{"title":"机械臂能量损失优化","authors":"Paulo A. Salgado, T. Perdicoulis, P. Santos","doi":"10.1109/CINTI-MACRo57952.2022.10029656","DOIUrl":null,"url":null,"abstract":"The use of robots is widely spread across the industry. It is paramount that the robot end-effector tracks a pre-defined trajectory with the lowest energy loss. To contribute to the solution of this problem, the robot trajectory is defined using a tracking parameter which is optimised using the Matlab® fntinunc function and the Particle Swam optimisation algorithm. This approach was tested for a case study with the energy loss being reduced in approximately 96.15%.","PeriodicalId":18535,"journal":{"name":"Micro","volume":"228 1","pages":"000101-000106"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy loss optimisation of a robotic arm\",\"authors\":\"Paulo A. Salgado, T. Perdicoulis, P. Santos\",\"doi\":\"10.1109/CINTI-MACRo57952.2022.10029656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of robots is widely spread across the industry. It is paramount that the robot end-effector tracks a pre-defined trajectory with the lowest energy loss. To contribute to the solution of this problem, the robot trajectory is defined using a tracking parameter which is optimised using the Matlab® fntinunc function and the Particle Swam optimisation algorithm. This approach was tested for a case study with the energy loss being reduced in approximately 96.15%.\",\"PeriodicalId\":18535,\"journal\":{\"name\":\"Micro\",\"volume\":\"228 1\",\"pages\":\"000101-000106\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Micro\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CINTI-MACRo57952.2022.10029656\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Micro","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINTI-MACRo57952.2022.10029656","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The use of robots is widely spread across the industry. It is paramount that the robot end-effector tracks a pre-defined trajectory with the lowest energy loss. To contribute to the solution of this problem, the robot trajectory is defined using a tracking parameter which is optimised using the Matlab® fntinunc function and the Particle Swam optimisation algorithm. This approach was tested for a case study with the energy loss being reduced in approximately 96.15%.