Research on Embedded Model-Aided Autonomous Navigation for Miniature AUVs

The concept of full autonomous navigation for Autonomous Underwater Vehicles (AUVs) is motivated by the needs of small AUVs, such as those described in [1~3]. Traditional navigation technologies, such as acoustic positioning system and Global Positioning System (GPS), are usually difficult to use for miniature AUVs characterized by complicated working environments, strictly volume constraints and low cost, which to some extent limited the application field of small AUVs. This work proposes a model-embedded integration of two aiding information sources to enhance the performance of full autonomous navigation system based on inertial navigation system (INS). A new methodology is derived to embed the vehicle model (VM) and ambient flow field model into EKF filter and use the pseudo-sensors information to improve navigation precision. By integrating the vehicle model and flow field model into the extended Kalman filter (EKF), the EKF state vector is carefully selected to exploit the dynamic characteristics of the vehicle to the maximum extend.. Navigation aided by model-embedded has the potential to achieve higher positioning accuracy and reduce the amount of real-time navigation calculation, and it may enable other navigation objectives, such as navigation of small AUVs relative to a larger AUV. The Matlab/Simulink simulation tool was used to produce the motion trajectory and the simulation results demonstrate the effectiveness and universality of the proposed approach. It greatly improves the positioning accuracy by utilizing object’s kinetic properties to maximal extent, and the calculation for embedded object modelling is also simplified as a part of the EKF algorithm.