A low cost fully autonomous GPS (Global Positioning System) based quad copter for disaster management

Saha, HimadriNath; Basu, Srijita; Auddy, Supratim; Dey, Ratul; Nandy, Arnab; Pal, Debjit; Roy, Nirjhar; Jasu, Subhadeep; Saha, Ankita; Chattopadhyay, SoummyoPriyo; Maity, Tamanna

doi:10.1109/ccwc.2018.8301782

Cited by 26 publications

(13 citation statements)

References 12 publications

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“…Quadrotor system has no real control input for the motion in the (x, y) plane, the following analysis is proposed to generate the appropriate control signals (U x and U y ) for the motion in the (x, y) plane, see (3). Simplifying the model of (3) by assuming the φ and θ angle deviations are small (i.e.…”

Section: Nonlinear Design For Olcmentioning

confidence: 99%

“…While the proposed NLPID controller showed an improved performance over the LPID one as shown in Figure 13. Case Study (3). The final case study is the square trajectory.…”

Section: Trajectory Trackingmentioning

confidence: 99%

“…Quadrotors applications have been increased in the last years because of their simple implementation, low cost, different sizes, and maneuverability. Many applications founded for danger places, disasters, and rescue [1][2][3], quadrotor applications in agriculture [4,5], and even in helpful jobs like [6,7]. The work in [8] is a good survey for quadrotor applications for entertainment.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Najm

Ibraheem

2019

Engineering Science and Technology, an International Journal

110

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A Nonlinear PID (NLPID) controller is proposed to stabilize the translational and rotational motion of a 6-DOF UAV quadrotor system and enforce it to track a given trajectory with minimum energy and error. The complete nonlinear model of the 6-DOF quadrotor system are obtained using Euler-Newton formalism and used in the design process, taking into account the velocity and acceleration vectors resulting in a more accurate 6-DOF quadrotor model and closer to the actual system. Six NLPID controllers are designed, each for Roll, Pitch, Yaw, Altitude, and the Position subsystems, where their parameters are tuned using GA to minimize a multi-objective Output Performance Index (OPI). The stability of the 6-DOF UAV subsystems has been analyzed in the sense of Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers. The simulations have been accomplished under MATLAB/SIMULINK environment and included three different trajectories, i.e., circular, helical, and square. The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the Linear PID (LPID) one and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady-state error.

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Section: Nonlinear Design For Olcmentioning

confidence: 99%

“…While the proposed NLPID controller showed an improved performance over the LPID one as shown in Figure 13. Case Study (3). The final case study is the square trajectory.…”

Section: Trajectory Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Najm

Ibraheem

2019

Engineering Science and Technology, an International Journal

110

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“…Another option is real-time drone crowd monitoring, which is becoming more popular in order to save lives, preserve the environment, protect property, and maintain peace and authority. According to the findings of this research, pedestrian crowd monitoring systems may be a viable alternative [7]. Crowd density was computed using image segmentation algorithms based on real-time images taken by UAVs; after which the data was evaluated and the results were presented.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicles and machine learning for detecting objects in real time

Albaghdadi

Manaa

2022

Bulletin EEI

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An unmanned aerial vehicle (UAV) image recognition system in real-time is proposed in this study. To begin, the you only look once (YOLO) detector has been retrained to better recognize objects in UAV photographs. The trained YOLO detector makes a trade-off between speed and precision in object recognition and localization to account for four typical moving entities caught by UAVs (cars, buses, trucks, and people). An additional 1500 UAV photographs captured by the embedded UAV camera are fed into the YOLO, which uses those probabilities to estimate the bounding box for the entire image. When it comes to object detection, the YOLO competes with other deep-learning frameworks such as the faster region convolutional neural network. The proposed system is tested on a wild test set of 1500 UAV photographs with graphics processing unit GPU acceleration, proving that it can distinguish objects in UAV images effectively and consistently in real-time at a detection speed of 60 frames per second.

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“…De 2011 a hoy, Saha et al (2018) coincide en que los smartphone que incluyen las potentes aplicaciones APP para captura, almacenamiento y procesamiento de datos, en combinación con drones de última generación a bajos costos, ofrecen una alternativa más que considerable para las mediciones y con cada vez menos errores de medida, gracias a que los drones en la actualidad se soportan en el Sistema de Posicionamiento Global (GPS), así mismo, las tecnologías relacionadas con la configuración de la aeronave constituyen la tendencia con mayor actividad inventiva con 779 invenciones a 2015 (CIGEPI, 2015, p. 32).…”

Section: Introductionunclassified

Estudio comparativo de Aplicaciones Móviles (APP), en el error de medición con Aeronaves Pilotadas a Distancia (RPA)

Zabala

Paternina

Cárdenas

2018

Ciencia y Poder Aéreo

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En la topografía actual se vienen incorporando las nuevas tecnologías de RPA para la medición de áreas, reduciendo los tiempos de toma de datos e incorporando las Tecnologías de Información y Comunicación (TIC) a través de las APP, facilitando su control y operación, haciendo hoy en día la toma de datos sistemática e intuitiva. En el presente artículo se reportan las 5 principales APP usadas en el mercado de fotogrametría, con el objetivo principal de evaluar el error de medición asociado a cada una de ellas, a través de la construcción de ortomosaicos con la cámara ZENMUSE X3 (FC350) de resolución de 12 Mp del dron DJI Inspire 1, estableciendo puntos de control a través de estaciones topográficas con precisión al milímetro, con la finalidad de hallar el error asociado a cada uno de los ortomosaicos construidos con las diferentes APP. Se encontró que el margen de error promedio para todas las APP es de 1.11 %, de igual manera, se pudo determinar que la altura mínima para marcación de puntos de control con cámara de 12 Mp es de 60 m, la precisión en la medición mejora en 0.35 %, siendo la mejor APP DroneDeploy, con un error promedio de 0.66 %. Ofreciendo una alternativa tecnológica importante para mejorar el desempeño en servicios topográficos.

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A low cost fully autonomous GPS (Global Positioning System) based quad copter for disaster management

Cited by 26 publications

References 12 publications

Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Unmanned aerial vehicles and machine learning for detecting objects in real time

Estudio comparativo de Aplicaciones Móviles (APP), en el error de medición con Aeronaves Pilotadas a Distancia (RPA)

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