{"title":"Intelligent ergonomic optimization in bimanual worker-robot interaction: A Reinforcement Learning approach","authors":"","doi":"10.1016/j.autcon.2024.105741","DOIUrl":null,"url":null,"abstract":"<div><p>Robots have the potential to enhance safety on construction job sites by assuming hazardous tasks. While existing safety research on physical human-robot interaction (pHRI) primarily addresses collision risks, ensuring inherently safe collaborative workflows is equally important. For example, ergonomic optimization in co-manipulation is an important safety consideration in pHRI. While frameworks such as Rapid Entire Body Assessment (REBA) have been an industry standard for these interventions, their lack of a rigorous mathematical structure poses challenges for using them with optimization algorithms. Previous works have tackled this gap by developing approximations or statistical approaches that are error-prone or data-dependent. This paper presents a framework using Reinforcement Learning for precise ergonomic optimization that generalizes to different types of tasks. To ensure practicality and safe experimentations, the training leverages Inverse Kinematics in virtual reality to simulate human movement mechanics. Results of a comparison between the developed framework and ergonomically naive approaches are presented.</p></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0926580524004771/pdfft?md5=2b2b2036cf1904a46046f0d067750809&pid=1-s2.0-S0926580524004771-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524004771","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Robots have the potential to enhance safety on construction job sites by assuming hazardous tasks. While existing safety research on physical human-robot interaction (pHRI) primarily addresses collision risks, ensuring inherently safe collaborative workflows is equally important. For example, ergonomic optimization in co-manipulation is an important safety consideration in pHRI. While frameworks such as Rapid Entire Body Assessment (REBA) have been an industry standard for these interventions, their lack of a rigorous mathematical structure poses challenges for using them with optimization algorithms. Previous works have tackled this gap by developing approximations or statistical approaches that are error-prone or data-dependent. This paper presents a framework using Reinforcement Learning for precise ergonomic optimization that generalizes to different types of tasks. To ensure practicality and safe experimentations, the training leverages Inverse Kinematics in virtual reality to simulate human movement mechanics. Results of a comparison between the developed framework and ergonomically naive approaches are presented.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.