Proposed system of artificial Neural Network for positioning and navigation of UAV-UGV

Kurdi, Moustafa M.; Dadykin, Aliaksei; Elzein, Imad A.; Ahmad, Ibrahim Sayed

doi:10.1109/ebbt.2018.8391459

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…1) Neural Network Algorithm: Artificial neural network (ANN) can process different types of input data, such as position information from GPS, image information, etc., and then output the next movement decision of UAV. [67] proposed a path planning algorithm based on ANN, which directly processed the image of the visual system on the UAV, and applied the back-propagation neural network to get the best control command of the next movement (forward, right, left, etc. ).…”

Section: B Heuristic Collision Avoidance Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Anti-collision Technologies for Unmanned Aerial Vehicles: Recent Advances and Future Trends

Wei¹,

Meng²,

Lai³

et al. 2021

Preprint

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Unmanned aerial vehicles (UAVs) are widely applied in civil applications, such as disaster relief, agriculture and cargo transportation, etc. With the massive number of UAV flight activities, the anti-collision technologies aiming to avoid the collisions between UAVs and other objects have attracted much attention. The anti-collision technologies are of vital importance to guarantee the survivability and safety of UAVs. In this article, a comprehensive survey on UAV anti-collision technologies is presented. We firstly introduce laws and regulations on UAV safety which prevent collision at the policy level. Then, the process of anti-collision technologies are reviewed from three aspects, i.e., obstacle sensing, collision prediction, and collision avoidance. We provide detailed survey and comparison of the methods of each aspect and analyze their pros and cons. Besides, the future trends on UAV anti-collision technologies are presented from the perspective of fast obstacle sensing and fast wireless networking. Finally, we summarize this article.

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Section: B Heuristic Collision Avoidance Algorithmsmentioning

confidence: 99%

“…Reference One-sentence summery Neural network algorithm [67] Proposed a path planning algorithm based on ANN. [68] Applied G-FCNN algorithm to evaluate the path.…”

Section: Categorymentioning

confidence: 99%

Anti-collision Technologies for Unmanned Aerial Vehicles: Recent Advances and Future Trends

Wei¹,

Meng²,

Lai³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For regulation of the vision line of UAVs in adverse conditions, neural networks were used 47 . The solution to the navigation and position of UAVs a neural network algorithm was proposed in Kurdi et al 48 Sampling base techniques …”

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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“…An enhanced A-star algorithm was used to generate the paths for robots. Kurdi et al [10] developed an intelligent controller for path planning using an ANN. The authors constructed collision-free paths for moving robots among obstacles on the basis of multiple inputs from different resources.…”

Section: Motion Planningmentioning

confidence: 99%

A Dynamic Route-Planning System Based on Industry 4.0 Technology

2020

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Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.

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Proposed system of artificial Neural Network for positioning and navigation of UAV-UGV

Cited by 9 publications

References 8 publications

Anti-collision Technologies for Unmanned Aerial Vehicles: Recent Advances and Future Trends

Anti-collision Technologies for Unmanned Aerial Vehicles: Recent Advances and Future Trends

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

A Dynamic Route-Planning System Based on Industry 4.0 Technology

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