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About

Jingge Tang is a robotics researcher specializing in the design, modeling, and control of underwater robotic systems, with a particular focus on the innovative intersection of snake-like locomotion and underwater gliding technology. Tang's most significant contribution is the conceptualization and development of the underwater gliding snake-like robot — a novel hybrid platform that integrates the high mobility of snake-like robots with the energy efficiency of underwater gliders, first introduced in a 2017 paper that laid the groundwork for an entire research direction. Building on this foundation, Tang advanced the field by applying sophisticated control strategies to these complex systems, including an Unscented Kalman Filter-based sliding mode controller (2019, 16 citations) that addresses the challenges of precise underwater navigation, and a reinforcement learning framework (2018) that enables the robot to adaptively handle difficult-to-model hydrodynamic environments. The 2021 work on hybrid-driven gliding motion further refined the mathematical modeling underpinning this robot class. With a growing citation record across multiple publications, Tang's research offers meaningful contributions to energy-efficient underwater robotics, with potential applications in ocean exploration and environmental monitoring.