An Active Perception Framework for Autonomous Underwater Vehicle Navigation Under Sensor Constraints

Inertial navigation for autonomous underwater vehicles (AUVs) is challenging because of the drift error caused by the noise and measurement errors of inertial sensors, typically packaged as an inertial measurement unit (IMU), integrated over time. To mitigate the drift error, recent AUV state estimation approaches incorporate external references or environmental information obtained from exteroceptive sensors, with increased costs and limited operational domains. For improved navigation under sensor constraints, this article proposes an active perception framework that exploits vehicle motion to estimate the flow state together with the vehicle state using IMU and depth sensors only. The proposed framework uses the estimated flow state as external information to improve vehicle state estimation. We construct a linear time-varying system for the flow state, separated from a nonlinear system for the vehicle state. This formulation allows us to analyze uniform complete observability for the flow state, which is found to depend on vehicle motion. Then, along with vehicle and flow state estimators, we design a vehicle controller to enable vehicle motion to maximize an information metric pertaining to estimation performance based on either observability or constructability Gramian for the flow state. The proposed framework is validated through simulations for a case study with a vehicle descending through the water column in a time-varying flow field. The effectiveness of the framework is demonstrated by comparing results obtained from its four implementations with those from baseline approaches without active perception.

I. Introduction

Autonomous underwater vehicles (AUVs) have significantly advanced in their capability of collecting data for environmental monitoring and exploration in coastal areas and deep oceans [1]. However, since the global positioning system (GPS) is not available underwater, AUV navigation has been challenging. A conventional approach for underwater navigation is inertial navigation based on dead reckoning using inertial sensors, typically packaged as an inertial measurement unit (IMU), that is, the position and orientation of the vehicle are calculated by integrating linear acceleration and angular velocity measurements from an IMU. However, due to the sensor noise and measurement errors integrated over time, inertial navigation suffers from the position error that grows over the course of the mission unless the error, also known as the drift error, is corrected using external references, e.g., through GPS updates.