Control Architecture and Design for a Multi-robotic Visual Servoing System in Automated Manufacturing Environment

TL;DR

This paper presents a multi-robot control system with a novel camera positioning algorithm to enhance precision in automated manufacturing, effectively reducing uncertainties and improving visual servoing performance.

Contribution

It introduces a multi-robot control architecture for manufacturing tasks and a new camera movement policy to optimize image quality in uncertain environments.

Findings

The control system significantly reduces positioning uncertainties.

The camera policy finds optimal locations with minimal noise.

Enhanced precision in micro-scale manufacturing 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 micro scale manufacturing, which requires high-precision robot manipulators. These sensory cues naturally compensate for high levels 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 modern robots, have compensated for many structural and dynamic errors in robot positioning, 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 that simulates the positioning process for fastening and unfastening applications can reduce various uncertainties, which may occur in this process, to a great extent. In addition, most research papers in visual servoing mainly focus on developing control and observation architectures in various scenarios, but few have discussed the importance of the camera's location in the configuration. In a manufacturing environment, the quality of camera estimations may vary significantly from one observation location to another, as the combined effects of environmental conditions result in different noise levels of a single image shot at different locations. Therefore, in this paper, we also propose a novel algorithm for the camera's moving policy so that it explores the camera workspace and searches for the optimal location where the image noise level is minimized.