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Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 12515

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Dr. Mariusz Specht

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Department of Transport and Logistics, Gdynia Maritime University, Morska 81-87, 81-225 Gdynia, Poland
Interests: global navigation satellite systems; civil engineering; geomatics; navigation; hydrography; mapping; earth observation; geospatial science; geoinformation; spatial analysis; geodesy; applied mathematics
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Dr. Łukasz Marchel

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Institute of Navigation and Maritime Hydrography, Polish Naval Academy, 81-103 Gdynia, Poland
Interests: UAV; USV; navigation; geodesy; hydrography; positioning systems; GNSS; mapping; failure analysis; transportation; civil engineering; digital mapping; geoinformation; spatial analysis; system modeling; information and communication technology; GIS

Special Issue Information

Dear Colleagues,

The beginning of the 21st century is the era of dynamic growth of unmanned systems. An unmanned system (US) or vehicle (UV) is a vehicle without a person on board. Depending on the environment in which they operate, the following types of unmanned systems can be distinguished: unmanned aerial vehicle (UAV), commonly known as drones, which are in the air; unmanned ground vehicle (UGV) on the ground; unmanned surface vehicle (USV) on the sea surface; and unmanned underwater vehicle (UUV) in the water column. Recently, research into the development of unmanned systems has been carried out in the field of autonomous vehicles that operate using Artificial Intelligence (AI)-powered navigation and operational software. The dynamically growing technological development of unmanned systems means that they are widely used in many areas of life, as well as for remote sensing.

Remote sensing research using unmanned vehicles is carried out in numerous fields, such as geodesy, geography, geophysics, and most Earth science disciplines (e.g., exploration geophysics, geology, glaciology, hydrology, meteorology, and oceanography). Potential topics include, but are not limited to, the following:

Application of remote sensing technologies for environmental monitoring;
Mobile robot navigation;
Remote sensing of the environment using unmanned systems;
Sensors used in unmanned systems;
Unmanned systems technology.

Dr. Mariusz Specht
Dr. Łukasz Marchel
Guest Editors

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Keywords

autonomous vehicle
drone
earth observation
remote sensing
sensors used in unmanned systems
unmanned aerial vehicle (UAV)
unmanned ground vehicle (UGV)
unmanned surface vehicle (USV)
unmanned system (US)
unmanned underwater vehicle (UUV)

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Related Special Issue

Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV) (Second Edition) in Remote Sensing

Published Papers (9 papers)

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Research

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22 pages, 56558 KiB

Open AccessArticle

An Improved Knowledge-Based Ground Moving Target Relocation Algorithm for a Lightweight Unmanned Aerial Vehicle-Borne Radar System

by Wencheng Liu, Yuan Zhang, Xuyang Ge, Yanlei Li, Yunlong Liu, Xiangxi Bu and Xingdong Liang

Remote Sens. 2025, 17(7), 1182; https://doi.org/10.3390/rs17071182 - 26 Mar 2025

Viewed by 267

Abstract

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground moving target indication (GMTI) radar systems has received great interest. In GMTI, moving target relocation is an essential requirement, because the positions of the moving targets are usually displaced. For a multichannel radar system, the position of moving targets can be accurately obtained by estimating their interferometric phase. However, the high position accuracy requirements of antennas and the computational resource requirements of algorithms limit the applications of relocation algorithms in UAV-borne GMTI radar systems. In addition, the clutter’s interferometric phase can be severely affected by the undesired phase error in the site. To overcome these issues, we propose an improved knowledge-based (KB) algorithm. In the algorithm, moving targets can be relocated by comparing their interferometric phase with the clutter’s phase. As for the undesired phase error, the algorithm first employs a random sample consensus (RANSAC) algorithm to iteratively filter the outliers. Compared with other classic relocation algorithms, the proposed algorithm shows better relocation accuracy and can be applied in real-time applications. The performance of the proposed improved KB algorithm was evaluated using both simulated and real experimental data. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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32 pages, 8125 KiB

Open AccessArticle

Real-Time Optimization Improved Model Predictive Control Trajectory Tracking for a Surface and Underwater Joint Observation System Based on Genetic Algorithm–Fuzzy Control

by Qichao Wu, Yunli Nie, Shengli Wang, Shihao Zhang, Tianze Wang and Yizhe Huang

Remote Sens. 2025, 17(5), 925; https://doi.org/10.3390/rs17050925 - 5 Mar 2025

Cited by 1 | Viewed by 543

Abstract

Aiming at the high-precision trajectory tracking problem of the new surface and underwater joint observation system (SUJOS) in the ocean remote sensing monitoring mission under complex sea conditions, especially at the problem of excessive tracking errors and slow convergence of actual trajectory oscillations caused by the wide range of angular changes in the motion trajectory, a real-time optimization improved model predictive control (IMPC) trajectory tracking method based on fuzzy control is proposed. Initially, the novel observation platform has been designed, and its mathematical model has been systematically established. In addition, this study optimizes the MPC trajectory tracking framework by integrating the least squares adaptive algorithm and the Extended Alternating Direction Method of Multipliers (EADMM). In addition, a fuzzy controller, optimized using a genetic algorithm, an output of real-time optimization coefficients, is employed to dynamically adjust and optimize the bias matrix within the objective function of the IMPC. Consequently, the real-time performance and accuracy of the system’s trajectory tracking are significantly enhanced. Ultimately, through comprehensive simulation and practical experimental verification, it is demonstrated that the real-time optimization IMPC algorithm exhibits commendable real-time and optimization performance, which markedly enhances the accuracy for trajectory tracking, and further validates the stability of the controller. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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19 pages, 8537 KiB

Open AccessArticle

Data-Driven Cooperative Localization Algorithm for Deep-Sea Landing Vehicles Under Track Slippage

by Zhenzhuo Wei, Wei Guo, Yanjun Lan, Ben Liu, Yu Sun and Sen Gao

Remote Sens. 2025, 17(5), 755; https://doi.org/10.3390/rs17050755 - 22 Feb 2025

Viewed by 463

Abstract

The deep-sea landing vehicle (DSLV) swarm exploration system is a novel platform for the detection of marine mineral resources. A high-precision cooperative localization system with Ultra-Short Baseline (USBL), Doppler Velocity Log (DVL), and electronic compass (EC) plays a vital role in the DSLV swarm exploration system. However, DVL measurements can be seriously interrupted due to the complex operational underwater environment, leading to unstable localization performance. The accuracy of the cooperative localization system could be further degraded by the persistent rubber track slippage during the vehicle’s movement over the soft seabed. In this study, a data-driven cooperative localization algorithm with a velocity prediction model is proposed to improve the positioning accuracy of DSLV under track slippage. First, a velocity prediction model for DVL measurements is constructed using multi-output least squares support vector regression (MLSSVR), and a genetic algorithm (GA) is further employed to optimize the model’s hyperparameters in order to enhance the robustness of the framework. Furthermore, the outputs of MLSSVR are fed into a DSLV position estimation framework based on the Unscented Kalman Filter (UKF) to improve localization accuracy in the presence of DVL failures. To validate the proposed method, the RecurDyn multibody dynamics simulation platform is applied for data synthesis, accounting for both the impact of the soft seabed and real-world motion simulation. The experimental results indicate that during DVL failure, the proposed algorithm can effectively compensate for the cooperative localization errors caused by track slippage, thereby significantly improving the accuracy and reliability of the DSLV cooperative localization system. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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26 pages, 11259 KiB

Open AccessArticle

Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism

by Ding Hu, Zihao Zheng, Yafei Liu, Chengkang Liu and Xiaoguo Zhang

Remote Sens. 2024, 16(23), 4585; https://doi.org/10.3390/rs16234585 - 6 Dec 2024

Viewed by 817

Abstract

The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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18 pages, 6699 KiB

Open AccessArticle

Prescribed-Time Dynamic Positioning Control for USV with Lumped Disturbances, Thruster Saturation and Prescribed Performance Constraints

by Bowen Sui, Jianqiang Zhang and Zhong Liu

Remote Sens. 2024, 16(22), 4142; https://doi.org/10.3390/rs16224142 - 6 Nov 2024

Viewed by 1159

Abstract

This work studies the dynamic positioning (DP) control issue of unmanned surface vessels subjected to thruster saturation, error constraints, and lumped disturbances composed of time-varying marine environmental disturbances and model parameter uncertainties. Combining the disturbance-accurate estimation technique and the prescribed performance control strategy, a novel prescribed-time DP control scheme is established to address this challenging problem. In particular, the prescribed-time lumped disturbance observer is designed to accurately estimate external marine disturbances, which guarantees that the estimation error converges to zero within a prescribed time. Subsequently, a prescribed performance control strategy is proposed to guarantee that the positioning errors of DP surface vessels with thruster saturation constraints meet the error constraints requirements within a prescribed time. Furthermore, an anti-windup compensator is presented to mitigate the thruster saturation and improve the robustness of the DP control system. The stability analysis demonstrates that all positioning errors of the closed-loop system can converge to predefined performance constraints within a prescribed time. Finally, the numerical simulation confirms the efficacy and superiority of the proposed PTDP scheme. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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21 pages, 13558 KiB

Open AccessArticle

A Low-Cost and Lightweight Real-Time Object-Detection Method Based on UAV Remote Sensing in Transportation Systems

by Ziye Liu, Chen Chen, Ziqin Huang, Yoong Choon Chang, Lei Liu and Qingqi Pei

Remote Sens. 2024, 16(19), 3712; https://doi.org/10.3390/rs16193712 - 5 Oct 2024

Cited by 7 | Viewed by 3533

Abstract

Accurate detection of transportation objects is pivotal for enhancing driving safety and operational efficiency. In the rapidly evolving domain of transportation systems, the utilization of unmanned aerial vehicles (UAVs) for low-altitude detection, leveraging remotely-sensed images and videos, has become increasingly vital. Addressing the growing demands for robust, real-time object-detection capabilities, this study introduces a lightweight, memory-efficient model specifically engineered for the constrained computational and power resources of UAV-embedded platforms. Incorporating the FasterNet-16 backbone, the model significantly enhances feature-processing efficiency, which is essential for real-time applications across diverse UAV operations. A novel multi-scale feature-fusion technique is employed to improve feature utilization while maintaining a compact architecture through passive integration methods. Extensive performance evaluations across various embedded platforms have demonstrated the model’s superior capabilities and robustness in real-time operations, thereby markedly advancing UAV deployment in crucial remote-sensing tasks and improving productivity and safety across multiple domains. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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24 pages, 1413 KiB

Open AccessArticle

Loop Detection Method Based on Neural Radiance Field BoW Model for Visual Inertial Navigation of UAVs

by Xiaoyue Zhang, Yue Cui, Yanchao Ren, Guodong Duan and Huanrui Zhang

Remote Sens. 2024, 16(16), 3038; https://doi.org/10.3390/rs16163038 - 19 Aug 2024

Viewed by 1152

Abstract

The loop closure detection (LCD) methods in Unmanned Aerial Vehicle (UAV) Visual Inertial Navigation System (VINS) are often affected by issues such as insufficient image texture information and limited observational perspectives, resulting in constrained UAV positioning accuracy and reduced capability to perform complex tasks. This study proposes a Bag-of-Words (BoW) LCD method based on Neural Radiance Field (NeRF), which estimates camera poses from existing images and achieves rapid scene reconstruction through NeRF. A method is designed to select virtual viewpoints and render images along the flight trajectory using a specific sampling approach to expand the limited observational angles, mitigating the impact of image blur and insufficient texture information at specific viewpoints while enlarging the loop closure candidate frames to improve the accuracy and success rate of LCD. Additionally, a BoW vector construction method that incorporates the importance of similar visual words and an adapted virtual image filtering and comprehensive scoring calculation method are designed to determine loop closures. Applied to VINS-Mono and ORB-SLAM3, and compared with the advanced BoW model LCDs of the two systems, results indicate that the NeRF-based BoW LCD method can detect more than 48% additional accurate loop closures, while the system’s navigation positioning error mean is reduced by over 46%, validating the effectiveness and superiority of the proposed method and demonstrating its significant importance for improving the navigation accuracy of VINS. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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Review

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26 pages, 4841 KiB

Open AccessReview

Methodology for Performing Bathymetric and Photogrammetric Measurements Using UAV and USV Vehicles in the Coastal Zone

by Mariusz Specht

Remote Sens. 2024, 16(17), 3328; https://doi.org/10.3390/rs16173328 - 8 Sep 2024

Cited by 8 | Viewed by 2116

Abstract

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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Other

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15 pages, 8391 KiB

Open AccessTechnical Note

Hydrographic Inspection Using a USV of a Harbour Bottom Deepened by the Periodic Actuation of SAR Vessel Propellers

by Cezary Specht and Dominika Śliwińska

Remote Sens. 2024, 16(14), 2522; https://doi.org/10.3390/rs16142522 - 9 Jul 2024

Viewed by 1254

Abstract

In contrast to classic hydrographic cutters, unmanned surface vehicles, due to their size, ease of transport and the equipment installed, enable the performance of quick and cost-effective bottom inspections in various water areas. Thanks to their shallow draught and high manoeuvrability, hydrographic drones are capable of the bathymetric exploration of shallow waters such as harbours, hydrotechnical structures and the areas where classic naval vessels could encounter implementation difficulties. The aim of this paper is to demonstrate, using a selected practical example, the specific ability of an unmanned surface vehicle (USV) to carry out the urgent and immediate inspection of the bottom of a specific water area. The freedom to move between restricted areas, the ease of transport and the satisfactory quality of the surveys make hydrographic drones ideal tools for projects of this type. The referenced study produced a bathymetric map of a section of the seabed adjacent to the quay at which a Search and Rescue (SAR) vessel is moored and regularly, at its permanent fixed location, actuates its propellers. The effect of its propellers is the local deepening of the bottom in two places. The research showed a local decrease in the depth from 5.5 m to less than 7 m, which may threaten the stability of the quay structure. In addition, it was noted that the washed bottom material had been moved approximately 10 m from the quay, causing shallowing in two places and reducing the depth from 5.5 m to 4.7 m. This study demonstrated that the use of USVs for applications of this type is very effective in terms of the implementation time and is economically justified. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing Application of Autonomous Unmanned Vehicles (UAV/UGV/USV/UUV))

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