5,630 research outputs found
Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform
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.Comment: 14 pages, 15 figures, journal pape
A microservice architecture for predictive analytics in manufacturing
Abstract This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multi-tenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
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