Inverse Dynamics Modeling as a State Estimation Aid for Underwater Robots Navigation

Abstract

Reliable navigation and localization systems are crucial for Autonomous Underwater Vehicles (AUVs). The limitations of electromagnetic signals underwater make the use of the Global Positioning System (GPS) impractical. As a result, real-time positioning often relies on expensive sensors like the Doppler Velocity Log (DVL) or acoustic ranging, integrated through sensor fusion techniques. This study investigates and compares three inverse dynamic models designed to estimate the velocity of AUVs using cost-effective Inertial Measurement Units (IMUs) and a model of thrust output based on Pulse Width Modulation (PWM) signals. These velocity estimates are then integrated with an Extended Kalman Filter (EKF) to determine the AUV’s state, and results are validated against ground truth data. Our findings demonstrate the feasibility and effectiveness of these models in providing a reliable and economical solution for low-cost AUV navigation, which is particularly interesting in swarm applications.