Cong Hung

Papers

Total Citations

H-Index

About

Cong Hung is a robotics and intelligent systems researcher whose work centers on autonomous mobile robot navigation, with particular expertise in simultaneous localization and mapping (SLAM) and bio-inspired optimization techniques. His most significant contribution lies in the development of neuro-fuzzy assisted adaptive Extended Kalman Filter (EKF) frameworks for solving the SLAM problem — a fundamental challenge in enabling robots and vehicles to build maps of unknown environments while tracking their own position within them. By integrating adaptive neuro-fuzzy inference systems with classical EKF methodology, Hung's approach addresses the critical mismatch between theoretical and actual covariance matrices, substantially improving estimation accuracy and robustness. His 2019 paper on this technique has garnered 14 citations, establishing it as a meaningful reference point in the field, while his earlier 2017 work laid the conceptual groundwork for this direction. Beyond SLAM, Hung has explored evolutionary computation for robotic motion planning, proposing differential evolution strategies to optimize mobile robot trajectories in demanding industrial contexts. Together, his research reflects a consistent drive to bridge soft computing intelligence with classical estimation theory, advancing the reliability and autonomy of next-generation robotic platforms.