Heuristic-driven strategy for boosting aerial photography with multi-UAV-aided Internet-of-Things platforms

Mohamadi, Houssem Eddine; Kara, Nadjia; Lagha, Mohand

doi:10.1016/j.engappai.2022.104854

Cited by 9 publications

(4 citation statements)

References 63 publications

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“…Unmanned aerial vehicles (UAVs) have received extensive attention from the engineering community due to their promising potential in military and civilian applications, such as disaster rescue [1,2], intelligent surveillance [3,4], smart agriculture [5][6][7][8], aerial photography [9,10], mapping [11], industry inspection [12,13] logistics [14][15][16], forest fire-fighting [16], and crop disease monitoring [17][18][19], etc.. Numerous types of UAVs have been fabricated and developed as research platforms, among which the quadcopters (also called quadrotors) are the most popular type because of their considerable merits including autonomous flight, easy construction, simple maintenance, low cost, onboard vision, vertical take-off and landing [20], hovering ability, and manoeuvrability.…”

Section: Introductionmentioning

confidence: 99%

“…The use of quadcopters is expected to grow rapidly in the future owing to their potentiality to transform many industries, increase efficiency, reduce costs, and improve safety. Moreover, the evolution of cutting-edge technologies such as artificial intelligence, machine learning, and computer vision has enhanced the capabilities of quadcopters, for example, once equipped with sensors and cameras [7,9,12], quadcopters can autonomously collect and analyze data, identify objects and anomalies, and conduct missions without human intervention. As a result, no doubt quadcopters are indispensable in many industries in the future, to fully exert their many advantages, it's a necessary work to design and develop advanced control systems for quadcopters, however, such work is quite challenging since the highly nonlinear systems [21], underactuated [22] and strongly coupled dynamics make it very difficult to design tracking control for quadcopters in the presence of various uncertainties such environmental disturbances and unknown loads.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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Objective of this study is to develop a novel, effective, and robust Sliding Mode Control (SMC) method for quadcopters (also called quadrotors) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for the purposes of enhancing trajectory tracking performance and realizing safe and reliable flight. In the paper, the ANFIS was combined with SMC technology to propose a scheme of adaptive robust controller, which is composed of three sub-controllers, x position controller, y position controller, and z position (altitude) controller. The proposed method can realize position tracking control of quadcopters in the presence of external disturbances. With the help of ANFIS, an adjustable gain rather than a fixed gain was established for the SMC controller, the optimal output could be attained based on a set of rules, and the position control gain was updated by ANFIS, enabling the SMC to adapt to environmental changes. Through modelling, simulation and comparison, experimental data verified that the proposed ANFIS-SMC controller outperformed conventional SMC controller in terms of convergence speed, robustness, accuracy, and stability with a maximum mean error of 0.125 meters in trajectory tracking. Research findings of this paper could contribute to the development of robust and responsive control strategies for Unmanned aerial vehicles (UAVs) trajectory tracking by providing valuable insights into the design of more effective and efficient control systems for UAVs, particularly in the context of dynamic environmental conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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“…Significant and rapid advances in unmanned aerial vehicle (UAV) technology have made them economically viable and easily maintainable. They have been used in research and in civil, military, and commercial applications, such as military UAVs, search and rescue, and agriculture [1][2][3][4][5]. Even though they can complete tasks, they still face many challenging problems because of the sensor accuracy and the coupled highly nonlinear system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

Candan,

Dik,

Kumbasar

et al. 2023

Algorithms

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This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers.

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“…Nowadays, the rapid need for aerial photos acquisition for some applications has changed the direction of UAVs technology growth [4], [5]. These applications are divided into some categories: agriculture (such as analysis of field soil [6], health assessment of crops [7], monitoring crops and irrigation system monitoring [8]), military (such as bomb recognition and military surveillance) [9], architecture-engineering-and construction (AEC) (such as structural and infrastructure inspections [10], transportation [11], cultural heritage conservation, city and urban planning [12]) and civilian (such as photography [13], [14], disaster management [15], [16], archeological survey [17], livestock surveillance [18], safety inspection [19], life observance [20], weather forecasting [21] and geographic mapping [22]).…”

Section: Introductionmentioning

confidence: 99%

Determining Forest Fire Position from UAV Photogrammetry using Color Filtration Algorithm

Muid

Evita²,

Aminah³

et al. 2022

JOIV : Int. J. Inform. Visualization

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Forest fires frequently happen worldwide, especially in the dry season. A forest fire early warning system (EWS) is needed to prevent this disaster. The main part of EWS is the hotspot detection system. On the other side, Unmanned Aerial Vehicle (UAV) technology offers an alternative solution to detect the hotspot for poor satellite image processing accuracy. Remote sensing techniques with UAV working drones are progressively challenging. Drones can provide results in 2D and 3D images with high resolution and real-time. Therefore, in this research, we have used a photogrammetry application from the number of images collected by a UAV with an optimum flight plan for the mission to determine the location of the forest fire. This paper describes remote sensing experiments using drones to detect land fires. The experiment was carried out using a quadcopter drone of the DJI Phantom 4 Pro. The photos are processed using Agisoft Metashape Professional image processing software and become a 2D image. These images captured a fire simulation in a known location. After a high resolution (GSD – Ground Sampling Distance – 0.87cm/px) orthophoto had been generated, a color filtration algorithm detected a hotspot to detect a fire at the exact location. The results are almost zero deviation of longitude and latitude from the real location with 1.44 m2 and 1.06 m2 fire area from 2 experiments. This algorithm program has TPR and FPR are 0,78 and 0, respectively. Further research can develop an EWS with a combination of UAV and Wireless Sensor Networks.

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Heuristic-driven strategy for boosting aerial photography with multi-UAV-aided Internet-of-Things platforms

Cited by 9 publications

References 63 publications

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

Determining Forest Fire Position from UAV Photogrammetry using Color Filtration Algorithm

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