Ning Song

Xi'an University of Science and Technology

Papers

Total Citations

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About

Ning Song is a leading researcher in intelligent robotics and autonomous navigation, with a focus on deep reinforcement learning for complex, unknown environments. Their most influential work, "Intelligent Navigation of Indoor Robot Based on Improved DDPG Algorithm" (2023, 5 citations), addresses a critical challenge in robotics: enabling indoor robots to navigate large-scale, complicated spaces without prior environmental modeling. By enhancing the Deep Deterministic Policy Gradient (DDPG) algorithm, Song developed an autonomous online decision-making framework that allows robots to adapt in real time, overcoming the limitations of traditional path planning methods that depend on static environment maps. This contribution has significant implications for service robotics, warehouse automation, and assistive technologies. Song’s research bridges the gap between theoretical reinforcement learning and practical robotic deployment, offering scalable solutions for dynamic settings. With a growing citation impact, their work is increasingly recognized for advancing the field of intelligent navigation, making them a key figure in the next generation of autonomous systems.