Immersive virtual learning for worker-robot teamwork on construction sites

建筑工地工人与机器人团队合作的沉浸式虚拟学习

• 批准号: 1822724
• 负责人: Burcin Becerik-Gerber
• 金额: $ 75万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822724&HistoricalAwards=false
• 关键词: Immersive; virtual; learning; worker; robot

The construction industry is one of the largest industries in the United States, employing millions of workers, however it is challenged by low productivity rates, worker shortages, and safety concerns. The industry has a tremendous opportunity to improve its productivity and safety with the recent advancements in technology. With the increased level of automation on construction sites, workers need to learn how to work with these new technologies and gain new knowledge and engineering skills. The goal of this project is to create and test a training program delivered through virtual reality (cyberlearning) to educate and re-educate workers for new work experiences that require collaboration with robots on construction sites. The project seeks to improve the teamwork among workers and robots on construction sites, focusing on more than one specific skill set and providing a comprehensive learning experience. The project is significant because it is a pioneering effort in providing learning opportunities to workers with varying levels of language proficiency and education, preparing them for work at the human-technology frontier. Fundamental questions addressed by the project include: 1) How does cyberlearning increase workers' knowledge, safety behavior and trust in automation compared to the traditional training methods? 2) How do individual differences impact workers' cyberlearning? 3) How does trust in automation change based on the construction task? 4) How does cyberlearning affect productivity and safety on construction sites? 5) Does the knowledge and level of trust gained through cyberlearning carry over to actual construction sites? 6) How does cyberlearning influence the development of next generation of construction automation? Through the exploration of these research questions, this project provides evidence for the utility of cost-effective training programs for vocational workforce of the construction industry. Numerous learning scenarios and types of construction robots are included in the cyberlearning platform, which is developed in Task 1. A construction site is simulated via the use of discrete event simulations and immersive virtual environments where construction workers interact with robots on a given task. To move away from today's lab-based hands-on training, the cyberlearning environment incorporates dynamic construction work environments, such as multiple tasks and crews sharing the same space and conditions, for example, uneven terrain, dust, rain through simulations and by using accurate models of construction sites. The work advances knowledge on the impact of cyberlearning for worker-robot teamwork at two levels: user-level and site-level. For the user-level investigations in Task 2, the work explores: 1) the extent to which cyberlearning increases workers' knowledge, safety behavior, and trust in automation, 2) how individual differences moderate workers' learning and 3) how trust-in-automation changes based on task type. For the site-level investigations in Task 3, the work explores: 1) the extent to which cyberlearning impacts productivity, safety, and trust-in-automation compared to in-person training across all workers on construction sites, and 2) how to both improve the cyberlearning environment and the construction robots for deployment on construction sites. Finally, through simulation-based studies in Task 4, the work explores how human-robot interactions impact construction tasks and workflows, safety procedures and productivity. The cyberlearning program is designed to maximize the extent to which the workers accept and trust construction robots, while at the same time freeing up human resources, improving productivity and safety, both of which are important to both individual workers and society at large.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

建筑业是美国最大的行业之一，雇用了数百万工人，但是由于生产率低，工人短缺和安全问题所面临的挑战。该行业有一个巨大的机会，可以随着技术的最新进步提高其生产率和安全性。随着建筑工地自动化水平的提高，工人需要学习如何使用这些新技术并获得新的知识和工程技能。该项目的目的是创建和测试通过虚拟现实（网络学习）提供的培训计划，以教育和重新教育工人，以获得需要与建筑工地上的机器人协作的新工作经验。该项目旨在改善建筑工地工人和机器人之间的团队合作，重点关注多种特定技能，并提供全面的学习体验。该项目之所以重要，是因为它是为具有不同语言水平和教育水平的工人提供学习机会的开创性努力，为他们在人类技术领域的工作做准备。该项目解决的基本问题包括：1）与传统培训方法相比，网络学习如何增加工人的知识，安全行为和自动化的信任？ 2）个体差异如何影响工人的网络学习？ 3）基于施工任务的自动化信任如何变化？ 4）网络学习如何影响建筑工地的生产力和安全性？ 5）通过网络学习获得的知识和信任水平是否会延续到实际的建筑工地？ 6）网络学习如何影响下一代施工自动化的发展？通过探索这些研究问题，该项目为建筑业职业劳动力的具有成本效益的培训计划提供了证据。在任务1中开发的网络学习平台中包括许多学习场景和类型的施工机器人。通过使用离散事件模拟和沉浸式虚拟环境模拟建筑工地，在给定任务上与机器人交互。为了摆脱当今基于实验室的动手培训，网络学习环境结合了动态的施工工作环境，例如多个任务和工作人员共享相同的空间和条件，例如，通过模拟和使用准确的施工站点模型，例如不均匀的地形，灰尘，降雨。这项工作促进了关于网络学习对工人 - 机器人团队合作的影响的知识：用户级别和站点级别。对于任务2中的用户级调查，工作探讨了：1）网络学习的程度增加了工人的知识，安全行为和对自动化的信任，2）2）个体差异如何中度工人的学习以及3）基于任务类型的信任自动化如何变化。对于任务3中的现场调查，与所有在建筑工地上的工人的面对面培训相比，网络学习会影响生产力，安全性和自动化信任的程度，以及如何改善网络学习环境以及在建筑工地上部署的建筑机器人。最后，通过在任务4中的基于模拟的研究，该工作探讨了人类机器人相互作用如何影响施工任务和工作流程，安全程序和生产力。该网络学习计划旨在最大程度地提高工人接受和信任建筑机器人的程度，同时释放人力资源，提高生产力和安全性，这对个人工人和整个社会都很重要。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识优点和广泛的crietia crietia crietia crietia crietia criteria criperia crietia criteria criperia criteria criteria crietia crietia criteria均值得通过评估。

项目成果

Participants matter: Effectiveness of VR-based training on the knowledge, trust in the robot, and self-efficacy of construction workers and university students

参与者很重要：基于 VR 的培训对建筑工人和大学生的知识、对机器人的信任以及自我效能感的有效性

• DOI: 10.1016/j.aei.2022.101837
• 发表时间: 2023
• 期刊: Advanced Engineering Informatics
• 影响因子: 8.8
• 作者: Adami, Pooya;Singh, Rashmi;Borges Rodrigues, Patrick;Becerik-Gerber, Burcin;Soibelman, Lucio;Copur-Gencturk, Yasemin;Lucas, Gale
• 通讯作者: Lucas, Gale

A multidimensional taxonomy for human-robot interaction in construction

• DOI: 10.1016/j.autcon.2023.104845
• 发表时间: 2023-06
• 期刊: Automation in Construction
• 影响因子: 10.3
• 作者: Patrick B. Rodrigues;Rashmi Singh;Mert Oytun;Pooya Adami;P. Woods;B. Becerik-Gerber;L. Soibelman;Yasemin Copur-Gencturk;Gale M. Lucas

Virtual Learning for Workers in Robot Deployed Construction Sites

• DOI: 10.1007/978-3-030-00220-6_107
• 发表时间: 2018-10
• 期刊: Advances in Informatics and Computing in Civil and Construction Engineering
• 作者: So-Yong Moon;B. Becerik-Gerber;L. Soibelman

An Immersive Virtual Learning Environment for Worker-Robot Collaboration on Construction Sites

建筑工地工人与机器人协作的沉浸式虚拟学习环境

• DOI: 10.1109/wsc48552.2020.9383944
• 发表时间: 2020
• 期刊: Proceedings of the Winter Simulation Conference
• 作者: Adami, Pooya;Becerik-Gerber, Burcin;Soibelman, Lucio;Doleck, Tenzin;Copur-Gencturk, Yasemin;Lucas, Gale
• 通讯作者: Lucas, Gale