Field Of Industrial Robots Research Articles

Improvement of visual servo system of industrial robot based on sliding mode control and deep reinforcement learning

Abstract. Visual servo system is more and more widely used in the field of industrial robots because it allows robots to sense external signals through sensors to convey control commands to themselves and complete tasks. However, the traditional visual servo has many limitations in the design of controller and image feature extraction, such as insufficient robustness and image extraction accuracy. This research focuses on the optimization of controller and image feature extraction, which can improve the overall performance and autonomy of the system by combining sliding mode control and Convolutional Neural Network (CNN). Sliding mode control performs well in terms of robustness and response speed, while CNN has excellent ability for image feature extraction. The research results show that the combination of the two and the visual servo has better performance in a variety of application scenarios, so this is also the development direction that industrial robots can adopt in the future.

Evolution of Industrial Robots from the Perspective of the Metaverse: Integration of Virtual and Physical Realities and Human–Robot Collaboration

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making.

Industrial robots and firm productivity

Industrial robotics has become a driving force in the development of high-quality manufacturing enterprises. This paper investigates the impact of industrial robotics on the total factor productivity of enterprises and its mechanism of action through the use of data from A-shares of Chinese companies listed on the Shanghai and Shenzhen exchanges from 2007 to 2019. We found that the use of industrial robots significantly increases the total factor productivity of enterprises, findings that still hold when Bartik instrumental variables are constructed. The mechanism of action suggests that industrial robot application improves both the human capital structure and the innovation capability of enterprises, thus increasing total factor productivity. Heterogeneity analysis shows that industrial robot applications are more likely to increase total factor productivity in firms that are non-state-owned and small scale and have political affiliations, low market concentration, low financialization, and high managerial competence. The findings of this study will help to accurately assess the productivity effects of industrial robotics, and the government should continue to promote the integration of industrial robotics and manufacturing development and actively cultivate independent innovation capabilities and professional skills in the field of industrial robotics in order to increase total factor productivity in enterprises.

Research on Real-Time Obstacle Avoidance Motion Planning of Industrial Robotic Arm Based on Artificial Potential Field Method in Joint Space

The artificial potential field method is a highly popular obstacle avoidance algorithm which is widely used in the field of industrial robotics due to its high efficiency. However, the traditional artificial potential field method has poor real-time performance, making it less suitable for modern factory work patterns, and it is difficult to handle situations when the robotic arm encounters singular configurations. In this paper, we propose an improved artificial potential field method in joint space, which effectively improves the real-time performance of the algorithm, and still performs well when the robotic arm falls into a singular configuration. This method solves the gradient of the repulsive potential field in advance by defining the shortest distance from each joint of the robotic arm to the obstacle, and only needs to calculate potential field function once per cycle, which significantly reduces the calculation time. In addition, when a robotic arm falls into a local minimum position in potential field, the algorithm adds a virtual obstacle to make it leave the position, while this virtual obstacle does not require additional input information. Experimental results show that the algorithm obtains short movement paths and requires very little computing time in the face of different obstacles.

Static Analysis of Manipulator Based on Solidworks and ANSYS Workbench

China has gradually achieved a far leading position in the field of industrial robots. The application of robots in various fields has also been widely promoted. Therefore, the safety and reliability of robot work need to be further improved. This paper is mainly for the safety and reliability of the robot arm in the work. Based on Solidworks and ANSYS, the workbench performs a static analysis of a robotic arm with six degrees of freedom. Firstly, the three-dimensional model of the robot arm is established by Solidworks. And then, it is introduced into the ANSYS workbench to simulate the actual force situation to analyze the force of the robot arm and obtain the change law of the stress on each component and the deformation of the key structure during operation. According to the analysis results of ANSYS workbench software, the design of each component of the robot arm has a certain degree of reliability, which can lay the foundation for subsequent research such as motion control.

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot.

Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%.

The Machines from Our Future

Abstract While the last sixty years have defined the field of industrial robots and empowered hard-bodied robots to execute complex assembly tasks in constrained industrial settings, the next sixty years will usher in our time with pervasive robots that come in a diversity of forms and materials and help people with physical tasks. The past sixty years have mostly been inspired by the human form, but the form diversity of the animal kingdom has broader potential. With the development of soft materials, machines and materials are coming closer together: machines are becoming compliant and fluid-like materials, and materials are becoming more intelligent. This progression raises the question: what will be the machines from our future?

Collaborative operation of 6-DOF industrial robot based on digital twin

In order to realize the collaborative operation between the physical prototype and the virtual prototype of industrial robot, a digital twin model of a six-degree-of-freedom industrial robot was established. The structure and motion parameters of the 6-DOF industrial robot are introduced. The virtual prototype model of the industrial robot was established based on Visual One; the digital twin modeling of the industrial robot was completed by the three methods communication between the virtual prototype of the industrial robot and the physical prototype was realized, and the virtual and real collaborative operation of the twin robot and the physical robot was realized. Experiments show that when manipulating the movement of the robot on the physical terminal, the digital twin was consistent with its behavior. The research results provide references for the application of digital twin technology in the field of industrial robots.

Mecanismo de evolução e análise empírica das redes de inovação na indústria transformadora avançada

Abstract: As the “chaotic” edge of innovation system has strong innovation potential and is easy to form and develop the emerging technology innovation network, the formation process of emerging technologies and their relationship with the development trajectory of original technologies are analyzed, and the evolution mechanism of innovation network of advanced manufacturing industry is deeply studied combined with life cycle theory. Firstly, empirical analysis is carried out by collecting patents data in the industrial robotics field. Then, the IPC co-occurrence network and patentee citation network are plotted by combining patents citation analysis with social network analysis. Next, the technical characteristics and knowledge flow characteristics of an advanced manufacturing innovation network are verified by calculating various indicators of the network. Finally, the empirical results show that the technology structure in the field of industrial robotics has high heterogeneity, wide integration among technical fields, and knowledge flow network has a small-world effect, characterized by easy flow, wide flow direction, high efficiency, fuzzy network boundary, and numerous and diversified core the key players in innovation.

Analysis of Core Technology Problems and Countermeasures in the Robot Industry

Since 2013, China has been the world’s largest market for industrial robots. Despite the gradual maturity of the industrial robot system, the lagging R&D and backward technology level of industrial robots have led to a strong dependence on the import of core components and key technologies, which to a certain extent has restricted the development and improvement of industrial robots. At present, the “neck problem” in the field of industrial robots in China is not only in the reducer, controller, and servo but also in the basic processing equipment, basic technology, and basic materials. In this paper, we propose measures to improve the “neck problem” of industrial robots to promote the high-quality development of industrial robots in China.

Cobot And Robot Risk Assessment (CARRA) method: an Automation Level-Based Safety Assessment Tool to Improve Fluency in Safe Human Cobot/Robot Interaction

Cobots and robots are integral to automated manufacturing operations. Although there are many studies in the field of industrial robots and cobots to make them safer the number of OSHA accidents due to cobots and robots has not decreased. Even though these cobots are considered inherently safe, they open more probability for accidents because they are not caged. Therefore, it is necessary for the manufacturing industries using cobots to consider the risk involved in human cobot interaction and the ways to attain safety and lower the risk of injury before installing cobots on assembly lines. A user-centric tool was developed to perform an ergonomic risk assessment using process- failure mode effect analysis for different automation levels in human-cobot interaction. The tool suggests recommended actions and various options to eliminate physical injuries. The results provide insights about safety analysis that can be used by manufacturers to improve safe human cobot interaction.

Review of performance testing of high precision reducers for industrial robots

The performance of a high precision reducer determines the accuracy and efficiency of the mechanical motion, particularly in the field of industrial robots. To meet the requirements of scientific research and the expansion of industrial production, comprehensive performance testing of high precision reducers has become an important focus in related fields. This review article summarizes the main test parameters, analyzes the characteristics of existing methods, and proposes requirements according to the aspects of the mechanical structure, metrology principles, and engineering applications. Moreover, measurement methods for basic physical quantities and specific calibration techniques are presented, and the load technology is summarized. Scientific research orientations and technical routes are emphasized for achieving high-precision measurements, which can appropriately determine the performance of high precision reducers.

The Collaboration between Humans and Robots in the Industrial Environment

This study will describe how the robotics industry evolved increasingly and a new phase of advanced robotics has emerged, and the relation between humans and robots in the same workplace. Problems of designing safer robots in human-machine interaction systems are urgent research topics in the field of industrial robotics. Many of the problems in industrial robotics are related not just to technological issues, but also to human-robot collaboration also will be discussed as an effective method to tackle this issue is the invention of Collaborative robots.

Adaptive control of manipulator based on neural network

With the development of economic science and technology, the development of computer vision has undergone rapid changes, and various products relying on computer vision are also more and more, such as smart home, robot technology, and so on. At present, robot technology has become a very important part of the development of human science and technology, and in the field of industrial robots, the most rapid development is the robot with robot arm adaptive motion. It is very necessary to study the adaptive motion control of the manipulator based on machine learning. The robot with the adaptive motion of the manipulator can carry out logistics express sorting, operate in the doors and windows outside the building, and pick fruits in the orchard, which can ensure the effective implementation of hard work. Therefore, this paper proposes a mechanical adaptive control method based on a neural network. According to the motion model of the manipulator, the RBF neural network model is used to judge the stability of the system according to the Lyapunov function. The related algorithms of machine learning and multi-degree of freedom manipulator are studied and improved. The RBF neural network model approximates the unknown function infinitely and then establishes the complex motion model. Aiming at the adaptive neural network of a manipulator, a network adaptive terminal control method is proposed. Firstly, a stable manipulator motion system is designed by using a neural network, and then the terminal synovial controller is designed by using backstepping control technology. The stability of the method is proved by using the approximation virtual control technology of the neural network. The adaptive control is realized by using the learning and self-adaptability of the neural network; thus, the stability analysis of the closed-loop system is realized.

Study on image processing method to classify objects on dynamic conveyor

Controlling a robotic arms for applications such as detection and classification moving object using the vision sensor is a trend in the field of industrial robots. In particular, the vision sensor is the "eye" of the robot. To solve this problem, we need an efficient image processing algorithm for object identification to optimize the speed. Our classification principle based on the color of the object to be classified first, then separating contour to classify according to the shape of the object. In addition, our paper also propose a classification method that rarely mentioned in the relevant documents that classify based on object's characteristic. In fact, the product packaging not only has one color, but also includes complex color and patterns. Being able to classify these products shows the practicality of the proposed method. For complex colors and patterns object, the PCASIFT algorithm is useful, where SIFT extracts the local characteristics of the object and PCA reduces the number of dimensionality and retain only the best characteristics for identification. To picking object, a proposed design with the optimal requirements of picking order so that picking time is the shortest to minimize the delay for the next picking. The other outstanding advantage is a system of robotic arm to perform pick-up and sorting. This helps to verify good running algorithms in real time. The items are randomly released and the rotation of items is random. The speed of the conveyor is 5cm/s, an average of more than 2 seconds to pick up an object and robot arm processing precisely at high speed. The experimental results using camera Logitech C270, Yamaha Scara YK-400X robotic arm, LabVolt conveyor and OpenCV library are satisfactory, reliable and applicable.

Optimization approaches of industrial serial manipulators to improve energy efficiency: A review

The objective of this paper is to provide a comprehensive review of existing approaches and techniques developed in the field of industrial robotics to make it energy efficient. The usage of industrial manipulators is increasing globally due to its repeatability and superiority. The considerable energy demand is also predicted because of the increased application of robots in industries, this can be overcome by making energy-efficient manipulators. Different approaches are already established in this field to make robots energy efficient such as; elimination of needless densities by topology optimization, selection of optimal path using trajectory optimization, usage of light-weight components, analysing speed, and providing energy storage devices, etc. Among different available approaches, major three methodologies are reviewed in this paper. The first group comprises the topology optimization method that optimizes the design space by eliminating needless densities based on given boundary conditions and constraints. Further, the topology optimization method sub-classified into two types based on loading conditions such as static analysis and dynamic analysis. The second group comprises trajectory optimization of robots; this is achieved by selecting the optimal path of the work-cycle, or minimization of torque. Again trajectory optimization classified into point-to-point and multi-point optimization. The third group includes the application of light-weight structural components in the field of industrial robotics and its challenges. Presented work will be useful to analyze different approaches to make the robot as energy efficient.

Open-Loop Position Control in Collaborative, Modular Variable-Stiffness-Link (VSL) Robots

Collaborative robots open up new avenues in the field of industrial robotics and physical Human-Robot Interaction (pHRI) as they are suitable to work in close approximation with humans. The integration and control of variable stiffness elements allow inherently safe interaction: Apart from notable work on Variable Stiffness Actuators, the concept of Variable-Stiffness-Link (VSL) manipulators promises safety improvements in cases of unintentional physical collision. However, position control of these type of robotic manipulators is challenging for critical task-oriented motions. In this letter, we propose a hybrid, learning based kinematic modelling approach to improve the performance of traditional open-loop position controllers for a modular, collaborative VSL robot. We show that our approach improves the performance of traditional open-loop position controllers for robots with VSL and compensates for position errors, in particular, for lower stiffness values inside the links: Using our upgraded and modular robot, two experiments have been carried out to evaluate the behaviour of the robot during task-oriented motions. Results show that traditional model-based kinematics are not able to accurately control the position of the end-effector: the position error increases with higher loads and lower pressures inside the VSLs. On the other hand, we demonstrate that, using our approach, the VSL robot can outperform the position control compared to a robotic manipulator with 3D printed rigid links.

Self‐Powered Sensor Based on Bionic Antennae Arrays and Triboelectric Nanogenerator for Identifying Noncontact Motions

AbstractThe sensor devices based on triboelectric nanogenerator (TENG) have been proved to have both high sensitivity and good self‐powered capability. Inspired by the cockroach antennae, a bionic‐antennae‐array (BAA) sensor based on the working principle of TENG is proposed for identifying the noncontact motions and a data processing module is specially designed for outputting electrostatic signal to the microcontroller unit (MCU). Based on the amplifying effect of antennae array, the detected maximum distance by this BAA sensor can reach 180 mm with a displacement resolution of 1 mm and maximum sensitivity of about 5.6 V mm−1 (at approaching angle of 90°). The influences of several critical factors, such as approaching angle, motion velocity, and target materials, to the performance of this BAA sensor are systematically studied. Meanwhile, the BAA sensor is integrated with proximity switch, intelligent robot, and mobile vehicles for realizing the function of motion alarming and obstacle detecting. The high sensitivity and the easy fabrication process of this TENG‐based BAA sensor show great application potentials in the field of industrial robotics, human–machine interactions, artificial intelligence, etc.

Semiautomatic Labeling for Deep Learning in Robotics

In this article, we propose an augmented reality semiautomatic labeling (ARS), a semiautomatic method which leverages on moving a 2-D camera by means of a robot, proving precise camera tracking, and an augmented reality pen (ARP) to define initial object bounding box, to create large labeled data sets with minimal human intervention. By removing the burden of generating annotated data from humans, we make the deep learning technique applied to computer vision, which typically requires very large data sets, truly automated and reliable. With the ARS pipeline, we created two novel data sets effortlessly, one on electromechanical components (industrial scenario) and other on fruits (daily-living scenario) and trained two state-of-the-art object detectors robustly, based on convolutional neural networks, such as you only look once (YOLO) and single shot detector (SSD). With respect to conventional manual annotation of 1000 frames that takes us slightly more than 10 h, the proposed approach based on ARS allows to annotate 9 sequences of about 35 000 frames in less than 1 h, with a gain factor of about 450. Moreover, both the precision and recall of object detection is increased by about 15% with respect to manual labeling. All our software is available as a robot operating system (ROS) package in a public repository alongside with the novel annotated data sets. Note to Practitioners —This article was motivated by the lack of a simple and effective solution for the generation of data sets usable to train a data-driven model, such as a modern deep neural network, so as to make them accessible in an industrial environment. Specifically, a deep learning robot guidance vision system would require such a large amount of manually labeled images that it would be too expensive and impractical for a real use case, where system reconfigurability is a fundamental requirement. With our system, on the other hand, especially in the field of industrial robotics, the cost of image labeling can be reduced, for the first time, to nearly zero, thus paving the way for self-reconfiguring systems with very high performance (as demonstrated by our experimental results). One of the limitations of this approach is the need to use a manual method for the detection of objects of interest in the preliminary stages of the pipeline (ARP or graphical interface). A feasible extension, related to the field of collaborative robotics, could be used to exploit the robot itself, manually moved by the user, even for this preliminary stage, so as to eliminate any source of inaccuracy.

Research Status and Tendency of Intelligent Industrial Robot

In order to adapt to the overall industrial upgrading of the global manufacturing industry, adding the artificial intelligence technology to the field of industrial robots has a very realistic and far-reaching significance. This paper will analys the research status of intelligent industrial robots at home and abroad. Then we will focus on the introduction of industrial robots based on deep learning and machine vision. According to the present situation of industrial robot industry at home and abroad, we will put forward the development tendency of intelligent industrial robot in the future.