Weiqi Jin

Beijing University of Posts and Telecommunications, Beijing Institute of Technology

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About

Weiqi Jin is a pioneering researcher whose work spans two transformative domains: intelligent routing protocols for robotic networks and advanced underwater laser imaging systems. In the field of unmanned robotic networks (URNs), Jin developed the groundbreaking Reward Function Learning for Q-learning-based Geographic routing (RFLQGeo) protocol, which tackles the critical challenge of maintaining reliable communication among highly mobile robotic nodes in dynamic environments. This 2019 work, cited 35 times, represents a significant advance in applying reinforcement learning to geographic routing, enabling more efficient and adaptive data transmission in autonomous systems. Jin’s earlier contributions to underwater imaging are equally impactful. Their 2008 study on range-gated underwater laser imaging systems, which has garnered 12 citations, demonstrated how intensified gate imaging technology can eliminate backscattering noise and extend imaging range by 4 to 6 times compared to conventional floodlight cameras. This innovation has become essential for oceanic research, deep-sea exploration, and underwater remote control operations. By bridging the gap between machine learning-driven networking and optical sensing in extreme environments, Jin’s work continues to inspire new generations of researchers tackling complex challenges in robotics and marine technology.