MAchIne learning for Design of industrial Robots MAID-Robot

Reference number
Coordinator Linköpings universitet - Institutionen för ekonomisk och industriell utveckling
Funding from Vinnova SEK 500 000
Project duration November 2018 - November 2019
Status Completed
Venture Groundbreaking ideas in industrial development
Call Banbrytande idéer inom industriell utveckling 2018

Purpose and goal

The goal of the MAID-robot project is to drastically reduce the development time for industrial robots through the use of machine learning based on virtual simulation models and large amounts of customer data. The project has shown that it is possible to automatically configure individual robots using statistical methods to run an optimization framework based on how customers actually use the robot in their production today.

Expected results and effects

The MAID-robot project has created a framework that enables the automatic assembly of a virtual simulation model, a digital twin, of the industrial robot´s drivetrain and then allow an optimization algorithm to vary its parameters so that it can best solve the tasks that customers use the robot for today. Thus, a product can be developed that fits the customer perfectly in a shorter time than today.

Planned approach and implementation

** Denna text är maskinöversatt ** The project´s first work package consisted of conducting a state-of-the-art analysis, and based on this, a digital framework was developed to put together a virtual simulation model of the drivetrain of an industrial robot. The next step was to develop a search algorithm where machine learning and optimization are linked so that the optimization algorithm searches for solutions that best meet the requirements that the machine learning algorithm has identified. Finally, the framework has been implemented and tested in ABB´s software.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 14 January 2020

Reference number 2018-04307

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