Minghao Jiang

Wuhan University of Science and Technology, Beijing Institute of Nanoenergy and Nanosystems

Papers

Total Citations

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About

Minghao Jiang is a robotics researcher whose work bridges autonomous navigation and human-robot interaction. His primary research areas include mobile robot path planning, dynamic movement primitives (DMPs), and deep learning-enhanced sensor systems. Jiang made significant contributions to robot autonomy by proposing a novel framework for establishing a Dynamic Movement Primitives Library (DMPL), enabling mobile robots to compute smooth, collision-free paths in unknown environments. His 2017 paper on this topic has garnered 15 citations, reflecting its influence in the field. Earlier, in 2016, he introduced a learning algorithm based on DMPs that allowed robots to autonomously plan paths by modeling human-demonstrated trajectories, a foundational work with 7 citations. Most recently, Jiang has ventured into affective computing, developing a deep learning-assisted acoustic sensor for real-time emotion recognition in auditory robots, a 2025 publication already attracting attention. His work demonstrates a clear progression from foundational path planning to sophisticated, perception-driven robotics, positioning him as a researcher who integrates classical control methods with modern AI to create more intelligent and responsive robotic systems.