Geometric Calibration Method of Robot Based on Measurement System Including Position and Orientation Parameters

Industrial robot which can acquire high accuracy has been widely used in automatic assembly.Usually,the geometric parameter of industrial robot should be inspected during manufacturing and application.High precision measurement equipment was utilized to acquire the position and orientation of robot’s end⁃effector,when calibrating the geometric parameter of robot.A kind of measurement system based on a draw⁃wire encoder was presented,since the current measurement equipment has some disadvantages,such as the cost and the requirements of working environment are high.According to this kind of measurement system,a sort of geometric calibration method of robot was presented including position and orientation parameters.The uncertain arc length of the cable length between robot end⁃effector and the measurement can be exactly acquired according to the position and orientation parameters.The pose⁃solving model of robot end⁃effector was associated with the kinematic model of robot,and robot’s geometric parameter can be computed by using the least⁃squares methods.Validate instance was conducted,the result showed that the optimal number of the calibration pose was 47 with little improvement in accuracy,even if increasing the number of calibration pose.Robot calibration experiment was performed and the results showed that the absolute accuracy of robot decreased from 4.32 mm to 0.87 mm after calibration,which improved the robot’s absolute accuracy effectively.

Smart Monitoring of Solar Photovoltaic Panels by the Approach of Machine Learning

The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependable smart asset management techniques within the solar power industry.

Advanced Electrical Motors and Control Strategies for High-quality Servo Systems-A Comprehensive Review

Recent technological advancements have propelled remarkable progress in servo systems,resulting in their extensive utilization across various high-end applications.A comprehensive review of high-quality servo system technologies,focusing specifically on electrical motor topologies and control strategies is presented.In terms of motor topology,this study outlines the mainstream servo motors used across different periods,as well as the latest theories and technologies surrounding contemporary servo motors.In terms of control strategies,two well-established approaches are presented:field-oriented control and direct torque control.Additionally,it discusses advanced control strategies employed in servo systems,such as model predictive control(MPC)and fault tolerance control,among others.