Effective Visual Calibration System for Parallel Kinematic Mechanism Robot Using Decision Tree with Cooperative Coevolutionary Network Approach

• Main Authors: Cheng-Hsun Hsieh, 謝政勳
• Other Authors: Ren C. Luo
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/53206668402342487600

碩士 === 國立臺灣大學 === 電機工程學研究所 === 102 === Calibration technique has been widely used in robotics, and it provides high accuracy and cost-effective solution for the robot. The calibration procedure only modifies the programming part instead of the hardware of robot design or tightening the manufacturing tolerances. This thesis proposed the highly accurate calibration method for the multi-DoF parallel robot. There are three steps in the standard calibration procedure: modeling, measurement, and correction. Kinematic modeling is a common way to modeling. Inverse kinematic model is used in parallel mechanism modeling. The high accuracy instrument is used to measure the position of end-effector. As for the position of end-effector, we can collect a complete set of 3D data and construct the error model. Correction starts after the measurement. The two different techniques are used in correction. The first method is model-based model. It constructs a correction relationship between the motor and operational coordinates of the robot. The correction relationship considers the geometric of robot and the environment error to decide the coefficients of mathematics function. The high accuracy multi-DoF parallel robot use polynomial function to construct the error model, and use this error model to compensate the error and calibration. The second method is model-free model. In this method, user doesn’t need to know the error source affecting the robot accuracy. The method compensates the error by learning method, and artificial neural network is commonly chosen for learning method. This thesis proposed an effective Visual Calibration System for Parallel Robot Using Cooperative Coevolutionary Network and Decision Tree Approach. The method can self-construct and optimize the neural network structure and parameters for the individual training set, and keeps the good prediction ability. This method combines with inverse kinematic model and it can find the accurate relationship between the motor and operational coordinates of the robot, and doesn’t need to consider the coefficient of polynomial and error model of the robot. The self-construction and optimization of the structure and parameters of neural network can help the robot achieve high accuracy in the workspace, and adapt in any source of error in the environment. The calibration technique brings a lot of benefit for the industrial applications, decrease the huge cost of error modification from the hardware and solves the problem using software technique.