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.

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.

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.

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.

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.

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.

Robots in Ecology:Welcome to the machine

Robots have primarily been developed for warfare, yet they also serve peaceful purposes. Their use in ecology is in its infancy, but they may soon become essential tools in a broad variety of ecological sub-disciplines. Autonomous robots, in particular drones sent to previously inaccessible areas, have revolutionized data acquisition, not only for abiotic parameters, but also for recording the behavior of undisturbed animals and collecting biological material. Robots will also play an essential role in population ecology, as they will allow for automatic census of individuals through image processing, or via detection of animals marked electronically. These new technologies will enable automated experimentation for increasingly large sample sizes, both in the laboratory and in the field. Finally, interactive robots and cyborgs are becoming major players in modern studies of animal behavior. Such rapid progress nonetheless raises ethical, environmental, and security issues.

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.

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.

From tunnel boring machine to tunnel boring robot: perspectives on intelligent shield machine and its smart operation

Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.

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.

Research on Workpiece Sorting System Based on Machine Vision Mechanism

This paper describes industrial sorting system, which is based on robot vision technology, introduces main image processing methodology used during development, and simulates algorithm with Matlab. Besides, we set up image processing algorithm library via C# program and realize recognition and location for regular geometry workpiece. Furthermore, we analyze camera model in vision algorithm library, calibrate the camera, process the image series, and resolve the identify problem for regular geometry workpiece with different colours.

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.

Trends of Robot Application——Ethical Response

With the rapid development of science and technology and the arrival of the information age,computer technology has also gained rapid development,and has a very wide application prospect in the current social environment.But at the same time,robots may also induce a variety of ethical problems,the existence of these ethical problems also affect the sustainable development of robots.This paper mainly aims at the change of robot application trend and related ethical issues,hoping to provide some reference for robot application and development.

Artificial Intelligence Trends and Ethics: Issues and Alternatives for Investors

Artificial intelligence (AI) based technology, machine learning, and cognitive systems have played a very active role in society’s economic and technological transformation. For industrial value chains and international businesses, it means that a structural change is necessary since these machines can learn and apply new information in making forecasts, processing, and interacting with people. Artificial intelligence (AI) is a science that uses powerful enough techniques, strategies, and mathematical modelling to tackle complex actual problems. Because of its inevitable progress further into the future, there have been considerable safety and ethical concerns. Creating an environment that is AI friendly for the people and vice versa might be a solution for humans and machines to discover a common set of values. In this context, the goal of this study is to investigate the emerging trends of AI (the benefits that it brings to the society), the moral challenges that come from ethical algorithms, learned or pre-set ideals, as well as address the ethical issues and malpractices of AI and AI security. This paper will address the consequences of AI in relation to investors and financial services. The article will examine the challenges and possible alternatives for resolving the potential unethical issues in finance and will propose the necessity of new AI governance mechanisms to protect the efficiency of the capital markets as well as the role of financial authority in the regulation and monitoring of the huge expansion of AI in finance.

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。

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.

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.

智能新闻业:生成式人工智能成为基础设施

生成式人工智能(GenAI)作为通用智媒时代的基础设施,正在快速变革新闻业。GenAI的典型应用ChatGPT在赋能新闻业,实现新闻素材检索的海量化、内容生成的智能化、新闻推荐的显性化、新闻反馈的人性化等方面发挥着重要作用。而且,ChatGPT的数据与模型缺陷也挑战着新闻价值及其专业性。因此,虚假信息生成,损害着新闻的真实性;数据更新缓慢,延误新闻的时效性;特定领域难以深耕,缺乏新闻的专业性;偏好与偏见共存,削弱新闻的客观性;AI茧房加剧,模糊新闻的公共性。人机协同新闻将成为智能新闻业的新范式,要求新闻工作者具备提问与评估能力,形成对GenAI的有效监督闭环。“CHAT”(careful、helpful、authentic、transparent)将作为新闻伦理调适的进路,实现人与技术、社会的良性互动。

Design and experiment of a picking robot for Agaricus bisporus based on machine vision

Harvesting represents the crucial stage in the cultivation process of Agaricus bisporus mushrooms.An important way for the production process of Agaricus bisporus to reduce costs and increase income is to ensure timely harvest of Agaricus bisporus,reduce harvesting costs,and improve harvesting efficiency.There are many disadvantages in manual picking,such as high labor intensity,time-consuming work and high cost.In this study,a set of mushroom picking platform including climbing mechanism,picking robot,and control system was designed and developed.The picking robot consisted of a truss mechanism,an image acquisition device,a mushroom collection device,and a picking actuator.The profile picking actuator could realize the function of constant force clamping.An online size detection algorithm for Agaricus bisporus based on deep image processing was proposed.The algorithm included removal of abnormal noise points,background segmentation,coordinate conversion,and diameter detection.The precision picking system for Agaricus bisporus with coordinate compensation function controlled by Industrial Personal Computer was designed,and the visual control interface was developed based on Labview.Through the performance test,the reliability of machine vision recognition and the overall operating stability of the picking platform were verified.The test results showed that in the process of machine vision recognition,the recognition accuracy rate was higher than 92.50%,the missed detection rate was lower than 4.95%,the false detection rate was lower than 2.15%,and the diameter measurement error was less than 4.50%.The image processing algorithm had high recognition rate and small diameter measurement error,which could meet the requirements of picking operation.The picking platform’s picking success rate was higher than 95.45%,the picking damage rate was lower than 3.57%,and the picking output rate was higher than 87.09%.Compared with manual picking,the recognition accuracy rate of the picking platform was increased by 6.70%,the picking output rate was increased by 1.51%.The overall performance of the picking platform was stable and practical.