Environment Uncertainty-Aware Dynamic Modeling for Autonomous Deep-Sea Robots

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.