The Polarized Light Field Enables Underwater Unmanned Vehicle Bionic Autonomous Navigation and Automatic Control

Abstract

In response to the critical need for autonomous navigation capabilities of underwater vehicles independent of satellites, this paper studies a novel navigation and control method based on underwater polarization patterns. We propose an underwater course angle measurement algorithm and develop underwater polarization detection equipment. By establishing the automatic control model of an ROV (Remote Operated Vehicle) with polarization information, we develop a strapdown navigation method combining polarization and inertial information. We verify the feasibility of angle measurement based on polarization in the water tank. The measurement accuracy of polarization azimuth is less than 0.69°. Next, we conduct ROV navigation at different water depths in a real underwater environment. At a depth of 5 m, the MSE (Mean Square Error) and SD (Standard Deviation) of angle error are 16.57° and 4.07°, respectively. Underwater navigation accuracy of traveling 100 m is better than 5 m within a depth of 5 m. Key technologies such as underwater polarization detection, multi-source information fusion, and the ROV automatic control model with polarization have been broken through. This method can effectively improve ROV underwater work efficiency and accuracy.

Study on the Polarization Pattern Induced by Wavy Water Surfaces

“…Remote Sens. 2023, 15, 4565 11 of 15 contrast, AOP patterns are more robust in general underwater environments and have better application potential [30,31]. Considering that the Cox-Munk wave model has a good simulation effect under moderate wind speed, the model in this paper has a high accuracy under moderate wind speed.…”

“…We find that the AOP patterns are in good agreement with the measured results and the value of AOP is about 0 • when the observed azimuth is 90 • . In contrast, AOP patterns are more robust in general underwater environments and have better application potential [30,31]. Considering that the Cox-Munk wave model has a good simulation effect under moderate wind speed, the model in this paper has a high accuracy under moderate wind speed.…”

Passive Polarized Vision for Autonomous Vehicles: A Review

“…The accuracy obtained was a 6 m error for every 1km traveled at depths ranging from 2 m to 20 m. A recent study based on deep learning reached geolocation accuracies of 55 km at a depth of 8m and 255 km at a depth of 50 m even in low-visibility waters [ 265 ]. Accurate heading estimation of an autonomous underwater vehicle was recently obtained by merging inertial and polarization information [ 266 , 267 ]. A standard deviation (SD) error of 0.83° was reached here at a depth of 2 m in real oceans in calm seas [ 267 ].…”

“…A standard deviation (SD) error of 0.83° was reached here at a depth of 2 m in real oceans in calm seas [ 267 ]. Cheng et al confirmed the deterioration of the heading measurement as a function of depth from 0.93° at a depth of 1 m to 4.07° at a depth of 5 m [ 266 ].…”