Papers by Author: Jie Liu

Abstract: Robot location is a key technology of quadruped robot with Hand-fused-Foot. The location method based on binocular vision system is studied for quadruped robot with Hand-fused-Foot. After obtaining image by a single camera, the object is segmented by using characteristic extraction method based on color characteristic. Then image processing such as filtering (de-noising) and opening is performed. The object is identified and its centroid coordinate in image is obtained. Location of robot based on environment reference--object coordinate is achieved. Experiments show the effectiveness and the accuracy (within 4cm) of the method.

Abstract: Adoption of machine vision inspection and computer image processing technology, an oil-seal dimension measuring system was developed to meet the requirement of online production and real time inspection. The makeup and principle of the system were introduced, as well as its working process and design requirements were described on detailed. The technique of quadratic filtering for image preprocessing combined with the principle of three points determining a circle, point Hough transform and the least squares was employed for image processing algorithm, and high precision sub-pixel edge detection was achieved. The measuring results of experiments demonstrated that the inspection goal on 100 percents of products could be realized successfully, and with many advantages such as non-contact, on-line, real time, appropriate precision and low cost, the system can be applied widely in other production fields.

Abstract: It were researched that the modeling methods of machine accuracy and the control techniques of the error compensation based on BP neural network(BPNN) for parallel machine tool(PMT)with five degrees of freedom(DOF). The samples are obtained to train the BP neural network which has good capacity for non- liner mapping, learning and generalization. The machine accuracy mathematics model is established for the error compensation, in order to study the nonlinear input and output problem of the parallel machine which difficultly modeling described. The trained neural network was applied to error compensation of PMT to realize modifying errors real-timely. Finally, simulation analysis was performed through the MATLAB software. The results expressed that the control strategies for error compensation were simple, efficient and practicable. Machine accuracy can be increased greatly after compensation.