Seam Tracking and Visual Control for Robotic Arc Welding Based on Structured Light Stereovision

A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type V groove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently.

Learning-based force servoing control of a robot with vision in an unknown environment

A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of the robot are derived and learned by a neural network. Secondly, a learning controller based on the neural network is designed for the robot to trace the object. Thirdly, a discrete time impedance control law is obtained for the force servoing of the robot, the on-line learning algorithms for three neural networks are developed to adjust the impedance parameters of the robot in the unknown environment. Lastly, wiping experiments are carried out by using a 6 DOF industrial robot with a CCD camera and a force/torque sensor in its end effector, and the experimental results confirm the effecti veness of the approach.

Vision Navigation Based PID Control for Line Tracking Robot

In a controlled indoor environment,line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots.A line tracking robot is a self-mobile machine that can recognize and track a painted line on thefloor.In general,the path is set and can be visible,such as a black line on a white surface with high contrasting colors.The robot’s path is marked by a distinct line or track,which the robot follows to move.Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation.Localization,automated map generation,autonomous navigation and path tracking is all becoming more frequent in vision applications.A visual navigation line tracking robot should detect the line with a camera using an image processing technique.The paper focuses on combining computer vision techniques with a proportional-integral-derivative(PID)control-ler for automatic steering and speed control.A prototype line tracking robot is used to evaluate the proposed control strategy.

Vision-based Stabilization of Nonholonomic Mobile Robots by Integrating Sliding-mode Control and Adaptive Approach

Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.

Visual Feedback Balance Control of a Robot Manipulator and Ball-Beam System

In this paper, we present a vision guided robotic ball-beam balancing control system, consisting of a robot manipulator (actuator), a ball-beam system (plant) and a machine vision system (feedback). The machine vision system feedbacks real-time beam angle and ball position data at a speed of 50 frames per second. Based on feedback data, the end-effector of a robot manipulator is driven to control the ball position by maneuvering of the inclination angle of the ball-beam system. The overall control system is implemented with two FPGA chips, one for machine vision processing, and one for robot joints servo PID controllers as well as ball position PD controller. Experiments are performed on a 5-axes robot manipulator to validate the proposed ball beam balancing control system.

Human-inspired lighting control in robot systems with the intention of glare avoidance

This paper presents some human-inspired strategies for lighting control in a robot system for best scene interpretation,where the main intention is to avoid possible glares or highlights occurring in images. It firstly compares the characteristics of human eyes and robot eyes. Then some evaluation criteria are addressed to assess the lighting conditions. A bio-inspired method is adopted to avoid the visual glare which is caused by either direct illumination from large light sources or indirect illumination reflected by smooth surfaces. Appropriate methods are proposed to optimize the pose and optical parameters of the light source and the vision camera.

Intelligent Control of Mobile Robot with Redundant Manipulator & Stereovision: Quantum / Soft Computing Toolkit

The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced.Design of robust knowledge bases is performed using a developed computational intelligence-quantum/soft computing toolkit(QC/SCOptKBTM).The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described.The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described.The general design methodology of a generalizing control unit based on the physical laws of quantum computing(quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal)is considered.The modernization of the pattern recognition system based on stereo vision technology presented.The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.The main objective of this article is to demonstrate the advantages of the approach based on quantum/soft computing.

Design of vision-based soccer robot using DSP

A new design of vision based soccer robot using the type TMS320F240 of DSPs for MiroSot series is presented. The DSP used enables cost effective control of DC motor, and features fewer external components, lower system cost and better performances than traditional microcontroller. The hardware architecture of robot is firstly presented in detail, and then the software design is briefly discussed. The control structure of decision making subsystem is illuminated also in this paper. The conclusion and prospect are given at last.

Dynamic Coordination of Uncalibrated Hand/Eye Robotic System Based on Neural Network

A nonlinear visual mapping model is presented to replace the image Jacobian relation for uncalibrated hand/eye coordination. A new visual tracking controller based on artificial neural network is designed. Simulation results show that this method can drive the static tracking error to zero quickly and keep good robustness and adaptability at the same time. In addition, the algorithm is very easy to be implemented with low computational complexity.

Modeling of Welding Process by Robotic Vision

Modeling of welding process by robotic vision is basically a theoretical problem, means mainly on physical problem, and also technological problem. To obtain a good model of welding process by robotic vision, theoretical researches are required but also constant experimental researches of several welding processes. Until today researches of welding processes has been based on empirical and detailed experimentation. This paper presents welding process by robotic and automation points of view with application of new technologies. Welding robotic system has been designed with possibility to control and inspect this process. Parameters that should be controlled during the process have been identified to reach desired quality. Figure of control system of welding process by robotic vision is given in this paper.

Task-oriented Hierarchical Control of Modular Soft Robots with External Vision Guidance

General,high-precision theoretical modeling method is not well developed in the field of soft robotics,which holds back motion control and practical application of soft robots.The concept of modularization brings novel structure,novel locomotion patterns as well as novel control method for soft robots.This paper presents the concept of hierarchical control method for modular soft robot system and a H-configuration pneumatic modular soft robot is designed as the control object.The H-configuration modular soft robot is composed of two basic motion units that take worm-like locomotion principle.The locomotion principle of the basic motion unit is analyzed and the actuation sequence is optimized by evolution strategy in VOXCAD simulation software.The differential drive method is applied to the H-configuration modular soft robot with multi motion modes and vision sensor is used to control the motion mode of the robot.The H-configuration modular soft robot and the basic motion unit are assembled by a cubic soft module made of silicone rubber.Also,connection mechanism is designed to ensure that the soft modules can be assembled in any direction and posture.Experiments are conducted to verify the effect of the hierarchical control method of the modular soft robots.

Robotic telemanipulation with EMG-driven strategy-assisted shared control method

The transfer of information between a human and a robot is of vital importance for robotic telemanipulation systems.In this paper,we propose a novel strategy-assisted shared control(SASC)scheme driven by electromyography(EMG)signals for robot telemanipulation.First,we develop an EMG decoding scheme to achieve reliable online performance,where ten features are extracted and selected and then classified by machine learning algorithms.Several feature selection methods are compared to learn a compact representation from original feature sets.Then,a vision-based module is designed for object detection and localization,which helps to grasp or release objects autonomously.Moreover,online visual feedback of environmental states and audio feedback of EMG decoding results are used for better perception of environmental contexts.A platform integrated with a robot arm,an RGB-D camera,and a pneumatic sucker,among others,is developed to evaluate the proposed method.The best accuracy of EMG recognition is 97.18%±10.61%,achieved by the support vector machine and features selected by recursive feature elimination.Two paradigms driven by a joystick or EMG are compared with SASC in a robotic telemanipulation task.The performance shows that SASC reaches a successful rate of 100% with the least time of 61.97±9.89 s.The completion times of SASC are only 67.78% and 55.71% of the two contrast methods,respectively.This shows that the proposed scheme has the potential to replace classical methods with reliable performance and less time,which provides a novel solution for this field of study.

Scene images and text information‐based object location of robot grasping

With the rapid development of artificial intelligence(AI),the application of this technology in the medical field is becoming increasingly extensive,along with a gradual increase in the amount of intelligent equipment in hospitals.Service robots can save human resources and replace nursing staff to achieve some work.In view of the phenomenon of mobile service robots'grabbing and distribution of patients'drugs in hospitals,a real‐time object detection and positioning system based on image and text information is proposed,which realizes the precise positioning and tracking of the grabbing objects and completes the grasping of a specific object(medicine bottle).The lightweight object detection model NanoDet is used to learn the features of the grasping objects and the object category,and bounding boxes are regressed.Then,the images in the bounding boxes are enhanced to overcome unfavourable factors,such as a small object region.The text detection and recognition model PP‐OCR is used to detect and recognise the enhanced images and extract the text information.The object information provided by the two models is fused,and the text recognition result is matched with the object detection box to achieve the precise posi-tioning of the grasping object.The kernel correlation filter(KCF)tracking algorithm is introduced to achieve real‐time tracking of specific objects to precisely control the robot's grasping.Both deep learning models adopt lightweight networks to facilitate direct deployment.The experiments show that the proposed robot grasping detection system has high reliability,accuracy and real‐time performance.

基于PLC控制机械手臂取放料的系统设计

随着工业自动化的不断深入,工业机器人已成为提升生产效率和降低成本的关键技术。尽管中大型机器人在工业生产中占据主导地位,但在精细化的生产流程中,小型机器人的应用需求日益凸显。为满足这一需求,该文设计了一种用于小型、多类型零部件抓取与投放的机器人系统。该系统集成了CCD检测单元,用于精确识别待放入物料的盒子状态;组件产品物料输送单元则实现了物料的自动、高效输送;系统中的4台机器人负责执行取料放料操作;系统通过触摸屏进行交互。整个系统通过PLC实现各部分的通信与控制,确保了信息传输的实时性和准确性。在实际应用中,该机器人系统展现出了稳定的运行性能、高精度的重复定位能力以及较低的成本投入。

基于Vision Card的机器人舵机高精度数字控制技术

针对目前机器人舵机控制技术调控性能差,工作过程不稳定的问题,引入Vision Card研究了一种新的高精度数字控制技术;同时分析Vision Card工作原理和机器人舵机控制原理,将二者融合,从视觉角度完成舵机控制;分别针对直流电机、驱动器和反馈电位器进行控制,通过计算直流电机的传递函数、反馈电位器上的电压和总行程,得到脉冲宽度调制(PWM)的电脉冲,利用得到的电脉冲来控制舵机;为验证控制技术有效性,与传统的开环控制技术、PID控制技术和模糊PID控制技术进行实验对比,结果表明,研究的控制技术稳定性高,调控性能好,可以有效提高机器人舵机的工作效率,保障机器人舵机的工作质量。

基于计算机视觉的采摘机器人作业优化研究

为进一步提高我国采摘机器人智能化作业水平及采摘效率,基于计算机视觉应用技术展开优化研究。以采摘机器人结构组成为设计基点,运用视觉控制核心理念建立采摘机器人视觉识别处理与控制数学模型,实施相适应的采摘路径规划和系统采摘状态输出设计。同时,进行视觉采摘验证试验,结果表明:基于计算机视觉的采摘机器人系统优化正确可行,整机综合采摘效率可达94.61%,系统识别准确率与控制精度分别相对提升了6.12%和5.25%,机器人采摘成功率可提升至95.98%。因此,将计算机视觉处理技术有效应用至采摘机器人设计改进,对于类似农业采摘与收获装备开发研究可提供创新及借鉴思路,推广价值良好。

农业信息采集机器人关键技术研究现状与发展趋势

智慧农业是农业现代化的标志,农业信息采集是智慧农业的重要环节之一,相较于人工信息采集具有的低效、准确度不高等不足,利用农业信息采集机器人代替人工进行农情信息采集,可降低农作强度、提升生产效率。本文针对不同场景下的农业信息采集机器人,概括了近几十年国内外农业信息采集机器人的应用现状,总结了自主导航技术、机器视觉技术、智能控制技术、智能云处理技术四大关键技术的研究现状,并结合农业生产中环境非结构化、作业对象具有娇嫩性等特点,指出了目前关键技术存在的问题,并提出复合导航技术、多机智能感知、视觉监测算法优化、通用化智能控制、智能云管控平台是未来的发展趋势。

基于深度学习的巡检机器人避障轨迹自动控制系统设计

为了提高巡检机器人在复杂环境下的避障能力,使机器人能够安全地完成巡检任务,设计基于深度学习的巡检机器人避障轨迹自动控制系统;设计由CCD传感器、信号处理芯片等设备组成的工业智能视觉CCD相机,基于FPGA和USB2.0的视频采集卡传输采集数据,完成硬件部分的设计;在软件设计中,对采集的图像实施目标分割、双目目标匹配等预处理,通过对摄像头实施双目视觉标定获取障碍物空间位置三维信息,基于深度学习中的CRNN设计机器人自主避障规划网络模型,并设计模糊轨迹控制器,实现避障中的轨迹自动控制;系统测试结果表明,设计系统最终成功避开了3个动态障碍物,最大轨迹控制误差的最大值为1.45°,最小轨迹控制误差的最大值为0.62°,动态避障巡检速度始终在3.5 m/s左右,表现出了精准而稳定的轨迹控制效果。

基于TwinCAT3的一种开源机器人控制系统设计

目前主流工业机器人为封闭式控制结构,存在不开源、二次开发难的问题,因此设计一种基于TwinCAT3(the windows control and automation technology)的跨平台、可移植性好的机器人控制系统架构。该架构包含视觉、运动控制和算法集成与仿真控制模块,采用倍福自动化设备规范(automation device specification,ADS)通信技术和实时工业以太网总线技术(ethernet for control automation technology,EtherCAT),建立以计算机(personal computer,PC)和倍福控制器为EtherCAT主站,控制多组从站执行器的一主多从工作模式。该模式结合离线与在线控制、集成数字孪生技术,完成虚拟样机与物理样机的联动;采用开源可扩展架构,便于视觉算法、智能算法等算法集成。经实验验证,此架构具有拓展性好、实时性强的特点。