Papers
18
Total Citations
754
H-Index
12
About
Hanzhen Xiao is a robotics and control systems researcher whose work sits at the intersection of model predictive control (MPC), neurodynamic optimization, and multi-robot coordination. With a career spanning nearly a decade of influential publications, Xiao has made substantial contributions to solving fundamental challenges in mobile robot stabilization, formation control, and autonomous navigation. Xiao's most celebrated work focuses on applying neural-network-based nonlinear model predictive control (NMPC) to leader-follower mobile robot formations, earning over 180 citations — a landmark contribution that advanced how constrained, nonholonomic robots achieve coordinated movement while navigating obstacle-rich environments. Complementary research on robust MPC for wheeled mobile robots (143 citations) and image-based visual servoing strategies (94 citations) further established Xiao as a leading voice in intelligent robot control under physical and perceptual constraints. Beyond foundational contributions, Xiao has pioneered incremental formation updating methods, self-triggered consensus protocols for Mecanum-wheeled robots, and reinforcement learning-driven obstacle avoidance — demonstrating a consistent ability to evolve with emerging paradigms. More recent work on distributed MPC with switching topology and RGB-D sensor-based target tracking highlights a broadening scope toward real-world deployment. Collectively, Xiao's publications have accumulated over 700 citations, reflecting deep, sustained impact on the autonomous robotics research community.
Research Focus
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