Cloud-based Robot Research Articles

Edge robotics: are we ready? an experimental evaluation of current vision and future directions

Cloud-based robotics systems leverage a wide range of Information Technologies (IT) to offer tangible benefits like cost reduction, powerful computational capabilities, data offloading, etc. However, the centralized nature of cloud computing is not well-suited for a multitude of Operational Technologies (OT) nowadays used in robotics systems that require strict real-time guarantees and security. Edge computing and fog computing are complementary approaches that aim at mitigating some of these challenges by providing computing capabilities closer to the users. The goal of this work is hence threefold: i) to analyze the current edge computing and fog computing landscape in the context of robotics systems, ii) to experimentally evaluate an end-to-end robotics system based on solutions proposed in the literature, and iii) to experimentally identify current benefits and open challenges of edge computing and fog computing. Results show that, in the case of an exemplary delivery application comprising two mobile robots, the robot coordination and range can be improved by consuming real-time radio information available at the edge. However, our evaluation highlights that the existing software, wireless and virtualization technologies still require substantial evolution to fully support edge-based robotics systems.

A Cloud-based Robot System for Long-term Interaction: Principles, Implementation, Lessons Learned

Making the transition to long-term interaction with social-robot systems has been identified as one of the main challenges in human-robot interaction. This article identifies four design principles to address this challenge and applies them in a real-world implementation: cloud-based robot control, a modular design, one common knowledge base for all applications, and hybrid artificial intelligence for decision making and reasoning. The control architecture for this robot includes a common Knowledge-base (ontologies), Data-base, “Hybrid Artificial Brain” (dialogue manager, action selection and explainable AI), Activities Centre (Timeline, Quiz, Break and Sort, Memory, Tip of the Day, \( \ldots \) ), Embodied Conversational Agent (ECA, i.e., robot and avatar), and Dashboards (for authoring and monitoring the interaction). Further, the ECA is integrated with an expandable set of (mobile) health applications. The resulting system is a Personal Assistant for a healthy Lifestyle (PAL), which supports diabetic children with self-management and educates them on health-related issues (48 children, aged 6–14, recruited via hospitals in the Netherlands and in Italy). It is capable of autonomous interaction “in the wild” for prolonged periods of time without the need for a “Wizard-of-Oz” (up until 6 months online). PAL is an exemplary system that provides personalised, stable and diverse, long-term human-robot interaction.

ECC-Based Authentication Scheme for Cloud-Based Robots

Security is one of the important aspects for many robotic applications. Cloud Robotics is one of the areas where security plays a vital role. Cloud-based robots access cloud resources to offload computational intensive tasks such as Simultaneous Localization and Mapping. Cloud-based robots can also get access to on-demand services. However, there is a strong need to provide these services secured manner. In security, authentication is one of the critical aspects because it ensures that only legitimate users get access to resources. In cloud-based robots, we intend to provide cloud services to the authorized robots only. In this paper, we have created a secure mutual authentication scheme between robots and cloud servers using Elliptic Curve Cryptography (ECC) with the key agreement for robots that access the cloud resources. The proposed scheme has been testified against the various well-known attacks using the Proverif tool. Moreover, when compared with other ECC-based authentication schemes, our proposed authentication scheme found efficient than them.

Intelligent task planner for cloud robotics using level of attention empowered with fuzzy system

Cloud robotics is a field of robotics that endeavors to summon cloud advances, for instance, cloud computing, and other internet advances focused on the advantages of merged framework and shared administrations for robotics. In this research, an intelligent task planning mechanism is proposed to attain a certain level of attention for knowledge sharing among cloud-based robots using mamdani fuzzy inference system (MFIS). The proposed task planning mechanism using mamdani fuzzy inference system (TPM-MFIS), can classify the different stages of attention such as very high, high, medium, low, very low and none. In this recommended intelligent system, cloud robotics services are used to maintain the workload of each robot. Whereas, all fuzzification and decision-making policies handled on cloud to avoid the redundancy task planning consumption on individual level. This system has two input variables that are facial expression and motion rate. Input variables detect the output level of attention to be very high, high, medium, low, very low, and none. This paper presents an analysis of the result’s accuracy using proposed TPM-MFIS to model the task planning process.

Overview and Framework of Quality Service Metrics for Cloud-Based Robotics Platforms

With the rapid development of big data, cloud computing and other technologies, Cloud-based robotic has become one of the key research directions for service robot, such as used in hospitals. A framework and set of metrics for evaluating the quality of service (QOS) of a cloud robotic platform would be greatly facilitate research into and actual practice of service robots. In this paper, a QOS metrics framework of cloud robotic computing is summarized and the research of components and metrics of a cloud robotic platform is reviewed. QOS metrics are organized into software, network, and robotic services. By summarizing and analyzing the above three groups of metrics, a QOS framework or index system is proposed. Finally, future research towards open source and standardization of components of robotic cloud platform is discussed.

A Tradeoff Analysis of a Cloud-Based Robot Navigation Assistant Using Stereo Image Processing

The use of Cloud Computing for computation offloading in the robotics area has become a field of interest today. The aim of this work is to demonstrate the viability of cloud offloading in a low level and intensive computing task: a vision-based navigation assistance of a service mobile robot. In order to do so, a prototype, running over a ROS-based mobile robot (Erratic by Videre Design LLC) is presented. The information extracted from on-board stereo cameras will be used by a private cloud platform consisting of five bare-metal nodes with AMD Phenom 965 $\,\times\,$ 4 CPU, with the cloud middleware Openstack Havana. The actual task is the shared control of the robot teleoperation, that is, the smooth filtering of the teleoperated commands with the detected obstacles to prevent collisions. All the possible offloading models for this case are presented and analyzed. Several performance results using different communication technologies and offloading models are explained as well. In addition to this, a real navigation case in a domestic circuit was done. The tests demonstrate that offloading computation to the Cloud improves the performance and navigation results with respect to the case where all processing is done by the robot.

클라우드보드: 클라우드 기반 지식 공유 및 제어 시스템

ABSTRACT As the importance of software to society has grown, more and more schools worldwide teach coding basics in the classroom. Despite the rapid spread of coding instruction in grade schools, experience in the classroom is certainly limited because there is a gap between the curriculum and the existing computing environment such as the mobile and cloud computing. We propose an approach to fill this gap by using a mobile environment and the robot on the cloud-based platform for effective teaching. In this paper, we propose an architecture called Cloudboard that enables knowledge sharing and collaboration among knowledge providers in the cloud-based robot platforms. We also describe five representative architectural patterns that are referenced and analyzed to design the Cloudboard architecture. Our early experimental results show that the Cloudboard can be effective in the development of collective robotic systems.Keywords:Cloud, Blackboard, Knowledge Sharing, Framework 클라우드보드: 클라우드 기반 지식 공유 및 제어 시스템

K-MORE: Design of a Kinect Motion Oriented iRobotics Environment

Internet and cloud-based robot services have been attractive and developed. Nowadays, how to enhance the controllability of intelligent robots is an important issue for researchers. This study designs a Kinect Motion Oriented Intelligent-Robot Environment, which is called K-MORE. K-MORE is designed for easily controlling intelligent robots, which is composed of Sensing-based Interactive Robot (SIR) service platform and Cloud Environment. K-MORE is based on open service platforms, including Arduinos, Google Android, and Google App Engine (GAE). Robots not only can be controlled by a smartphone but also can be triggered by events via GAE or Ontology Case-based Reasoning (OCBR). Furthermore, users can use a smartphone to control robots. K-MORE is well-integrated with users, intelligent robots, sensors and the cloud environment. Finally, K-MORE can be an inference model for researchers when developing a robotics-based open service platform in the future.