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Collaborative robots using mixed reality (MR) and artificial intelligence (AI)

Reference number
Coordinator Karlstads universitet - Karlstads universitet Inst f ingenjörsvetenskap & fysik
Funding from Vinnova SEK 148 285
Project duration December 2021 - November 2022
Status Completed

Important results from the project

Purpose This study aims to implement novel sustainable solutions based on collaborative robots using mixed reality and artificial intelligence in the robotic platforms available within the participants. Goal + An assembly tasks demonstration was implemented for a collaborative robot + An artificial intelligence algorithm for body gesture recognition was integrated to command a collaborative robot. + A mixed reality algorithms was preliminary integrated for displaying information of the current state of the collaborative robot

Expected long term effects

The implementation of an assembly task demonstration, the integration of the artificial intelligence algorithm and the mixed reality algorithm have been integrated to the respective collaborative robots available. The results were presented at the MIRAI 2.0 R&I Week in Japan. The developed concept/demonstration has been presented to the local industry as an example of the future industrial systems gave an understanding of the effect on the industry and the need to address the current issues in assembly tasks for facilitating the development in the production systems.

Approach and implementation

The technical development and integration of the algorithms for body language recognition, mixed reality and the implementation of a demonstration of assembly tasks progressed according to plan. A simple demonstration for assembly tasks was implemented in a collaborative robot. Basic functions of an artificial intelligence algorithm to control a collaborative robot were tested. Basic functions of a mixed reality algorithm to display information about the state of a collaborative robot were preliminary tested.

External links

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

Last updated 22 December 2022

Reference number 2021-04810

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