Autonomous Underwater Vehicle Navigation and Localization in Underwater Environments

Autonomous underwater vehicles (AUVs) are programmable robotic vehicles used for various tasks, including the military, demining, oceanography surveys, ocean environments and bathymetric data collection in marine. This project considers the vehicle navigation problem for an autonomous underwater veh

Project Title

Autonomous Underwater Vehicle Navigation and Localization in Underwater Environments

Project Area of Specialization

Robotics

Project Summary

Autonomous underwater vehicles (AUVs) are programmable robotic vehicles used for various tasks, including the military, demining, oceanography surveys, ocean environments and bathymetric data collection in marine. This project considers the vehicle navigation problem for an autonomous underwater vehicle (AUV) with six degrees of freedom. We approach this problem using an error state formulation of the Kalman filter. Integration of the vehicle’s high-rate inertial measurement unit’s (IMU’s) accelerometers and gyros allow time propagation while other sensors provide measurement corrections. The low-rate aiding sensors include a Doppler velocity log (DVL), an acoustic long baseline (LBL) system that provides round-trip travel times from known locations, a pressure sensor for aiding depth, and an attitude sensor. Measurements correct
the filter independently as they arrive, and as such, the filter is not dependent on the arrival of any particular measurement. We propose novel tightly coupled techniques for the incorporation of the LBL and DVL measurements. In particular, the LBL correction properly accounts for the error state throughout the measurement cycle via the state transition matrix. Alternate tightly coupled approaches ignore the error state, utilizing only the navigation state to account for the physical latencies in the measurement cycle. These approaches account for neither the uncertainty of vehicle trajectory between interrogation and reply, nor the error state at interrogation. The navigation system also estimates critical sensor calibration parameters to improve performance.

Project Objectives

AUV navigation and localization is a challenging problem due primarily to the rapid attenuation of higher frequency signals and the unstructured nature of the undersea environment.
Above water, most autonomous systems rely on radio or spread-spectrum communications and global positioning. However, underwater, such signals propagate only short distances and acoustic-based sensors and communications perform better. AUV navigation and localization is the main object of this system.

Project Implementation Method

This project presents an error state formulation of a navigation algorithm for an underwater vehicle. We derive the system kinematics model, then augment this model with unknown parameters from the sensor models to formulate the augmented system equations. Mechanization equations represent our estimate of the true augmented system equations, where the difference between the true and mechanization equations is the error state system. We design a Kalman filter to estimate this error state via measurement residuals from aiding sensors. The IMU propagates the navigation state, error state, and error state covariance through time, while aiding sensors provide corrections. Aiding sensors include an attitude sensor, DVL, LBL system, and a pressure sensor. Of particular interest are the measurement correction routines for the DVL and LBL system. The DVL correction utilizes a tightly coupled approach to alleviate the need for bottom lock. When fewer than three beams respond, the filter can still incorporate valuable information. Hardware, Simulation results and observability analysis will show the incorporation of a single-beam measurement is far superior to a total dropout. Unaided inertial performance quickly degrades. A loosely coupled instrument frame approach would not be able to incorporate less than three beams, and thus would be equivalent to a total dropout. Further, individual beam corrections have statistically uncorrelated noise and permit individual beam validation. The result is a more robust system.

Benefits of the Project

• Highly stable and agile.
• Excellent navigational algorithms for providing accurate target location.
• It has low deployment costs as it does not require bulky and complex support equipment.
• High reliability and improved data quality.

Technical Details of Final Deliverable

The IMU
propagates the navigation state, error state, and error state covariance through time, while aiding sensors provide corrections. Aiding sensors include an attitude sensor, DVL, LBL system, and a pressure sensor. Of particular interest are the measurement correction routines for the DVL and LBL system. The DVL correction utilizes a tightly coupled approach to alleviate the need for bottom lock. When fewer than three beams respond, the filter can still incorporate valuable information. Hardware, Simulation results and observability analysis will show the incorporation of a single-beam measurement is far superior to a total dropout. Unaided inertial performance quickly degrades. A loosely coupled instrument frame approach would not be able to incorporate less than three beams, and thus would be equivalent to a total dropout. Further, individual beam corrections have statistically uncorrelated noise and permit individual beam validation. The result is a more robust system.The LBL correction utilizes a tightly coupled approach that
properly accounts for the error state throughout the measurement cycle via the state transition matrix. During the interrogation cycle, the navigation algorithm propagates error state separate from the navigation state. All measurement corrections, including those from other aiding sensors, accumulate into the error state. The navigation state continues to propagate via integration of the inertial measurements, but does not incorporate aiding measurement corrections until the completion of the
LBL measurement cycle. The state transition matrix accumulates over the entire measurement cycle to relate the error state at interrogation to that at the final response. Alternate tightly coupled approaches ignore the error state, utilizing only the navigation state to account for the physical latencies in the measurement cycle. These approaches do not account for the uncertainty of vehicle trajectory between interrogation and reply, or the error state at interrogation. Simulation and experimental results will confirm our approach and implementation. Monte Carlo simulations of partial DVL and LBL acoustic failures indicate that partial failures have little or no effect on overall performance, provided that the vehicle continues to maneuver. Prolonged failure of multiple DVL beams or LBL transponders will degrade performance; however, the filter will continue to incorporate all available information.

Final Deliverable of the Project

Hardware System

Core Industry

Education

Other Industries

Transportation , Security , Telecommunication

Core Technology

Robotics

Other Technologies

Clean Tech

Sustainable Development Goals

Quality Education, Industry, Innovation and Infrastructure

Required Resources

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