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About

Zhenbo Wang is a leading researcher in guidance, navigation, and control (GNC) for advanced vehicular systems, with a particular focus on autonomous aerial vehicles and urban air mobility (UAM). His work bridges convex optimization, reinforcement learning, and multimodal data fusion to solve complex, safety-critical control problems in constrained environments. Wang’s most impactful contribution is his comprehensive survey on convex optimization for guidance and control of vehicular systems, which has already garnered 73 citations since its 2024 publication, establishing a foundational reference for the field. He has also pioneered the application of deep Q-networks for autonomous landing of eVTOL vehicles, a key enabler for UAM operations, with his 2023 paper on this topic featuring a video presentation that demonstrates real-world implementation. Additionally, Wang introduced MTFR, a universal multimodal fusion method that enhances perception and decision-making through modality transfer and fusion refinement. His work is characterized by its practical orientation toward real-time, computationally efficient solutions for next-generation autonomous systems, making significant strides in translating theoretical advances into deployable GNC technologies.