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.

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.

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.

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.

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.

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.

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.

Design of intelligent controller for mobile robot based on fuzzy logic

In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mobile robot to safely navigate in an indoor environment. First, the designs of two behaviors for a robot＇s autonomous navigation are described, including path tracking and obstacle avoidance, which emulate human driving behaviors and reduce the complexity of the robot＇s navigation problems in unknown environments. Secondly, the two behaviors are combined by using a finite state machine （FSM）, which ensures that the robot can safely track a predefined path in an unknown indoor environment. The inputs to this controller are the readings from the sensors. The corresponding output is the desired direction of the robot. Finally, both the simulation and experimental results verify the effectiveness of the proposed method.

基于RobotStudio的异形轴加工生产线仿真设计

以多机床加工、多机器人作业的生产线为研究对象,基于RobotStudio平台建立了带驱动的生产线仿真模型.首先在仿真环境下完成了机器人手部的设计、工具坐标的标定、工件坐标系的标定以及动态Smart组件设计.其次对机器人上下料工作站做了轨迹规划、并介绍了该站的程序结构及程序示例.最后进行了上下料工作站的轨迹跟踪仿真以及整条生产线的生产仿真.仿真结果表明,生产线的轨迹规划和连续生产可靠性都达到了预期目的.该方案不仅可为多机床多机器人生产线的规划布局与研发提供理论依据,也可为研究生产过程的优化、生产效率的提高提供验证平台.

基于知识管理技术的翻译Robot的设计与应用

从机器翻译实用化角度出发,提出了基于知识管理技术构建翻译Robot的设计方案。通过知识管理与机器翻译技术的融合,进一步改善翻译系统对知识的获取能力和应用能力,从而提高机器翻译系统的智能性和实用性。

人工智能在脊髓神经损伤与修复领域研究热点的可视化分析

背景:近年来人工智能逐渐兴起,在多方面应用于脊髓神经损伤与修复领域,对临床治疗也有诸多积极影响。目的:研究人工智能在脊髓神经损伤与修复领域的诊断、治疗和康复中的应用进展,明确该领域的研究热点和不足,为今后研究工作提供建议。方法:在Web of Science核心集数据库检索建库至2023年收录的人工智能在脊髓神经损伤与修复领域相关文献,使用CiteSpace 6.1.R6和VOSviewer 1.6.19软件对文献数据进行一般文献学分析、文献共被引、期刊共被引、期刊双图叠加及关键词聚类等可视化分析。结果与结论:①共筛选出1713篇文章,此领域年发文量呈波动上升趋势,其中美国占据主导地位,Kadone Hideki是发文量最多的作者,《ARCH PHYS MED REHAB》是被引用次数最多的期刊。②关键词共现和聚类分析显示,去除与检索词相近的关键词后,主要关键词被分为3个主要集群:外骨骼与运动康复(为最大核心热点)、机器学习和神经可塑性、机器人和康复训练。③关键词爆发分析显示,深度学习和人工智能在过去5年中已成为突发术语。④文献共被引和高被引文献分析结果显示,人工智能在脊髓神经损伤与修复领域热点集中于动力外骨骼(powered exoskeleton)、步态(gait)、神经电刺激(electrical nerve stimulation)、皮质内脑机接口(intracortical brain-computer interface,IBCI)、机器人(robot)、高分子生物材料(polymer biomaterials)及神经干细胞(neural stem cell)等内容。⑤人工智能在脊髓神经损伤与修复领域的研究近年来呈现上升趋势,该领域的关注点从外骨骼、电刺激等单一的治疗手段,逐渐向智能化、精准化和个性化等方向转变。⑥该领域存在一些局限性,例如数据缺失或不平衡的后果、数据准确性和可重复性低以及伦理问题(如隐私、研究透明度和临床可靠性等),未来的研究应该解决数据收集的问题,需要大样本、高质量的临床数据集来建立有效的人工智能模型;同时该领域的基因组学等机制研究十分薄弱,未来可利用类脑芯片等多种机器学习技术,运用基因编辑治疗及单细胞空间转录组等方法,进行再生相关基因上调和轴突生长结构蛋白产生等基础机制研究。

A novel algorithm of normal attitude regulation for the designed end-effector of a flexible drilling robot

An end-effector for a flexible drilling robot is designed, and a novel four-point algorithm of normal attitude regulation for this end-effector is presented. Four non-coplanar points can define a unique sphere tangent to them in spatial geometry, and the center point of the sphere and the radius can be calculated. The shape of a workpiece surface in the machining area is approximately regarded as such a sphere. A vector from the machining point to the center point is thus approximately regarded as a normal vector to the workpiece surface. By this principle, the algorithm first measures four coordinates on the curve in the drilling region using four sensors and calculates the normal vector at the drilling point, then calculates the error between the normal vector and the axis of the spindle. According to this error, the algorithm further figures out the angles of two revolving axes on the end- effector and the displacements of three linear axes on the robot main body, thus it implements the function of adjusting the spindle to be perpendicular to the curve at the drilling point. Simulation results of two kinds of curved surfaces show that accuracy and efficiency can be realized using the proposed algorithm.

Computational dynamics of soft machines

Soft machine refers to a kind of mechanical system made of soft materials to complete sophisticated missions, such as handling a fragile object and crawling along a narrow tunnel corner, under low cost control and actuation. Hence, soft machines have raised great challenges to computational dynamics. In this review article, recent studies of the authors on the dynamic modeling, numerical simulation, and experimental validation of soft machines are summarized in the framework of multibody system dynamics. The dynamic modeling approaches are presented first for the geometric nonlinearities of coupled overall motions and large deformations of a soft component, the physical nonlinearities of a soft component made of hyperelastic or elastoplastic materials, and the frictional contacts/impacts of soft components, respectively. Then the computation approach is outlined for the dynamic simulation of soft machines governed by a set of differential-algebraic equations of very high dimensions, with an emphasis on the efficient computations of the nonlinear elastic force vector of finite elements. The validations of the proposed approaches are given via three case studies, including the locomotion of a soft quadrupedal robot, the spinning deployment of a solar sail of a spacecraft, and the deployment of a mesh reflector of a satellite antenna, as well as the corresponding experimental studies. Finally, some remarks are made for future studies.

A Fast RLE-based Reconstruction Technique for Real-time Robot Tracking

Wheeled Mobile Robots (WMRs) are more and more widely used in advanced manufacturing.For real time tracking of WMRs,a novel way for real-time color image reconstruction based on Run Length Encoding (RLE) is present.In- dexed from a fast look up table (FLUT),color image can be encoded into multiple llne structures with different specified colors. Through object-oriented method,the RLE elements reconstruct the image features.Successful application of this technique for mo- bile robots identification is reported.

Novel Biological Based Method for Robot Navigation and Localization

The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.

Design and Experiments of Ultrasound Image-Guided Multi-DOF Robot System for Brachytherapy

To implant radioactive seeds through a needle precisely and safely, a novel multi-DOF surgical robotic system is presented in this paper for percutaneous prostate intervention through the patient’s perineum under real-time ultrasound image guidance. The proposed robot, which is designed with 9-DOF, consists of a 3-DOF automatic location platform for position adjustment, 2-DOF for automatic ultrasonic probe adjustment mounted with electromagnetic trackers, and 4-DOF for manually adjusting the guided template. Meanwhile, a new registration method based on the quaternion algorithm and least square method is developed, and the needle insertion is performed under the real-time guidance of a navigation system. Furthermore, the robot system has undergone some preliminary experiments with a laser tracker to evaluate the repeatability and accuracy of the robot system. The location error of the puncture needle tip can be controlled under 0.7 mm in air for the whole robotic system. The acquired results endorse the precision of the robot system for prostate seed implantation brachytherapy. © 2017, Tianjin University and Springer-Verlag GmbH Germany.

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.