Developing of a Video-Based Model for UAV Autonomous Navigation

Aguilar, Wilbert G.; Salcedo, Vinicio S.; Sandoval, David; Cobeña, Bryan

doi:10.1007/978-3-319-71011-2_8

Cited by 28 publications

(8 citation statements)

References 16 publications

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“…within the range [α, β] (α < β), the following α ≤ x (1) < x (2) ≤ x * implies g(x (1) ) > g(x (2) ), and x * ≤ x (1) < x (2) ≤ β implies g(x (1) ) < g(x (2) ). It is assumed that g(x) is calculated/measured with finite accuracy.…”

Section: Discussion Of Optimalitymentioning

confidence: 99%

“…It is assumed that g(x) is calculated/measured with finite accuracy. In this onedimensional search, g is evaluated at N distinct points x (1) , x (2) , . .…”

Section: Discussion Of Optimalitymentioning

confidence: 99%

“…Yet other tuning techniques may include hybridization approach including [3] fuzzy logic approach combined with with MRAC MIT-oriented tuning, which inherently is limited by the MIT rule and its local convergence, or by building knowledge concerning the UAV on the basis of vision-gained information [2], to improve the performance of the system.…”

Section: Contribution and Organization Of The Papermentioning

confidence: 99%

“…-2 bootstraps are performed, -k P is sought in the range [4,12], and k D in [2,7], -= 0.05 in DIC1, GLD, FIB, or = 0.125 in EQL, DIC2, -δ = 0.0625 in EQL, δ = 0.01 in FIB, δ = 0.04 in DIC1, and δ = 0.1 in DIC2, -a total of 48 iterations are required in FIB, DIC2 and GLD (6 iterations per half of a bootstrap), while 56 iterations are required in DIC1, EQL (7 iterations), -the initial values for the second parameter (k D ) take the following values: 1, 3, 5, 7, 9, -for all methods apart from the DIC family, the algorithms used the calculated/averaged cost function, or skipped the points known from previous iterations, which gave rise to 45 simulations/algorithm, and 15 for DICs.…”

Section: Simulations With An Accurate Modelmentioning

confidence: 99%

See 3 more Smart Citations

AL-TUNE: A Family of Methods to Effectively Tune UAV Controllers in In-flight Conditions

Horla

Giernacki

Báča

et al. 2021

J Intell Robot Syst

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In the paper, a family of novel real-time tuning methods for an unmanned aerial vehicle (UAV) altitude controller in in-flight conditions. The methods allow the controller’s gains to be adapted only on the basis of measurements from a basic sensory equipment and by constructing the optimization cost function in an on-line fashion with virtually no impeding computational complexity; in the case of the altitude controller as in this paper for a hexacopter, altitude measurements were used only. The methods are not dependent on the measurement level, and present the approach in a generally applicable form to tuning arbitrary controllers with low number of parameters. Real-world experimental flights, preceded by simulation tests, have shown which method should behave best in a noisy environment when e.g. wind disturbances act on a UAV while it is in autonomous flight. As the methods can potentially be extended to other control loops or controller types, making this a versatile, rapid-tuning tool. It has been shown that a well-tuned controller using the proposed AL-TUNE scheme outperforms controllers that are tuned just to stabilize the system. AL-TUNE provides a new way of using UAVs in terms of adaptivity to changing their dynamic properties and can be deployed rapidly. This enables new applications and extends the usability of fully autonomous UAVs, unlike other tuning methods, which basically require the availability of a UAV model. The core difference with respect to other research from the field is that other authors either use a model of a UAV to optimize the gains analytically or use machine learning techniques, what increases time consumption, whereas the presented methods offer a rapid way to tune controllers, in a reliable way, with deterministic time requirements.

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Section: Discussion Of Optimalitymentioning

confidence: 99%

“…It is assumed that g(x) is calculated/measured with finite accuracy. In this onedimensional search, g is evaluated at N distinct points x (1) , x (2) , . .…”

Section: Discussion Of Optimalitymentioning

confidence: 99%

Section: Contribution and Organization Of The Papermentioning

confidence: 99%

Section: Simulations With An Accurate Modelmentioning

confidence: 99%

See 2 more Smart Citations

AL-TUNE: A Family of Methods to Effectively Tune UAV Controllers in In-flight Conditions

Horla

Giernacki

Báča

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…Moreover, drones with high maneuverability in small spaces fits both urban missions [ 14 , 15 , 16 , 17 , 18 ], such as monitoring road traffic, and rescue missions [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ], such as patrolling dangerous zone after natural disasters. In many of these applications Vision-Based Navigation (VBN) algorithms are often used to improve accuracy in the positioning [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Film makers and video studios are successfully using UAVs for aerial shooting [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

Bigazzi

Gherardini

Innocenti

et al. 2021

Sensors

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In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a “cyber-pilot”, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a “virtual sensor” which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.

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Convolutional Neuronal Networks Based Monocular Object Detection and Depth Perception for Micro UAVs

Aguilar

Quisaguano

Rodríguez

et al. 2018

Lecture Notes in Computer Science

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Developing of a Video-Based Model for UAV Autonomous Navigation

Cited by 28 publications

References 16 publications

AL-TUNE: A Family of Methods to Effectively Tune UAV Controllers in In-flight Conditions

AL-TUNE: A Family of Methods to Effectively Tune UAV Controllers in In-flight Conditions

Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

Convolutional Neuronal Networks Based Monocular Object Detection and Depth Perception for Micro UAVs

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