Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plasti...Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plastics,aerospace,food,etc.Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots,has limited their further developments and applications in the field of high requirements for machining accuracy,e.g.,aircraft assembly.In this paper,a neural-network-based approach is proposed to improve the robots’positioning accuracy.Firstly,the neural network,optimized by a genetic particle swarm algorithm,is constructed to model and predict the positioning errors of an industrial robot.Next,the predicted errors are utilized to realize the compensation of the target points at the robot’s workspace.Finally,a series of experiments of the KUKA KR 500–3 industrial robot with no-load and drilling scenarios are implemented to validate the proposed method.The experimental results show that the positioning errors of the robot are reduced from 1.529 mm to 0.344 mm and from 1.879 mm to 0.227 mm for the no-load and drilling conditions,respectively,which means that the position accuracy of the robot is increased by 77.6%and 87.9%for the two experimental conditions,respectively.展开更多
This paper presents an error modeling methodology that enables the tolerance design, assembly and kinematic calibration of a class of 3-DOF parallel kinematic machines with parallelogram struts to be integrated into a...This paper presents an error modeling methodology that enables the tolerance design, assembly and kinematic calibration of a class of 3-DOF parallel kinematic machines with parallelogram struts to be integrated into a unified framework. The error mapping function is formulated to identify the source errors affecting the uncompensable pose error. The sensitivity analysis in the sense of statistics is also carried out to investigate the influences of source errors on the pose accuracy. An assembly process that can effectively minimize the uncompensable pose error is proposed as one of the results of this investigation.展开更多
Abstract Industrial robots are used for automatic drilling and riveting. The absolute position accuracy of an industrial robot is one of the key performance indexes in aircraft assembly, and can be improved through er...Abstract Industrial robots are used for automatic drilling and riveting. The absolute position accuracy of an industrial robot is one of the key performance indexes in aircraft assembly, and can be improved through error compensation to meet aircraft assembly requirements. The achiev- able accuracy and the difficulty of accuracy compensation implementation are closely related to the choice of sampling points. Therefore, based on the error similarity error compensation method, a method for choosing sampling points on a uniform grid is proposed. A simulation is conducted to analyze the influence of the sample point locations on error compensation. In addition, the grid steps of the sampling points are optimized using a statistical analysis method. The method is used to generate grids and optimize the grid steps of a Kuka KR-210 robot. The experimental results show that the method for planning sampling data can be used to effectively optimize the sampling grid. After error compensation, the position accuracy of the robot meels the position accuracy require- ments.展开更多
基金co-supported by the Natural Science Foundation of Jiangsu Province(No.BK20190417)the National Natural Science Foundation of China(No.52005254)the National Key R&D Program of China(No.2018YFB1306800)。
文摘Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plastics,aerospace,food,etc.Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots,has limited their further developments and applications in the field of high requirements for machining accuracy,e.g.,aircraft assembly.In this paper,a neural-network-based approach is proposed to improve the robots’positioning accuracy.Firstly,the neural network,optimized by a genetic particle swarm algorithm,is constructed to model and predict the positioning errors of an industrial robot.Next,the predicted errors are utilized to realize the compensation of the target points at the robot’s workspace.Finally,a series of experiments of the KUKA KR 500–3 industrial robot with no-load and drilling scenarios are implemented to validate the proposed method.The experimental results show that the positioning errors of the robot are reduced from 1.529 mm to 0.344 mm and from 1.879 mm to 0.227 mm for the no-load and drilling conditions,respectively,which means that the position accuracy of the robot is increased by 77.6%and 87.9%for the two experimental conditions,respectively.
基金This work was supported by the National Natural Science Foundation of China (Grant No.50075006) the Royal Society UK-China Joint Research Grant and Tianjin Scientce and Technology Commission(Grant No.003802111).
文摘This paper presents an error modeling methodology that enables the tolerance design, assembly and kinematic calibration of a class of 3-DOF parallel kinematic machines with parallelogram struts to be integrated into a unified framework. The error mapping function is formulated to identify the source errors affecting the uncompensable pose error. The sensitivity analysis in the sense of statistics is also carried out to investigate the influences of source errors on the pose accuracy. An assembly process that can effectively minimize the uncompensable pose error is proposed as one of the results of this investigation.
基金co-supported by the National Natural Science Foundation of China(No.51475225)the Aeronautical Science Foundation of China(No.2013ZE52067)
文摘Abstract Industrial robots are used for automatic drilling and riveting. The absolute position accuracy of an industrial robot is one of the key performance indexes in aircraft assembly, and can be improved through error compensation to meet aircraft assembly requirements. The achiev- able accuracy and the difficulty of accuracy compensation implementation are closely related to the choice of sampling points. Therefore, based on the error similarity error compensation method, a method for choosing sampling points on a uniform grid is proposed. A simulation is conducted to analyze the influence of the sample point locations on error compensation. In addition, the grid steps of the sampling points are optimized using a statistical analysis method. The method is used to generate grids and optimize the grid steps of a Kuka KR-210 robot. The experimental results show that the method for planning sampling data can be used to effectively optimize the sampling grid. After error compensation, the position accuracy of the robot meels the position accuracy require- ments.
