Mingze Li

Jiangsu University of Science and Technology, Harbin Engineering University

Papers

Total Citations

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About

Mingze Li is a robotics researcher whose work focuses on intelligent motion planning, visual servoing, and fault-tolerant control for robotic systems. His research addresses critical challenges in autonomous robot navigation and manipulation, particularly in constrained and uncertain environments. Li’s most cited work, “A probability smoothing Bi-RRT path planning algorithm for indoor robot” (2023, 61 citations), introduces an enhanced rapidly-exploring random tree method that improves path smoothness and efficiency for indoor mobile robots. He has also made significant contributions to vision-based robot control, proposing a model predictive control strategy tuned by reinforcement learning for constrained image-based visual servoing (IBVS) of robot manipulators (2023, 14 citations). This work bridges optimal control and learning-based methods to handle visual servo tasks under constraints. More recently, Li developed a fault-tolerant control strategy for IBVS that ensures task completion despite actuator faults in robotic arms (2024, 7 citations). His work demonstrates a strong integration of theoretical control methods with practical robotic applications, offering valuable insights for researchers working on autonomous systems, robot manipulation, and human-robot interaction.