System Architecture for Real-Time Surface Inspection Using Multiple UAVs

Hoang, Van Truong; Phung, Manh Duong; Dinh, Tran Hiep; Ha, Q.P.

doi:10.1109/jsyst.2019.2922290

Cited by 48 publications

(18 citation statements)

References 44 publications

(74 reference statements)

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“…The experiment is carried out in the search area of 60 m × 60 m located in a park in Sydney. The UAV used is a 3DR Solo drone with a control architecture developed for infrastructure inspection [36] that can be controlled via a ground control station (GCS) software named Mission Planner. The detection sensor is a Hero 4 camera attached to the drone via a three-axis gimble responsible for adjusting and stabilizing the camera.…”

Section: Methodsmentioning

confidence: 99%

Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs

Phung¹

2020

Preprint

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This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.

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Section: Methodsmentioning

confidence: 99%

Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs

Phung¹

2020

Preprint

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“…[27], [28]). In [15], a system for infrastructure monitoring based on the use of multiple UAVs is proposed: the paper is focused, mostly, on the real-time path planning and on the introduction of a classical yet effective image segmentation method based on iterative thresholding for crack detection and quantification. Moving from the domain of civil buildings and infrastructures to the domain of CH, specific features and constraints must be considered: for instance, the physical and chemical properties of the material inspected can significantly vary, even in the same structure; the condition in which data are collected may be challenging (with area difficult to access, or variable environmental conditions); the methods used for acquiring data and monitoring relevant parameters should be not invasive.…”

Section: State Of the Artmentioning

confidence: 99%

Monitoring Ancient Buildings: Real Deployment of an IoT System Enhanced by UAVs and Virtual Reality

et al. 2020

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The historical buildings of a nation are the tangible signs of its history and culture. Their preservation deserves considerable attention, being of primary importance from a historical, cultural, and economic point of view. Having a scalable and reliable monitoring system plays an important role in the Structural Health Monitoring (SHM): therefore, this paper proposes an Internet of Things (IoT) architecture for a remote monitoring system that is able to integrate, through the Virtual Reality (VR) paradigm, the environmental and mechanical data acquired by a wireless sensor network set on three ancient buildings with the images and context information acquired by an Unmanned Aerial Vehicle (UAV). Moreover, the information provided by the UAV allows to promptly inspect the critical structural damage, such as the patterns of cracks in the structural components of the building being monitored. Our approach opens new scenarios to support SHM activities, because an operator can interact with real-time data retrieved from a Wireless Sensor Network (WSN) by means of the VR environment. INDEX TERMS Internet of Things, structural health monitoring, virtual reality, unmanned aerial vehicles, condition monitoring.

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“…However, many previous algorithms suffer from excessive computational overheads. More relevant to this work, [16] has used multiple UAVs for infrastructure inspection, using a triangular formation for the UAV swarm positioning. Those researchers exploited an angle-coded particle swarm optimization approach to swarm path planning.…”

Section: A Uav Swarmsmentioning

confidence: 99%

Towards Persistent Surveillance and Reconnaissance Using a Connected Swarm of Multiple UAVs

et al. 2020

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In situations where surveillance or communication infrastructure has collapsed, it is important to keep monitoring affected areas. We leverage unmanned aerial vehicles (UAVs) to collect and provide up-to-date on-site information to a data consumer in an efficient way, for later complete yet agile analysis. We propose a distributed dynamic data collection scheme for persistent surveillance and reconnaissance, using a swarm of connected UAVs with two phases of operation: 1) network formation; and 2) UAV traversal of a region of interest. The main task of a UAV is to continuously collect data within its sensing range, while the UAV swarm travels along the calculated paths. When UAVs are newly connected to form a swarm, or disconnected from an already-formed swarm, a formation phase begins. In the formation phase, UAVs become a single group and produce a compact, dynamically alternating formation called DiagonalX to cover broad areas, including boundary parts, in a fair and effective manner. During the traversal phase, each UAV swarm finds a simple yet efficient navigation path based on data freshness to cover sub-areas and continuously obtain up-to-date information evenly throughout the whole region of interest. Simulation experiments confirm that both formation and traversal procedures perform essential tasks in a distributed manner, while maintaining better data freshness than other counterpart algorithms, with a freshness factor of up to 5.77, and reasonable overheads. An additional feature, a dynamically aperiodic formation change, achieves a more stable performance.

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System Architecture for Real-Time Surface Inspection Using Multiple UAVs

Cited by 48 publications

References 44 publications

Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs

Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs

Monitoring Ancient Buildings: Real Deployment of an IoT System Enhanced by UAVs and Virtual Reality

Towards Persistent Surveillance and Reconnaissance Using a Connected Swarm of Multiple UAVs

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