Ming Wang

Capital Normal University, Shandong Jianzhu University, Ningxia University

Papers

Total Citations

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About

Ming Wang is a robotics and intelligent systems researcher whose work spans mobile robot navigation, bionic robotics, and robotic manipulation. His research addresses some of the most practically challenging problems in autonomous systems, combining probabilistic modeling, computer vision, and control theory to advance robotic capabilities in dynamic, real-world environments. Wang's most influential contribution applies Markov Decision Process-based probabilistic formal modeling to obstacle-avoidance strategies for mobile robots operating in dynamic environments, a work that has garnered 9 citations and offers a rigorous framework for reasoning about the inherently stochastic behaviors of both robots and obstacles. His foray into bionic robotics produced a YOLOv3-based detection method for robotic fish, enabling rapid and precise target localization in underwater systems — a study that has attracted 6 citations and reflects growing interest in bio-inspired robotics. More recently, Wang introduced a novel multidimensional uncalibrated technique for six-axis manipulators, allowing robotic arms to adaptively perform vision-guided grasping without cumbersome calibration procedures, broadening the practical applicability of machine vision in complex settings. Across his body of work, Wang consistently bridges theoretical rigor with engineering practicality, making meaningful contributions to the advancement of intelligent, adaptive robotic systems.