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About

Shenshen Gu is a leading researcher at the intersection of deep learning, robotics, and multi-objective optimization, with a particular focus on intelligent sports robotics. His most impactful work centers on developing tennis ball collection robots that combine computer vision and autonomous navigation, where he has pioneered the application of advanced neural network architectures including AlexNet, SSD, YOLOv3, and MobileNet-SSD for real-time ball recognition and detection. His seminal 2017 paper on deep learning-based tennis ball recognition has garnered 42 citations, establishing a foundation for subsequent innovations in sports robotics. Gu's contributions extend beyond perception to include multi-objective path planning algorithms, as demonstrated in his 2024 work on deep reinforcement learning for the traveling salesman problem, which addresses the complex optimization challenges inherent in autonomous collection tasks. Notably, his 2018 implementation on the NVIDIA Jetson TX1 board (25 citations) showcases his commitment to deploying computationally efficient solutions on embedded systems. With a growing body of work accumulating over 96 citations, Gu's research continues to bridge the gap between theoretical AI advances and practical robotic applications, making significant strides toward reducing human labor in sports environments while advancing the broader fields of deep learning and optimization.