Shilong Dai

Nankai University, Shenyang Institute of Automation

Papers

Total Citations

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About

Shilong Dai is a researcher working at the intersection of mobile robotics, indoor localization, and reinforcement learning — fields that are rapidly transforming how autonomous systems navigate and interact with complex environments. His most recognized contribution lies in autonomous WiFi fingerprinting for indoor localization, a body of work that has garnered over 22 citations and addresses one of the field's persistent challenges: reducing the costly overhead of constructing and maintaining WiFi fingerprint maps. By leveraging robotic systems to automate this process, Dai's research pushes WiFi-based localization closer to practical, wide-scale deployment. Beyond localization, Dai has made meaningful strides in autonomous decision-making for mobile robots. His reinforcement learning-based hierarchical framework for pursuit-evasion games demonstrates a sophisticated approach to target tracking in dynamic environments, while his work on an improved Double Deep Q-Network (HERDDQN) tackles critical limitations like overestimation bias and sparse rewards in robot path planning. Together, these contributions reflect a coherent research vision: building smarter, more autonomous robots capable of navigating and reasoning in real-world settings. Dai's growing publication record signals an emerging voice in the robotics and AI research community.