Zhang De
Papers
6
Total Citations
134
H-Index
5
About
Dr. Zhang De is a leading researcher at the intersection of robotics, artificial intelligence, and nuclear safety engineering. His work focuses on solving the critical challenge of autonomous navigation in hazardous radioactive environments, where minimizing radiation exposure while completing complex missions is paramount. Dr. Zhang has pioneered novel hybrid optimization algorithms for multi-objective path planning, most notably integrating improved ant colony optimization with A* and PSO techniques, achieving over 56 citations for his foundational 2023 work. He has also advanced deep reinforcement learning approaches, developing the Improved Dueling Deep Double Q Network (ID3QN) for radiation-aware navigation. Beyond path planning, Dr. Zhang has contributed to radiation source localization using Bayesian unscented particle filtering, enabling mobile detection robots to predict and map unknown contamination. His recent comprehensive review on mobile robots in nuclear power plants (2025) synthesizes the fragmented literature on design, protection, perception, and planning. With a growing citation record exceeding 130 total citations, Dr. Zhang’s work is instrumental in making nuclear accident response safer and more efficient through intelligent robotic systems.
Research Focus
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