Research On Clustering Analysis Of Pre-grasping Pattern For Multi-fingered Hands Based On Computer Vision

With the continuous development of science and technology, the robot applications become more and more extensive, and grasping manipulation is essential to the robot applications. Imitating the grasping manipulation of human, the pre-grasping phase of the robot is researched in the paper, it is a decision process, in which the robot perceives the grasping relevant parameters of the object by the vision sensors, and grasps the object using one of the pre-grasping patterns, according to the robot hand posture.Firstly, the four fingered hand model is designed with reference to the BH-4 and Rutgers robot hands. According to the Cutkosky grasp taxonomy, the multi-fingered hand pre-grasping patterns are divided into 13 categories.Secondly, 26 kinds of objects are selected to the research, and every two objects are corresponding to a pre-grasping pattern. In the same environment, each kind of object is collected an image in five different angles. After a series of image processing, the gesture, size, shape and surface roughness characteristics of the object are extracted as characteristic parameters for the pre-grasping pattern classification.Then the application of the clustering analysis in the multi-fingered hand grasping pattern classification is emphatically studied. The feasibility of the clustering analysis is verified by using the fuzzy c-means clustering algorithm. Because of the low clustering accuracy rate and the unstable clustering results, the improved algorithms are studied. Based on these algorithms, a new improved algorithm– the two-stage weighted fuzzy c-means clustering algorithm is presented, various performance indexes of this algorithm are improved obviously, and its clustering accuracy rate is 96.15%, especially.Then the kernel method in the applications of feature extraction and clustering analysis are further studied. A new algorithm, combined with the kernel principal component analysis and the kernel fuzzy c-means clustering algorithm, is presented. The real-time performance and the clustering accuracy rate of the algorithm are both perfect.Finally, the 3D simulation platform for the pre-grasping of the multi-fingered hand is developed by using OpenGL and Visual C++.Visual simulation of the pre-grasping process of the robot hand is demonstrated in the platform.