Luciano Angelini, Manuela Uliano, Angela Mazzeo, Mattia Penzotti, M. Controzzi
{"title":"Self-collision avoidance in bimanual teleoperation using CollisionIK: algorithm revision and usability experiment","authors":"Luciano Angelini, Manuela Uliano, Angela Mazzeo, Mattia Penzotti, M. Controzzi","doi":"10.1109/Humanoids53995.2022.10000179","DOIUrl":null,"url":null,"abstract":"One of the challenges in teleoperation is avoiding self-collisions, which is particularly critical in bi-manual systems. Available solutions are usually developed for redundant robots or introduce significant delays during teleoperation. We propose a revised version of the CollisionIK algorithm, dubbed revised_CollisionIK, to solve this issue. The algorithm has been tested in a bi-manual system teleoperated by naïve users and compared with the original version CollisionIK monitored by a standard emergency brake strategy. Based on objective and subjective metrics, results show that the revised_CollisionIK can be successfully used for teleoperating bimanual pick-handover-place tasks. Participants find the manipulation of small object easier with this strategy and don't perceive any difference in terms of accuracy and delay, despite these being significantly worse than CollisionIK combined with a standard emergency brake strategy.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Humanoids53995.2022.10000179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
One of the challenges in teleoperation is avoiding self-collisions, which is particularly critical in bi-manual systems. Available solutions are usually developed for redundant robots or introduce significant delays during teleoperation. We propose a revised version of the CollisionIK algorithm, dubbed revised_CollisionIK, to solve this issue. The algorithm has been tested in a bi-manual system teleoperated by naïve users and compared with the original version CollisionIK monitored by a standard emergency brake strategy. Based on objective and subjective metrics, results show that the revised_CollisionIK can be successfully used for teleoperating bimanual pick-handover-place tasks. Participants find the manipulation of small object easier with this strategy and don't perceive any difference in terms of accuracy and delay, despite these being significantly worse than CollisionIK combined with a standard emergency brake strategy.