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Multi-Objective Optimization in Industrial Robotic Cell Design
ABB Corporate Research, Västerås, Sweden.
ABB Robotics, Västerås, Sweden.
ABB Corporate Research, Västerås, Sweden.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
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2010 (English)In: ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: Volume 1: 36th Design Automation Conference, Parts A and B, The American Society of Mechanical Engineers (ASME) , 2010, Vol. 1, 815-823 p.Conference paper (Refereed)
Abstract [en]

It has become a common practice to conduct simulation-based design of industrial robotic cells, where Mechatronic system model of an industrial robot is used to accurately predict robot performance characteristics like cycle time, critical component lifetime, and energy efficiency. However, current robot programming systems do not usually provide functionality for finding the optimal design of robotic cells. Robot cell designers therefore still face significant challenge to manually search in design space for achieving optimal robot cell design in consideration of productivity measured by the cycle time, lifetime, and energy efficiency. In addition, robot cell designers experience even more challenge to consider the trade-offs between cycle time and lifetime as well as cycle time and energy efficiency. In this work, utilization of multi-objective optimization to optimal design of the work cell of an industrial robot is investigated. Solution space and Pareto front are obtained and used to demonstrate the trade-offs between cycle-time and critical component lifetime as well as cycle-time and energy efficiency of an industrial robot. Two types of multi-objective optimization have been investigated and benchmarked using optimal design problem of robotic work cells: 1) single-objective optimization constructed using Weighted Compromise Programming (WCP) of multiple objectives and 2) Pareto front optimization using multi-objective generic algorithm (MOGA-II). Of the industrial robotics significance, a combined design optimization problem is investigated, where design space consisting of design variables defining robot task placement and robot drive-train are simultaneously searched. Optimization efficiency and interesting trade-offs have been explored and successful results demonstrated.

Place, publisher, year, edition, pages
The American Society of Mechanical Engineers (ASME) , 2010. Vol. 1, 815-823 p.
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URN: urn:nbn:se:liu:diva-62605DOI: 10.1115/DETC2010-28488ISI: 000320093200074ISBN: 978-0-7918-4409-0 (print)ISBN: 978-0-7918-3881-5 (online)OAI: diva2:373562
36th Design Automation Conference, Montreal, Quebec, Canada, August 15–18, 2010
Available from: 2010-11-30 Created: 2010-11-30 Last updated: 2016-05-17Bibliographically approved

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