Author:
Feld-Cook Elisabeth,Shome Rahul,Zaleski Rosemary T.,Mohan Krishnan,Kourtev Hristiyan,Bekris Kostas E.,Weisel Clifford P.,Shin Jennifer
Abstract
AbstractObtaining valid, reliable quantitative exposure data can be a significant challenge for industrial hygienists, exposure scientists, and other health science professionals. In this proof-of-concept study, a robotic platform was programmed to perform a simple task as a plausible alternative to human subjects in exposure studies for generating exposure data. The use of robots offers several advantages over the use of humans. Research can be completed more efficiently and there is no need to recruit, screen, or train volunteers. In addition, robots can perform tasks repeatedly without getting tired allowing for collection of an unlimited number of measurements using different chemicals to assess exposure impacts from formulation changes and new product development. The use of robots also eliminates concerns with intentional human exposures while removing health research ethics review requirements which are time consuming. In this study, a humanoid robot was programmed to paint drywall, while volatile organic compounds were measured in air for comparison to model estimates. The measured air concentrations generally agreed with more advanced exposure model estimates. These findings suggest that robots have potential as a methodology for generating exposure measurements relevant to human activities, but without using human subjects.
Publisher
Springer Science and Business Media LLC
Subject
Public Health, Environmental and Occupational Health,Pollution,Toxicology,Epidemiology
Reference32 articles.
1. Chua PY, Ilschner T, Caldwell DG. Robotic manipulation of food products—a review. Ind Robot. 2003;30:345–54.
2. Engelberger JF. Robotics in practice: management and applications of industrial robots. US: Springer; 2012.
3. Dautenhahn K, Nehaniv CL, Walters ML, Robins B, Kose-Bagci H, Mirza NA, et al. KASPAR—a minimally expressive humanoid robot for human robot interaction research. Appl Bionics and Biomech. 2009;6:369–97.
4. Littlefield Z, Krontiris A, Kimmel A, Dobson A, Shome R, Bekris KE. An extensible software architecture for composing motion and task planners. Simulation, modeling, and programming for autonomous robots. Cham: Springer International Publishing; 2014.
5. Khan ZH, Khalid A, Iqbal J. Towards realizing robotic potential in future intelligent food manufacturing systems. Innov Food Sci Emerg Technol. 2018;48:11–24.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献