摘要
对欠驱动手爪自主抓取进行了研究 ,将其分为自主决策和抓取控制两个过程 .首先分析了欠驱动手爪的特点、主要的抓取模式 ,并借鉴人的抓取经验 ,采用模糊输入方法 ,综合考虑抓取任务要求和物体本身的特征属性 ,利用模糊神经网络良好的分类特性选择合适的抓取模式 .在此基础上 ,完成手指姿势调整 ,采用基于传感器反馈的控制策略 ,在被抓物体上形成的合适的力分布以获得稳定抓取 ,并通过抓取实例验证了抓取决策和控制的正确性 ,提高了欠驱动手爪抓取的自动化水平 .
The novel underactuated robot hand can achieve versatile grasp modes with the ingenious mechanism. This paper focuses on the problem of autonomous grasp of the underactuated robot hand, and divides the problem into two processes, autonomous decision and grasp control. Firstly, the characteristics and grasp modes of the underactuated robot hand are presented. Secondly, proper maps between the fuzzy inputs, including grasp task and object features, and the outputs of grasp modes are established using a fuzzy neural network. According to the grasp modes, the finger orientations are regulated. The control tact based on force sensor feedback is proposed to impose proper forces on all phalanges to achieve stable grasp. At last grasp experiments validate the correctness of autonomous decision and control.
出处
《机器人》
EI
CSCD
北大核心
2005年第1期20-25,30,共7页
Robot
基金
国家自然科学基金资助项目 ( 50 2 7514 1)
关键词
机器人
欠驱动手爪
自主抓取
模糊神经网络
robot
underactuated robot hand
autonomous grasp
fuzzy neural network
