Dancheng Li

Papers

Total Citations

H-Index

About

Dancheng Li is a researcher focused on advancing autonomous vehicle control and 3D spatial perception, with key contributions in the integration of deep reinforcement learning with industrial robotics. His most cited work, "The Method for Automatic Adjustment of AGV’s PID Based on Deep Reinforcement Learning" (2022, 5 citations), introduces a novel approach to optimizing PID controllers for Automated Guided Vehicles (AGVs) using reinforcement learning, directly addressing the challenge of smooth, adaptive motion control. Building on this, his paper "The Determination of Reward Function in AGV Motion Control Based on DQN" (2022, 4 citations) further refines reinforcement learning frameworks by designing effective reward functions that enhance AGV stability and performance. Earlier in his career, Li contributed to 3D perception with "3D Point Sets Matching Method Based on Moravec Vertical Interest Operator" (2012, 2 citations), demonstrating a lasting interest in spatial data processing. While his citation counts are modest, his work represents a practical bridge between theoretical reinforcement learning algorithms and real-world industrial automation, offering valuable insights for students and researchers exploring AI-driven control systems in robotics and autonomous navigation.