Modeling and Multi-objective Optimization of a Kind of Teaching Manipulator

TL;DR

This paper presents a modeling and multi-objective optimization approach for a novel six-degree-of-freedom teaching manipulator without actuators, enhancing design performance and providing reusable design insights.

Contribution

It introduces a new manipulator design, formulates a multi-objective optimization problem, and applies an innovative optimization and knowledge extraction method for improved design.

Findings

Optimized manipulator designs outperform human expert designs.

The NSGA-II-CDP algorithm effectively balances multiple design objectives.

An innovization study reveals implicit relationships useful for future designs.

Abstract

A new kind of six degree-of-freedom teaching manipulator without actuators is designed, for recording and conveniently setting a trajectory of an industrial robot. The device requires good gravity balance and operating force performance to ensure being controlled easily and fluently. In this paper, we propose a process for modeling the manipulator and then the model is used to formulate a multi-objective optimization problem to optimize the design of the testing manipulator. Three objectives, including total mass of the device, gravity balancing and operating force performance are analyzed and defined. A popular non-dominated sorting genetic algorithm (NSGA-II-CDP) is used to solve the optimization problem. The obtained solutions all outperform the design of a human expert. To extract design knowledge, an innovization study is performed to establish meaningful implicit relationship between the objective space and the decision space, which can be reused by the designer in future design process.