Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

Javaid, Shumaila; Saeed, Nasir; Qadir, Zakria; Fahim, Hamza; He, Bin; Song, Houbing; Bilal, Muhammad

doi:10.1109/tits.2023.3248841

Cited by 21 publications

(7 citation statements)

References 179 publications

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“…There are two main categories: multi-oriented object detection and classification and single object recognition. DL approaches for object detection and recognition and classification of images mainly focus on accurate object recognition (improving detection and recognition performance), speed of testing, training, computational processes, and accurate object classification (minimizing the error rate) [ 8 , 9 ].…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo

Khan

Fang

et al. 2023

Sensors

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Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.

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Section: Deep Learning Techniquesmentioning

confidence: 99%

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo

Khan

Fang

et al. 2023

Sensors

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“…Javaid et al [16] review collaborative communication technologies for UASs as a key enabler of multi-UAS systems. Furthermore, Wilson et al [17] review the sensors, communication technologies, and ML techniques commonly employed in UAS applications.…”

Section: Intelligent Vehicular Cyber-physical Systemsmentioning

confidence: 99%

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

2023

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Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems.

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“…Currently, there are three main methods for a huge-region remote-sensing mission: Single-UAV with multiple flights: As shown in Figure 1 b, this method means a single UAV covers the entire region with multiple flights [ 6 ]. Multi-UAV cooperation: This method employs multiple UAVs to simultaneously cover the entire region [ 7 ]. Figure 1 c shows a paradigm of this method.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-UAV cooperation: This method employs multiple UAVs to simultaneously cover the entire region [ 7 ]. Figure 1 c shows a paradigm of this method.…”

Section: Introductionmentioning

confidence: 99%

A PAD-Based Unmanned Aerial Vehichle Route Planning Scheme for Remote Sensing in Huge Regions

Shao,

Li,

Gao

et al. 2023

Sensors

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Unmanned aerial vehicles (UAVs) have been employed extensively for remote-sensing missions. However, due to their energy limitations, UAVs have a restricted flight operating time and spatial coverage, which makes remote sensing over huge regions that are out of UAV flight endurance and range challenging. PAD is an autonomous wireless charging station that might significantly increase the flying time of UAVs by recharging them in the air. In this work, we introduce PADs to simplify UAV-based remote sensing over a huge region, and then we explore the UAV route planning problem once PADs have been predeployed throughout a huge remote sensing region. A route planning scheme, named PAD-based remote sensing (PBRS), is proposed to solve the problem. The PBRS scheme first plans the UAV’s round-trip routes based on the location of the PADs and divides the whole target region into multiple PAD-based subregions. Between adjacent subregions, the UAV flight subroute is planned by determining piggyback points to minimize the total time for remote sensing. We demonstrate the effectiveness of the proposed scheme by conducting several sets of simulation experiments based on the digital orthophoto model of Hutou Village in Beibei District, Chongqing, China. The results show that the PBRS scheme can achieve excellent performance in three metrics of remote sensing duration, the number of trips to charging stations, and the data-storage rate in UAV remote-sensing missions over huge regions with predeployed PADs through effective planning of UAVs.

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Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

Cited by 21 publications

References 179 publications

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

A PAD-Based Unmanned Aerial Vehichle Route Planning Scheme for Remote Sensing in Huge Regions

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