摘要
机器人灵巧手的抓持分类是抓持规划的一个主要问题 .本文应用模式识别技术设计和实现了一种基于高斯混合模型 GMM的分类器 .采用 Expectation Maximization(EM)算法估计 GMM的参数 ,对人手的抓持动作进行识别与分类 ,经过人手到机器人手的关节空间运动映射 ,实现了机器人灵巧手的抓持动作分类 .
The grasp taxonomy is one of the key problems in grasp planning of dexterous robot hands. This paper describes the design and implement of a system of grasp identification based on pattern recognition technology. The feature of human grasp is represented by Gaussian Mixture Model (GMM). The parameters of GMM are estimated by the Expectation Maximization algorithm (EM). The taxonomy of robot hand grasps is achieved by mapping grasps from human hand to robot hand. The effectiveness of the GMM identification method is proved by the experiments. The system can be used in grasp planning.
出处
《机器人》
EI
CSCD
北大核心
2003年第3期259-263,共5页
Robot
基金
国家自然科学基金 ( 5 9985 0 0 1)
国家教育部博士点基金 ( 2 0 0 0 0 0 0 60 5 )资助项目
