Papers
5
Total Citations
159
H-Index
3
About
Haiyi Kong is a robotics and control systems researcher whose work sits at the intersection of autonomous mobile robotics, human-robot interaction, and intelligent control theory. Their most significant contribution lies in advancing trajectory-tracking and model predictive control (MPC) for nonholonomic mobile robots, most notably through a tube-based MPC framework combined with adaptive control to simultaneously handle kinematic and dynamic constraints — a paper that has garnered over 120 citations since 2018 and established Kong as a notable voice in mobile robot control. Building on this foundation, Kong has explored robust predictive and adaptive control architectures that tackle real-world challenges such as coupled input constraints and unknown system dynamics. Their research extends into human-robot interaction, proposing an innovative sEMG-based shared control system that enables intuitive human guidance of omnidirectional robots while integrating obstacle avoidance for enhanced safety. More recently, Kong has ventured into impedance control for robots operating in soft environments, employing neural networks to address the limitations of linear environmental models. Complementing these efforts, contributions to artificial potential field-based path planning further demonstrate a broad commitment to making autonomous robotic systems smarter, safer, and more adaptable in complex real-world scenarios.
Research Focus
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Top Papers
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