Mingwu Zhang

Beijing Academy of Artificial Intelligence

Papers

Total Citations

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About

Mingwu Zhang is a robotics researcher whose work bridges simulation, modeling, and real-world control for camera and service robots. His primary research areas include robot kinematics, model conversion algorithms, and the practical deployment of robotic systems. Zhang’s most notable contribution is the development of a model conversion algorithm from Unified Robot Description Format (URDF) to Denavit-Hartenberg (DH) parameters, specifically designed for camera robots. This work addresses a critical engineering challenge: while URDF offers flexibility in describing kinematic structures, DH parameters provide superior efficiency for inverse kinematics control at the position level. By enabling seamless translation between these two representations, Zhang’s algorithm allows engineers to leverage the strengths of both formats, particularly when only URDF files are available from design or simulation environments. His 2022 paper on this topic has garnered 4 citations, reflecting its practical value in robotics development. Zhang’s work is especially relevant for researchers and engineers working on robot arm calibration, camera positioning systems, and service robotics, where accurate kinematic modeling is essential for precise motion control.