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

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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.