A Real-Time Autonomous Flight Navigation Trajectory Assessment for Unmanned Aerial Vehicles

Mostafa, Salama A.; Shamsudin, Abu Ubaidah; Ahmad, Azhana; Ahmad, Mohd Sharifuddin; Gunasekaran, Saraswathy Shamini

doi:10.1109/isamsr.2018.8540544

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Some works have focused on solving the problem using more traditional AI techniques. For example, in [Mostafa et al 2018], the Performance Visualized Assessment (PVA) model was introduced to evaluate UAV performance in indoor settings. This model combined traditional assessment tools with AI components, notably the Chi-square Inference (CSI) module.…”

Section: Related Workmentioning

confidence: 99%

Position Estimation of Unmanned Aerial Vehicles in Contested Environments using Dense Matching Networks

Afonso,

de Oliveira,

Lamosa

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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This paper presents an approach to predicting the trajectory and the position of unmanned aerial vehicles (UAVs) in contested global navigation satellite system (GNSS) environments. Our approach utilizes dense matching networks to analyze and predict patterns in UAV movement and then effectively recovers temporal trajectory information and accurately predicts UAV position changes using images and altitude data. This problem was part of the KDDBR 2022 Kaggle Competition, where we obtained a Root Mean Squared Error (RMSE) of 0.29603, which made it the runner-up solution. This could have significant implications for the future of UAV navigation, potentially leading to safer and more efficient operations in various applications.

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Section: Related Workmentioning

confidence: 99%

Position Estimation of Unmanned Aerial Vehicles in Contested Environments using Dense Matching Networks

Afonso,

de Oliveira,

Lamosa

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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“…With the development of CNN architecture, based on its powerful features expression ability and efficient way of feature extraction, the CNN architecture has been developed in many application scenarios (Mostafa et al , 2021; Al-Waisy et al , 2021). Computer vision-based CNN models have been designed and constantly built for specific problems, and successfully applied to various image processing tasks (Mohd et al , 2020; Mostafa et al , 2018). In the field of visual tracking, with the emergence of many top CNN-based deep visual object tracking algorithms, the effectiveness of such application in tracking tasks has been fully researched during recent years (Khaleefah et al , 2018).…”

Section: Related Workmentioning

confidence: 99%

Auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking

Huang

2021

IJICC

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PurposeMulti-domain convolutional neural network (MDCNN) model has been widely used in object recognition and tracking in the field of computer vision. However, if the objects to be tracked move rapid or the appearances of moving objects vary dramatically, the conventional MDCNN model will suffer from the model drift problem. To solve such problem in tracking rapid objects under limiting environment for MDCNN model, this paper proposed an auto-attentional mechanism-based MDCNN (AA-MDCNN) model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approachFirst, to distinguish the foreground object between background and other similar objects, the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other. Then, the bidirectional gated recurrent unit (Bi-GRU) architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps. Finally, the final feature map is obtained by fusion the above two feature maps for object tracking. In addition, a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.FindingsIn order to validate the effectiveness and feasibility of the proposed AA-MDCNN model, this paper used ImageNet-Vid dataset to train the object tracking model, and the OTB-50 dataset is used to validate the AA-MDCNN tracking model. Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75% and success rate 2.41%, respectively. In addition, the authors also selected six complex tracking scenarios in OTB-50 dataset; over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes. In addition, except for the scenario of multi-objects moving with each other, the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/valueThis paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features. By using the proposed AA-MDCNN model, rapid object tracking under complex background, motion blur and occlusion objects has better effect, and such model is expected to be further applied to the rapid object tracking in the real world.

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“…For UAV tracking, these models feature the process of decision making 89 ; ACO Gaussian filter, 90 the chi‐square method 91 have been used. To check the ability of the UAV to perform in an indoor environment, a visualized assessment model was proposed in Mostafa et al 92 For the trajectory and localization model, they used the chi‐square method. The moderate time of finalization after the simulation result comes at 4.08 min.…”

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Sharma

Obaidat

Sharma

et al. 2020

Int J Communication

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SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

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A Real-Time Autonomous Flight Navigation Trajectory Assessment for Unmanned Aerial Vehicles

Cited by 9 publications

References 16 publications

Position Estimation of Unmanned Aerial Vehicles in Contested Environments using Dense Matching Networks

Position Estimation of Unmanned Aerial Vehicles in Contested Environments using Dense Matching Networks

Auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

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