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About

Zhengping Fan is a leading robotics researcher whose work bridges autonomous exploration, control theory, and real-time optimization. His primary research areas include large-scale autonomous navigation for mobile robots and unmanned aerial vehicles (UAVs), as well as advanced control systems for robotic platforms. Fan’s most impactful contribution is the FAEL framework (2023, 77 citations), which enables fast autonomous exploration of large-scale environments by addressing computational overhead challenges that typically overwhelm mobile platforms. This work has become a cornerstone for researchers tackling real-world exploration in complex, expansive spaces. He also developed singularity-conquering ZG controllers (2014, 46 citations) for tracking control of inverted pendulum systems, advancing the field of autonomous robotics and intelligent vehicles. Earlier in his career, Fan contributed to the simplified LVI-based primal-dual neural network (2007, 33 citations), which efficiently solves linear and quadratic programming problems for real-time robotic applications. His most recent work (2024) introduces low-memory mapping techniques for efficient UAV exploration in large-scale 3D environments, addressing critical onboard resource limitations. With over 160 citations across his key publications, Fan’s research continues to shape how robots autonomously navigate and interact with complex, large-scale environments.