Accuracy Improvement of Robot-Based Milling Using an Enhanced Manipulator Model

TL;DR

This paper enhances robot-based milling accuracy by developing an improved manipulator model that considers geometric and elastostatic factors, including gravity effects, leading to a 2.4 times accuracy improvement confirmed by experiments.

Contribution

It introduces a novel approach for manipulator model parameter identification that accounts for gravity and link weights, improving milling precision.

Findings

Achieved 2.4 times accuracy improvement in robot milling

Developed an industry-oriented measurement and identification method

Validated approach through experimental calibration of industrial robot

Abstract

The paper is devoted to the accuracy improvement of robot-based milling by using an enhanced manipulator model that takes into account both geometric and elastostatic factors. Particular attention is paid to the model parameters identification accuracy. In contrast to other works, the proposed approach takes into account impact of the gravity compensator and link weights on the manipulator elastostatic properties. In order to improve the identification accuracy, the industry oriented performance measure is used to define optimal measurement configurations and an enhanced partial pose measurement method is applied for the identification of the model parameters. The advantages of the developed approach are confirmed by experimental results that deal with the elastostatic calibration of a heavy industrial robot used for milling. The achieved accuracy improvement factor is about 2.4.