Unmanned Ground Vehicles for Continuous Crop Monitoring in Agriculture: Assessing the Readiness of Current ICT Technology

Machines, Год журнала: 2024, Номер 12(11), С. 750 - 750

Опубликована: Окт. 23, 2024

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions yielding economic, environmental, health benefits. The work organization modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, report experts in real time. This review evaluates current state technology determine if it supports autonomous, focus on shifting from traditional cloud-based approaches, where data are sent remote computers deferred processing, a hybrid design emphasizing edge computing real-time analysis field. Key aspects considered include algorithms in-field navigation, AIoT models detecting agricultural emergencies, advanced devices that capable managing sensors, collecting data, performing deep learning inference, ensuring precise mapping sending alert reports minimal intervention. State-of-the-art research development suggest general, necessarily crop-specific, prototypes fully UGVs now at hand. Additionally, demand low-power consumption affordable solutions be practically addressed.

Язык: Английский

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Exploring advancements and emerging trends in robotic swarm coordination and control of swarm flying robots: A review

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, Год журнала: 2024, Номер unknown

Опубликована: Сен. 22, 2024

Swarm Robotics (SR) is an interdisciplinary field that rapidly advancing to address complex industrial challenges. This paper provides a comprehensive review of recent advancements and emerging trends in SR, with specific focus on the coordination control Flying Robots (SFRs). The motivation behind this explore scalable robust solutions for SFRs enhance their performance adaptability across various applications. Key objectives include examining characteristics essential behaviors analyzing challenges so lutions implementing SR (FRs), highlighting current future research directions. delves into critical areas such as multiple robot path planning, Intelligence (SI), combinatorial optimization, formation flying using SFR. Special attention given techniques, including GPS-denied environments, underscore significance SR. also addresses ethical, privacy, security considerations, emphasizing importance responsible practices development. Major takeaways from identification key technical potential SFR, exploration SI algorithms, directions necessary fully realizing technologies. By offering detailed insights state-of-the-art its implications, serves foundational guide studies dynamic promising domain swarm robotics.

Язык: Английский

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A Review on Deep Learning for UAV Absolute Visual Localization

Drones, Год журнала: 2024, Номер 8(11), С. 622 - 622

Опубликована: Окт. 29, 2024

In the past few years, use of Unmanned Aerial Vehicles (UAVs) has expanded and now reached mainstream levels for applications such as infrastructure inspection, agriculture, transport, security, entertainment, real estate, environmental conservation, search rescue, even insurance. This surge in adoption can be attributed to UAV ecosystem’s maturation, which not only made these devices more accessible cost effective but also significantly enhanced their operational capabilities terms flight duration embedded computing power. conjunction with developments, research on Absolute Visual Localization (AVL) seen a resurgence driven by introduction deep learning field. These new approaches have improved localization solutions comparison previous generation based traditional computer vision feature extractors. paper conducts an extensive review literature learning-based methods AVL, covering significant advancements since 2019. It retraces key developments that led rise provides in-depth analysis related sources Inertial Measurement Units (IMUs) Global Navigation Satellite Systems (GNSSs), highlighting limitations advantages integration AVL. The concludes current challenges proposes future directions guide further work

Язык: Английский

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Mission Results: Creating a 3D Map of a Very High Radiation Confined Space Using the LiDAR-Equipped Elios 3 Drone

Nuclear Science and Engineering, Год журнала: 2024, Номер unknown, С. 1 - 13

Опубликована: Июнь 24, 2024

A prototype configuration of an Elios 3 indoor inspection drone, made by Flyability, comprising a lightweight light detection and ranging (LiDAR) system wide-range, electronic dosimeter was developed tested to quickly measure radiation levels collect three-dimensional (3D) spatial data from within very high nuclear waste storage facility at the U.S. Department Energy−operated Idaho National Laboratory (INL) site.

Язык: Английский

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Enhanced UAV localization in GPS-denied environments using acoustic TDOA and EKF integration

Intelligent Service Robotics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 27, 2025

Язык: Английский

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GrapeSLAM: UAV-based monocular visual dataset for SLAM, SfM and 3D reconstruction with trajectories under challenging illumination conditions

Data in Brief, Год журнала: 2025, Номер unknown, С. 111495 - 111495

Опубликована: Март 1, 2025

SLAM (Simultaneous Localization and Mapping) is an efficient method for robot to percept surrendings make decisions, especially robots in agricultural scenarios. Perception path planning automatic way crucial precision agriculture. However, there are limited public datasets implement develop robotic algorithms environments. Therefore, we collected dataset "GrapeSLAM". The ``GrapeSLAM'' comprises video data from vineyards support robotics research. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) capturing videos under different illumination conditions, (2) trajectories of the UAV during each flight by RTK IMU. used was Phantom 4 RTK, equipped with a high resolution camera, flying at around 1 3 meters above ground level.

Язык: Английский

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Target Enclosing Control of Symmetric Unmanned Aerial Vehicle Swarms Based on Crowd Entropy

Symmetry, Год журнала: 2025, Номер 17(4), С. 552 - 552

Опубликована: Апрель 4, 2025

Drone swarms often need to fly cooperatively in complex spaces filled with multiple obstacles. In such scenarios, they must meet the requirements of both external obstacle avoidance and internal collision while maintaining a certain topological configuration among individuals. This easily leads problems as congestion, oscillation, poor stability, including being out control. Thus, it is essential measure system-wide regulate autonomous cooperative evolution swarms, enhance their adaptation environmental changes. To solve this problem, using symmetric unmanned aerial vehicle (UAV) swarm research object, group entropy measurement theory for stability drone proposed. We introduce an entropy-based metric motion consistency. serves fitness index individual collaboration, enabling adaptive adjustment coherence under multi-obstacle conditions. Finally, simulation experiments are conducted verify effectiveness established algorithm.

Язык: Английский

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Fundamentals of Drone Navigation and the Role of Computer Vision

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 129 - 166

Опубликована: Март 28, 2025

Drone navigation is based on precise for efficient and secure performance delivery, surveillance, or rescue. Traditional GPS, inertial measurement units, magnetometers provides good guidance but inefficient in conditions with weakened signals unpredictable obstacles. Computer vision changing this. By equipping drones to perceive understand visual information about their surrounding space, it makes decision-making independent, allows better past obstacles, builds real-time maps. Object detection, optical flow, SLAM are some techniques being applied aerial robotics today. Vision complemented enhanced when combined other sensors like LiDAR making feasible complex terrains. However, processing high volumes of data remains a challenge. Advances edge computing AI-driven perception helping overcome these limitations, bringing faster more onboard processing.

Язык: Английский

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Benchmarking of monocular camera UAV-based localization and mapping methods in vineyards

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 227, С. 109661 - 109661

Опубликована: Ноя. 15, 2024

Язык: Английский

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Key-Region-Based UAV Visual Navigation

˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences, Год журнала: 2024, Номер XLVIII-2-2024, С. 173 - 179

Опубликована: Июнь 11, 2024

Abstract. Visual navigation has recently seen significant developments with the rise in autonomous navigation. Keypoint-based mapping and localization served as a reliable method for many applications, but push to run more applications on less expensive hardware becomes extremely limiting. In this paper, we present novel approach visual geolocalization that improves landmark detection reliability while reducing reference map complexity. Similar prior techniques, use process of point based matching schemes solve image-to-map transform. The critical difference is object identify key-regions instead keypoints. During an initial flight are mapped into identity dictionary their geolocations few-shot learning encoded descriptors. Then subsequent flights, detected matched using re-identification. Using identified vehicles key-regions, results show proposed key-region produces GPS like maintaining higher resilience image noise compared keypoint-based techniques.

Язык: Английский

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