Simulation of Unmanned Aircraft Vehicle Flight Precision

Sedláčková, Alena Novák; Kurdel, Pavol; Labun, Ján

doi:10.1016/j.trpro.2020.02.037

Cited by 23 publications

(15 citation statements)

References 8 publications

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“…However, these areas demand drones capable of providing large amounts of data and analysing data in real-time. In addition, drones must detect obstacles using colour segmentation [38], deep learning and RGB cameras [39,40], depth information and deep learning [41], obstacle-free navigation [42], monocular depth prediction [43], and agile and precise flight [44].…”

Section: Related Workmentioning

confidence: 99%

Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera

Rojas-Perez

Martínez-Carranza

2021

Sensors

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Recent advances have shown for the first time that it is possible to beat a human with an autonomous drone in a drone race. However, this solution relies heavily on external sensors, specifically on the use of a motion capture system. Thus, a truly autonomous solution demands performing computationally intensive tasks such as gate detection, drone localisation, and state estimation. To this end, other solutions rely on specialised hardware such as graphics processing units (GPUs) whose onboard hardware versions are not as powerful as those available for desktop and server computers. An alternative is to combine specialised hardware with smart sensors capable of processing specific tasks on the chip, alleviating the need for the onboard processor to perform these computations. Motivated by this, we present the initial results of adapting a novel smart camera, known as the OpenCV AI Kit or OAK-D, as part of a solution for the ADR running entirely on board. This smart camera performs neural inference on the chip that does not use a GPU. It can also perform depth estimation with a stereo rig and run neural network models using images from a 4K colour camera as the input. Additionally, seeking to limit the payload to 200 g, we present a new 3D-printed design of the camera’s back case, reducing the original weight 40%, thus enabling the drone to carry it in tandem with a host onboard computer, the Intel Stick compute, where we run a controller based on gate detection. The latter is performed with a neural model running on an OAK-D at an operation frequency of 40 Hz, enabling the drone to fly at a speed of 2 m/s. We deem these initial results promising toward the development of a truly autonomous solution that will run intensive computational tasks fully on board.

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Section: Related Workmentioning

confidence: 99%

Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera

Rojas-Perez

Martínez-Carranza

2021

Sensors

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“…One is the UAV hover execution controller, which will control the rotor to maintain the rotor in the desired position. The other is a four-rotor control for tracking arbitrary trajectories in 3D [11,16,17].…”

Section: Position Controlmentioning

confidence: 99%

A Mathematical Model for Controlling a Quadrotor UAV

Kowalik¹,

Novák

2021

Transactions on Aerospace Research

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Given the recent surge in interest in UAVs and their potential applications, a great deal of work has lately been done in the field of UAV control. However, UAVs belong to a class of nonlinear systems that are inherently difficult to control. In this study we devised a mathematical model for a PID (proportional integral derivative) control system, designed to control a quadrotor UAV so that it follows a predefined trajectory. After first describing quadrotor flight dynamics, we present the control model adopted in our system (developed in MATLAB Simulink). We then present simulated results for the use of the control system to move a quadrotor UAV to desired locations and along desired trajectories. Positive results of these simulation support the conclusion that a quadrotor UAV spatial orientation control system based on this model will successfully fulfil its task also in real conditions.

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“…An MROs maintenance technician has access to the corporate network, therefore is able to work anytime from any location. Electronic handheld devices enable him to track, compare and record maintenance task [9].…”

Section: Digitization Of Environment and Virtualization Of Tasksmentioning

confidence: 99%

“…After establishment of such innovative work environment unmanned aerial vehicles will be able to perform aircraft visual inspections prior, during or after the maintenance tasks are carried out. Further implementation of Automated Guided Vehicles (AGV) as well as Intelligent cabinet will help in search for and point-to-point transport of tools and aircraft parts from the storage to the place of the maintenance (point-to-use) in real time [9].…”

Section: Robotization and Automation Of An Environmentmentioning

confidence: 99%

Use of Unmanned Aerial Vehicles in Aircraft Inspection

Novák¹,

Bugaj²,

Sedláčková³

et al. 2021

Adv. sci. technol. eng. syst. j.

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The article further extends the researched issue of the unmanned aircraft use in the preflight and post-flight visual check of aircraft. Procedures of pre-flight inspection are fulfilled by the aircraft maintenance certified staff or the crew member before flight. The process is similar for all categories of aircraft, but its implementation differs for individual specific types of aircraft. Therefore, the article will deal only with small training aircraft, which will be used to verify the use of UAV (Unmanned Aerial Vehicle) in normal operation. It identifies and defines the problem of using multiple UAV in swarms and their usage in standard activities in aircraft operation. The outcome should be a reduction of the number of possible failures cause by the human factor with impact on the safety in operations. Proportionately important fact is the desirable minimization in the time necessary to carry out a pre-flight inspection process, which will improve the final indicator of the efficiency in aeroplane operations.

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Simulation of Unmanned Aircraft Vehicle Flight Precision

Cited by 23 publications

References 8 publications

Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera

Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera

A Mathematical Model for Controlling a Quadrotor UAV

Use of Unmanned Aerial Vehicles in Aircraft Inspection

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