An Approach towards Survey and Analysis of Cloud Robotics

Companion Proceedings of the10th International Conference on Utility and Cloud Computing, 2017

Robots are moving out of factories, service robotics is bringing them to our homes, work environments, cities, and outdoors. While the Robot Operating System (ROS) is promising to open the world of robotics to developers, a proper platform and ecosystem supporting robotic applications development is still missing. This work presents an example of cloud robotics application in which cloud computing is not just complementing limited robot capabilities, but is leveraged to provide a development and operations environment supporting the complete life-cycle of a robotics-enabled application. We relate on our experience building cloud robotics applications spanning heterogeneous hardware (i.e., robots and cloud servers) through a use case scenario.

IEEE Network, 2000

We extend the computation and information sharing capabilities of networked robotics by proposing a cloud robotic architecture. The cloud robotics architecture leverages the combination of a virtual ad-hoc cloud formed by machine-to-machine (M2M) communications among participating robots, and an infrastructure cloud enabled by machine-to-cloud (M2C) communications. Cloud robotics utilizes elastic computing models, in which resources are dynamically allocated from a shared resource pool in the cloud, to support task offloading and information sharing in robotic applications. We propose communication protocols, and several elastic computing models to handle different applications. We discuss the technical challenges in computation, communications and security, and illustrate the potential benefits of cloud robotics in several applications.

Ijca Proceedings on National Conference Cum Workshop on Bioinformatics and Computational Biology, 2014

The application of the cloud computing concept to robots is called Cloud Robotics. It is a concept that utilizes the services of the cloud so that robots can have learning abilities. Since applications for Cloud Robotics have to be developed in a platform, majority of the cloud application developers choose ROS for it. Robot Operating System (ROS) is an open source middleware that has a collection of inter-programming language headers to allow the sharing of data between independent programs. ROS provides a graph-like structure for cloud robotics. A new library for ROS that is a pure Java implementation, called rosjava, allows Android applications to be developed for robots. Since Android has a booming market and billion users, it would be a huge leap in the field of Cloud Robotics.

2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), 2018

Advances in robotics and cloud computing have led to the emergence of cloud robotics where robots can benefit from remote processing, greater memory and computational power, and massive data storage. The integration of robotics and cloud computing has often been regarded as a complex aspect due to the various components involved in such systems. In order to address this issue, different studies have attempted to create cloud robotic architectures to simplify representation into different blocks or components. However, limited study has been undertaken to critically review and compare these architectures. As such, this paper investigates and performs a comparative analysis of existing cloud robotic architectures in order to identify key limitations and recommend on the future of cloud robotic architectures. As part of this study, 7 such architectures have been reviewed and compared and results showed limited evaluation of existing architectures in favour of security weaknesses.

2016 World Automation Congress (WAC), 2016

The paper proposes a software architecture for cloud robotics which intends three subsystems in the cloud environment: Middleware Subsystem, Background Tasks Subsystem, and Control Subsystem. The architecture invokes cloud technologies such as cloud computing, cloud storage, and other networking platforms arranged on the assistances of congregated infrastructure and shared services for robotics, for instance Robot Operating System (ROS). Since the architecture is looking for reliable, scalable, and distributed system for the heterogeneous large-scale autonomous robots, Infrastructure as a Service (IaaS) is chosen among the cloud services. Three major tasks can be handled by the proposed software architecture Computing, Storage, and Networking. Hadoop-MapReduce provides the appropriate framework in the cloud environment to process and handle these tasks.

IEEE Access

Networked robotics involves a collection of robots working together to perform complex tasks, such as search and rescue task in disaster management. Because such tasks are beyond the capacity of a single powerful robot, networked robotics has been widely researched. However, the modes of cooperation in traditional networked robotics have been restricted by the inherent physical constraint that all computations are performed in the robotic network, with knowledge sharing being limited to the collective storage in the network. Cloud robotics, which allows robots to benefit from the rich storage, computation, and communication resources of modern data centers, is widely accepted as a promising approach to efficient robot cooperation in applications, such as disaster management. In this paper, we study robotic cooperation in cloud robotics. We first give a conceptual view of the nature of this cooperation. We then propose three novel robotic cooperation frameworks for cloud robotics: robotic knowledge sharing cooperation, robotic physical-task cooperation, and robotic computation task cooperation. Finally, we identify several critical challenges, and illustrate the potential benefits of robotic cooperation in cloud robotics. INDEX TERMS Cloud robotics, multi-robot system, robot cooperation, quality of service.

—The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation , and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data, 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning, 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes, and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to a) datasets, publications, models, benchmarks, and simulation tools, b) open competitions for designs and systems, and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: Note to Practitioners—Most robots and automation systems still operate independently using onboard computation, memory, and programming. Emerging advances and the increasing availability of networking in the " Cloud " suggests new approaches where processing is performed remotely with access to dynamic global datasets to support a range of functions. This paper surveys research to date.

IOP Conference Series: Materials Science and Engineering, 2018

In recent years, with the development of science and technology, new technologies have emerged continuously, such as cloud computing, big data, etc., and cloud computing technology has been used in robot research, making the designed robot have high real-time performance and high energy efficiency, low cost and a series of advantages. Among robot technologies, controlling robots is one of the key technologies. One of the classical problems in the field of robots is the synchronous map construction and positioning (SLAM) problem. This problem is a typical computationally intensive task. In traditional robots, the implementation method is to rely on the robot's local computing resources to calculate and solve in real time. The disadvantages of this method are slow speed, low precision, and very large computational overhead. This paper aims at the current research status, based on the robot architecture and the "Software as a Service" (SaaS) idea in cloud computing, proposes a SLAM service framework based on cloud computing, and studies the control in the robot, and finally the development of cloud computing and robotics is looking into the future.

Recently, robots and automation systems have been at the front of research with the majority of systems still operating independently using onboard computation, memory manipulation and communication. With improvements in communication technology and the increasing availability of network, new approaches where robot and automation processing is performed remotely with access to large scale datasets, support a range of functions. Cloud Robotics supplements performance enhancement of robotics and autonomous systems by providing a global infrastructure in innovative ways. This paper summarizes recent research into five traits of Cloud Robotics for performance enhancement in robotics and autonomous systems: 1) Remote Brain, 2) Big Data and Shared Knowledge-base, 3) Collective Learning, 4) Intelligence and Behavior, and 5) Cloud architectures. Towards the end, in this survey, we present future directions for research in cloud robotics.

2017 European Conference on Mobile Robots (ECMR)

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.