Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle

2020

Energies

Unmanned Surface Vehicles (USV) are increasingly used to perform numerous tasks connected with measurements in inland waters and seas. One of such target applications is hydrography, where traditional (manned) bathymetric measurements are increasingly often realized by unmanned surface vehicles. This pertains especially to restricted or hardly navigable waters, in which execution of hydrographic surveys with the use of USVs requires precise maneuvering. Bathymetric measurements should be realized in a way that makes it possible to determine the waterbody’s depth as precisely as possible, and this requires high-precision in navigating along planned sounding profiles. This paper presents research that aimed to determine the accuracy of unmanned surface vehicle steering in autonomous mode (with a Proportional-Integral-Derivative (PID) controller) along planned hydrographic profiles. During the measurements, a high-precision Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) positioning system based on a GNSS reference station network (positioning accuracy: 1–2 cm, p = 0.95) and a magnetic compass with the stability of course maintenance of 1°–3° Root Mean Square (RMS) were used. For the purpose of evaluating the accuracy of the vessel’s path following along sounding profiles, the cross track error (XTE) measure, i.e., the distance between an USV’s position and the hydrographic profile, calculated transversely to the course, was proposed. The tests were compared with earlier measurements taken by other unmanned surface vehicles, which followed the exact same profiles with the use of much simpler and low-cost multi-GNSS receiver (positioning accuracy: 2–2.5 m or better, p = 0.50), supported with a Fluxgate magnetic compass with a high course measurement accuracy of 0.3° (p = 0.50 at 30 m/s). The research has shown that despite the considerable difference in the positioning accuracy of both devices and incomparably different costs of both solutions, the authors proved that the use of the GNSS RTK positioning system, as opposed to a multi-GNSS system supported with a Fluxgate magnetic compass, influences the precision of USV following sounding profiles to an insignificant extent.

mentioning

confidence: 70%

Assessment of the Steering Precision of a Hydrographic USV along Sounding Profiles Using a High-Precision GNSS RTK Receiver Supported Autopilot

Marchel

2020

Energies

“…The analyses performed indicate that currently the most effective and optimal method from the point of view of accuracy, coverage, and rate is the realization of bathymetric surveys in ultra-shallow waters (at depths of less than 1 m) using an USV on which a multi-GNSS receiver and a miniature SBES or a MultiBeam EchoSounder (MBES) can be mounted [71][72][73][74][75]. The measurements performed in the littoral zone can be supplemented with the use of an UAV equipped with a high-resolution camera [76][77][78][79][80].…”

Section: Discussionmentioning

confidence: 99%

Methodology for Carrying out Measurements of the Tombolo Geomorphic Landform Using Unmanned Aerial and Surface Vehicles near Sopot Pier, Poland

Lewicka

et al. 2020

JMSE

The human impact on the ecosystem has been particularly evident in the last century; it transforms the Earth’s surface on an unprecedented scale and brings about irreversible changes. One example is an oceanographic phenomenon known as a tombolo, i.e., a narrow belt connecting the mainland with an island lying near the shore formed as a result of sand and gravel being deposited by sea currents. The phenomenon contributes to an increase in the biogenic substance content in the littoral zone, which leads to increased cyanobacteria blooming in the summer period. Moreover, the debris accumulation in the littoral zone results in the mud formation, which makes the beach landscape less attractive. One of the main features of the tombolo phenomenon is its variability of shape, which includes the form of both the shore and the seabed adjacent to it. Therefore, to describe its size and spatio-temporal variability, it is necessary to apply methods for geodetic (the land) and hydrographic (the sea) measurements that can be carried out in different ways. The aim of the paper is to present the methodology for carrying out measurements of the tombolo oceanographic phenomenon using Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV) on the example of a waterbody adjacent to the Sopot pier. It also presents the results of surveys carried out in November 2019 within this area. The study demonstrated that the integration of two measuring devices whose development began in the second decade of the 20th century, i.e., UAVs and USVs, enables accurate (even up to several centimeters) and reliable determination of the scale and variability of the phenomena occurring in the littoral zone.

“…Other authors have developed solutions based on machine learning (ML) or deep learning (DL): Algabri and Choi, [ 28 ] present a method to detect and track people in indoor environments, whereas Qiu et al [ 29 ] focused on different types of moving obstacles in outdoor environments. More recently, other authors, Chang et al [ 30 ], Yang et al [ 31 ] and Qiu et al [ 32 ] have developed solutions for obstacles detection for self-driving cars. However, the main drawback of these solutions based on ML/DL is that, most of them, require a lot of training and thus existing data.…”

Section: Introductionmentioning

confidence: 99%

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

González-de-Soto

Mora

Martín-Jiménez

et al. 2020

Sensors

A new roadway eventual obstacle detection system based on computer vision is described and evaluated. This system uses low-cost hardware and open-source software to detect and classify moving elements in roads using infra-red and colour video images as input data. This solution represents an important advancement to prevent road accidents due to eventual obstacles which have considerably increased in the past decades, mainly with wildlife. The experimental evaluation of the system demonstrated that the proposed solution detects and classifies correctly different types of moving obstacles on roads, working robustly under different weather and illumination conditions.