Max Robotics — Global Service Robot Directory + US Certification

About

Zhaoming Miao is a leading researcher in intelligent fault diagnosis for mobile robotics, specializing in advanced deep learning architectures that fuse multi-sensor data. His core contributions lie in pioneering graph-based neural network approaches to model complex spatial-temporal relationships in robotic systems. Miao’s seminal work, “Fault Diagnosis of Wheeled Robot Based on Prior Knowledge and Spatial-Temporal Difference Graph Convolutional Network” (2022, 47 citations), introduced a novel method that leverages prior knowledge to enhance graph convolutional networks, enabling comprehensive health evaluation from heterogeneous sensor arrays. He further advanced the field with “Multi-heterogeneous sensor data fusion method via convolutional neural network for fault diagnosis of wheeled mobile robot” (2022, 37 citations), demonstrating how CNNs can effectively integrate diverse sensor modalities. Most recently, his 2023 study on spatial-temporal graph attention networks (17 citations) addresses the critical challenge of imbalanced data in real-world fault diagnosis, a problem that severely impacts traditional deep learning methods. Collectively, Miao’s work has accumulated over 100 citations, establishing him as a key innovator in reliable, data-driven robotic health monitoring systems that push beyond conventional temporal or spatial-only extraction techniques.