper describes a Virtual Reality (VR) based system for automating data collection and surveying in a retail store using mobile robots. The manpower cost for surveying and monitoring the shelves in retail stores are high, because of which these activities are not repeated frequently causing reduced customer satisfaction and loss of revenue. Further, the accuracy of data collected may be improved by avoiding human related factors. We use a mobile robot platform with on-board cameras to monitor the shelves either autonomously or through tele-operation. A remote operator can control the robot from a console which shows a 3D of view of the store as well as, capture real images and videos of the store. The robot is designed to facilitate automatic detection of Out-of-Stock (OOS) situations. It would be possible for a single operator to control multiple robots placed at different stores thus optimizing the available resources. As the deployment of the proposed system does not require modifying existing infrastructure of the store, the cost of the entire solution is cheaper with shorter return-on-investment (ROI) period.

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