Guanghsu Chang

Western Carolina University, East Tennessee State University

Papers

Total Citations

H-Index

About

Guanghsu Chang’s research lies at the intersection of industrial robotics and intelligent decision-making systems, with a focus on making automation more accessible and efficient. His most influential work, “An Effective Learning Approach for Industrial Robot Programming” (2020, 8 citations), addresses a critical bottleneck in manufacturing: the complexity of teaching industrial robots through traditional teach pendants. Chang proposes a novel learning framework that simplifies robot programming, reducing the need for extensive robotics expertise and enabling faster deployment on factory floors. This contribution is particularly valuable for small and medium enterprises seeking to adopt automation. Earlier, Chang explored knowledge-based systems in “A Case-Based Reasoning Approach to Robot Selection” (2005, 4 citations), where he developed a CBR framework to help engineers choose the optimal robot for a given workcell. By integrating browsing and preference-based selection tools, his work streamlines a traditionally complex decision process. Though his citation counts are modest, Chang’s research demonstrates a consistent commitment to practical, human-centered robotics—lowering barriers to entry and empowering non-experts to leverage advanced automation.