Cloud Robotics: Current Status and Open Issues

Wan, Jiafu; Tang, Shenglong; Yan, Hehua; Li, Di; Wang, Shiyong; Vasilakos, Athanasios V.

doi:10.1109/access.2016.2574979

Cited by 121 publications

(64 citation statements)

References 59 publications

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“…The cloud part allows adding this computing power while not using the limited battery power of the mobile robot. A real-time connection to the cloud-based cognitive operator is challenging [30] and not necessary, as the reflective operator defines the immediate reaction of the robot. This ideally fits into the Internet of Things (IoT) domain and opens the path for…”

Section: Autonomy Vs Cloud Computingmentioning

confidence: 99%

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Concepts of a Modular System Architecture for Distributed Robotic Systems

2019

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Modern robots often use more than one processing unit to solve the requirements in robotics. Robots are frequently designed in a modular manner to fulfill the possibility to be extended for future tasks. The use of multiple processing units leads to a distributed system within one single robot. Therefore, the system architecture is even more important than in single-computer robots. The presented concept of a modular and distributed system architecture was designed for robotic systems. The architecture is based on the Operator-Controller Module (OCM). This article describes the adaption of the distributed OCM for mobile robots considering the requirements on such robots, including, for example, real-time and safety constraints. The presented architecture splits the system hierarchically into a three-layer structure of controllers and operators. The controllers interact directly with all sensors and actuators within the system. For that reason, hard real-time constraints need to comply. The reflective operator, however, processes the information of the controllers, which can be done by model-based principles using state machines. The cognitive operator is used to optimize the system. The article also shows the exemplary design of the DAEbot, a self-developed robot, and discusses the experience of applying these concepts on this robot.

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Section: Autonomy Vs Cloud Computingmentioning

confidence: 99%

“…For example, an FPGA board can be added to the robot to run computer vision algorithms much more efficiently than ARM-based processing units. The cloud connection is typically a Machine-to-Machine (M2M) communication architecture [30].…”

Section: Computing Power and Complexitymentioning

confidence: 99%

Concepts of a Modular System Architecture for Distributed Robotic Systems

2019

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“…While a cloudbased system offers several advantages, it also poses several challenges which if solved can greatly enhance the usability of such platforms. Some of these challenges include network latency, data interaction and security [27].…”

Section: B Cloud Robotics Platform: Rapyutamentioning

confidence: 99%

Managing a fleet of autonomous mobile robots (AMR) using cloud robotics platform

Singhal

Pallav

Kejriwal

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

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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.

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“…Robots are increasingly linked to the network and thus not limited by onboard resources for computation, memory, or software. Internet of Things (IoT) applications and the volume of sensory data continues to increase, leading to a higher latency, variable timing, limited bandwidth access than deemed feasible for modern robotics applications [10], [11]. Moreover, stability issues arise in handling environmental uncertainty with any loss in network connectivity.…”

Section: Introductionmentioning

confidence: 99%

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

Tanwani

Mor

Kubiatowicz

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a 'Fog Robotics' approach to deep robot learning that distributes compute, storage and networking resources between the Cloud and the Edge in a federated manner. Deep models are trained on non-private (public) synthetic images in the Cloud; the models are adapted to the private real images of the environment at the Edge within a trusted network and subsequently, deployed as a service for lowlatency and secure inference/prediction for other robots in the network. We apply this approach to surface decluttering, where a mobile robot picks and sorts objects from a cluttered floor by learning a deep object recognition and a grasp planning model. Experiments suggest that Fog Robotics can improve performance by sim-to-real domain adaptation in comparison to exclusively using Cloud or Edge resources, while reducing the inference cycle time by 4× to successfully declutter 86% of objects over 213 attempts.

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Cloud Robotics: Current Status and Open Issues

Cited by 121 publications

References 59 publications

Concepts of a Modular System Architecture for Distributed Robotic Systems

Concepts of a Modular System Architecture for Distributed Robotic Systems

Managing a fleet of autonomous mobile robots (AMR) using cloud robotics platform

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

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