Ching-Lung Chang

National Yunlin University of Science and Technology

Papers

Total Citations

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About

Ching-Lung Chang is a robotics and intelligent systems researcher whose work spans autonomous robot navigation, sensor fusion, and adaptive control systems. His research addresses real-world challenges in service robotics, particularly as societies grapple with aging populations and shrinking workforces, making the development of reliable, intelligent robots an urgent priority. Chang's most influential contributions include a high-efficiency automatic recharging mechanism for cleaning robots, which intelligently combines onboard motor data with multi-sensor input to minimize downtime — work that has garnered 12 citations. His research on ROS-based multi-sensor fusion for accurate indoor positioning and SLAM (Simultaneous Localization and Mapping) has further advanced the mobility capabilities of intelligent robots, earning 11 citations. Equally notable is his pioneering application of reinforcement learning to two-wheeled self-balancing robots — a notoriously nonlinear control problem — demonstrating that adaptive learning algorithms can effectively stabilize complex dynamic systems, also cited 11 times. Across his body of work, Chang consistently bridges theoretical control methods with practical hardware implementation, using platforms like BeagleBone Black to validate his designs. With over 37 total citations, his contributions offer valuable insights for researchers developing the next generation of autonomous service robots.