Hostname: page-component-77c78cf97d-cfh4f Total loading time: 0 Render date: 2026-05-04T19:04:06.737Z Has data issue: false hasContentIssue false

Simultaneous task placement and sequence optimization in an inspection robotic cell

Published online by Cambridge University Press:  08 April 2021

MohammadHadi FarzanehKaloorazi*
Affiliation:
École de technologie supérieure, 1100 Notre-Dame St W, Montreal H3C 1K3, Canada. E-mail: ilian.bonev@etsmtl.ca
Ilian A. Bonev
Affiliation:
École de technologie supérieure, 1100 Notre-Dame St W, Montreal H3C 1K3, Canada. E-mail: ilian.bonev@etsmtl.ca
Lionel Birglen
Affiliation:
Polytechnique Montréal, 2900 Édouard-Montpetit Blvd, Montreal H3T 1J4, Canada. E-mail: lionel.birglen@polymtl.ca
*
*Corresponding author. Email: hamidfarzane88@gmail.com

Abstract

In this article, we improve the efficiency of a turbine blade inspection robotic workcell. The workcell consists of a stationary camera and a 6-axis serial robot that is holding a blade and presenting different zones of the blade to the camera for inspection. The problem at hand consists of a 6-DOF (degree of freedom) continuous optimization of the camera placement and a discrete combinatorial optimization of the sequence of inspection poses (images). For each image, all robot configurations (up to eight) are taken into consideration. A novel combined approach is introduced, called blind dynamic particle swarm optimization (BD-PSO), to simultaneously obtain the optimal design for both domains. The objective is to minimize the cycle time of the inspection process, while avoiding any collisions. Even though PSO is vastly used in engineering problems, the novelty of our combinatorial optimization method is in its ability to be used efficiently in traveling salesman problems where the distances between the cities are unknown and subject to change. This highly unpredictable environment is the case of the inspection cell where the cycle time between two images will change for different camera placements.

Information

Type
Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable