Review on Type of Sensors and Detection Method of Anti-Collision System of Unmanned Aerial Vehicle

Chandran, Navaneetha Krishna; Sultan, Mohammed Thariq Hameed; Łukaszewicz, Andrzej; Shahar, Farah Syazwani; Holovatyy, Andriy; Giernacki, Wojciech

doi:10.3390/s23156810

Cited by 9 publications

(8 citation statements)

References 81 publications

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“…Additionally, multiple sensor types and their applications for UAV collision avoidance are discussed. Reference [42] reviews the sensors and detection methods used in anti-collision systems. Classification, control applications, and future industry and research interest areas for UAVs are present in [32].…”

Section: A Related Surveysmentioning

confidence: 99%

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.

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Section: A Related Surveysmentioning

confidence: 99%

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…This encompasses options such as using active LiDAR sensors or relying on passive remote sensing sensors like cameras (Gupta and Shukla, 2018;Chandran et al, 2023;Telli et al, 2023).…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

The cross-disciplinary influence of aerial measurement techniques: Exploring archaeological studies through photogrammetry and LiDAR

FILIP-GHERMAN,

BRUMA,

SABOU

et al. 2024

Nova Geodesia

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This research aimed to evaluate the efficacy of contemporary digitization methods for archaeological sites, specifically focusing on aerial approaches. The study is concentrated on the examination of two primary methods: LiDAR sensor and photogrammetry. The chosen case study revolves around the Camp of the V Macedonica Legion, a pivotal feature of the former Roman city now known as Turda, a city in Cluj County, Transylvania, Romania. Through analysis and comparison, the paper revealed that each of these aerial 3D scanning techniques possesses unique strengths, which, when combined, offer a comprehensive approach to archaeological digitization. These complementary attributes must be carefully considered and integrated considering the specific requirements and objectives of the archaeological project at hand when selecting the appropriate method. Furthermore, the research underscores the pivotal role of presenting archaeology in 3D, emphasizing its significant impact on both public and academic audiences. Achieving this presentation necessitates the utilization of specialized software for modelling, rendering, and animating objects of interest, thus enhancing the accessibility and engagement of archaeological findings. The comprehensive findings of this study demonstrate the vast potential offered by aerial 3D scans in the field of archaeology. Moreover, it serves as a potential call for the meticulous selection of the analysis method, recognizing its crucial role as a valuable tool for researchers and archaeologists. By leveraging 3D technologies in their activities, professionals in the field can significantly enhance the accuracy, depth, and accessibility of their archaeological investigations, thereby enriching our understanding of the past.

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“…Moreover, obstacle detection utilizes image-based, sensor-based, and hybrid methods. Image-based approaches include analyzing appearance, motion, depth, and size changes, while sensor-based techniques rely on devices like LiDAR and ultrasonic sensors [11]. Recently, deep learning and edge computing have become popular for real-time obstacle detection and tracking, offering improved accuracy.…”

Section: Introductionmentioning

confidence: 99%

YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility

Ywet,

Maw,

Nguyen

et al. 2024

Aerospace

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Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, enhancing safety and efficiency. This study introduces a YOLOTransfer-DT framework specifically designed for Artificial Intelligence (AI) training in simulated environments, focusing on its utility for experiential learning in realistic scenarios. The framework’s objective is to augment AI training, particularly in developing an object detection system that employs visual tasks for proactive conflict identification and mission support, leveraging deep and transfer learning techniques. The proposed methodology combines real-time detection, transfer learning, and a novel mix-up process for environmental data extraction, tested rigorously in realistic simulations. Findings validate the use of existing deep learning models for real-time object recognition in similar conditions. This research underscores the value of the ODT framework in bridging the gap between virtual and actual environments, highlighting the safety and cost-effectiveness of virtual testing. This adaptable framework facilitates extensive experimentation and training, demonstrating its potential as a foundation for advanced detection techniques in UAM.

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Review on Type of Sensors and Detection Method of Anti-Collision System of Unmanned Aerial Vehicle

Cited by 9 publications

References 81 publications

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

The cross-disciplinary influence of aerial measurement techniques: Exploring archaeological studies through photogrammetry and LiDAR

YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility

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