Peifang Dong
Papers
1
Total Citations
165
H-Index
1
About
Peifang Dong is a leading researcher in intelligent robotics and autonomous navigation, with a primary focus on deep reinforcement learning for dynamic path planning. Their most influential work, "Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning" (2018), has garnered 165 citations and represents a significant breakthrough in mobile robotics. In this seminal paper, Dong pioneered the application of Double Deep Q-Network (DDQN) architecture to enable robots to navigate entirely unknown, dynamic environments without prior mapping. By designing innovative reward and punishment functions and training mechanisms, Dong effectively solved the long-standing challenge of real-time obstacle avoidance in unpredictable settings. This work has had substantial impact on the fields of autonomous driving, warehouse robotics, and search-and-rescue operations. Dong's contributions bridge the gap between theoretical reinforcement learning algorithms and practical robotic applications, establishing them as a key figure in advancing intelligent navigation systems. Their research continues to influence how autonomous agents learn to operate safely and efficiently in complex, real-world environments.
Research Focus
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Top Papers
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