Cloud Robotic System Research Articles

RoboEC2: A Novel Cloud Robotic System With Dynamic Network Offloading Assisted by Amazon EC2

RoboEC2: A Novel Cloud Robotic System With Dynamic Network Offloading Assisted by Amazon EC2

Improving makespan in dynamic task scheduling for cloud robotic systems with time window constraints

A scheduling method in a robotic network cloud system with minimal makespan is beneficial as the system can complete all the tasks assigned to it in the fastest way. Time window constraints on tasks are a natural way to order tasks. The makespan is the maximum amount of time between when the first processing unit starts executing its first task and when all processing units have completed their last scheduled task. Load balancing allocation and scheduling ensures that the time between when the first processing unit completes its scheduled tasks and when all other processing units complete their scheduled tasks is as short as possible. We propose a method to ensure that the time window constraints are met. We propose the grid of all tasks balancing algorithm (GTBA) for distributing and scheduling tasks with minimum makespan. The GTBA method is a combinatorial method that describes a way to distribute tasks among processing units and schedule them when a set of tasks arrives in such a way that the makespan is minimized, the loads of all processing units are nearly balanced, and it is ensured that the time window constraints are met. We prove the correctness of the proposed algorithm and present simulations illustrating the obtained results.

Building and evaluating cloud robotic systems: A systematic review

Accelerated advancements in artificial intelligence and robotics have recently boosted the proliferation and adoption of robotic systems within various industries. In order to improve the capabilities of such systems that range from enabling remote processing to enhanced access to data, there has been integration of the cloud, and this gave rise to cloud robotics. Nevertheless, minimal published literature is available on a generic cloud robotic architecture that computer scientists or software developers can utilise and adapt to a particular use case scenario, as most existing cloud robotic architectures are scenario specific. To address this gap, a systematic review is conducted in this paper to illustrate the key components of cloud robotic systems through an architectural representation, in addition to enlighten how such systems are built and evaluated. For this, 44 recently published papers were systematically reviewed and analysed. Following the review, a generic architecture for cloud robotic system was realised, and it was found that there is no agreed consensus on the tools and technologies used in the build-up of such systems. Similarly, various approaches are used to evaluate such systems and the most common one is through case studies. However, there is a lack of frameworks that guide the evaluation process. This paper presents fascinating research concerns and potential for future study based on the data reported.

Optimizing Camera Stream Transport in Cloud-Based Industrial Robotic Systems

Combining visual-guided robotics with cloud networking brought a new era into industrial robotic research and development. New challenges have to be tackled with a focus on providing proper communication and data processing setup: sensor data processing as well as the control software should be decoupled from the local robot hardware and should move into the cloud. In the emerging field of cloud robotics, there are trade-offs that have to be handled. More and more sensors such as cameras are being integrated but it comes with a cost. All sensory data have to be sent through often limited networking resources, while latency must be kept as low as possible. In this paper we propose a general solution for efficient camera stream transportation in cloud robotic systems. After introducing our test scenario with the used hardware and software elements, a detailed overview of the architecture is presented with describing each task of the components. The goal of this paper is to examine the current stream transportation implementations in ROS environment and implement a more efficient method. The performance of the proposed method is investigated and compared with other solutions evidenced by measurements.

Survey on Cloud Robotics and Robot Operating System

The paper proposes cloud robotics which intends five parts which are for software architecture, trends and challenges, design based on a web technology, cloud computing platforms and cloud robotics in practical smart city applications. Software architecture involves lots of different cloud technologies like cloud storage and shared services for robotics for Robot Operating System. There are lots of trends in cloud robotics as it uses cloud technologies for robotics. The use of the cloud for automation brings some largely ameliorating the performance of robotics. Besides, the trends, there are some challenges in cloud robotics. Robot Operating System has been a standard tool in the design for cloud robotics. It always runs at the on-board computer but some resources of CPU do not allow the ability of robotics. In this problem, a good solution is to transfer the task to the cloud. Hence, the design of a cloud robotics middleware is based on web technology. Robotics with cloud computing gives lots of research scopes and research fields. In Smart City Applications, Real-Time Cloud Robotics is present. Real Time Cloud Robotics has emerged as an efficient computing means in data processing applications. By this we introduce CR20 cloud robotics platform that uses real time stream technology virtually to connect to different devices.

Novel denial-of-service attacks against cloud-based multi-robot systems

The development of robotics technology is accelerated by the strong support from cloud computing. Massive computation resources and services from the cloud make modern multi-robot systems more efficient and powerful. However, the introduction of cloud servers to multi-robot systems can also incur potential Denial-of-Service (DoS) threats, where an adversary can utilize the shared cloud resources to degrade or bring down the robot systems. In this paper, we conduct a comprehensive study about this security issue. By analyzing different attack vectors in cloud-robotic platforms, we propose three new DoS attacks, which manipulate the network resources, micro-architecture resources, and function parameters respectively. We conduct extensive evaluations and case studies to demonstrate the feasibility and severity of our techniques. We alert the robotics community to these catastrophic attacks on the safety and performance of cloud-robotic systems, and encourage building better defenses for higher reliability, in addition to automation and intelligence.

Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data

Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So, how can robots achieve this? To address the issue, we present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service requests. After that, we introduce a knowledge transfer scheme to facilitate local robots acquiring knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning in accuracy and efficiency. Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a self-driving task for robots (cars). The experimental results demonstrate that the shared model generated by FIL increases imitation learning efficiency of local robots in cloud robotic systems.

Cloud technologies for STEM education

Cloud technologies being used in STEM education for providing robotics studying are highlighted in this article. Developing cloud robotic systems have not been used to their fullest degree in education but are applied by limited specialists’ number. Advantages given by cloud robotics (an access to big data, open systems, open environments development) lead to work with mentioned systems interfaces improving and having them more accessible. The potential represented by these technologies make them worth being shown to the majority of teachers. Benefits of cloud technologies for robotics and automatization systems are defined. An integrated approach to knowledge assimilation is STEM education basis. The demanded stages for robotics system development are shown and cloud sources which could be possibly used are analyzed in this article.

Ontologies for cloud robotics

AbstractCloud robotics (CR) is currently a growing area in the robotic community. Indeed, the use of cloud computing to share data and resources of distributed robotic systems leads to the design and development of cloud robotic systems (CRS) which constitute useful technologies for a wide range of applications such as smart manufacturing, aid and rescue missions. However, in order to get coherent agent-to-cloud communications and efficient agent-to-agent collaboration within these CRS, there is a need to formalize the knowledge representation in CR. Hence, the use of ontologies provides a mean to define formal concepts and their relations in an interoperable way. This paper presents standard robotic ontologies and their extension in the CR domain as well as their possible implementations in the case of a real-world CR scenario.

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL.

Predictor-Based Motion Tracking Control for Cloud Robotic Systems with Delayed Measurements

This paper addresses the problem of motion prediction and tracking control for cloud robotic systems with time-varying delays in measurements. A novel method using an observer-based structure for position and velocity prediction is developed to estimate the real-time information of robot manipulator. The prediction error can converge to zero even if model uncertainties exist in the robot manipulator. Based on the predicted positions and velocities, some sufficient conditions are derived to design suitable tracking controllers such that semi-globally uniformly ultimately bounded tracking performance of the predictor–controller couple can be guaranteed. Finally, the effectiveness and robustness to model uncertainties of the proposed method are verified by a two degree-of-freedom (DOF) robot system.

Dynamic Path Selection for Cloud-based Multi-Hop Multi-Robot Wireless Networks

ABSTRACTIn a cloud robotics system, communication between robots and between robots and the cloud are crucial from the application prospective. Due to the nature of the network technology (predominantly wireless) and nature of the network devices, direct communication between robots and between robots and the cloud is not feasible. Multi-hop communication is the only feasible solution in such scenarios. In Multi-hop communication, finding the optimal route between the end points is a complicated task. This becomes further complicated when the communicating nodes are mobile and do not have enough communication and computational power as in the case of robots. Though robots can compute the end-to-end path, it is a complicated task for them and is sometimes infeasible in practice due to their low computation and communication power. To overcome this problem, we propose a multi-hop communication model called Dynamic Path Selection using Cloud-based Multi-hop Multi-Robot (DPS-CMM) model, where instead of the robots, the cloud decides the end-to-end communication path which the robots need to use to communicate among themselves. In this model, the cloud provides the optimal path information of the robots to the Access Points (APs) such that source robot establishes a reliable communication path through the APs to reach the destination robot. In this paper, the DPS-CMM model provides the information of network topology and computes the path selection at the cloud instead of computing the same in robots level thereby increasing the life time of robotic networks. To validate this model, we use combination of NS-3 and Matlab-based simulations and compare our scheme with the existing state-of-the-art solutions.

Constrained Control for Cloud Robotic Under Time Delay Based on Command Governor With Interval Estimation

The constrained command tracking problem for the cloud robotic system with unknown bounded time-varying delays is considered. Based on a novel predictor-observer approach, a command governor is proposed to modify the received command in order to guarantee the constraint enforcement of remote robot. We firstly use a prediction algorithm in the forward channel to generate a virtual command, which is close to the original one as much as possible when the danger of constraint violation exists. Then, to deal with the measurement delay and disturbance in the backward channel, interval estimations of the system states are applied instead of delayed measurements in the prediction algorithm of the command governor. The constraints can be rigorously enforced with the upper and lower estimations. The efficiency of the proposed approach is demonstrated by simulations in the case of a single-degree-of-freedom manipulator.

Microservice-based cloud robotics system for intelligent space

Cloud robotics (CR) is a red-hot branch of the burgeoning field of service robots that is centered on the benefits of integrating infrastructure and shared services via a cloud computing environment. Although it extends the computation power and information sharing capabilities of the network robots, the development and operations (DevOps) of the CR system are currently limited for enterprise-scale projects due to the heavy framework. In fact, current developed CR systems are typical distributed monomer architectures followed by a “top-down” design. As the scale of the applications gets larger, the operation and maintenance of CR systems will become a very difficult task. In this paper, a new architecture for a microservice-based cloud robotics system in intelligent space is proposed to solve the present dilemma. To enable this, we design a service management architecture based on a microservice to provide a highly efficient and flexible development/deployment mechanism. The container technology based on the docker engine is then used to functionally decompose the application into a set of collaborating services to ensure the software design methods, based on microservice, easy for implementation. Finally, a real experiment on SLAM (Simulation localization and mapping) in an intelligent space is implemented to verify the proposed architecture. Compared with traditional monomer architectures, the results show that the proposed framework is more productive, flexible and cost effective.

IAPcloud: A Cloud Control Platform for Heterogeneous Robots

The cloud robotic technology makes multiple robots share resources and collaborate with each other more flexibly. Although this concept has been widely accepted by academia since it was put forward, it still faces many challenges in engineering. The main problems are that the functions of industrial robotic systems become more and more complex, and the programming language and computing environment of multiple vendor robots are significantly different, accordingly the cooperative control of different devices and the online testing become extremely difficult. Therefore, this paper proposes a new control platform for cloud robots, called IAPcloud, which has significant advantages in solving the problem of collaborative control among heterogeneous robots and their auxiliary devices, declining the programming difficulties of cloud robotic control systems and shortening the development and deployment cycle of the application. Finally, we verify the scientific and effectiveness of the IAPcloud platform by three cases, including the image recognition and tracking, the human–machine gobang game, and the online dynamic reconfiguration of control algorithms.

Three dimensional memristor-based neuromorphic computing system and its application to cloud robotics

Neuromorphic computing based on three-dimensional inetgraed circuits (3D-NCs) offers a novel hardware implementation of neuromorphic computing, and provides high device density, massively parallel signal processing capability, low power consumption, and direct analog signal processing capability. In this paper, by replacing conventional CPUs based on Von Neumann architecture with 3D-NCs, a novel neuromorphic computing based cloud robotics (NC-robotics) system is proposed, which is constructed by 1) cloud server center using 3D-NCs as computing units, 2) neuromorphic robotics based on neural network control technology. Besides the benefits of normal Cloud Robotics platform, this NC- Robotics system has more advantages on massive parallel-computing, analog signals processing, and lower power consumption. In order to implement this NC––Robotics system, a novel 3D-NCs architecture combining vertical RRAM structure is investigated and its concise equivalent circuit model is created, evaluated, and analyzed through SPICE simulations.

Architectural Design of a Cloud Robotic System for Upper-Limb Rehabilitation with Multimodal Interaction

The rise in the cases of motor impairing illnesses demands the research for improvements in rehabilitation therapy. Due to the current situation that the service of the professional therapists cannot meet the need of the motor-impaired subjects, a cloud robotic system is proposed to provide an Internet-based process for upper-limb rehabilitation with multimodal interaction. In this system, therapists and subjects are connected through the Internet using client/server architecture. At the client site, gradual virtual games are introduced so that the subjects can control and interact with virtual objects through the interaction devices such as robot arms. Computer graphics show the geometric results and interaction haptic/force is fed back during exercising. Both video/audio information and kinematical/physiological data are transferred to the therapist for monitoring and analysis. In this way, patients can be diagnosed and directed and therapists can manage therapy sessions remotely. The rehabilitation process can be monitored through the Internet. Expert libraries on the central server can serve as a supervisor and give advice based on the training data and the physiological data. The proposed solution is a convenient application that has several features taking advantage of the extensive technological utilization in the area of physical rehabilitation and multimodal interaction.

An overview of internet-enabled cloud-based cyber manufacturing

This paper presents an overview of the latest advancement of Internet-enabled cloud-based cyber manufacturing and new approaches for hardware-in-the-loop real-time applications, including cloud-based remote monitoring and control of industrial robots, remote assembly in a cyber-physical environment, and a cloud robotic system for energy-efficient operations. Altogether, they form an integrated cloud-based cyber manufacturing system. In terms of enabling technologies, they are the unique combination of virtual 3D models driven by real sensors, and image-to-model based representation of dynamic environment to guide cyber users. The objective of this research is to significantly reduce network traffic over the Internet for cyber manufacturing. This paper includes case studies, the results of which show that the integrated cyber-physical system consumes less than 1% of network bandwidth of traditional camera-based systems with a 30 msec latency of real-time operations. They are feasible and practical as cyber manufacturing solutions.

Cloud based Real-time Multi-robot Collision Avoidance for Swarm Robotics

Nowadays, Cloud Computing has brought many new and efficient approaches for computation-intensive application areas. One typical area is Cloud based real-time device control system, such as the IoT Cloud Platform. This kind of platform shifts computation load from devices to the Cloud and provides powerful processing capabilities to a simple device. In Swarm robotics, robots are supposed to be small, energy efficient and low-cost, but still smart enough to carry out individual and swarm intelligence. These two goals are normally contradictory to each other. Besides, in real world robot control, real-time on-line data processing is required, but most of the current Cloud Robotic Systems are focusing on off-line batch processing. However, Cloud based real-time device control system may provide a way that leads this research area out of its dilemma. This paper explores the availability of Cloud based real-time control of massive complex robots by implementing a relatively complicated but better performed local collision avoidance algorithm. The Cloud based application and corresponding Cloud driver, which connects the robot and the Cloud, are developed and deployed in Cloud environment. Simulation tests are carried out and the results show that, when the number of robots increases, by simply scaling computation resources for the application, the algorithm can still maintain the preset control frequency. Such characteristics verify that the Cloud Computing environment is a new platform for studying massive complex robots in swarm robotics.

A Cloud Robotics Solution to Improve Social Assistive Robots for Active and Healthy Aging

Technological innovation in robotics and ICT represents an effective solution to tackle the challenge of providing social sustainable care services for the ageing population. The recent introduction of cloud technologies is opening new opportunities for the provisioning of advanced robotic services based on the cooperation of a number of connected robots, smart environments and devices improved by the huge cloud computational and storage capability. In this context, this paper aims to investigate and assess the potentialities of a cloud robotic system for the provisioning of assistive services for the promotion of active and healthy ageing. The system comprised two different smart environments, located in Italy and Sweden, where a service robot is connected to a cloud platform for the provisioning of localization based services to the users. The cloud robotic services were tested in the two realistic environments to assess the general feasibility of the solution and demonstrate the ability to provide assistive location based services in a multiple environment framework. The results confirmed the validity of the solution but also suggested a deeper investigation on the dependability of the communication technologies adopted in such kind of systems.