Collaborative Research: FW-HTF-R: Wearable Safety Sensing and Assistive Robot-Worker Collaboration for an Augmented Workforce in Construction

合作研究：FW-HTF-R：可穿戴安全传感和辅助机器人工人协作，增强建筑劳动力

• 批准号: 2222881
• 负责人: Chao Wang
• 金额: $ 72万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222881&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; Wearable

Construction workers exert intense physical effort and experience serious safety and health risks in hazardous working environments. Thus, the construction industry is one of the highest-risk sectors in the US. A significant shortage of skilled workers in the construction industry amplifies the need to improve workers’ safety and health. Furthermore, the current workforce is aging and retiring; approximately 39% of construction workers were between 45-64 years old in 2020. Low interest among young adults and very low representation of women workers (only 4% in 2020) is exacerbating the existing labor shortage. As a result, there is an urgent need to develop new technology that keeps workers safe and injury-free, makes the industry more inclusive and economically sustainable, and eventually changes negative images that construction jobs are unsafe, low tech, and too male-dominated. The objective of this FW-HTF research project is to develop wearable safety sensing and assistive robot-worker collaboration for an augmented workforce, thereby improving worker retention and attracting women and young workers to construction careers. The researchers will also develop a number of integrated research and education programs to attract students from underrepresented groups into engineering and involve undergraduate students in research.Although robotics technologies are increasingly used, most research focuses on how they support construction tasks and yield economic benefits. Few studies discuss how to deploy wearable exoskeletons to prevent work-related musculoskeletal disorders and improve workers’ safety and health. New interventions are needed to address current safety and health knowledge gaps, identify social and economic benefits, risks, and barriers to the adoption of emerging technologies, and contribute to the development of an inclusive, diverse, and sustainable workforce in construction. Wearable devices, machine learning, and virtual-, augmented- and mixed-reality technologies offer great promise for revolutionizing existing practices in construction. This potential motivates the PIs to develop an integrated, multidisciplinary approach to bring these emerging technologies to individual workers, organizations, and the construction industry to enhance worker safety and health, improve productivity, address gender- and age-related labor shortages and expand employment opportunities. In this research project, the team of researchers plans to develop wearable occupational safety sensing and assistive robotic collaboration technology for skilled construction workers. Specifically, this project will emphasize: (1) machine learning-enabled, real-time worker activity recognition and pose estimation; (2) user-centered design of soft exoskeletons; (3) mixed reality-enhanced work skill transferring and workplace-based learning; (3) wearable safety sensing and assistive robotic collaboration for an augmented workforce; (4) analyses of social-economic impacts of the proposed technology; and (5) pilot studies, industrial deployment and workforce training. Academic collaborations and multi-stakeholder partnerships will provide the intellectual and personnel infrastructure necessary to address the multi-disciplinary, multi-faceted challenges by integrating best practices in construction with emerging technologies.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.

建筑工人体力劳动强度大，在危险的工作环境中面临严重的安全和健康风险，因此建筑行业是美国风险最高的行业之一，建筑行业技术工人的严重短缺加剧了改进的必要性。此外，目前的劳动力正在老龄化和退休；到 2020 年，大约 39% 的建筑工人年龄在 45-64 岁之间。年轻人的兴趣较低，女性工人的比例很低（2020 年仅占 4%）。 2020）正在加剧现有的劳动力短缺，因此迫切需要开发新技术来保证工人的安全和免受伤害，使该行业更具包容性和经济可持续性，并最终改变建筑工作不安全的负面形象。该 FW-HTF 研究项目的目标是开发可穿戴安全传感和辅助机器人工人协作，以增强劳动力，从而提高工人保留率并吸引女性和年轻工人从事建筑职业。研究人员还将开发一些综合研究和教育项目，以吸引代表性不足群体的学生进入工程领域，并让本科生参与研究。尽管机器人技术的使用越来越多，但大多数研究都集中在它们如何支持建筑任务并产生经济效益，很少有研究讨论。如何部署可穿戴外骨骼来预防与工作相关的肌肉骨骼疾病并改善工人的安全和健康，需要采取新的干预措施来解决当前的安全和健康知识差距，确定采用新兴技术的社会和经济效益、风险和障碍，并做出贡献可穿戴设备、虚拟现实、增强现实和混合现实技术为建筑业的现有实践带来了巨大的变革，这种潜力促使 PI 开发一种可穿戴设备。在这个研究项目中，团队采用综合的、多学科的方法将这些新兴技术带给个体工人、组织和建筑行业，以增强工人的安全和健康，提高生产力，解决与性别和年龄相关的劳动力短缺问题并扩大就业机会。的研究人员计划开发可穿戴职业具体来说，该项目将强调：（1）支持机器学习的实时工人活动识别和姿势估计；（2）以用户为中心的软外骨骼设计； ) ) 混合现实增强工作技能转移和基于工作场所的学习；(3) 用于增强劳动力的可穿戴安全传感和辅助机器人协作；(4) 拟议技术的社会经济影响分析； 、产业布局和劳动力学术合作和多方利益相关者伙伴关系将通过将建筑最佳实践与新兴技术相结合，提供解决多学科、多方面挑战所需的知识和人员基础设施。该奖项是 NSF 的法定使命，并被认为值得支持。通过使用基金会的智力优点和更广泛的影响审查标准进行评估。