Work

Cycle-Based Robot Drive Train Optimization Utilizing SVD Analysis

Year: 2007

Type: conference-paper

Abstract: Designing a drive train for an industrial robot is a demanding task where a set of design variables need to be determined so that optimal performance is obtained for a wide range of different duty cycles. The paper presents a method where singular value decomposition (SVD) is used to reduce the design variable set. The application is a six degree of freedom serial manipulator, with nine drive train parameters for each axis and the objective is to minimize the cycle time on 122 representative design cycles without decreasing the expected lifetime of the robot. The optimization is based on a simulation model of the robot and conducted on a reduced set of the initial duty cycles and with the design variables suggested by the SVD analysis. The obtained design reduces the cycle time with 1.6% on the original design cycles without decreasing the life time of the robot.

Source: ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE)

DOI: 10.1115/detc2007-34772

Authors: Xiaolong Feng, Shiva Sander-Tavallaey, Johan O¨lvander

Institutions: ABB (Sweden), Linköping University

Language: English

Topic: Robotic Mechanisms and Dynamics

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: VINNOVA