Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV

Janoušek, Jiří; Marcoň, P.; Klouda, Jan; Pokorny, Jiri; Raichl, Petr; Siruckova, A.

doi:10.1109/piers53385.2021.9694683

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…In general, captured and acquired images are sent to pre-trained NNs, which first classify the obtained images into different classes and then pass this information to the underlying multirotor controller, as discussed in Bartak and Vykovsky [14], Janousek et al [22], Giusti et al [20], and Kaufmann et al [21]. The specifics are offered next.…”

Section: Machine Learningmentioning

confidence: 99%

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A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Sönmez,

Rutherford,

Valavanis

2024

Drones

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Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time.

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Section: Machine Learningmentioning

confidence: 99%

“…Janousek et al [22] developed a method to accurately guide an autonomous UAV to land in a specific area, which is labeled as the 'ground object'. The landing area includes a QR code, which, after it is identified and recognized, provides specific instructions/commands to the UAV for landing.…”

Section: Machine Learningmentioning

confidence: 99%

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Sönmez,

Rutherford,

Valavanis

2024

Drones

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“…In [11], the YOLO algorithm is modified to improve the recognition and positioning accuracy of the Aruco marker. In [12], the QR code is used as a landing marker and the convolutional neural network is used to identify the target. In the experiments, the QR code can be identified and the location information is accurately obtained.…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Autonomous Landing Guidance Strategy for a Micro-UAV by the Modified Camera View

Wang

et al. 2023

Drones

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Autonomous landing is one of the key technologies for unmanned aerial vehicles (UAVs) which can improve task flexibility in various fields. In this paper, a vision-based autonomous landing strategy is proposed for a quadrotor micro-UAV based on a novel camera view angle conversion method, fast landing marker detection, and an autonomous guidance approach. The front-view camera of the micro-UAV video is first modified by a new strategy to obtain a top-down view. By this means, the landing marker can be captured by the onboard camera of the micro-UAV and is then detected by the YOLOv5 algorithm in real time. The central coordinate of the landing marker is estimated and used to generate the guidance commands for the flight controller. After that, the guidance commands are sent by the ground station to perform the landing task of the UAV. Finally, the flight experiments using DJI Tello UAV are conducted outdoors and indoors, respectively. The original UAV platform is modified using the proposed camera view angle-changing strategy so that the top-down view can be achieved for performing the landing mission. The experimental results show that the proposed landing marker detection algorithm and landing guidance strategy can complete the autonomous landing task of the micro-UAV efficiently.

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A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

Jiang

Song

2022

2022 International Conference on Computing, Robotics and System Sciences (ICRSS)

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Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV

Cited by 5 publications

References 6 publications

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

A Vision-Based Autonomous Landing Guidance Strategy for a Micro-UAV by the Modified Camera View

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

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