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About

Gaowei Yan is a researcher whose work bridges robotics, artificial intelligence, and nuclear safety. His primary research areas include robotic grasping, path planning, and autonomous radiation detection. Yan’s most significant contribution is the development of a multi-modal deep extreme learning machine for robotic grasping recognition, a 2016 paper that has garnered 42 citations and demonstrates how deep learning can enhance a robot’s ability to perceive and manipulate objects. In the domain of path planning, Yan proposed an immune evolution algorithm for soccer robots, addressing the limitations of traditional immune algorithms to achieve faster and more efficient navigation. More recently, he has ventured into nuclear safety, co-authoring a 2022 study on an alpha/beta radiation mapping method that uses simultaneous localization and mapping (SLAM) on mobile robots, offering a safer alternative to manual detection in nuclear power plants. This work highlights his ability to apply robotics to critical real-world challenges. With a career spanning over a decade, Yan’s research consistently integrates machine learning, evolutionary computation, and autonomous systems, making him a versatile contributor to both theoretical and applied robotics.