ERI: Design of Reliable Autonomous Engineering Systems with Active Failure Prevention

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The objective of this Engineering Research Initiation (ERI) project is to create a design framework using computer simulations to design reliable autonomous engineering systems (AES) that can proactively prevent catastrophic failures during operation. Autonomous engineering systems, such as autonomous vehicles and industrial robots, are emerging engineering systems that are replacing humans in many aspects of society. It is envisioned that AES in the future should have the capability of not only autonomously accomplishing a mission, but also proactively preventing failures in operation (i.e., active failure prevention). Through simulation-based design under uncertainty, this research will incorporate post-design active failure prevention into the early design stage of AES, thus reducing the probability of failure when the designed AES is put into operation. The research will establish a theoretical foundation that enables: (1) efficient and accurate analysis of the reliability of AES equipped with active failure prevention in the presence of uncertainty; and (2) co-design of AES physical and cyber systems for active failure prevention subject to specific reliability requirements. This research will enable the design of AES with assured reliability. It will accelerate the development of AES by reducing the reliability certification effort in the design process. Additional deliverables of this project include improvement of reliability and design-related curricula, research experiences for undergraduate and graduate students, and outreach initiatives for students and under-represented minorities.The overarching goal of this project is to create a new design paradigm shift from the conventional reliability-based design of engineered systems through passive reliability assurance to the model-based design of reliable AES to account for both passive reliability assurance at the population level and active failure prevention at the individual level. Personalized active failure prevention and autonomy set AES apart from conventional engineered systems and make current design under uncertainty methodologies inapplicable to AES. The research draws upon model-based design, statistics, machine learning, and optimization to fill the gap in the current methods. It will create innovations in reliability analysis by tackling the computational challenges through a first-passage method and co-simulations, while accounting for active failure prevention, along with innovations in design optimization by decoupling and decentralizing reliability-based co-design problems. The research has broad societal impacts by enabling the design of AES with active failure prevention with the consideration of reliability, which accelerates the development of next-generation reliable autonomous engineering systems to replace humans in safety-critical or dangerous mission/working environments. The integrated education plan involves curriculum improvement, engaging undergraduate students in research, dissemination of research results, and outreach to K-12 students and teachers.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.