Mattias Hovgard, B. Lennartson, Kristofer Bengtsson
{"title":"Simulation Based Energy Optimization of Robot Stations by Motion Parameter Tuning","authors":"Mattias Hovgard, B. Lennartson, Kristofer Bengtsson","doi":"10.1109/COASE.2019.8843152","DOIUrl":null,"url":null,"abstract":"This paper presents energy optimization of a welding station from a manufacturing line in an automotive factory. The aim of the optimization is to find free time between operations, where it is possible to extend the execution time of the robot movements, and thereby saving energy, without extending the cycle time of the whole station. The station is modeled and optimized in a simulation platform. The optimization algorithm works by iteratively limiting the maximum velocity of the robot movements, until no more free time exists. Simulation results show that the energy use, peak power and jerk of the robots can be reduced significantly.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"79 1","pages":"456-461"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COASE.2019.8843152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper presents energy optimization of a welding station from a manufacturing line in an automotive factory. The aim of the optimization is to find free time between operations, where it is possible to extend the execution time of the robot movements, and thereby saving energy, without extending the cycle time of the whole station. The station is modeled and optimized in a simulation platform. The optimization algorithm works by iteratively limiting the maximum velocity of the robot movements, until no more free time exists. Simulation results show that the energy use, peak power and jerk of the robots can be reduced significantly.