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About

Tengteng Gao is a leading researcher in mobile robotics, specializing in intelligent local path planning for autonomous navigation in complex, unknown environments. His work directly addresses critical challenges such as local deadlock, path redundancy, and the "curse of dimensionality" that plague traditional algorithms. Gao’s major contributions lie in pioneering the fusion of deep learning with reinforcement learning and classical methods. He developed a novel algorithm integrating Long Short-Term Memory (LSTM) neural networks with reinforcement learning, significantly improving a robot’s ability to understand and react to its surroundings. He also advanced the Artificial Potential Field method by resolving its local minimum problem and introduced a Double BP Q-Learning algorithm to enhance model generalization. With his most-cited papers—including "A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning" (29 citations) and "Local Path Planning of Mobile Robot Based on Artificial Potential Field" (26 citations)—Gao’s work is foundational for creating more robust, adaptable, and intelligent mobile robots capable of navigating real-world challenges.