Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots

The forward kinematics of parallel robots is a challenging issue due to its highly coupled non‐linear relation among branch chains.This paper presents a novel approach to for-ward kinematics of parallel robots based on kernel extreme learning machine(KELM).To tackle with the forward kinematics solution of fully parallel robots,the forward ki-nematics solution of parallel robots is equivalently transformed into a machine learning model first.On this basis,a computational model combining sparrow search algorithm and KELM is then established,which can serve as both regression and classification.Based on SSA‐optimised KELM(SSA‐KELM)established in this study,a binary discriminator for judging the existence of the forward kinematics solution and a multi‐label regression model for predicting the forward kinematics solution are built to obtain the forward kinematics general solution of parallel robots with different structural configurations and parameters.To evaluate the proposed model,a numerical case on this dataset collected by the inverse kinematics model of a typical 6‐DOF parallel robot is conducted,followed by the results manifesting that the binary discriminator with the discriminant accuracy of 88.50%is superior over ELM,KELM,support vector machine and logistic regression.The multi‐label regression model,with the root mean squared error of 0.06 mm for the position and 0.15°for the orientation,outperforms the double‐hidden‐layer back propagation(2‐BP),ELM,KELM and genetic algorithm‐optimised KELM.Furthermore,numerical cases of parallel robots with different structural con-figurations and parameters are compared with state‐of‐the‐art models.Moreover,these results of numerical simulation and experiment on the host computer demonstrate that the proposed model displays its high precision,high robustness and rapid convergence,which provides a candidate for the forward kinematics of parallel robots.

Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning

Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.

Robotic Process Automation with New Future Trends

The invention concept of Robotic Process Automation (RPA) has emerged as a transformative technology that has revolved the local business processes by programming repetitive task and efficiency adjusting the operations. This research had focused on developing the RPA environment and its future features in order to elaborate on the projected policies based on its comprehensive experiences. The current and previous situations of industry are looking for IT solutions to fully scale their company Improve business flexibility, improve customer satisfaction, improve productivity, accuracy and reduce costs, quick scalability in RPA has currently appeared as an advance technology with exceptional performance. It emphasizes future trends and foresees the evolution of RPA by integrating artificial intelligence, learning of machine and cognitive automation into RPA frameworks. Moreover, it has analyzed the technical constraints, including the scalability, security issues and interoperability, while investigating regulatory and ethical considerations that are so important to the ethical utilization of RPA. By providing a comprehensive analysis of RPA with new future trends in this study, researcher’s ambitions to provide valuable insights the benefits of it on industrial performances from the gap observed so as to guide the strategic decision and future implementation of the RPA.

Error Prediction in Industrial Robot Machining: Optimization Based on Stiffness and Accuracy Limit

Among the advantages of using industrial robots for machining applications instead of machine tools are flexibility, cost effectiveness, and versatility. Due to the kinematics of the articulated robot, the system behaviour is quite different compared with machine tools. Two major questions arise in implementing robots in machining tasks: one is the robot’s stiffness, and the second is the achievable machined part accuracy, which varies mainly due to the huge variety of robot models. This paper proposes error prediction model in the application of industrial robot for machining tasks, based on stiffness and accuracy limits. The research work includes experimental and theoretical parts. Advanced machining and inspection tools were applied, as well as a theoretical model of the robot structure and stiffness based on the form-shaping function approach. The robot machining performances, from the workpiece accuracy point of view were predicted.

Robotic system for adding tundish-covering flux based on machine vision

Tundish-covering flux bags can be depalletized and moved in the steel casting region using industrial robots and monocular vision simultaneously.An industrial robot mounted with a flexible vacuum sucker was used as the executor.For a structured bag model,a visual scheme based on the support vector machine and the histogram of oriented gradients was adopted.The computer was trained using a number of sample bag images that relied on the feature recognition algorithm.Finally,the automatic stacking and moving of the flux bags were realized.

Research of 6-DOF Serial-Parallel Mechanism Platform for Stability Training of Legged-Walking Robot

The concept of legged-robot stability training with a training platform is proposed and a serial-parallel mechanism platform with 6 degrees of freedom is designed for this target. The designed platform is composed of 4-DOF parallel mechanism with spherical joints and prismatic pairs,and 2-DOF serial mechanism with prismatic pairs. With this design,the platform has advantages of low platform countertop,big workspace,high carrying capacity and high stiffness. On the basis of DOF analysis and computation of space mechanism,weight supporting auxiliary mechanism and raceways-balls supporting mechanism are designed,so as to improve the stiffness of designed large platform and payload capacity of servo motors. And then the whole structure design work of the platform is done. Meanwhile,this paper derives the analytical solutions of forward kinematics, inverse kinematics and inverse dynamics. The error analysis model of position and orientation is established. And then the simulation is done in ADAMS to ensure the correctness and feasibility of this design.

Integration of data-intensive,machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions

Technological advancements in recent decades have greatly transformed the field of material chemistry.Juxtaposing the accentuating energy demand with the pollution associated,urgent measures are required to ensure energy maximization,while reducing the extended experimental time cycle involved in energy production.In lieu of this,the prominence of catalysts in chemical reactions,particularly energy related reactions cannot be undermined,and thus it is critical to discover and design catalyst,towards the optimization of chemical processes and generation of sustainable energy.Most recently,artificial intelligence(AI)has been incorporated into several fields,particularly in advancing catalytic processes.The integration of intensive data set,machine learning models and robotics,provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques.The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst,with extreme accuracy and swiftness comparable to a number of human researchers.Although,the utilization of robots in catalyst discovery is still in its infancy,in this review we summarize current sway of artificial intelligence in catalyst discovery,briefly describe the application of databases,machine learning models and robots in this field,with emphasis on the consolidation of these monomeric units into a tripartite flow process.We point out current trends of machine learning and hybrid models of first principle calculations(DFT)for generating dataset,which is integrable into autonomous flow process of catalyst discovery.Also,we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors.

Least Squares Support Vector Machine Kalman Filter for Physiological Tremor Suppression in Minimally Invasive Surgical Robot

This paper studies the physiological tremor filtering in minimally invasive surgical robot.The surgeon􀆳s physiological tremor of the hand can cause the vibration of the tip of the surgical instrument,which may reduce operative accuracy and limit the application of surgical robots.Aiming at the vibration caused by physiological tremor of hand,we propose a Least Squares Support Vector Machine Kalman Filter(LSSVMKF),which can filter the tremor by estimating and modeling the tremor signal by Kalman filter and then superimposing it reversely in the control signal.When estimating and modeling the tremor signal,the filter uses the Least Squares Support Vector Machine(LS⁃SVM)to build the regression model of the constant parameters(Process Noise Covariance and Measurement Noise Covariance)of the traditional Kalman filter,which can dynamically adjust these parameters during the operation and improve the accuracy of Kalman filter.The simulation results show that the LSSVMKF can effectively filter out the tremor signal,thereby improving the accuracy of surgery.

基于PowerMill Robot结合Roboguide的机器人数控加工方法

针对传统数控加工企业迫切提升数控加工自动化的问题,设计了一种CAD/CAM与机器人离线编程技术结合的方法,通过PowerMill Robot结合FANUC仿真软件Roboguide和LR MATE200iD/4S机器人,配合切削动力头实现对iPhone6S后盖从出刀路到仿真再到联机调试的全过程自动化,为数控加工产业升级提供参考方向。通过iPhone 6S后盖打磨的机器人数控加工实例,重点介绍基于PowerMill Robot的数控加工编程方法,结合FANUC仿真软件Roboguide检验程序的可行性。仿真实验及真实环境下工业机器人的程序联调加工结果证明了所提出方法的可行性。

Learning control of nonhonolomic robot based on support vector machine

A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.

Internet of robotic things for mobile robots:Concepts,technologies,challenges,applications,and future directions

Nowadays,Multi Robotic System(MRS)consisting of different robot shapes,sizes and capabilities has received significant attention from researchers and are being deployed in a variety of real-world applications.From sensors and actuators improved by communication technologies to powerful computing systems utilizing advanced Artificial Intelligence(AI)algorithms have rapidly driven the development of MRS,so the Internet of Things(IoT)in MRS has become a new topic,namely the Internet of Robotic Things(IoRT).This paper summarizes a comprehensive survey of state-of-the-art technologies for mobile robots,including general architecture,benefits,challenges,practical applications,and future research directions.In addition,remarkable research of i)multirobot navigation,ii)network architecture,routing protocols and communications,and iii)coordination among robots as well as data analysis via external computing(cloud,fog,edge,edge-cloud)are merged with the IoRT architecture according to their applicability.Moreover,security is a long-term challenge for IoRT because of various attack vectors,security flaws,and vulnerabilities.Security threats,attacks,and existing solutions based on IoRT architectures are also under scrutiny.Moreover,the identification of environmental situations that are crucial for all types of IoRT applications,such as the detection of objects,human,and obstacles,is also critically reviewed.Finally,future research directions are given by analyzing the challenges of IoRT in mobile robots.

A Petri Net Model for Part Sequencing and Robot Moves Sequence in A 2-Machine Robotic Cell

This paper deals with part sequencing and optimal robot moves sequence in 2-machine robotic cells according to Petri net graph. We have assumed that the robotic cell is capable of producing same and different parts. We have considered a new motion cycle for robot moves sequence which is the development of existing motion cycles in 2-machine robotic cells. The main goal of this study is to minimize the cycle time by determining the optimal part sequencing and robot moves sequence in the robotic cell. So, we have proposed a model based on Petri network.

Fault Diagnosis in Robot Manipulators Using SVM and KNN

In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.

多机协同智能发展战略研究

多个自主智能系统通过信息、行为交互构成的多机协同智能,代表着未来智能系统的必然发展趋势,是我国新一代人工智能规划部署的主攻方向,也是支撑国防、社会安全的核心技术和推动制造业由大到强的必由之路。开展突破多机协同智能技术发展研究,对于推动我国军事智能、智能产业高质量发展、加快工业转型升级具有重要意义。本文基于多机协同智能系统当前面临的难以适应复杂任务这一挑战,从基础理论和核心关键技术两个层面出发,系统地梳理了多机协同智能的研究现状,分析了制约基础理论与关键技术发展的主要瓶颈性问题,并以多机协同智能制造为典型应用,剖析理论与技术发展中存在的问题。研究认为,多机协同智能将朝着人机群组智能的方向发展,为抢占发展先机,需及早布局人机群组智能的基础理论探索,加速核心技术突破,并加快应用示范。

机器人磨抛复杂曲面加工轨迹对表面质量的影响研究

为了探究不同加工轨迹及其排布对工件磨抛加工表面质量的影响,本文进行了机器人磨抛轨迹对工件表面质量影响规律的研究。基于Preston去除方程和Hertz接触理论建立了砂带磨抛加工材料去除深度模型,分析了表面残留纹理的生成机理。以曲面航空发动机叶片为试验样件,利用自行搭建的机器人磨抛系统,分别使用等距轨迹、摆线轨迹进行加工试验,分析材料去除效果及表面纹理情况。试验结果表明,采用传统直线加工的等距轨迹于搭接处产生条带状纹理;摆线因其多方向性的加工动作,均化了表面纹理,提高了加工表面一致性。

结合实例分割与抓取筛选的堆叠目标抓取方法

为了在种类多样、位姿随机、背景散乱的堆叠场景下得到机器人抓取目标物体的最优次序与抓取位姿,本文提出一种结合实例分割与抓取筛选的堆叠目标抓取方法,包含抓取次序推理与抓取位姿检测两部分.抓取次序推理部分,首先设计基于自注意力与边界细化的实例分割网络,利用自注意力模块提升特征提取能力,并通过边界细化模块提高堆叠场景下物体边界的分割精度;其次提出一种抓取筛选方法,运用判断物体间完整掩码是否重叠与优先抓取分数排序的策略,筛选出目标最上层未被遮挡的物体为待抓取目标,以减少机器人多余抓取动作.抓取位姿检测部分,为适应不同尺度物体的检测,设计融合多尺度密集残差模块的抓取位姿检测网络,以保留物体的多尺度特征,从而适应不同尺度物体的检测.实验结果表明,本方法能够有效推理出堆叠场景下的抓取次序,并准确检测出物体的位姿,以实现目标的抓取.

基于机器视觉的智能物流搬运机器人的设计与研究

第七届全国大学生工程训练综合能力竞赛智能+赛道基于机器视觉的智能物流搬运机器人,对OpenMV4视觉模块进行研究,应用该模块进行二维码、不同颜色物料等多种目标的识别,信息经过模块上STM32F427微控制器的处理,与Arduino Mega 2560板通信,机器人识别场地上的三个区域,用PID算法驱动麦克纳姆轮机器人的四个直流电机旋转并且定位机器人,通过PCA9685模块的I2C通信协议,发送PWM脉冲信号给机械臂上的四个关节舵机,使智能物流搬运机器人对场地上的物料进行自动搬运。实验证明其可以较为精确地完成各项搬运任务。

芒果采收车机械臂运动特性分析及试验

为提高芒果采收车机械化采摘的适应性,该研究采用多体动力学理论方法对其机械臂的运动特性进行分析。首先,结合芒果生长和农艺特征及采摘机械臂几何构型与向量特性,运用D-H法建立了机械臂运动学模型以探索其运动特性,并进行末端执行器运动轨迹规划。轨迹规划结果表明末端执行器运动平稳,满足芒果采摘运动要求。进一步地,利用拉格朗日法构建了机械臂的动力学模型,对其进行了正逆动力学仿真验证,以深入了解机械臂的关节运动特性。动力学分析结果表明,在恒力矩工况下,伸缩装置和摆动装置的运动呈现一定的周期性,摆动装置、旋转装置角加速度和伸缩装置加速度均约在1.25、2.30、4.15 s达到阶段峰值;在仅做摆动周期运动的情况下,旋转装置、摆动装置所受驱动力矩均近似呈周期性变化,峰值分别为72.5与52 N·mm,且在一个运动周期内,均有两个极大值点。对机械臂结构进行仿真模态计算和模态试验,结果表明前六阶固有频率误差在5%以内,验证了芒果采收车机械臂多体动力学仿真建模的准确性。研究结果可为保证有效实现机械臂的采摘效果及提高其可靠性与稳定性提供依据。

两种方法清洗消毒达芬奇机器人手术臂的效果比较

目的探讨达芬奇机器人手术臂有效的清洗消毒方法。方法回顾性分析2022年1月至2023年4月江西省人民医院手术室使用后的达芬奇机器人手术臂200件达芬奇机器人手术臂作为研究对象,按照不同的操作流程将其分为观察组(100件)与对照组(100件)。观察组采用流动水冲洗+腹腔镜酶剂浸泡+超声波清洗机超声+蒸汽清洗机清洗手术臂操作端+高压水枪纯水漂洗+全自动清洗机清洗消毒。对照组清洗消毒流程为流动水冲洗+腹腔镜酶剂浸泡+超声波清洗机超声+刷洗手术臂操作端+高压水枪纯水漂洗+全自动清洗机清洗消毒。采用目测检查法(四倍带光源放大镜)、腺苷三磷酸(ATP)生物荧光快速检测法进行检测,计算清洗质量不合格的返洗率,比较两组的清洗消毒操作平均耗时。结果观察组的目测检查法返洗率低于对照组,观察组的ATP生物荧光检测返洗率低于对照组,差异有统计学意义(P<0.05)。观察组清洗消毒平均耗时短于对照组,差异有统计学意义(P<0.05)。结论蒸汽清洗机清洗消毒达芬奇机器人手术臂效果比手工刷洗清洗消毒效果更为理想,可节约大量清洗消毒时间,有效节约稀有医疗资源。

工业机器人单目视觉装配系统研究

为了解决传统机器人只能通过示教或离线编程固定执行点到点装配任务的问题,进行了视觉引导的工业机器人装配方面的研究。首先对工业机器人视觉装配过程建立数学模型并标定,然后研究了基于机器视觉的零件位姿估计方法,针对法兰盘类金属零件弱纹理及无角点特征的位姿估计问题,提出了一种基于自身特征和外部标记辅助的单目视觉位姿计算方法。最后搭建了工业机器人单目视觉装配实验平台验证该位姿计算方法的可行性。实验结果表明,该系统能够实现对法兰盘类零件的定位和装配,最小位置误差为2.07mm,最小姿态误差0.13。