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并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，美国传统的油气革命正受到其老化的管道基础设施的阻碍，其中一半以上的管道基础设施已有60年以上的历史。因此，在天然气管道行业内，迫切需要检查和认证新兴燃料运输的现有基础设施。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恶化，这往往会导致灾难性的后果。确保管道的完整性需要在未来几十年内使用先进的检测和诊断工具。由于管道的复杂几何形状、阀门尺寸过大或过小、弯道半径较小以及其他条件，美国相当大一部分管道基础设施仍然无法进行检修--无法由传统的在线检测机器人进行检测。允许管道运营商使用新开发的机器人、分析和人工集成来检查不可搬运的天然气管道，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，以降低与运输、储存和获取易燃和腐蚀性材料有关的固有危险的行业的风险和提高效率。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个人提供渠道，例如残疾人和现场经验和教育不足的操作员。该项目探索网络-物理-人类互动如何使石油和天然气管道工人能够更迅速和有效地转化新技术进步。该项目的总体目标是了解管道检测的工作背景，检查新型机器人检测系统的可行性，并为NSF人类技术前沿(FW-HTF)倡议的未来工作的更大项目提案生成经验基础。未来的FW-HTF提案旨在创建一个新的人类-机器人-人工智能框架，以实现自动化检查、安全评估和工人培训。这笔开发赠款的具体计划涉及：(A)约谈利益攸关方，以了解管道检查的当前工作做法，并评估他们对机器人和自动化技术的需求和看法；(B)通过与利益攸关方一起对机器人原型进行人工分析，了解人的因素(如信任)，以加强人与机器人之间的伙伴关系；(C)了解机器人管道检测工具的潜在安全危险，并为减少这种风险制定机器人设计准则；(D)了解人类知识和检测数据融合及其对管道风险评估的影响，以及上述任务的初步结果。拟议的研究将极大地促进对工作、工人和技术的未来的了解，以便有效地检查关键管道基础设施。如果成功，这笔开发赠款将导致FW-HTF设计和实施网络-物理-人类系统的全面研究建议，以最大限度地利用人类工人的能力和新技术来实现高生产率和安全的工作条件。该奖项反映了NSF的法定使命，并已通过使用基金会的智力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.