Max Robotics — Global Service Robot Directory + US Certification

About

Hochul Shin is a leading researcher at the intersection of robotics, smart city security, and human-robot collaboration. His work focuses on developing intelligent surveillance systems that leverage multi-modal sensor data—including LiDAR, thermal imaging, and cameras—to enhance urban safety and operational efficiency. Shin’s most influential paper, “Multimodal layer surveillance map based on anomaly detection using multi‐agents for smart city security” (2022, 35 citations), introduces a framework that integrates data from diverse agents like CCTV and security robots to detect anomalies in real environments. He also contributed the X-MAS dataset (2023, 10 citations), an extremely large-scale multi-modal sensor dataset for outdoor surveillance, providing a critical resource for advancing deep learning in human detection and tracking. In the domain of industrial robotics, Shin’s work on “Evaluation of force pain thresholds to ensure collision safety in worker-robot collaborative operations” (2024, 7 citations) addresses key safety challenges in human-robot interaction. His earlier research on anomaly detection using elevation and thermal maps (2020, 7 citations) further demonstrates his commitment to practical, real-world applications. With a career spanning from robot-assisted orthopaedic surgery to cutting-edge smart city technologies, Shin’s contributions are shaping safer, more autonomous urban environments.