Dapeng Jiang
Papers
4
Total Citations
26
H-Index
3
About
Dapeng Jiang is a leading researcher in autonomous underwater vehicle (AUV) systems, with a career focused on enhancing the reliability and coordination of underwater robots. His key research areas include fault diagnosis, neural network control, and multi-vehicle coordination. Jiang's most impactful contribution is his pioneering work on recurrent neural networks (RNNs) for thruster fault diagnosis in underwater robots, as detailed in his 2009 paper (15 citations), which significantly improved system reliability by enabling real-time detection of thruster failures. He further advanced the field by developing a market-based coordination approach for multiple AUVs using the MOOS-IvP framework (2010, 4 citations), enabling efficient oceanographic data gathering. In his most recent work (2024, 2 citations), Jiang designed and implemented an AUV control system based on the Robot Operating System (ROS), demonstrating a modern, modular approach to vehicle control. With a total of 26 citations across his key papers, Jiang's research has laid critical groundwork for fault-tolerant and collaborative underwater robotics, making him a notable contributor to the field.
Research Focus
Key Achievements
Top Papers
- 1Recurrent neural network applied to fault diagnosis of Underwater Robots15 citations · 2009
- 2Fault diagnosis of Underwater Robots based on recurrent neural network5 citations · 2009
- 3Coordination of multiple AUVs based on MOOS-IvP4 citations · 2010
- 4Design and Experiments on AUV Control System based on ROS2 citations · 2024