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About

Shilei Cheng is a robotics researcher focused on advancing autonomous systems for critical infrastructure inspection, with a particular emphasis on airport pavement monitoring and multi-robot coordination. His work addresses the complex challenge of deploying robot teams for large-area detection tasks, such as inspecting airport runways, taxiways, and parking aprons under tight time constraints. Cheng’s most-cited paper, "Multi-robot Task Allocation for Airfield Pavement Detection Tasks" (2021, 8 citations), introduces novel strategies to minimize inspection time by optimizing robot entry and exit positions. He further develops partition coverage planning methods using adjustable rectangular decomposition to enable safe human-robot interaction during high-stakes airport inspections. In the realm of human-aware navigation, Cheng proposes an asymmetric Gaussian model that accounts for human gaze direction, preventing robots from intruding into personal visual space—a subtle but critical improvement for social robotics. His research on multi-robot task allocation for runway inspection directly supports Foreign Object Debris (FOD) removal, enhancing aviation safety. With a growing citation record and practical contributions to both algorithmic efficiency and human-robot interaction, Cheng is establishing himself as a key innovator in field robotics for safety-critical environments.