Tianxiang Cui

Dalian Maritime University, University of Nottingham Ningbo China

Papers

Total Citations

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About

Tianxiang Cui is a researcher advancing the frontiers of autonomous robotics and intelligent perception systems. His work centers on three interconnected domains: deep reinforcement learning for robotic decision-making, computer vision for object detection, and human-aware autonomous navigation. Cui’s most impactful contribution is his 2023 paper on underwater object detection, which introduces a novel multiple information perception-based attention mechanism for YOLO architectures—a method that has already garnered 32 citations for its practical improvements in challenging visual environments. In 2024, he published a pioneering study on mobile robot sequential decision-making using a deep reinforcement learning hyper-heuristic approach, earning 22 citations for bridging the gap between traditional DRL limitations and complex, real-world robotic tasks. His ongoing work includes a safety-driven end-to-end navigation framework for autonomous mobile robots that predicts human behavior from sparse sensor data, addressing the critical challenge of human-robot coexistence in industrial spaces. Cui also contributes to applied automation, having developed a low-cost visual screw inspection system for small-scale industries. Through these contributions, he is shaping how robots perceive, decide, and move safely in human-centered environments.