Role of Uncertainty in Model Development and Control Design for a Manufacturing Process

TL;DR

This paper discusses how a multi-robot control system can significantly reduce uncertainties in high-precision manufacturing processes, highlighting the importance of control algorithms in compensating for measurement noise, model inaccuracies, and joint compliance.

Contribution

It demonstrates that multi-robot control strategies effectively mitigate uncertainties in micro-scale manufacturing, offering a cost-effective alternative to advanced sensors.

Findings

Multi-robot control reduces manufacturing uncertainties significantly.

Control algorithms can compensate for measurement noise and model inaccuracies.

Multi-robot systems outperform single robots in precision tasks.

Abstract

The use of robotic technology has drastically increased in manufacturing in the 21st century. But by utilizing their sensory cues, humans still outperform machines, especially in the micro scale manufacturing, which requires high-precision robot manipulators. These sensory cues naturally compensate for high level of uncertainties that exist in the manufacturing environment. Uncertainties in performing manufacturing tasks may come from measurement noise, model inaccuracy, joint compliance (e.g., elasticity) etc. Although advanced metrology sensors and high-precision microprocessors, which are utilized in nowadays robots, have compensated for many structural and dynamic errors in robot positioning, but a well-designed control algorithm still works as a comparable and cheaper alternative to reduce uncertainties in automated manufacturing. Our work illustrates that a multi-robot control system can reduce various uncertainties to a great amount.