Robots In Factories Research Articles

Impact of Artificial Intelligence on Indian Banking Sector- A Study of Banks

Artificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. It is a critical branch of cognitive science, and its influence is increasingly being felt in other areas, including the humanities. Intelligence might be defined as the ability to learn and perform suitable techniques to solve problems and achieve goals, appropriate to the context in an uncertain, ever-varying world. A fully pre-programmed factory robot is flexible, accurate, and consistent but not intelligent. Artificial Intelligence (AI), a term coined by emeritus Stanford Professor John McCarthy in 1955, was define as “the science and engineering of making intelligent machines”. Much research has humans program machines to behave in a clever way, like playing chess, but, today, people emphasize machines that can learn, at least somewhat like human beings do. Autonomous systems can independently plan and decide sequences of steps to achieve a specified goal without micro-management. A hospital delivery robot must autonomously navigate busy corridors to succeed in its task. In AI, autonomy doesn’t have the sense of being self-governing common in politics or biology.

L-NORM: Learning and Network Orchestration at the Edge for Robot Connectivity and Mobility in Factory Floor Environments

Robotic factory floors will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, these robots will need low-latency, reliable connectivity throughout the work zone through links established by wireless access points (APs). This will allow the robot to assuredly respond to programming directives that rely on the real-time relaying of robot-generated sensor data to the Mobile Edge Computing (MEC) server. In this paper, we propose L-NORM, a multi-AP and multi-robot coordination framework, as a multi-tiered solution for such autonomous edge networks. First, multi-robot motion planning through reinforcement learning occurs at the MEC, using as input multi-modal robot sensor data. Second, multi-AP resource orchestration is performed using another reinforcement learning-based method that maps a subset of available APs to each robot toward meeting their sensor data delivery requirements. Furthermore, we suggest diversity combination of uplink channels with the 802.11ax scheduled access mode that will (i) support high reliability of multi-robot uplink sensor packets and (ii) enable multi-AP coordination, for optimized resource utilization. Through extensive simulation studies, we show that the probability of robot deviation to remain within 0.5 m from its optimal path, is 19% more in L-NORM compared to classical 802.11ax based edge network solution, considering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1 MB of sensor data per robot.

Opportunistic Block Validation for IoT Blockchain Networks

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintain the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.

A review of robotic grasp detection technology

AbstractIn order to complete many complex operations and attain more general-purpose utility, robotic grasp is a necessary skill to master. As the most common essential action of robots in factory and daily life environments, robotic autonomous grasping has a wide range of application prospects and has received much attention from researchers in the past decade. However, the accurate grasp of arbitrary objects in unstructured environments is still a research challenge that has not yet been completely overcome. A complete robotic grasp system usually involves three aspects: grasp detection, grasp planning, and control subsystem. As the first step, identifying the location of the object and generating the grasp pose is the premise of successful grasp, which is conducive to planning the subsequent grasp path and the realization of the entire grasp action. Therefore, this paper conducts a literature review focusing on grasp detection technology and concludes two significant aspects: the analytic and data-driven methods. According to the previous grasp experience of the target object, this paper divides the data-driven methods into the grasp of known and unknown objects. Then it describes in detail the typical grasp detection methods and related characteristics of each classification in the grasp of unknown objects. Finally, current research status and potential research directions in this field are discussed to provide some reference for related research.

Neuro-inspired continual anthropomorphic grasping

Humans can learn continuously grasping various objects dexterously. This ability is enabled partly by underlying neural mechanisms. Most current works of anthropomorphic robotic grasping learning lack the capability of continual learning (CL). They utilize large datasets to train grasp models and the trained models are difficult to improve incrementally. By incorporating several discovered neural mechanisms supporting CL, we propose a neuro-inspired continual anthropomorphic grasping (NICAG) approach. It consists of a CL framework of anthropomorphic grasping and a neuro-inspired CL algorithm. Compared with other methods, our NICAG approach achieves better CL capability with lower loss and forgetting, and gets higher grasping success rate. It indicates that our approach performs better on alleviating forgetting and preserving grasp knowledge. The proposed system offers an approach for endowing anthropomorphic robotic hands with the ability to learn grasping objects continually and has great potential to make a profound impact on robots in households and factories.

Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory.

Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology is required for mobile inspection terminals, picking robots, and intelligent sorting equipment in tomato production and management in plant factories. However, due to the limitations of computer power, storage capacity, and the complexity of the plant factory (PF) environment, the precision of small-target detection for tomatoes in real-world applications is inadequate. Therefore, we propose an improved Small MobileNet YOLOv5 (SM-YOLOv5) detection algorithm and model based on YOLOv5 for target detection by tomato-picking robots in plant factories. Firstly, MobileNetV3-Large was used as the backbone network to make the model structure lightweight and improve its running performance. Secondly, a small-target detection layer was added to improve the accuracy of small-target detection for tomatoes. The constructed PF tomato dataset was used for training. Compared with the YOLOv5 baseline model, the mAP of the improved SM-YOLOv5 model was increased by 1.4%, reaching 98.8%. The model size was only 6.33 MB, which was 42.48% that of YOLOv5, and it required only 7.6 GFLOPs, which was half that required by YOLOv5. The experiment showed that the improved SM-YOLOv5 model had a precision of 97.8% and a recall rate of 96.7%. The model is lightweight and has excellent detection performance, and so it can meet the real-time detection requirements of tomato-picking robots in plant factories.

Integrating Robot Manufacturer Perspectives into Legible Factory Robot Light Communications

In a world with increasing numbers of robots operating in everyday human spaces, the employees at this robotics company are pioneers, with intelligent point-to-point path planning and autonomous transport operations in 150+ factory and warehouse locations in North America. At the time of research, this robotics company consisted of 250 employees. Unlike other industry models, their robots are designed to operate with people in mixed human-machine spaces, yet no HRI style evaluations had previously been run with their robots. As early observers of how factory workers and transport robot interact, across varied job roles ranging from technology design to customer relations, this work sought to leverage employee knowledge and experiences to identify opportunities for improving the communication capabilities of the robots, resulting in the addition of several robot state communications to their initial software set leveraging both employee- and social robotics literature- sourced ideas for communicating with lights. To achieve this a social robotics researcher spent a summer onsite at the robotics company, getting to know their software stack and culture. Her research activities included: (1) a company-wide survey relative to the robot’s light, sound, and motion communications was sent out and analyzed, (2) the development of three new light sets (car-like, sweeping, heartbeat) and five overall states (blocked, at goal, turning, idle), and (3) a user study evaluating the developed light sets relative to the current robot default light patterns, all significantly improving the overall legibility of the targeted robot state communications: at goal, blocked, turning, and idle. Our initial findings advance knowledge in which style of light patterns is best for different communication states, showing that eye-catching lights are best for high urgency states, such as blocked, and subtle lights are best for low urgency states, such as idle. Finally, the latest software release for this robot has deployed a subset of these light patterns to all of their currently operating client sites, i.e., anyone who updates their robots to the latest release will benefit from these research results. This deployment sets the ground for future researchers exploring how end-users at different sites have responded to the new, more communicative light patterns.

Biomechanical Modeling of Human–Robot Accident Scenarios: A Computational Assessment for Heavy-Payload-Capacity Robots

Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing with increased productivity and flexibility. Workspaces are being transformed into fully shared spaces for performing tasks during human–robot collaboration (HRC), increasing the possibility of accidents as compared to the fully restricted and partially shared workspaces. The next technological epoch of Industry 5.0 has a heavy focus on human well-being, with humans and robots operating in synergy. However, the reluctance to adopt heavy-payload-capacity robots due to safety concerns is a major hurdle. Therefore, the importance of analyzing the level of injury after impact can never be neglected for the safety of workers and for designing a collaborative environment. In this study, quasi-static and dynamic analyses of accidental scenarios during HRC are performed for medium- and low-payload-capacity robots according to the conditions given in ISO TS 15066 to assess the threshold level of injury and pain, and is subsequently extended for high speeds and heavy payloads for collaborative robots. For this purpose, accidental scenarios are simulated in ANSYS using a 3D finite element model of an adult human index finger and hand, composed of cortical bone and soft tissue. Stresses and strains in the bone and tissue, and contact forces and energy transfer during impact are studied, and contact speed limit values are estimated. It is observed that heavy-payload-capacity robots must be restricted to 80% of the speed limit of low-payload-capacity robots. Biomechanical modeling of accident scenarios offers insights and, therefore, gives confidence in the adoption of heavy-payload robots in factories of the future. The analysis allows for prediction and assessment of different hypothetical accidental scenarios in HRC involving high speeds and heavy-payload-capacity robots.

Simulated and Real Robotic Reach, Grasp, and Pick-and-Place Using Combined Reinforcement Learning and Traditional Controls

The majority of robots in factories today are operated with conventional control strategies that require individual programming on a task-by-task basis, with no margin for error. As an alternative to the rudimentary operation planning and task-programming techniques, machine learning has shown significant promise for higher-level task planning, with the development of reinforcement learning (RL)-based control strategies. This paper reviews the implementation of combined traditional and RL control for simulated and real environments to validate the RL approach for standard industrial tasks such as reach, grasp, and pick-and-place. The goal of this research is to bring intelligence to robotic control so that robotic operations can be completed without precisely defining the environment, constraints, and the action plan. The results from this approach provide optimistic preliminary data on the application of RL to real-world robotics.

An effective self-collision detection algorithm for multi-degree-of-freedom manipulator

In order to address the self-collision problem associated with the operation of modern industrial robots, this paper proposes a multi-degree-of-freedom collision detection algorithm that can detect self-collision in single arm and double arm robots as well as collision with the load. Firstly, the zero pose of the Denaviti−Hartenberg model is built based on the manipulator configuration, and the coordinate information of each key point is obtained through a rotation and translation operation of the matrix. Then, the positional relation and distance between the detected objects are determined by the spatial geometry theory, and finally, collision is detected using a collision matrix. By simulating two groups of single arms and two groups of double arms, and from the laboratory testing of SR10C in the SIASUN robot factory, it has been verified that the proposed algorithm has good collision detection capability. Without the use of sensors, cameras, and other external devices, the collision between the arm and the load, and the collision between the cooperative robot and the load may be effectively detected and mitigated.

Trustworthy Transaction Spreading Using Node Reliability Estimation in IoT Blockchain Networks

Blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically comprise various sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or reliable drone deliveries in smart cities. The wide spread of transactions and shared smart contracts across blockchain networks guarantees ultimate network security. A typical wired blockchain network maintains sufficient redundancy within a stable configuration. However, IoT blockchain networks exhibit unavoidable instability. The dynamic configuration changes caused by flexible node membership make it impossible to achieve the same level of redundancy as a stable network. A trustworthy transaction spreading method provides practical transaction sharing for dynamic IoT networks. We propose a Q-learning framework and a graph convergence network (GCN) to search for the proper spreading path of each transaction. The proposed Q-learning framework determines the next spreading hop using node features. The GCN determines the reliable area based on the Q-learning results. The discovered reliable area guides the proper spreading path of transactions to the destination node. In addition, the proposed trustworthy transaction spreading was implemented over an InterPlanetary File system (IPFS). The IPFS-powered experiments confirmed the practicability of the proposed transaction spreading mechanism.

Neural Network-Based Pose Estimation Approaches for Mobile Manipulation

Abstract This paper illustrates two approaches for the mobile manipulation of factory robots using deep neural networks. The networks are trained using synthetic datasets unique to the factory environment. Approach I uses depth and red-green-blue (RGB) images of objects for its convolutional neural network (CNN) and Approach II uses computer-aided design models of the objects with RGB images for a deep object pose estimation (DOPE) network and perspective-n-point (PnP) algorithm. Both the approaches are compared based on their complexity, required resources for training, robustness, pose estimation accuracy, and run-time characteristics. Recommendations of which approach is suitable under what circumstances are provided. Finally, the most suitable approach is implemented on a real mobile factory robot in order to execute a series of manipulation tasks and validate the approach.

Application Layer-Forward Error Correction Raptor Q Codes in 5G Mobile Networks for Factory of the Future

The future communication requirements for industrial automation will differ significantly from existing technologies. Traffic in Industry 4.0 imposes real-time requirements, requiring ultra-reliable communication (URC) with high reliability and minimal latency. The demand for ultra-high reliability as high as 99.999999 percent and as low as 1 ms end-to-end latency is the major challenge of the NOMA communication system in the factory of the future. The high expectations on reliability and latency need modifications to the radio system’s baseband signal processing, medium access control (MAC) layer, and application layer to protect against packet losses. Thus, this paper investigates the utilization of Raptor Q codes, which is a type of Application Layer Forward Error Correction (AL-FEC), by developing an end-to-end system level simulator to evaluate and analyze the performance of the transmission signals based on cross-layer approach; from the physical layer (PHY), MAC layer, and application layer parameters in an indoor factory network setting. The factory is assumed to be operated with various factory robots of different speeds, from static to 10 km/h. The 5G technology relies heavily on flexible network operations. High user density, high user mobility, deployment, and coverage are all qualities that allow for this flexibility. Through extensive simulations, the results showed that the Raptor Q codes are not only able to give good results, i.e., packet reception rate PRR = 0.9 of 10 m or 1.8% to 10 m or 3.4% depending on different scenarios, but are also able to meet PRR = 0.9 in the mobility scenario at 10 km/h. Thus, the Raptor Q codes can be seen as a good candidate for obtaining results within a strict range of requirements set by URC communications for the factory of the future replacing RLNC.

Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation

Robotic systems for research and development of factory automation are complex and unavailable for broad deployment in robotic laboratory settings. The usual robotic factory automation setup consists of series of sensors, robotic arms and mobile robots integrated and orchestrated by a central information system. Cloud-based integration has been gaining traction in recent years. In order to build such a system in a laboratory environment, there are several practical challenges that have to be resolved to come to a point when such a system can become operational. In this paper, we present the development of one such system composed of (i) a cloud-based system built on top of open platform for innovation in logistics, (ii) a prototyped mobile robot with a forklift to manipulate pallets in a “factory” floor, and (iii) industrial robot ABB IRB 140 with a customized gripper and various sensors. A mobile robot is designed as an autonomous four Mecanum wheels system with on-board LiDAR and RGB-D sensor for simultaneous localization and mapping. The paper shows a use case of the overall system and highlights the advantages of having a laboratory setting with real robots for the research of factory automation in a laboratory environment. Moreover, the proposed solution could be scaled and replicated in real factory automation applications.

The Energy, Information, Robotics, and Industrial Revolution [Students and Young Professionals News

While the IEEE Industrial Electronics Society (IES) is celebrating its 70th anniversary, the energy, information, robotics, and industrial revolution is speeding up. It seems that soon everyone in the world will have access to electricity. The electric power supply is a common factor for electrical and electronics devices. It does not matter if we are considering electrifying a Honduran primary school or automating a Japanese robotic factory—without a reliable, flexible, environmentally friendly energy source, modern education and industrialization is not possible. Therefore, one role of the IES is to attract as many new engineers as possible. We need to show girls that engineering is as attractive as other study fields. We must propose a discussion platform for undergraduates, postgraduates, and young professionals from industry. This kind of platform was presented at the IES 70th Anniversary Joint Forum of Women in Engineering (WiE) and Students & Young Professionals (S&YP) at the 2021 International Symposium on Industrial Electronics (ISIE2021)-Kyoto online conference by IES Vice President for Planning and Development, Prof. Makoto Iwasaki, Nagoya Institute of Technology, Japan.

The use of digital twin in the industrial sector

The paper discusses one of the most active areas of production digitalization, capable of reducing the risks of environmental pollution - a solution for the virtual representation of the real static and dynamic characteristics of a technological system, briefly called “Digital Twin”. The concept and classification of Digital Twin are described. The application of Digital Twins is also considered, starting with the analysis of the work of smart products in real operating conditions and ending with the creation of a fully digital robot factory.

ReLy: Machine Learning for Ultra-Reliable, Low-Latency Messaging in Industrial Robots

Robotic factory floors are transforming the manufacturing sector by delivering an unprecedented boost to productivity. However, such a paradigm raises questions on safety and coordination, especially when in the presence of unexpected events. Time-critical communication messages for such industrial robots mandate the requirement of ultra-reliable low-latency communication (URLLC). Classical WiFi-connected industrial robots often suffer from the traditional dense network problems prevalent in production WiFi networks, where transmission of an emergency notification packet is “best effort,” devoid of time guarantees. In this work, we propose a machine-learning-based framework called ReLy that intelligently embeds the time-critical messages in the preamble of outgoing frames at the transmitter. These messages are inferred from the channel state information variations at the receiver. As ReLy is implemented entirely at the physical layer, the transmitter is able to deliver information within 5 ms latency and ultra-high reliability of 99 percent. We experimentally demonstrate the feasibility of achieving URLLC with moving robots in a busy workshop with a number of other peer robots, equipment, desks, and robotic arms, as expected in a typical factory setting.

Autonomous Operation Control of IoT Blockchain Networks

Internet of Things (IoT) networks are typically composed of many sensors and actuators. The operation controls for robots in smart factories or drones produce a massive volume of data that requires high reliability. A blockchain architecture can be used to build highly reliable IoT networks. The shared ledger and open data validation among users guarantee extremely high data security. However, current blockchain technology has limitations for its overall application across IoT networks. Because general permission-less blockchain networks typically target high-performance network nodes with sufficient computing power, a blockchain node with low computing power and memory, such as an IoT sensor/actuator, cannot operate in a blockchain as a fully functional node. A lightweight blockchain provides practical blockchain availability over IoT networks. We propose essential operational advances to develop a lightweight blockchain over IoT networks. A dynamic network configuration enforced by deep clustering provides ad-hoc flexibility for IoT network environments. The proposed graph neural network technique enhances the efficiency of dApp (distributed application) spreading across IoT networks. In addition, the proposed blockchain technology is highly implementable in software because it adopts the Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed methods.

A General Technique for Real-Time Robotic Simulation in Manufacturing System

This paper describes a real-time simulator that allows the user in the factories to simulate arbitrary interaction between machinery and equipment. We discussed in details not only the general technique for developing such a real-time simulator but also the implementation of the simulator in its actual use. As such, people on the production line could benefit from observing and controlling robots in factories for preventing or reducing the severity of a collision, using the proposed simulator and its related technique. For that purpose, we divided the simulator into two main models: the real-time communication model and the simulation model. For the communication model, we present the distributed messaging server structure and the database semaphore technique for handling the real-time peer-to-peer (P2P) connections and control. For the simulation model, we used parallel programming and the general-purpose GPU (GPGPU) technique to speed up the processing time of the simulation. In the paper, we exhaustively explain how the simulator’s techniques are designed and implemented and verify two real world models with robots and robot-operating systems (ROS). Moreover, we compare the results between our technique and the traditional method.

A Cloud-Based Platform for Big Data-Driven CPS Modeling of Robots

This paper proposes an improved cyber-physical systems (CPS) architecture for a smart robotic factory based on an industrial cloud platform driven by big data based on the traditional CPS architecture. This paper uses the architecture analysis and design language to model and design a total of three scales for the underlying cell-level robot, the system-level robot shop, and the overall robotic smart factory CPS, respectively, to complete the conceptual scheme for building a robotic smart factory from a local to an overall CPS system. Using the advantages of cloud computing and combining robotic CPS with cloud computing, an architecture for an industrial management system for CPS cloud computing is proposed. Base based distributed storage architecture with Storm based distributed real-time processing architecture. In terms of modeling, the advantages and disadvantages of using AADL, structural analysis, and design language, and modelers, a physical device modeling language, are combined to analyze the advantages and disadvantages of architecture analysis & design language (AADL) for modeling CPS and propose a CPS analysis and design based on AADL and applicable to it. The paper also investigates the use of LeNet models for state identification in the HSV color space. The algorithm was verified on a self-built power equipment indicator dataset with a 100% detection rate and 99.8% state recognition accuracy after four consecutive frames of fusion detection. Simulink simulation of the trolley was carried out in terms of a cell-level robotic trolley CPS system to demonstrate the effectiveness of the design of a robotic CPS system driven by soaring data based on the industrial cloud platform proposed in this paper.