Collaborative Research: FW-HTF-P: Efficient Inspection of Unpiggable Pipelines through Human-Robot Integration

可再生天然气和氢气等新兴燃料的最新趋势为美国提供了成为全球能源市场强国和真正实现能源完全独立的黄金机会。然而，最近的经济和商业分析表明，美国传统石油和天然气革命正在受到其老化的管道基础设施的阻碍，其中一半以上的管道已经超过60年。因此，在天然气管道行业内，迫切需要对现有基础设施进行检查和认证，以用于新兴燃料运输。天然气管道基础设施面临的一个重大挑战是与老化相关的老化，这往往会导致灾难性的后果。在未来几十年里，确保管道的完整性需要先进的检查和诊断工具。由于管道几何形状复杂、阀门尺寸过大或过小、弯曲半径短以及其他条件，美国很大一部分管道基础设施仍然是“无法清管”的——无法通过传统的在线检测机器人进行检测。通过新开发的机器人、分析和人工集成技术，管道运营商可以检查不可清管的天然气管道，这将极大地帮助天然气行业管理关键基础设施。拟议的研究旨在解决这一具有挑战性的问题，为运输、储存和获取可燃和腐蚀性材料的行业降低风险，提高效率。该项目中创建的机器人、决策支持和培训工具将为招聘和留住传统上不被考虑担任管道检查职位的人员提供渠道，例如残疾人和缺乏现场经验和教育的操作人员。该项目探讨了网络-物理-人类的相互作用如何使石油和天然气管道工人能够更有效地响应新技术的进步。该项目的总体目标是了解管道检查的工作环境，检查新型机器人检查系统的可行性，并为NSF“人类技术前沿工作的未来”（FW-HTF）计划的更大项目提案提供经验基础。未来的FW-HTF提案旨在创建一个新的人-机器人-人工智能框架，以实现自动检查、安全评估和工人培训。这项发展拨款的具体计划包括(a)与利益相关者面谈，以了解管道检查的当前工作实践，并评估他们对机器人和自动化技术的需求和看法；(b)通过与利益相关者一起对机器人原型进行人工分析，了解加强人与机器人之间伙伴关系的人为因素（如信任）；(c)了解机器人管道检查工具的潜在安全危害，并制定机器人设计指南，以减轻此类风险；(d)通过上述任务的初步结果，了解人的知识和检测数据的融合及其对管道风险评估的影响。拟议的研究将大大促进对未来工作、工人和技术的理解，以有效检查关键的管道基础设施。如果成功，该开发拨款将导致一项全面的FW-HTF研究计划，用于设计和实施网络-物理-人类系统，该系统最好地利用人类工人和新技术的能力，以实现高生产率和安全的工作条件。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Recent trends in emerging fuels, such as renewable natural gas and hydrogen, provide the United States a golden opportunity to become a powerhouse in global energy markets and truly achieve full energy independence. However, recent economic and business analytics has indicated that the US conventional oil and gas revolution is being held back by its aging pipeline infrastructure, more than half of which is over 60 years old. Therefore, within the gas pipeline industry, an urgent need is to inspect and certify existing infrastructure for emerging fuel transport. A grand challenge in gas pipeline infrastructure is aging-related deterioration that often leads to catastrophic consequences. Ensuring the integrity of pipelines requires advanced inspection and diagnostic tools in the next few decades. Due to the complex geometries of pipelines, over or under-sized valves, short-radius bends, and other conditions, a significant portion of pipeline infrastructure in the US remains “unpiggable” – unable to be inspected by the conventional inline inspection robots. Allowing pipeline operators to inspect unpiggable gas pipelines with newly developed robots, analytics, and human integration will significantly help the gas industry to manage critical infrastructure. The proposed study aims to tackle this challenging problem to reduce risk and improve efficiency for an industry with inherent dangers related to transporting, storing, and accessing combustible and corrosive materials. The robotic, decision-support, and training tools created in this project will provide a conduit to recruiting and retaining individuals who have not traditionally been considered for pipeline inspection positions, such as persons with disabilities and operators with insufficient field experience and education.This project explores how a cyber-physical-human interplay enables a more responsive and effective translation of new technology advancements for oil and gas pipeline workers. The overall goal of this project is to understand the work context of pipeline inspection, examine the feasibility of a novel robotic inspection system, and generate the empirical groundwork for a larger project proposal to the NSF’s Future of Work at the Human-Technology Frontier (FW-HTF) Initiative. The future FW-HTF proposal aims to create a novel human-robot-AI framework to enable automated inspection, safety assessment, and worker training. Specific plans for this development grant involve (a) interviewing stakeholders to understand the current work practices of pipeline inspection and assess their needs and perceptions of robotics and automation technologies; (b) gaining an understanding of human factors (such as trust) that strengthen the partnership between humans and robots by conducting artifact analysis of robot prototypes with stakeholders; (c) understanding potential safety hazards of robotic pipeline inspection tools and developing guidelines on robot design for mitigating such risks; and (d) understanding the human knowledge and inspection data fusion and their impact on the risk assessment for the pipeline with the preliminary results from the above tasks. The proposed study will significantly advance the understanding of the future of work, workers, and technology for efficient inspection of critical pipeline infrastructure. If successful, this development grant will lead to a comprehensive FW-HTF research proposal for designing and implementing cyber-physical-human systems that best utilize the capacities of human workers and new technologies to achieve high productivity and safe work conditions.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.