Gianluca Bianchin

University of California, Riverside, University of Colorado Boulder

Papers

Total Citations

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About

Gianluca Bianchin’s research sits at the intersection of control theory, robotics, and cybersecurity, with a focus on ensuring the safe and resilient operation of autonomous systems under adversarial conditions. His most influential work addresses the critical challenge of secure navigation, where he developed methods for trajectory planning that remain effective even when sensor data is corrupted by undetectable spoofing attacks. This foundational paper, “Secure trajectory planning against undetectable spoofing attacks” (2019), has garnered 49 citations and established a framework for guaranteeing robot localization and path execution despite malicious interference. Expanding on this, his 2019 study “Secure Navigation of Robots in Adversarial Environments” (29 citations) formalized strategies for robots with single-integrator dynamics to navigate while countering unknown attacker actions. More recently, Bianchin has pioneered the integration of deep learning perception with online optimization for dynamical systems, as seen in his 2022 work (11 citations), which enables data-driven control when state measurements and performance metrics are incomplete. His contributions are vital for deploying autonomous vehicles, drones, and mobile robots in security-sensitive environments, making him a leading voice in resilient autonomy.