Yin Kuang

Xi'an University of Science and Technology

Papers

Total Citations

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About

Yin Kuang is a researcher at the forefront of autonomous robotics and intelligent navigation systems, with a primary focus on deep reinforcement learning for real-world robotic control. His most cited work, "Intelligent Navigation of Indoor Robot Based on Improved DDPG Algorithm" (2023, 5 citations), addresses a critical challenge in robotics: enabling autonomous navigation in large-scale, complex, and unknown environments without relying on pre-built environmental models. By enhancing the Deep Deterministic Policy Gradient (DDPG) algorithm, Kuang developed an online decision-making framework that allows robots to learn and adapt their path planning in real time, overcoming the limitations of traditional, model-dependent approaches. This contribution is particularly significant for applications in service robotics, warehouse automation, and search-and-rescue operations. Kuang’s research bridges the gap between theoretical reinforcement learning and practical robotic deployment, demonstrating how adaptive algorithms can improve both efficiency and safety in dynamic settings. His work has been recognized for its potential to advance the field of intelligent robotics, and he continues to explore novel methods for integrating perception, planning, and control in autonomous systems.