Mingxiao Ma

Papers

Total Citations

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About

Mingxiao Ma is a robotics researcher whose work centers on vision-based control and autonomous navigation for mobile robots. His most cited paper, "Vision-based Adaptive Tracking Control of a Mobile Robot: Algorithms and Experimental Validation" (2022), addresses the critical challenge of enabling robots to track targets reliably using visual input. Ma’s key contribution lies in developing the multi-feature fusion Kernel Correlation Filters (MF-KCF) algorithm, which leverages RGB-D cameras to integrate depth information and multiple visual features, significantly improving tracking robustness in dynamic environments. This work bridges the gap between theoretical control design and practical validation, offering a scalable solution for real-world robotic applications. With 2 citations, this paper has already garnered interest from peers exploring sensor fusion and adaptive control. Ma’s research is particularly valuable for students and engineers working on autonomous systems, as it provides a clear, experimentally validated framework for vision-based tracking. His achievements underscore a commitment to advancing robotic perception and control, making his work a foundational reference for those seeking to enhance mobile robot autonomy in complex, unstructured settings.