Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing

TL;DR

This paper presents an energy-efficient camera location search algorithm that optimizes observation accuracy in automated manufacturing by minimizing image noise through adaptive exploration, improving precision without filtering high-frequency details.

Contribution

It introduces a novel camera movement algorithm that adaptively searches for optimal locations to reduce image noise, enhancing observation accuracy in manufacturing environments.

Findings

Algorithm effectively finds near-optimal camera positions with limited energy.

Simulation results show improved observation precision in manufacturing tasks.

Adaptive exploration outperforms brute-force methods in noise minimization.

Abstract

Visual servoing technology has been well developed and applied in many automated manufacturing tasks, especially in tools' pose alignment. To access a full global view of tools, most applications adopt eye-to-hand configuration or eye-to-hand/eye-in-hand cooperation configuration in an automated manufacturing environment. Most research papers mainly put efforts into developing control and observation architectures in various scenarios, but few of them have discussed the importance of the camera's location in eye-to-hand 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. In this paper, we propose an algorithm for the camera's moving policy so that it explores the camera workspace and searches for the optimal location where the images' noise level is minimized. Also, this algorithm ensures the camera ends up at a suboptimal (if the optimal one is unreachable) location among the locations already searched, with limited energy available for moving the camera. Unlike a simple brute force approach, the algorithm enables the camera to explore space more efficiently by adapting the search policy from learning the environment. With the aid of an image averaging technique, this algorithm, in use of a solo camera, achieves the observation accuracy in eye-to-hand configurations to a desirable extent without filtering out high-frequency information in the original image. An automated manufacturing application has been simulated and the results show the success of this algorithm's improvement of observation precision with limited energy.