Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Curves and surfaces for computer aided geometric design (3rd ed.): a practical guide
Curves and surfaces for computer aided geometric design (3rd ed.): a practical guide
Camera Aided Robot Calibration
Camera Aided Robot Calibration
Fundamentals of Manipulator Calibration
Fundamentals of Manipulator Calibration
Membership function modification of fuzzy logic controllers withhistogram equalization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improve the robot calibration accuracy using a dynamic online fuzzy error mapping system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image interpolation using neural networks
IEEE Transactions on Image Processing
| Hi-index | 0.01 |
The robot manipulator calibration process utilizes model or modeless techniques. In modeless method, the compensation of position error is to move the end-effector of robot to a target position in the workspace, and to find the position error of the target position by using an estimation method based on the errors of neighboring points around the target position. In this paper, both traditional and newly developed soft computing techniques used to estimate position error of robot manipulators are investigated. After an introduction, the paper presents four methods for manipulator position error estimation. These include two commonly used analytical techniques, bilinear and cubic spline, and two soft computing based techniques, fuzzy logic and neural network. A range of simulation experiment is made using three common used error models. Various aspects of each method, including accuracy, complexity, and limitation are investigated. A comparison is made to demonstrate the advantages and drawback of each method.