Ye-bo Yin
Papers
4
Total Citations
122
H-Index
4
About
Ye-bo Yin is a leading researcher in mobile robotics, specializing in path planning for hazardous environments, particularly nuclear accident sites. His major contributions lie in developing hybrid optimization algorithms that integrate ant colony optimization, A*, and particle swarm optimization to solve multi-objective path planning problems. These algorithms uniquely balance competing constraints such as path length, collision avoidance, and accumulated radiation dose, enabling robots to navigate safely in radioactive settings. His most cited work, "Multi-objective path planning for mobile robot in nuclear accident environment based on improved ant colony optimization with modified A∗" (2023, 56 citations), introduced a two-layer cost grid map that realistically models nuclear accident environments. This was followed by his hybrid IACO-A*-PSO algorithm (39 citations) and a novel deep reinforcement learning approach using an Improved Dueling Deep Double Q Network (ID3QN) (20 citations), which leverages asymmetric neural networks for radiation-aware navigation. His recent bi-level hybrid algorithm (2024) extends this work to multi-target inspection tasks in complex indoor radioactive environments. With over 120 total citations in just two years, Yin's research is rapidly shaping the field of autonomous navigation in extreme environments, offering practical solutions for disaster response and nuclear safety.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4