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About

Jianguang Shi is a robotics and marine autonomy researcher whose work sits at the intersection of autonomous underwater vehicles (AUVs), coverage path planning, and seafloor mapping. His research addresses one of ocean science's most pressing challenges: how to efficiently and intelligently survey the seabed using robotic systems, reducing reliance on costly crewed vessels while improving data resolution and environmental sustainability. Shi's most notable contribution is his development of data-driven and uncertainty-driven approaches to online coverage path planning (CPP) — algorithms that allow marine robots to adaptively navigate and map underwater terrain in real time. His 2020 paper on intermittent online CPP for AUV-based bathymetric mapping, his most cited work with 15 citations, demonstrated how intelligent path planning can dramatically improve survey efficiency. Complementing this, his work on interferometric sonar-based planning and uncertainty-driven sampling methods reflects a commitment to making autonomous systems robust under the unpredictable conditions of underwater environments. Collectively, Shi's contributions push the frontier of marine robotics toward smarter, more autonomous, and more ecologically conscious ocean exploration — work of growing importance as demand for high-resolution seabed data continues to rise globally.