Industrial Robot Control System Research Articles

Retracted: Application of Microcontroller-Based Multipath Servos in Industrial Robot Control Systems

Retracted: Application of Microcontroller-Based Multipath Servos in Industrial Robot Control Systems

Research on automatic control and servo control technology of industrial robots for high-precision assembly requirements

Abstract Industrial robots find widespread application in machining, part assembly, and welding processes. This paper focuses on the precise control of industrial robots, using the permanent magnet synchronous motor (PMSM) as the driving device of the manipulator. By constructing a mathematical model of the PMSM and implementing a vector control strategy, we design an adaptive control algorithm based on the feature model. We also introduce the golden section adaptive control and the position outer-loop control, which are based on the nonlinear proportional regulator. This approach enables the creation of industrial robots that utilize high-precision servo control technology. After analyzing the response speed and positional accuracy of the servo control technology through testing experiments, we integrated this technology into the design of the automatic control system for industrial robots and conducted simulation tests to explore its performance in various applications. The results demonstrate the superior speed response speed and position control accuracy of the servo control technology; the response times for speed and position are approximately 0.2 seconds and 1 second, respectively, with a maximum deviation of 31.75 rpm when the speed remains constant. The industrial robot control system’s structural displacement and tool switching time are both within 2.88 mm and 0.65 s, and it has a large torque. The study confirms that servo control technology and automatic control of industrial robots have superior performance.

Cluster optimization and algorithm design for machine vision in industrial robot control systems

Abstract Machine vision technology improves the ability to detect the environment of industrial robots, which contributes to the improvement of the collaboration efficiency of swarm robots. This paper proposes a roadmap extraction algorithm that utilizes the improved Mean Shift algorithm to extract the roadmap information from images acquired by binocular cameras. Subsequently, the IEO algorithm with K-Means++ is used to optimize the task allocation of the swarm robots. The experiments show that the average error of this paper’s algorithm’s road sign extraction is 0.025m, the ratio of full scene and homing reaches 90.6%, and the results of the scheduling algorithm under the three kinds of task volume are 59.89, 773.08, and 2704.67. The efficiency of scheduling task completion in dispensing experiments is 9.56% higher than that of the comparative algorithms. The experiment proves that the algorithm proposed in this paper has good performance and practical effects on optimizing the industrial robot control system.

Retracted: Hardware Realization of Kinematic Mechanism and Control System of Multifunctional Industrial Robot

Retracted: Hardware Realization of Kinematic Mechanism and Control System of Multifunctional Industrial Robot

Intelligent control system for industrial robots based on multi-source data fusion

Abstract Industrialization has advanced quickly, bringing intelligent production and manufacturing into people’s daily lives, but it has also created a number of issues with the ability of intelligent control systems for industrial robots. As a result, a study has been conducted on the use of multi-source data fusion methods in the mechanical industry. First, the research analyzes and discusses the existing research at home and abroad. Then, a robot intelligent control system based on multi-source fusion method is proposed, which combines multi-source data fusion with principal component analysis to better fuse data of multiple control periods; In the process, the experimental results are dynamically evaluated, and the performance of the proposed method is compared with other fusion methods. The results of the study showed that the confidence values and recognition correctness of the intelligent control system under the proposed method were superior compared to the Yu, Murphy, and Deng methods. Applying the method to the comparison of real-time and historical data values, it is found that the predicted data under the proposed method fits better with the actual data values, and the fit can be as high as 0.9945. The dynamic evaluation analysis of single and multi-factor in the simulation stage demonstrates that the control ability in the training samples of 0–100 is often better than the actual results, and the best evaluation results may be obtained at the sample size of 50 per batch. The aforementioned findings demonstrated that the multi-data fusion method that was suggested had a high degree of viability and accuracy for the intelligent control system of industrial robots and could offer a fresh line of enquiry for the advancement and development of the mechanical industrialization field.

ОСОБЕННОСТИ СОВМЕСТНОГО ИСПОЛЬЗОВАНИЯ ДВУХОСЕВОГО ПОВОРОТНОГО СТОЛА И ШЕСТИОСЕВОГО МАНИПУЛЯТОРА В АДДИТИВНОМ ПРОИЗВОДСТВЕ

The features of using a two-axis rotary table and a six-axis industrial robot to increase the efficiency of additive manufacturing are considered. Methods and algorithms of joint control of devices are proposed. Synchronization of the industrial robot control system with third-party devices for two-way transmission of information without time delay is implemented.

Research on the design of mechanical robot control system for industrial PC in the context of big data

Abstract In the digitalization of big data information, how to use big data technology to design and study the mechanical robot control system of industrial PC has become a key topic of concern in today’s society. In this paper, robot kinematic modeling is mainly used to analyze and study the robot motion equations and control system operation mechanism, and the motion controller of the industrial robot control system is selected from the CLIPPERPMAC controller of the PMAC motion controller series. The approach to reducing the operation error of the industrial robot system from the control perspective is discussed. The error is reduced by adjusting the PID parameters of the PMAC motion controller in automatic and manual ways, and the error is further reduced by introducing fuzzy control algorithms. By comparing the original curve, the curve after the PID adjustment of PMAC itself and the curve after the introduction of the fuzzy controller, the curve error is kept below 5%, which is determined to be an effective method to reduce the error. This study can precisely realize the design and research of mechanical robot control systems, thus providing a guiding reference value for the research of industrial robot mechanization.

Design of a decentralized Internet-based control system for industrial robot robotic arms

Abstract To meet the needs of small and medium-sized processing plants by establishing a software and hardware platform for a robotic arm control system with a hierarchical structure and designing a robot robotic arm motion control system that is adaptable to multiple environments and cost-effective. In this paper, the industrial robot robotic arm motion control system is designed based on the consistent fusion tracking algorithm under the decentralized Internet. While using the VisualC++ development environment for writing human-machine interface programs using the MFC framework, the device controls the robotic arm body to complete predetermined movements or operational tasks based on command information, sensing information, and the robot controller. The AMS1086CD-3.3 low-voltage differential linear regulator chip used in this design effectively implements the circuit design from 4.8V to 3.4V and successfully meets the 3.4V voltage supply required by the controller. The industrial robot robotic arm control system adopts an adaptive backstepping algorithm controller, and the error of the tracking system at 0 seconds in the simulation results is always kept within the range of [-1~1] that meets the requirements, which effectively reduces the control error of the robotic arm position. Therefore, the robotic arm control studied in this paper is composed of a complete control system in hardware and software, and the designed adaptive backstepping algorithm controller achieves the demand for good control performance of the robotic arm proper.

EMG-Based Dynamic Hand Gesture Recognition Using Edge AI for Human–Robot Interaction

Recently, human–robot interaction technology has been considered as a key solution for smart factories. Surface electromyography signals obtained from hand gestures are often used to enable users to control robots through hand gestures. In this paper, we propose a dynamic hand-gesture-based industrial robot control system using the edge AI platform. The proposed system can perform both robot operating-system-based control and edge AI control through an embedded board without requiring an external personal computer. Systems on a mobile edge AI platform must be lightweight, robust, and fast. In the context of a smart factory, classifying a given hand gesture is important for ensuring correct operation. In this study, we collected electromyography signal data from hand gestures and used them to train a convolutional recurrent neural network. The trained classifier model achieved 96% accuracy for 10 gestures in real time. We also verified the universality of the classifier by testing it on 11 different participants.

A data-driven approach for motion planning of industrial robots controlled by high-level motion commands.

Most motion planners generate trajectories as low-level control inputs, such as joint torque or interpolation of joint angles, which cannot be deployed directly in most industrial robot control systems. Some industrial robot systems provide interfaces to execute planned trajectories by an additional control loop with low-level control inputs. However, there is a geometric and temporal deviation between the executed and the planned motions due to the inaccurate estimation of the inaccessible robot dynamic behavior and controller parameters in the planning phase. This deviation can lead to collisions or dangerous situations, especially in heavy-duty industrial robot applications where high-speed and long-distance motions are widely used. When deploying the planned robot motion, the actual robot motion needs to be iteratively checked and adjusted to avoid collisions caused by the deviation between the planned and the executed motions. This process takes a lot of time and engineering effort. Therefore, the state-of-the-art methods no longer meet the needs of today's agile manufacturing for robotic systems that should rapidly plan and deploy new robot motions for different tasks. We present a data-driven motion planning approach using a neural network structure to simultaneously learn high-level motion commands and robot dynamics from acquired realistic collision-free trajectories. The trained neural network can generate trajectory in the form of high-level commands, such as Point-to-Point and Linear motion commands, which can be executed directly by the robot control system. The result carried out in various experimental scenarios has shown that the geometric and temporal deviation between the executed and the planned motions by the proposed approach has been significantly reduced, even if without access to the "black box" parameters of the robot. Furthermore, the proposed approach can generate new collision-free trajectories up to 10 times faster than benchmark motion planners.

Hardware Realization of Kinematic Mechanism and Control System of Multifunctional Industrial Robot

In order to improve the position control accuracy of industrial robots and solve the problems of poor real-time and reconfigurability of traditional motion controllers, a hardware implementation method for the motion mechanism and control system of multi-functional industrial robots is proposed. The video acquisition system is suitable for infrared detectors containing 4 channels or readout circuits in 4 channels. The video display system is compatible with the old AV/S-Video interface, and is also suitable for computer system video graphics array (VGA) analog video interface and high-definition video interface. Multimedia Interface (HDMI) digital high-definition video interface display terminal equipment. The hardware circuit of video acquisition and corresponding video interface image output is designed, including signal amplification circuit, analog-to-digital conversion circuit, video information buffer circuit, video display digital-to-analog conversion circuit, and interface hardware circuit, to realize the digitization of image information acquisition and image information. Display simulation, construct VGA, AV/S-Video, and HDMI timing sequence with hardware description language through Field programmable gate array (FPGA) to complete the display of corresponding terminal equipment. The experimental results show that the experimental data was substituted into the formula and the variance σ = 0.09 mm was found, indicating that the detection error of the system is less than 0.27 mm, which meets the detection requirements. Through the 3D contour reconstruction experiment of the workpiece, the expected function realization of each module is proved, and the feasibility of the system software and hardware system is verified. This design has good scalability and stability, reducing labor costs.

Safety assurance of an industrial robotic control system using hardware/software co-verification

As a general trend in industrial robotics, an increasing number of safety functions are being developed or re-engineered to be handled in software rather than by physical hardware such as safety relays or interlock circuits. This trend reinforces the importance of supplementing traditional, input-based testing and quality procedures which are widely used in industry today, with formal verification and model-checking methods. To this end, this paper focuses on a representative safety-critical system in an ABB industrial paint robot, namely the High-Voltage electrostatic Control system (HVC). The practical convergence of the high-voltage produced by the HVC, essential for safe operation, is formally verified using a novel and general co-verification framework where hardware and software models are related via platform mappings. This approach enables the pragmatic combination of highly diverse and specialised tools. The paper's main contribution includes details on how hardware abstraction and verification results can be transferred between tools in order to verify system-level safety properties. It is noteworthy that the HVC application considered in this paper has a rather generic form of a feedback controller. Hence, the co-verification framework and experiences reported here are also highly relevant for any cyber-physical system tracking a setpoint reference.

Industrial robot control system optimized by wireless resources and cloud resources based on cloud edge multi-cluster containers

Industrial robot control system optimized by wireless resources and cloud resources based on cloud edge multi-cluster containers

Bio-inspired algorithms for industrial robot control using deep learning methods

The biologically inspired algorithm is a significant embranchment of sequence on computational intelligence and plays a critical role in industrial robot control. Industrial robots are well suited for conducting manipulation or material handling functions in extremely constrained and predictable environments. By comparison, living organisms are highly resilient and able to perform tasks in evolving environments, such as stable locomotion over uneven terrain. The robotics challenge is to use inspiration from biology to build devices capable of functioning in unrestrained or moderately constrained environments. Hence, in this study, a Bio-inspired Intelligent Industrial Robot Control System (BIIRCS) has been suggested using Deep Learning methods. A bio-inspired neural network is considered to model the complex environment and to guide a team of robots for the coverage task. The collected data is fed into a Deep Learning Neural Network to comprehend the localization and recognition of present objects from various classes. With the derived data, appropriate robot actions can be planned and executed. Usable objects are identified and seized in the robot's workspace, or that feed is sufficient for unattainable objects. This study confirms the ability to create intelligent systems using existing Deep Learning algorithms and industrial robotics. The simulations' findings reveal that the new approach achieves a high-performance ratio of 83.5%, accuracy ratio of 95.4%, less operational time of 7.8%, low RMSE rate of 11.2%, and increased coverage task rate of 96.7% when compared to other existing approaches.

Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback

There are some industrial tasks that are still mainly performed manually by human workers due to their complexity, which is the case of surface treatment operations (such as sanding, deburring, finishing, grinding, polishing, etc.) used to repair defects. This work develops an advanced teleoperation and control system for industrial robots in order to assist the human operator to perform the mentioned tasks. On the one hand, the controlled robotic system provides strength and accuracy, holding the tool, keeping the right tool orientation and guaranteeing a smooth approach to the workpiece. On the other hand, the advanced teleoperation provides security and comfort to the user when performing the task. In particular, the proposed teleoperation uses augmented virtuality (i.e., a virtual world that includes non-modeled real-world data) and haptic feedback to provide the user an immersive virtual experience when remotely teleoperating the tool of the robot system to treat arbitrary regions of the workpiece surface. The method is illustrated with a car body surface treatment operation, although it can be easily extended to other surface treatment applications or even to other industrial tasks where the human operator may benefit from robotic assistance. The effectiveness of the proposed approach is shown with several experiments using a 6R robotic arm. Moreover, a comparison of the performance obtained manually by an expert and that obtained with the proposed method has also been conducted in order to show the suitability of the proposed approach.

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

Комплексная система управления промышленными роботами

The article considers the control system for industrial robots (IR), including precision robots (РIR), representing the integration of systems of different levels of the control hierarchy into a single (integrated) system. There are the analyzes of the structural diagram and the original mathematical formulation of the control task and, accordingly, the principles of constructing algorithms for the functioning of such systems as a whole are given. It is shown that the expansion of the range of manufactured products requires continuous improvement of technological equipment, including both "mechanics" and the entire complex of control devices: electrics, electronics, pneumatics, hydraulics, optics and their possible various "complexes" (combinations). The presented hierarchical control systems can be used not only for stationary and mobile ground-based robotic systems, but also for controlling the movement of single and group UAVs, since in both cases it is necessary to move the IR unit in space to fulfill the assigned task.

Visual Servo Control of Industrial Robot Based on Convolutional Neural Network

Aiming at the problem of poor flexibility of traditional industrial robot, the vision system and industrial robot control system are combined to propose a visual servo control method based on Faster RCNN. The image information of the target object is collected through the camera, and the Convolutional Neural Network(CNN) is used to detect the entire picture, then the category and position of the object to be grasped are obtained, which improves the control performance of the robot system. Experiments show that the visual servo robot control system can quickly complete the position recognition of different objects. Mean average precision of this method can reach 0.867. The maximum error between the recognized grasping position and the actual position on the coordinate axis does not exceed 3.4 mm, the detection time of each picture in the GPU environment is 17.1ms, which has certain guiding significance in the visual servo control of industrial robots.

Exploring Edge Computing for Multitier Industrial Control

Industrial automation traditionally relies on local controllers implemented on microcontrollers or programmable logic controllers. With the emergence of edge computing, however, industrial automation evolves into a distributed two-tier computing architecture comprising local controllers and edge servers that communicate over wireless networks. Compared to local controllers, edge servers provide larger computing capacity at the cost of data loss over wireless networks. This article presents switching multitier control (SMC) to exploit edge computing for industrial control. SMC dynamically optimizes control performance by switching between local and edge controllers in response to changing network conditions. SMC employs a data-driven approach to derive switching policies based on classification models trained based on simulations while guaranteeing system stability based on an extended Simplex approach tailored for two-tier platforms. To evaluate the performance of industrial control over edge computing platforms, we have developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WCPS-EC</i> , a real-time hybrid simulator that integrates simulated plants, real computing platforms, and real or simulated wireless networks. In a case study of an industrial robotic control system, SMC significantly outperformed both a local controller and an edge controller in face of varying data loss in a wireless network.

A New Type of Industrial Robot Trajectory Generation Component Based on Motion Modularity Technology

Motion modularity is the main method of motion control for higher animals. That means the complex movements of the muscles are made up of basic motion primitives, and the brain or central nervous system does not care about the specific details of the movement. However, the industrial robot control system does not adopt the technical roadmap of motion modularity, it generates complex trajectories by providing a large number of sampling points. This approach is equivalent to using the brain to directly guide the specific movement of the muscle and has to rely on a faster Fieldbus system to obtain complex motion trajectories. This work constructs a modularized industrial robot trajectory generation component based on Dynamic Movement Primitives (DMP) theory. With this component, the robot controller can generate complex trajectories without increasing the sampling points and can obtain good trajectory accuracy. Finally, the rationality of this system is proved by simulations and experiments.