CPS: Medium: Collaborative Research: Real-time Subsurface Sensing with Cognitive Networked Robotic System

This Cyber-Physical Systems (CPS) project will investigate cognitive and cooperative sensing and imaging systems for rapid near real-time subsurface infrastructure monitoring and mapping. This research advances the frontier of subsurface sensing to a new paradigm enabling practical large-area surveys not possible by existing means. By significantly enhancing subsurface knowledge acquisition, the systems in this project will have far-reaching implications for maintenance and planning related to urban subsurface infrastructure, improving resilience, security, emergency response and urban renewal. The interdisciplinary nature of this collaborative research project broadens inter-institute and institute-community interactions. The research activities and outcomes will enhance and enrich existing STEM education curricula, CPS research efforts and summer programs for K-12 students, undergraduates, graduates and underrepresented groups in both Burlington, Vermont, and Chattanooga, Tennessee, leading to the development of a highly competitive and diverse STEM workforce for Internet of things, smart cities, public safety, and transportation industries. The goal and scope of this project are to create faster and more accurate subsurface infrastructure sensing systems using teams of coordinated autonomous ground penetrating radars (GPRs) equipped with innovative and feedback-controlled cognitive slant sensing (CSS) capabilities. The research methods will be a collaborative and integrated development of hardware, communication networking, data acquisition and analytics, fundamental algorithms and models. The research approaches are to: 1) Build autonomous mobile GPR agents with slant scanning and edge-enhanced communication and computing. The CSS-GPRs can operate in both distributed and collective modes, with agents scanning individually or as a team in a scalable architecture; 2) Create synergistic multi-agent monostatic and multistatic teaming to map and construct 3-D images of subsurface infrastructure using novel slant imaging methods; 3) Assemble teams of autonomous GPRs with networking capabilities to enable adaptive switching between distributed and collective sensing modes; and 4) Validate with laboratory and field tests in challenging urban environments. The potential contribution of this research is advanced sensing systems that swiftly traverse designated terrains, employing data-driven adaptive methodologies to yield high-fidelity and scalable tomographic renderings of subsurface conditions and built infrastructure. 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.