Haokun Geng

University of Auckland, University of Jinan

Papers

Total Citations

H-Index

About

Haokun Geng’s research bridges robotics, control theory, and computer vision, with a focus on enabling precise and robust autonomous systems. His early work on vision-based ego-motion estimation introduced a feature-matching method combined with an extended Kalman filter for stereo camera systems, providing a reliable approach for determining robot and vehicle pose from visual data—a foundational technique for autonomous navigation. More recently, Geng has advanced control systems for flexible-joint robots, developing a dual-channel disturbance rejection controller that ensures prescribed performance constraints even under dynamic uncertainties. This work addresses critical challenges in compliant robotics, where joint flexibility introduces instability, and his proposed method enhances both safety and precision in human-robot interaction. Though his citation counts are still growing—with his 2013 paper on stereo ego-motion accumulating 2 citations and his 2025 work on disturbance rejection already garnering 1—the novelty and practical relevance of his contributions are evident. Geng’s trajectory from visual perception to advanced control showcases a versatile researcher tackling core problems in robotics, with potential for significant impact as his methods gain traction in both academic and applied settings.