Internet Of Robotic Things Research Articles

Internet of Robotic Things: A Review

The Internet of Things (IoT) gives a strong structure for connecting things to the internet to facilitate Machine to Machine (M2M) communication and data transmission through basic network protocols such as TCP/IP. IoT is growing at a fast pace, and billions of devices are now associated, with the amount expected to reach trillions in the coming years. Many fields, including the army, farming, manufacturing, healthcare, robotics, and biotechnology, are adopting IoT for advanced solutions as technology advances. This paper offers a detailed view of the current IoT paradigm, specifically proposed for robots, namely the Internet of Robotic Things (IoRT). IoRT is a collection of various developments such as Cloud Computing, Artificial Intelligence (AI), Machine Learning, and the (IoT). This paper also goes over architecture, which would be essential in the design of Multi-Role Robotic Systems for IoRT. Furthermore, includes systems underlying IoRT, as well as IoRT implementations. The paper provides the foundation for researchers to imagine the idea of IoRT and to look beyond the frame while designing and implementing IoRT-based robotic systems in real-world implementations.

Internet of Things Applied on Assistive Robotics

With the Americans with Disabilities Act (ADA), the past two decades have seen a proliferation of Assistive Technology (AT) and its enabling impact on the lives of people with disabilities in the areas of accessing information, communication, and daily living activities. Due to recent emergence of the Internet of Things (IoT), the research of assistive robotics has been contributing to assisting humans to manipulate and communicate with the robot in complex unstructured environments. The ongoing revolution of Internet of Things (IoT), together with the growing diffusion of robots applied in everyday life and industry, makes Internet of Robotic Things (IoRT) as the future direction for assistive robotics research. New advanced technologies and services are explored in assisting humans. This study provides an overview of the IoT and applications into robotics based on the building blocks of the IoT, along with recent trends and issues relevant to accessing technology for people with disabilities. This research also discusses the technologies in IoT that would benefit the applications of assistive robotics. The most important research challenges to be faced are also highlighted.

An Introduction to Patterns for the Internet of Robotic Things in the Ambient Assisted Living Scenario

The Internet of Things paradigm envisions the interoperation among objects, people, and their surrounding environment. In the last decade, the spread of IoT-based solutions has been supported in various domains and scenarios by academia, industry, and standards-setting organizations. The wide variety of applications and the need for a higher level of autonomy and interaction with the environment have recently led to the rise of the Internet of Robotic Things (IoRT), where smart objects become autonomous robotic systems. As mentioned in the recent literature, many of the proposed solutions in the IoT field have to tackle similar challenges regarding the management of resources, interoperation among objects, and interaction with users and the environment. Given that, the concept of the IoT pattern has recently been introduced. In software engineering, a pattern is defined as a general solution that can be applied to a class of common problems. It is a template suggesting a solution for the same problem occurring in different contexts. Similarly, an IoT pattern provides a guide to design an IoT solution with the difference that the software is not the only element involved. Starting from this idea, we propose the novel concept of the IoRT pattern. To the authors’ knowledge, this is the first attempt at pattern authoring in the Internet of Robotic Things context. We focus on pattern identification by abstracting examples also in the Ambient Assisted Living (AAL) scenario. A case study providing an implementation of the proposed patterns in the AAL context is also presented and discussed.

Best strategy to control data on internet-of-robotic-things in heterogeneous networks

The control and transmission of huge data constitute an immense challenge in various types of networks (wired and wireless). Congestion caused by the high traffic and low throughput of huge data continues to be major problems in a heterogeneous platforms such as internet of things (IoT) technology and internet-of-robotic-things (IoRT). The heterogeneous network requires new models and mechanisms to deal with the increased challenges posed by IoT and IoRT. Accordingly, eliminating the issues that emerge has compelled finding improved solutions as a new strategy. This study proposed a new strategy called routing information and distance vector (RIDV) to create the best improvement of a heterogeneous network. The RIDV strategy activates the routing information protocol (RIPv2) on a router in wire network parallel with the ad-hoc on-demand distance vector (AODV) protocol on the wireless network. The RIDV strategy is used to solve the problems of the diversity of heterogeneous networks as the basis of the infrastructure IoRT technology. Hence, this strategy can reduce or avoid congestion through the use of enhanced and effective best routing protocols. Simulation results using OPNET show that the proposed method improved the quality of service (QoS) compared with other related strategies and AODV and RIPv1 protocols in terms of data drop, traffic drop, queue delay, and throughput.

Design of a Social Robot Interact With Artificial Intelligence by Versatile Control Systems

Robots are a combination of mechatronics, computer science, and artificial intelligence. Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of actions. Whenever a robot has to interact with the human-society, it has to adopt a special skill called Human-Robot Interaction and thus the term Social-Robot comes into account. A social robot has to be able to express emotions, communicate with high-level dialogue, use natural cues, and learn to recognize models of other agents. An autonomous social robot cannot follow orders instead of doing something on its own. To make the robot more interactive and communicative, lots of sensors and modules have to be used along with its moving mechanism. Therefore, a social robot becomes complex and expensive. To overcome the issue of the complexity and costliness, in this paper, a design of social robot using a combination of embedded systems, the Internet of Robotic Things (IORT) and Android operating system has been introduced to be interactive and communicative to human, be intelligent enough to solve complex mathematics and be able to follow the operator’s command simultaneously. By using the Internet as the robot’s source of information and the Android phone as the robot’s sensory and control system partially, and adding them all to the robot’s embedded system wirelessly, we have not only become able to make the robot more advanced and intelligent, but also reduce the cost of construction by a significant amount.

A Highly Reliable Communication System for Internet of Robotic Things and Implementation in RT-Middleware With AMQP Communication Interfaces

Since the Internet of Robotic Things (IoRT) is composed of robots with actuators, interferences with the real-world activities are necessary, and safety is essential. In addition, some IoRT services may require bidirectional communication between multiple machines. One of the communication protocols that satisfy these requirements is AMQP, a broker architecture that emphasizes reliability and high functionality in communication. Therefore, using AMQP as the communication infrastructure between components in an IoRT system, it can contribute to improving the reliability of robot operations in IoRT and diversifying messaging between robots. To verify AMQP’s communication advantages, we have implemented the communication interface of AMQP in RT-Middleware, which is one of the robot middlewares, and have conducted performance and reliability tests on the effectiveness of RT-Middleware as a platform for constructing a reliable IoRT system. In the tests, we compared the communication performance of the platform with CORBA and MQTT communication interfaces implemented in RT-Middleware. The results show that although AMQP causes a small amount of latency compared to other communication interfaces, the distribution range of the latency is small, and relatively stable communication is performed. Furthermore, in the messaging quality test results, the data loss during communication by AMQP is almost a hundred times better than CORBA, and ten times better than MQTT, which mean that highly reliable messaging is possible. Based on this study’s findings, we conclude that AMQP should be fully used as a communication protocol for constructing IoRT systems.

Distributed Control with Web-based 3D Visualization using Kinematics Analysis for IoRT

The use of graphical user interface in web platform has been increasing in the recent decades. The concept of Internet of Robotic Things (IoRT) integrates heterogenous intelligent devices into a distributed architecture of platforms. This research is conducted to deliver proof of concept of distributed control of 2 DOF robot manipulator with web-based 3D visualization. The 3D modeling was developed using Three.js and the WebGL library. Synchronization was made using WebSocket communication on TCP network layer. This protocol allowed 3D mesh data broadcasted from web interface to manipulator with average response 7.86 ms. Moreover, using Denavit-Hartenberg convention, kinematic model facilitates the convention of end-effector position data into angular form and vice versa. The error percentage at a joint 1 is 4% and joint 2 of 2.96%. The error occurs because of the characteristic of inverse function called singularity in which the application value in each joint has the input values of and respectively to get the end-effector position on the 2 DoF Manipulator Robot.

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things.

Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.

A secure anonymous tracing fog-assisted method for the Internet of Robotic Things

PurposeEmploying a fog computing (FC) network system in the robotic network system is an effective solution to support robotic application issues. The interconnection between robotic devices through an FC network can be referred as the Internet of Robotic Things (IoRT). Although the FC network system can provide number of services closer to IoRT devices, it still faces significant challenges including real-time tracing services and a secure tracing services. Therefore, this paper aims to provide a tracking mobile robot devices in a secure and private manner, with high efficiency performance, is considered essential to ensuring the success of IoRT network applications.Design/methodology/approachThis paper proposes a secure anonymous tracing (SAT) method to support the tracing of IoRT devices through a FC network system based on the Counting Bloom filter (CBF) and elliptic curve cryptography techniques. With the proposed SAT mechanism, a fog node can trace a particular robot device in a secure manner, which means that the fog node can provide a service to a particular robot device without revealing any private data such as the device's identity or location.FindingsAnalysis shows that the SAT mechanism is both efficient and resilient against tracing attacks. Simulation results are provided to show that the proposed mechanism is beneficial to support IoRT applications over an FC network system.Originality/valueThis paper represents a SAT method based on CBF and elliptic curve cryptography techniques as an efficient mechanism that is resilient against tracing attacks.

Internet of Robotic Things Intelligent Connectivity and Platforms.

The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and “things” have evolved significantly. “Things” now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of “intelligent things” (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.

Semantic approach to RIoT autonomous robots mission coordination

Internet of Things (IoT) has recently become the key for innovation and progress in many industrial sectors and scientific areas. However, it brings many challenges and issues, such as growing number of connected devices, heterogeneity, amount of generated data, security and privacy issues, interoperability and many others. Since devices not only collect data, but also take actions that affect the environment, device coordination in the context of IoT systems is becoming more and more important, especially if the IoT convergence with robotics, known as “Internet of Robotic Things” (RIoT), is taken into consideration. In novel cyber–physical systems coordination is very important for situations when many devices working parallel have higher potential to achieve the given task more effectively, than a single device operating independently. RIoT experimentation testbeds facilitate development of such cyber–physical systems where devices need to be aware of the environment while interacting with other devices in order to achieve a common goal. In this paper, we propose a semantic-driven framework for automated autonomous robots coordination in the context of RIoT-based experimentation testbeds. Framework for automatic coordinated mission generation within the robotics experimentation platform testbed is evaluated. Results of evaluation are presented and discussed.

An intelligent and multi-terrain navigational environment monitoring robotic platform with wireless sensor network and internet of robotic things

Wireless sensors network (WSN) and internet of robotic things (WSN&IRT) have been emerged as fundamental data-gathering tools for the deployment of an environment monitoring system. The challenging issue in the environment monitoring system is the critical design of system architecture, coverage of monitoring area, energy efficiency of sensors and validity of the data. In this paper, design and implementation of an intelligent and multi-terrain navigational environment monitoring system with WSN&IRT is discussed. It measures and analyze the air quality, whether it is breathable by human or not. The robot measures four chemical and four physical parameters, dust particle size and communicates it to server in real-time with autonomous navigation. Artificial vision system, by using a camera is placed in the robot head for image capturing which is processed at server. The energy efficient reporting mechanism is implemented to prevent the redundant transmission of sensor data and equipped with a docking station to charge it.

An intelligent and multi-terrain navigational environment monitoring robotic platform with wireless sensor network and internet of robotic things

An intelligent and multi-terrain navigational environment monitoring robotic platform with wireless sensor network and internet of robotic things

Analyzing the robotic behavior in a smart city with deep enforcement and imitation learning using IoRT

Smart City aims to develop an environment in which different things goes to different people. The smart city provides a core infrastructure by improving the status of the inhabitants by providing a smart environment by applying Smart Solutions. The Internet of Things (IoT) is a revolutionary concept which finds its way in many applications such as business, industry, healthcare, Transportation, modern Information Technology applications and many more. IoT combined with Artificial Intelligence (AI) can be applied to many day to day applications in superior systems like transportation, robotics, industrial, and automation systems applications. This research focuses on developing a robotic behavior control using the Internet of Robotic Things (IoRT) using deep learning for a Smart City. The IoRT is a promising standard that brings together autonomous robotic systems in the midst of the IoT vision of connected sensors and smart objects pervasively embedded in the day to day environments. Robotic behavioral control models the robot with the essential features to react with the immediate environment via sensory-motor links. Robotic behavioral control has a direct interconnection between the sensors and actuators and controls the functions necessary to move around the environment and carry out necessary tasks. This deep learning solution applied to the robotic behavioral control for robotic application uses two main paradigms: Deep Reinforcement Learning (DRL) and Imitation Learning (IL).DRL merges the concept of deep learning architecture using neural networks and Reinforcement learning algorithms to identify the behavior of the robots.IL focus on imitating human learning or expert demonstration for controlling the robot behavior. The behavior of the robot is monitored in a diner and its performance is estimated to be 92% in realtime

Complex industrial automation data stream mining algorithm based on random Internet of robotic things

ABSTRACTIn recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumulated a large amount of data while transforming and improving enterprise management modes and means. How to mine useful data, discover important knowledge and extract useful information has become a hot topic of current research. Industrial big data is significantly different from traditional big data. The traditional big data is based on the Internet environment. Although the data has a high degree of discretization and distribution, its association is relatively simple. The collection of industrial process data is relatively easy, but the mathematical and physical and chemical mechanism models involved make the inherent relationship of data complex, so it is difficult to use common analytical models and methods for processing. In this paper, we propose a complex industrial automation data stream Mining algorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher data mining efficiency and robustness.

Designing Smart Sewerbot for the Identification of Sewer Defects and Blockages

Internet of thing (IoT) is a new concept where the term ‘thing’ is associated with the configurable sensors and devices no matter domestic or industrial, whereas bridging up a relationship in between these things and internet protocol is known as Internet of thing. Moreover, the same concept has been introduced in the field of robotics as ‘Internet of Robotic Things (IoRT)’, which is mainly concerned with active sensorization of sensors dully interfaced with any type of robots i.e. autonomous unmanned ground vehicle (UGV). This paper describes the prototyping of an autonomous sewerbot that will not only identify the sewer defects in sewerage pipelines but will also identify the type of blockages using the technique of digital image processing. Furthermore, the deployed configurable sensors will also share the attributes of particular sewerage line on IoT such that temperature, humidity, availability of hazardous gases, exact depth at which it is available and global positioning using GPS module. The paper also provides the brief construction of this mechatronic and amphibian system via which it can extricate the blockages from sewerage lines along with wireless camera surveillance.

Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Robots are moving from well-controlled lab environments to the real world, where an increasing number of environments has been transformed into smart sensorized IoT spaces. Users will expect these robots to adapt to their preferences and needs, and even more so for social robots that engage in personal interactions. In this paper, we present declarative ontological models and a middleware platform for building services that generate interaction tasks for social robots in smart IoT environments. The platform implements a modular, data-driven workflow that allows developers of interaction services to determine the appropriate time, content and style of human-robot interaction tasks by reasoning on semantically enriched IoT sensor data. The platform also abstracts the complexities of scheduling, planning and execution of these tasks, and can automatically adjust parameters to the personal profile and current context. We present motivational scenarios in three environments: a smart home, a smart office and a smart nursing home, detail the interfaces and executional paths in our platform and present a proof-of-concept implementation.

Neuro-Dominating set scheme for a fast and efficient robot deployment in internet of robotic things

Internet of Robotic Things (IoRT) is a new concept introduced for the first time by ABI Research. Unlike the Internet of Things (IoT), IoRT provides an active sensorization and is considered as the new evolution of IoT. In this context, we propose a Neuro-Dominating Set algorithm (NDS) to efficiently deploy a team of mobile wireless robots in an IoRT scenario, in order to reach a desired inter-robot distance, while maintaining global connectivity in the whole network. We use the term Neuro-Dominating Set to describe our approach, since it is inspired by both neural network and dominating set principles. With NDS algorithm, a robot adopts different behaviors according whether it is a dominating or a dominated robot. Our main goal is to show and demonstrate the beneficial effect of using different behaviors in the IoRT concept. The obtained results show that the proposed method outperforms an existing related technique (i.e., the Virtual Angular Force approach) and the neural network based approach presented in our previous work. As an objective, we aim to decrease the overall traveled distance and keep a low energy consumption level, while maintaining network connectivity and an acceptable convergence time.

IoRT cloud survivability framework for robotic AALs using HARMS

The Internet of Robotic Things, which includes ambient assisted living systems has been pushed to be developed by the research community for reasons such as the population gap between elderly people and their caregivers. Due to the critical mission that is assigned to those systems; interruptions, failures, worse still, full malfunction should not be allowed to materialize. Such systems ought to keep running in a proper way notwithstanding problems caused either by internal and external system collapses or bad intentioned actions in their surroundings. Therefore, including survivability features must be insured to Ambient Assisted Living systems (AALs) using Humans, software Agents, Robots, Machines, and Sensors (HARMS). HARMS stands for the model that allows through the indistinguishability feature to any type of actor to communicate and interact. This work proposes a framework which takes advantage of the Cloud to overcome the state explosion problem encountered when using model checking. Model checking techniques are used to find a possible solution when a problem is already faced by the system — instead of its original purpose to detect errors on the systems during the design stage. This paper presents the implementation of the proposed framework and validates the functionality with experiments. The conducted experiments evaluate the advantages of using cloud tools to offload the model checking capability for applications such as multi-agent systems.

An integrated semantic framework for designing context-aware Internet of Robotic Things systems

The paradigm of Internet of Robotic Things (IoRT) extends the scope of the Internet of Things by endowing any object with the three main typical functions of any robotic system: perception, actuation, and control. This paper presents a semantic framework for context-aware IoRT systems to support the development of applications for monitoring and managing IoRT systems. A knowledge representation framework, called SmartRules, is proposed for context modeling. SmartRules is a production rules language that enables reactive reasoning based on the closed world and unique name assumptions. It allows producing actions based on contextual information represented in a dedicated ontology language, called μ-Concept. An operational platform, centered on the notion of manageable object (MO), is also proposed to abstract the access to any physical or virtual device, which can communicate through the Internet. In addition, an integrated methodology and tools are proposed for guiding the development and deployment of context-aware Semantic IoRT systems, and, in particular, for defining context semantics and creating context management rules. To show the effectiveness of the proposed framework in ambient assisted living (AAL) applications, an IoRT system dedicated to the monitoring and assistance of an elderly person during his/her daily living activities is described and evaluated.