Optimized Convolutional Neural Network architecture for UAV navigation within unstructured trail

Menfoukh, Khaoula; Touba, Mostefa Mohamed; Khenfri, Fouad; Guettal, Lemya

doi:10.1109/ccssp49278.2020.9151783

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Deep Learning (DL), on the other hand, with its different types such as Fully connected Neural Networks (FNNs) [125] and Convolutional Neural Networks (CNNs) [126], helps in autonomous UAV navigation under harsh environments by only utilizing the DNN part of the DRL. For instance, a UAV can be navigated through DNN by means of an image augmentation method [127], as well as via real-time photos and CNNs [128].…”

Section: (B2) Training Modelsmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

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Section: (B2) Training Modelsmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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“…Deep learning comprises only the deep neural network (DNN) part of the DRL. Considering recent improvements in a variety of tasks such as object identification and localization, image segmentation, and depth recognition from monocular or stereo images, the DNN method has been successfully utilized by several researchers for the identification of roads and streets on key routes and metropolitan regions by focusing on achieving a high level of autonomy for self-driving cars [133]. DNNs can be used to achieve autonomous navigation for UAVs in extremely difficult environments.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

“…Menfoukh [133] proposed an image augmentation method utilizing a CNN for vision-based UAV navigation. Back et al proposed vision-based UAV navigation utilizing CNN in [134], where UAVs perform trail following, disturbance recovery, and obstacle avoidance.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Rezwan

Choi

2022

IEEE Access

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Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learningbased approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.

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“…Another recent paper about autonomous navigation in an unstructured forest trail environment is [135]. The work presents an optimized CNN architecture trained and tested using a patch of the unstructured environment dataset IDSIA available at [3].…”

Section: Uavsmentioning

confidence: 99%

Classical and Deep Learning based Visual Servoing Systems: a Survey on State of the Art

Machkour¹,

Ortiz-Arroyo²,

Løhndorf³

2021

J Intell Robot Syst

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Computer vision, together with bayesian estimation algorithms, sensors and actuators are used in robotics to solve a variety of critical tasks such as localization, obstacle avoidance, and navigation. Visual servoing uses computer vision algorithms to guide robot movements. Classical approaches in visual servoing systems relied on extracting features from images to control robot movements. Now, state of the art computer vision systems use deep neural networks for object recognition, detection, segmentation, and tracking. These networks and specialized controllers play a predominant role in the design and implementation of modern visual servoing systems due to their accuracy, flexibility, and adaptability. Recent research in direct systems for visual servoing has created robotic systems that rely only on the information extracted from images. Furthermore, end-to-end systems eliminate entirely the controller by learning the control laws during training.This paper presents a comprehensive survey on the state of the art in visual servoing systems, discussing the latest classical methods not included in other surveys but emphasizing the new approaches based on deep neural networks and their applications within robotics.

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Optimized Convolutional Neural Network architecture for UAV navigation within unstructured trail

Cited by 8 publications

References 14 publications

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Classical and Deep Learning based Visual Servoing Systems: a Survey on State of the Art

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