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About

Cheng-Lung Jen is a robotics researcher specializing in assistive technologies for wheelchair robots, with a focus on simultaneous localization and mapping (SLAM), human tracking, and intelligent navigation. His work integrates multi-sensor fusion, combining RGB-D cameras, laser range finders, and wireless local area networks (WLAN) to enable autonomous and safe wheelchair operation. Jen’s major contributions include developing a Bayesian framework for SLAM and human tracking using Microsoft Kinect sensors, which achieved 13 citations, and a fuzzy-based navigation system leveraging WLAN positioning with 9 citations. He also advanced monocular vision-based localization and target tracking, cited 8 times, and created adaptive online learning methods for human tracking. His research addresses critical challenges in human-robot interaction, such as accompanist detection and following, using fuzzy controllers and multisensory data fusion. With a total of over 40 citations across his most-cited works, Jen’s innovations aim to reduce caregiver burden and improve mobility for wheelchair users, showcasing his impact in assistive robotics. His notable achievements include pioneering the use of RGB-D sensors for real-time SLAM and developing dynamic path planning with graphic feature nodes, making his work a valuable resource for students and researchers in autonomous navigation and human-robot collaboration.