Double-Parameter Regression Design of Drive Trains for Lightweight Robotic Arms

摘要：

　　This paper presents a new design approach for lightweight robotic arms.In this method,the drive trains and structural dimensions are parameterized as design variables,and a major objective is to minimize the total mass of robotic arms satisfying the constraint conditions.To solve the optimization problem,the relationship among mass,the moment of inertia and torque of drive trains are introduced as their power-density curves,which is the basis of the double-parameter regression design.In this design approach,there are two modules: structure optimization and drive trains optimization.The orthogonal design method is adopted to implement the structure optimization.The double-parameter regression design is used for drive trains optimization.Finally,a design example for a four degree of freedom (DOF) robotic arm is demonstrated to verify the validity of the proposed scheme.

关键词： Lightweight robotic arm Structure optimization Drive trains optimization Double-parameter regression design

作者: Haibin Yin Cheng Kong Mingchang He Shansheng Huang

作者单位: School of Mechanical and Electronic Engineering,Wuhan University of Technology, Wuhan, Hubei, China; School of Mechanical and Electronic Engineering,Wuhan University of Technology, Wuhan, Hubei, China

会议类型: 国际会议

会议名称: 2016中国机构与机器科学国际会议

会议地点: 广州

会议语种:英文

页码: 223-236

在线出版日期: 2016-12-15（万方平台首次上网日期，不代表论文的发表时间）