Cloud Robotics with ROS

Toffetti, Giovanni; Bohnert, Thomas Michael

doi:10.1007/978-3-030-20190-6_5

Cited by 20 publications

(7 citation statements)

References 24 publications

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“…In Task 1, the vocabulary of the ROS pocketsphinx package [12,14] is stored in the RSP-AHEP, which the new voice instruction information is defined based on. When the RSP-AHEP received the voice signal from the user, the voice_recog.py node in the ROS recognized the voice information as the defined instruction ID in the pound-etsphinx.…”

Section: Verification and Discussionmentioning

confidence: 99%

A Method of Constructing Robotics Service Platform for Assisting Handicapped or Elderly People

Yin

Wang

Liu

2020

Journal of Robotics

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In order to solve the problem of demand for large capacity storage and high-performance computing resource of intelligent robot navigation and demand-service matching in the field of assisting handicapped or elderly people, overcoming the limitations of carrying resources, the method of constructing robot service platform for assisting handicapped or elderly people (RSP-AHEP) was proposed based on cloud robotics technology. Firstly, the demand of assisting handicapped or elderly people and the corresponding robot service category was analyzed to assure relationship-matching subjects. Secondly, based on VMware and Hadoop cluster technology, the architecture of a three-layer robot service platform was designed, which were universal interface layer, resource service layer, and application layer. Complex computing tasks, such as the matching computing between robot service and demand of the handicapped or elderly people, and the robot path-planning service, were placed in the robot service platform with the advantages of storage and computing resources. Thirdly, the remote communication between the robot and the service platform was realized based on the ROS (Robot Operation System) technology; finally, the function experiments, which included the remote dispatch, path planning, and service response between the service platform and robot, were carried out in the simulation environment. The result verified the feasibility of the proposed method.

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Section: Verification and Discussionmentioning

confidence: 99%

A Method of Constructing Robotics Service Platform for Assisting Handicapped or Elderly People

Yin

Wang

Liu

2020

Journal of Robotics

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“…While in [20] an OpenStack-based cloud is used for data processing and storage, this cloud is a local deployment on the same network as the remaining nodes, thus not incurring any problems of traditional cloud computing. We can find an actual cloud computing setup in [21], overcoming network boundaries by using rosbridge an a custom cloud bridge. Apart from rosbridge -identified as the most common tool to enable cloud robotics applications with ROS by [22] -we can find ROSLink [23] as another tool to solve the problem of connecting ROS applications over multiple network segments.…”

Section: Ros and Docker -Related Workmentioning

confidence: 99%

Proxying ROS communications -- enabling containerized ROS deployments in distributed multi-host environments

Wendt,

Schüppstuhl

2022

Preprint

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With the ability to use containers at the edge, they pose a unified solution to combat the complexity of distributed multi-host ROS deployments, as well as individual ROS-node and dependency deployment. The bidirectional communication in ROS poses a challenge to using containerized ROS deployments alongside non-containerized ones spread over multiple machines though. We will analyze the communication protocol employed by ROS, and the suitability of different container networking modes and their implications on ROS deployments. Finally, we will present a layer 7 transparent proxy server architecture for ROS, as a solution to the identified problems. Enabling the use of ROS not only in containerized environments, but proxying ROS between network segments in general.

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“…From cloud and edge computing for data storage and processing to 4G/LTE and 5G networks for low latency, high bandwidth, data transferring [1]- [3]. Researchers seek to take advantage of these technologies and integrate them to their robotic applications [4]- [11]. Edge computing -5G enabled robotic applications will provide the advantages of computational resources, features for scalable, automated and self-healing applications, and low latency.…”

Section: Introductionmentioning

confidence: 99%

E-CNMPC: Edge-Based Centralized Nonlinear Model Predictive Control for Multiagent Robotic Systems

et al. 2022

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With the wide deployment of autonomous multi-agent robotic systems, control solutions based on centralized algorithms have been developed. Even though these centralized algorithms can optimize the performance of the multi-agent robotic systems, they require a lot of computational effort, and a centralized unit to undertake the whole process. Yet, many robotic platforms like some ground robots and even more, aerial robots, do not have the computing capacity to execute this kind of frameworks on their on onboard computers. While cloud computing has been used as a solution for offloading computationally demanding robotic applications, from the robots to the cloud servers, the latency they introduce to the system has set them unsuitable for time sensitive applications. To overcome these challenges, this article promotes an Edge computing-based Centralized Nonlinear Model Predictive Control (E-CNMPC) framework to control, and optimize, in swarm formation, the trajectory of multiple ground robotic agents, while taking under consideration potential collisions. The data processing procedure for the time critical application of controlling the robots in a centralized manner, is offloaded to the edge machine, thus the framework benefits from the provided edge resources, features, and centralized optimal performance, while the latency remains bounded in desired values. Besides, real experiments were conducted as a proof-of-concept of the proposed framework to evaluate the system's performance and effectiveness. INDEX TERMSEdge-based Centralized Nonlinear Model Predictive Control (E-CNMPC), Edge Computing, Kubernetes, Robotics

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Cloud Robotics with ROS

Cited by 20 publications

References 24 publications

A Method of Constructing Robotics Service Platform for Assisting Handicapped or Elderly People

A Method of Constructing Robotics Service Platform for Assisting Handicapped or Elderly People

Proxying ROS communications -- enabling containerized ROS deployments in distributed multi-host environments

E-CNMPC: Edge-Based Centralized Nonlinear Model Predictive Control for Multiagent Robotic Systems

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