Intelligent Robot Control System Research Articles

Intelligent robotic control system based on computer vision technology

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

Stability analysis of braking and control system of pipeline intelligent plugging robot

In the maintenance and emergency repair of oil and gas long-distance pipelines, the pipeline intelligent plugging robot is a new type of intelligent and efficient plugging device inside the pipeline. The stability of its braking and control system determines whether it can successfully seal high-pressure oil and gas inside the pipeline. This research is about intelligent plugging robots for pipelines that use the friction between the rubber hose and the pipe wall for braking. A hydraulic control system is designed, and a braking model for the robot is established. The hydraulic control system is simulated using a numerical simulation method, and the influence of different braking distances on system stability is investigated for various initial velocities of the robot. The results indicate that the fuzzy PID control hydraulic system exhibits a faster response with a maximum overshoot acceleration of −12.8 m/s2. The system achieves the desired acceleration of −0.14 m/s2 within 7 seconds and successfully completes the braking process. The fuzzy PID control approach effectively reduces parameter fluctuations, improves system stability, and decreases acceleration variations when faced with different initial speeds and braking distance signals. Consequently, the positioning accuracy of the robot is enhanced, and power consumption is reduced. This research provides important guidance for the development of new technologies in pipeline maintenance and emergency repair, particularly in the area of plugging operations under pressure.

Retracted: Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

Retracted: Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

Retracted: Active Compliance Control System for Intelligent Inspection Robot in Power Room

Retracted: Active Compliance Control System for Intelligent Inspection Robot in Power Room

Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

In order to further optimize the precision and efficiency of intelligent robot navigation system control, an IoT intelligent robot motion control system based on the improved ResNet model is proposed. Based on the deep learning method, using the Faster R-CNN target detection architecture and the ResNet50 convolutional neural network, the network is trained according to the characteristics of the operation target of the distribution line maintenance robot system. On this basis, combined with the binocular vision ranging principle, the coordinates of the job target in the camera coordinate system are measured, and the coordinates are converted into the robot base coordinate system through hand-eye calibration, so as to complete the spatial positioning of the job target. The results showed that the errors of the binocular measurement methods adopted by the system are all within 1%. Conclusion. The method can well adapt to the complex background of the operation scene, the change of illumination, and the partial occlusion of the target and can meet the requirements of the distribution line maintenance robot for the measurement and positioning of the target space.

Active Compliance Control System for Intelligent Inspection Robot in Power Room

In order to solve the problems of blind spot and time-consuming and laborious monitoring in manual inspection of power information and communication rooms, a kind of active compliant control system for intelligent inspection robots of power information and communication rooms is proposed. This research includes the noncontact detection method of the inspection robot in the power room based on machine vision; the optimization environment of the power room; the control of the inspection robot through the monitoring system terminal; the automatic visual inspection of the machine through the monitoring network transmission; the display of the monitoring terminal and the use of the calibration of the machine; and the edge detection method of the inspection image. The motion model of the inspection robot in the power room is constructed and the compliant control method of the inspection robot is designed. The experimental results show that the ineffective fluctuation range of torque of each joint is the smallest when the inspection robot is disturbed by obstacles in the process of executing signal change instructions, and the average fluctuation range of robot limbs is 0.65%. Conclusion. Based on the research results of this essay, it is proved that the controllability of power signal lamp change can be optimized separately in the future, and the safety of circuit operation can be improved.

Система інтелектуалізованого відеоспостереження для цілеспрямованої взаємодії з автономним мобільним роботом

Introduction. One of the conditions for increasing the autonomous capabilities of mobile robots is their awareness of the state of the environment in which they operate. A class of so-called indoor robots requires the timely acquisition of position data both on obstacle objects and goal interest objects for the robot actions. As a rule, the sources of data on surrounding objects are sensor devices of various types installed on the board of the robot. In modern robotics, such devices are rangefinders or scanners with various data acquisition principles and video cameras. It is images from the video cameras that contain the most complete information about the state of the mobile robot’s working room. But computer analysis and content interpretation of visual data require the development of appropriate information technologies. Successful software implementations of rather complex algorithms for visual object recognition and methods for determining these object positions relative to the room coordinate system, which were previously created as separate functional modules of the intelligent mobile robot control system, can be used to create other applied artificial intelligence systems. The formation of generalized images (models) of the environment and the states of the robot in it is important for the intelligent control system of autonomous mobile robots. Images from the robot’s onboard video camera are the most informative source of timely detection of the positions of static and dynamic objects in the robot’s working areas. Using additional information from an external video camera can not only increase the general situational awareness of an autonomous robot but also speed up the achievement of goal states, as long as the external video monitoring control system has an interface for goal-directed interaction with the mobile robot intelligent control system and conforming data structures for the representation of surrounding objects. The purpose of the article is to describe the results of the development of a control system of intellectualized video monitoring in the autonomous mobile robot working environment, as an additional external source of the object’s location data. Methods. 3D modeling of technical systems and spatial scenes, content interpretation of data, and object-oriented programming. Results. The functioning of the developed system of intellectualized video monitoring has been studied. Accuracy of the positions of static and dynamic objects obtained by this system both as a separate applied system of artificial intelligence and as an additional source of data on the presence and position of objects in the working room while interacting with an autonomous mobile robot has been estimated. Conclusions. The analysis of the functioning results of the system of intellectualized video monitoring which is intended for detecting objects and determining their positions in the room, and which is described in the article, shows that such a system can have a number of independent applications. Its most productive use is in the autonomous mobile robot working environment. Wide awareness of the autonomous mobile robot about the surrounding objects due to the interaction of two technical systems of artificial intelligence, namely, the intelligent control system of the mobile robot and the control system of the intellectualized video monitoring leads to the increase in the autonomous capabilities of the robot to perform complex tasks given by the user in a shorter time.

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.

Design of intelligent robot control system based on human–computer interaction

Design of intelligent robot control system based on human–computer interaction

Autonomous robotic rock breaking using a real‐time 3D visual perception system

AbstractCrushing of blasted ore is an essential phase in extraction of valuable minerals in mining industry. It is typically performed in multiple stages with each stage producing finer fragmentation. Performance and throughput of the first stage of crushing is highly dependent on the size distribution of the blasted ore. In the crushing plant, a metal grate prevents oversized boulders from getting into the crusher jaws, and a human‐controlled hydraulic manipulator equipped with a rock hammer is required to break oversized boulders and ensure continuous material flow. This secondary breaking task is event‐based in the sense that ore trucks deliver boulders at irregular intervals, thus requiring constant human supervision to ensure continuous material flow and prevent blockages. To automatize such breaking tasks, an intelligent robotic control system along with a visual perception system (VPS) is essential. In this manuscript, we propose an autonomous breaker system that includes a VPS capable of detecting multiple irregularly shaped rocks, a robotic control system featuring a decision‐making mechanism for determining the breaking order when dealing with multiple rocks, and a comprehensive manipulator control system. We present a proof of concept for an autonomous robotic boulder breaking system, which consists of a stereo‐camera‐based VPS and an industrial rock‐breaking manipulator robotized with our retrofitted system design. The experiments in this study were conducted in a real‐world setup, and the results were evaluated based on the success rates of breaking. The experiments yielded an average success rate of 34% and a break pace of 3.3 attempts per minute.

Backstepping Output Feedback Control for the Stochastic Nonlinear System Based on Variable Function Constraints with the Subsea Intelligent Electroexecution Robot System

The output feedback controller is designed for a class of stochastic nonlinear systems that satisfy uncertain function growth conditions for the first time. The multivariate function growth condition has greatly relaxed the restrictions on the drift and diffusion terms in the original stochastic nonlinear system. Here, we cleverly handle the problem of uncertain functions in the scaling process through the function maxima theory so that the Ito differential system can achieve output stabilization through Lyapunov function design and the solution of stochastic nonlinear system objects satisfies the existence of uniqueness, ensuring that the system is globally asymptotically stable in the sense of probability. Furthermore, it is concluded that the system is inversely optimally stable in the sense of probability. Finally, we apply the theoretical results to the practical subsea intelligent electroexecution robot control system and obtain good results.

Intelligent wearable rehabilitation robot control system based on mobile communication network

A large number of disabled people are caused by major diseases and accidents. Because disabled patients cannot exercise freely on their own, in order to prevent muscle atrophy, the most effective treatment is to exercise the patients’ limbs. Therefore, it is necessary to choose a more suitable rehabilitation training method to replace the traditional artificial rehabilitation training method, and the use of intelligent wearable rehabilitation robots to treat hemiplegia patients is particularly important for the rehabilitation treatment of hemiplegia patients. Rehabilitation robots can promote the development of medical technology and medical equipment, and have important practical significance for patients to overcome disease and recover health and build a harmonious family. This paper mainly studies the EMG research of the control system of intelligent wearable rehabilitation robot based on mobile communication network. This paper studies the way of stimulating signals and the processing of signal feedback in the process of electromyographic stimulation of rehabilitation robots to improve the rehabilitation effect of robots after stimulation; explores the problem of using biofeedback and fuzzy control rules to control patients for rehabilitation training, which urges patients actively participate in rehabilitation treatment, effectively guide the recovery of patients’ self-consciousness, and build a variety of training modes for rehabilitation robots to enhance the robot’s ability to adapt to different groups of people. In the experiments of this paper, the time window for data processing is 256ms, and the delay is also 256ms. It can be seen that the original SEMG signal of the control signal has some delay compared with the filtered SEMG signal.

Intelligent mobile robot control system based on PC+ underlying motion controller

Open-structure mobile robot motion control system based on “PC + underlying motion controller” could take advantage of the high degree of good versatility, real-time performance, high processing capacity of PC, and high controlling capacity of underlying motion controller. And the overall design of the open- structure mobile robot motion controller based on PC + stm32 is developed, which contains the system control architecture, the layered architecture of the control system and the human-computer interface. An algorithm also proposed for estimating the robot’s position and orientation, and the experimental system built for the prototype, provided the platform for moving, monitoring and automation. Experimental results of the proposed control system show that the open-structure mobile robot owns the stability, accuracy, rapid response and exact localization.

Artificial intelligence in robot control systems

This paper analyzes modern concepts of artificial intelligence and known definitions of the term "level of intelligence". In robotics artificial intelligence system is defined as a system that works intelligently and optimally. The author proposes to use optimization methods for the design of intelligent robot control systems. The article provides the formalization of problems of robotic control system design, as a class of extremum problems with constraints. Solving these problems is rather complicated due to the high dimensionality, polymodality and a priori uncertainty. Decomposition of the extremum problems according to the method, suggested by the author, allows reducing them into a sequence of simpler problems, that can be successfully solved by modern computing technology. Several possible approaches to solving such problems are considered in the article.

Intelligent Computational Control of Multi-Fingered Dexterous Robotic Hand

We discuss the intelligent computational control theory and introduce the hardware structure of HIT/DLR II dexterous robotic hand, which is the typical dexterous robotic hand. We show that how DSP or FPGA controller can be used in the dexterous robotic hand. A popular intelligent dexterous robotic hand control system, which named Electromyography (EMG) control is investigated. We introduced some mathematical algorithms in EMG controlling, such as Gauss mixture model (GMM), artificial neural network (ANN) and Support vector machine (SVM).

Intelligent Fire-Fighting Robot

In this paper, intelligent fire-fighting robot as the research object, based on Mitsubishi FX2N series PLC, the establishment of intelligent fire-fighting robot control model and control system optimization, so that the fire-fighting robot among the various modules can be smooth and stable work together. Using L298 chip control two motors, the speed achieved by controlling the robot flexible steering, and using PLC produce different wave PWM stepless duty to ensure the smooth operation of the robot. Meanwhile, the detection of motor speed using encoder feedback to PLC will constitute a closed-loop control to achieve precise control and adjustment of robots routes. Intelligent fire-fighting robot each module to work together more quickly and safely find and reach the fire source and fight it.

Design of Multi-Intelligent Robot Wireless Communication System Based on ZigBee Automation Control

This paper designs a new ZigBee intelligent robot communication control system, which realizes one to many data transmission. Upper computer can send commands to control multiple mobile intelligent robots to do the action response through wireless module. Slave computer can also send data to host computer to achieve remote monitoring, and has high relay ability. In order to verify the effectiveness and reliability of the system, this paper tests the system and obtains 3D signal and robot node control distribution. Through the analysis of the system time delay and control signal features, we found that the system has good stability, which can satisfy the design need of multi-intelligent robot control. It provides the theoretical reference for the study of ZigBee automation control.

Intelligent Robot Motion Control System Based on Immune Genetic Algorithm

The robot's motion control system is the core technology of intelligent robot. In this paper based on immune genetic algorithm, we improve the intelligent robot control system, and design the intelligent robot action output system with adjustable ratio combined with the PID algorithm. The system has the adaptive adjustment function, by adjusting the proportional coefficient P, which can reduce the output error of the system, improve the adaptability of the system and accelerate the speed of motion control. Finally, we use Siemens S7-200 series products to simulate the action output, and obtain the output time and residual of action under different proportional coefficient P by simulation. It provides the technical reference for the research on control algorithm of intelligent robot.

Chaotic artificial bee colony approach to step planning of maintaining balance for quadruped robot

Purpose – Artificial bee colony (ABC) algorithm is a relatively new optimization method inspired by the herd behavior of honey bees, which shows quite intelligence. The purpose of this paper is to propose an improved ABC optimization algorithm based on chaos theory for solving the push recovery problem of a quadruped robot, which can tune the controller parameters based on its search mechanism. ADAMS simulation environment is adopted to implement the proposed scheme for the quadruped robot. Design/methodology/approach – Maintaining balance is a rather complicated global optimum problem for a quadruped robot which is about seeking a foot contact point prevents itself from falling down. To ensure the stability of the intelligent robot control system, the intelligent optimization method is employed. The proposed chaotic artificial bee colony (CABC) algorithm is based on basic ABC, and a chaotic mechanism is used to help the algorithm to jump out of the local optimum as well as finding the optimal parameters. The implementation procedure of our proposed chaotic ABC approach is described in detail. Findings – The proposed CABC method is applied to a quadruped robot in ADAMS simulator. Using the CABC to implement, the quadruped robot can work smoothly under the interference. A comparison among the basic ABC and CABC is made. Experimental results verify a better trajectory tracking response can be achieved by the proposed CABC method after control parameters training. Practical implications – The proposed CABC algorithm can be easily applied to practice and can steer the robot during walking, which will considerably increase the autonomy of the robot. Originality/value – The proposed CABC approach is interesting for the optimization of a control scheme for quadruped robot. A parameter training methodology, using the presented intelligent algorithm is proposed to increase the learning capability. The experimental results verify the system stabilization, favorable performance and no chattering phenomena can be achieved by using the proposed CABC algorithm. And, the proposed CABC methodology can be easily extended to other applications.

The Application of Intelligent Industrial Robotic Control System Based on PLC in Mechanical Automation

In the field of mechanical automation, intelligent industrial robot technology is an important branch in the research field of robot; it is always the hot spots of the world robot research, and it being used to get the application in the industry today. Robot experiment platform of PLC and motor control technology, it based on the control method used by the robot control system improvements to make it more perfect run more precise, reasonable. In the same time, the man-machine interface state run monitoring, to ensure the normal operation of the system. Improved control method of the improvement of the work efficiency, reduce the work of the workers a duplication degree have a significant effect, and the system control at the scene, especially PLC control has excellent control function and good cost performance .