Chengwen Luo

Papers

Total Citations

H-Index

About

Chengwen Luo is a leading researcher in human–machine fusion interaction, autonomous robotic systems, and indoor localization. His work bridges the gap between robotics and ambient intelligence, with major contributions to multi-modal sensing and simultaneous localization and mapping (SLAM). Luo’s 2024 paper on multi-modal autonomous ultrasound scanning, which integrates visual and tactile information for efficient human–machine interaction, has garnered 10 citations and represents a breakthrough in robotic decision-making for complex spatial tasks. He also developed rWiFiSLAM, a WiFi-ranging-based indoor localization system that addresses GPS-denied environments, earning 6 citations, and proposed a novel edge-enabled SLAM solution using projected depth images (7 citations). With a focus on real-world applications, Luo’s research impacts mobile robotics, healthcare automation, and smart environments. His work is notable for its practical edge-computing approach, enabling efficient, real-time performance in resource-constrained settings. Luo’s achievements highlight his role in advancing autonomous systems that seamlessly collaborate with humans, making him a key figure in the evolution of intelligent robotics and pervasive computing.