Nancong Chen

Guilin University of Aerospace Technology

Papers

Total Citations

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About

Nancong Chen is a researcher focused on intelligent robotics and autonomous navigation, with a particular emphasis on improving the safety and efficiency of automated guided vehicles (AGVs) in industrial settings. Chen’s most cited work, “Study on Obstacle Avoidance of AGV based on Fuzzy Neural Network” (2019), introduces a novel approach that combines fuzzy logic with neural networks to enable AGVs to navigate complex, unpredictable environments. This paper, with 4 citations, addresses a critical challenge in wheeled robotics: real-time obstacle avoidance without pre-programmed paths. By integrating adaptive learning with rule-based decision-making, Chen’s method enhances the vehicle’s ability to respond to dynamic obstacles, a key advancement for industries like warehousing and manufacturing. Though early in their career, Chen’s contributions lay important groundwork for more resilient autonomous systems. Their work stands out for bridging theoretical AI techniques with practical robotics applications, offering a scalable solution for safer AGV operations. As the demand for intelligent logistics grows, Chen’s research provides a foundational step toward fully autonomous material handling.