Yixiang Gao

Papers

Total Citations

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About

Yixiang Gao is a robotics researcher whose work bridges intelligent control, computer vision, and embedded systems. His primary research areas include inverse kinematics for serial robots, neural network-based control, and real-time object detection for assistive technology. Gao’s most cited paper, “A Study on Solving the Inverse Kinematics of Serial Robots using Artificial Neural Network and Fuzzy Neural Network” (2019, 53 citations), addresses a fundamental challenge in robotics: efficiently computing joint configurations to achieve desired end-effector positions. By applying artificial and fuzzy neural networks, he offers a robust alternative to traditional analytical methods, improving computational efficiency and adaptability in complex robotic systems. His work on “Object Detection and Pose Estimation Using CNN in Embedded Hardware for Assistive Technology” (2019, 8 citations) demonstrates practical innovation, deploying MobileNet SSD on low-cost hardware to enable real-time object tracking for assistive devices. This contribution highlights his commitment to making advanced robotics accessible for real-world applications, particularly in healthcare and human support. With a growing citation impact, Gao’s research continues to influence both theoretical advances in robot control and the development of cost-effective, intelligent systems for everyday use.