@article{Lei2025, 
author = {Lei Lei and Xi Chen and Ben M. Chen},
title = {Environment Uncertainty-Aware Dynamic Modeling for Autonomous Deep-Sea Robots},
year = {2025},
journal = {Unmanned Systems},
volume = {13},
number = {5},
pages = {1295-1306},
keywords = {neural network, dynamic modeling, ocean exploration, Unmanned systems, deep-sea exploration},
url = {https://www.sciopen.com/article/10.1142/S2301385025440029},
doi = {10.1142/S2301385025440029},
abstract = {The complexity and variability of the deep-sea environment present significant challenges for autonomous underwater robots, particularly in dynamic modeling considering environmental disturbances. This paper presents a novel environment uncertainty-aware dynamic modeling approach for autonomous deep-sea robots. First, a robot multibody dynamic model is established under ideal environmental conditions. Then, environmental disturbances, including pressure, temperature, and density, are incorporated to capture their environment–robot coupling effects. Finally, a neural network compensator is designed to predict pose deviations caused by uncertain ocean disturbances. Experimental studies under deep-sea conditions show that the proposed model can accurately predict the motion state of the deep-sea robot with most depth errors within 5 m and pitch angle errors within 0.1 rad, providing a solid foundation for future autonomous deep-sea robot motion control and autonomous navigation.}
}