Ya-qi CHANG

Papers

Total Citations

H-Index

About

Ya-Qi Chang is a researcher whose work centers on advancing robotic precision through intelligent optimization algorithms. His primary research areas include robot calibration, evolutionary computation, and industrial automation. Chang’s most notable contribution is his development of a modified differential evolution algorithm for robot calibration, a method that significantly improves the accuracy of robotic manipulators by optimizing kinematic parameters. This work, published in 2021, has garnered 7 citations, reflecting its growing relevance in the field of robotics. By addressing the critical challenge of calibration—essential for tasks ranging from manufacturing to surgical robotics—Chang’s approach offers a more efficient and robust alternative to traditional techniques. His research bridges the gap between theoretical optimization and practical application, demonstrating how evolutionary algorithms can enhance real-world robotic performance. For students and researchers exploring robot kinematics or metaheuristic optimization, Chang’s work provides a clear example of how algorithmic innovation can directly impact precision engineering. His contributions are particularly valuable in contexts where high accuracy is paramount, such as automated assembly or quality inspection.