Xinyang Xiong
Papers
1
Total Citations
16
H-Index
1
About
Xinyang Xiong is a rising researcher at the forefront of autonomous underwater vehicle (AUV) technology, with a primary focus on intelligent control systems and deep reinforcement learning. His most influential work, "Position-based acoustic visual servo control for docking of autonomous underwater vehicle using deep reinforcement learning" (2025), has already garnered 16 citations—a strong indicator of its early impact in the field. In this landmark study, Xiong pioneered a novel approach that integrates acoustic and visual sensing with reinforcement learning algorithms, enabling AUVs to perform precise docking maneuvers in complex underwater environments without human intervention. This contribution addresses a critical challenge in marine robotics: achieving reliable, autonomous station-keeping and docking for long-duration missions. Xiong's work bridges the gap between simulation-based learning and real-world deployment, offering a scalable solution for applications ranging from oceanographic monitoring to offshore infrastructure inspection. By demonstrating how deep reinforcement learning can overcome the limitations of traditional control methods in dynamic, low-visibility underwater settings, Xiong has established himself as a key innovator in intelligent marine systems. His research not only advances the autonomy of underwater robots but also opens new pathways for adaptive, sensor-fusion-based control in extreme environments.
Research Focus
Key Achievements
Top Papers
- 1