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FW-HTF-R: Collaborative Research: Partnering Workers with Interactive Robot Assistants to Usher Transformation in Future Construction Work

FW-HTF-R：协作研究：工人与交互式机器人助手合作，引领未来建筑工作的转型

基本信息

批准号：
2128623
负责人：
Carol Menassa
金额：
$ 158万
依托单位：
Regents of the University of Michigan - Ann Arbor
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：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128623&HistoricalAwards=false
关键词：
FW HTF Collaborative Research Partnering

项目摘要

Construction is a $10 trillion industry that employs about 180 million workers worldwide. However, the future of construction work is at crossroads. First, productivity in construction work has been stagnant relative to other industries (e.g., manufacturing), and the industry has historically been slow to adopt innovations that affect efficiency. Second, it has been difficult to offset the aging and retiring workforce with younger and more diverse workers, causing the workforce supply to fall short of rising demand. This is mainly because construction work tends to be physically strenuous leading to occupational hazards that often force workers to retire early. Robotization has been suggested as a potential solution to these problems. However, the unstructured nature of construction work presents several technical, social and economic impediments that hinder the direct adoption and integration of such innovations by the construction industry. For construction workers, robotic technology can only be transformative if it allows them to channel their passion for the work while avoiding the chronic pain and health outcomes associated with its physical demands.This project investigates if construction work can be conceived as a human-robot partnership, where human workers play the critical role of planning the work, and training and supervising robotic assistants to adapt to presented workspace conditions and perform useful work. The project team is integrating advances in interactive task learning, mixed reality, and reinforcement learning to enable construction workers to naturally collaborate with robot assistants through direct physical interaction and virtual supervision and training. For such a symbiotic human-robot partnership to benefit construction workers and result in widespread deployment, workers need to be equipped with new skills. The project team is exploring new educational and professional development programs to support worker aspirations for upskilling and lifelong learning, and to open avenues for people of diverse abilities to be productive members of the construction workforce. Tight-knit partnerships with industry collaborators will inform the project activities and provide access to construction work sites and training facilities for testing and evaluation.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.

建筑业是一个价值10万亿美元的行业，在全球雇佣了约1.8亿工人。然而，建筑业的未来正处于十字路口。首先，建筑业的生产率相对于其他行业一直停滞不前（例如，制造业），而且该行业在采用影响效率的创新方面历来进展缓慢。其次，很难用更年轻和更多样化的工人来抵消老龄化和退休的劳动力，导致劳动力供应无法满足不断增长的需求。这主要是因为建筑工作往往是体力劳动，导致职业危害，往往迫使工人提前退休。机器人化被认为是解决这些问题的一个潜在方案。然而，建筑工作的非结构化性质提出了一些技术，社会和经济障碍，阻碍了建筑行业直接采用和整合这些创新。对于建筑工人来说，机器人技术只有在能够让他们释放对工作的热情，同时避免与其身体需求相关的慢性疼痛和健康后果时，才能带来变革。本项目研究建筑工作是否可以被视为人类与机器人的合作伙伴关系，其中人类工人在规划工作中发挥关键作用，以及训练和监督机器人助理以适应所呈现的工作空间条件并执行有用的工作。该项目团队正在整合交互式任务学习、混合现实和强化学习方面的进展，使建筑工人能够通过直接的物理交互和虚拟监督和培训，自然地与机器人助手协作。为了让这种共生的人类-机器人伙伴关系使建筑工人受益并导致广泛部署，工人需要配备新技能。项目团队正在探索新的教育和专业发展计划，以支持工人提高技能和终身学习的愿望，并为具有不同能力的人成为建筑劳动力的生产力成员开辟途径。与行业合作者紧密的伙伴关系将为项目活动提供信息，并提供进入施工现场和培训设施进行测试和评估的机会。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Automatic high-level motion sequencing methods for enabling multi-tasking construction robots

DOI：
10.1016/j.autcon.2023.105071
发表时间：
2023-09-02
期刊：
AUTOMATION IN CONSTRUCTION
影响因子：
10.3
作者：
Wang，Xi;Wang，Shuoqi;Mcgee，Wes
通讯作者：
Mcgee，Wes

Reducing Uncertainty in Multi-Robot Construction through Perception Modelling and Adaptive Fabrication

通过感知建模和自适应制造减少多机器人构建的不确定性

DOI：
10.22260/isarc2023/0006
发表时间：
2023
期刊：
The International Association for Automation and Robotics in Construction
影响因子：
0
作者：
Ruan, Daniel;Mcgee, Wes;Adel, Arash
通讯作者：
Adel, Arash

DANLI: Deliberative Agent for Following Natural Language Instructions

DOI：
10.48550/arxiv.2210.12485
发表时间：
2022-10
期刊：
Environ. Model. Softw.
影响因子：
0
作者：
Yichi Zhang;Jianing Yang;Jiayi Pan;Shane Storks;N. Devraj;Ziqiao Ma;Keunwoo Peter Yu;Yuwei Bao;J. Chai
通讯作者：
Yichi Zhang;Jianing Yang;Jiayi Pan;Shane Storks;N. Devraj;Ziqiao Ma;Keunwoo Peter Yu;Yuwei Bao;J. Chai

Trajectory-Based Skill Learning for Overhead Construction Robots Using Generalized Cylinders with Orientation

使用具有定向的广义圆柱体的架空施工机器人的基于轨迹的技能学习