scholarly journals Estimate the Unknown Environment with Biosonar Echoes—A Simulation Study

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4186
Author(s):  
Muhammad Hassan Tanveer ◽  
Antony Thomas ◽  
Waqar Ahmed ◽  
Hongxiao Zhu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Simulation Study ◽  
Processing Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Tree Leaves ◽  
Forest Environment ◽  
Aerial Vehicles ◽  
Scattering Technique ◽  
Percent Accuracy

Unmanned aerial vehicles (UAVs) have shown great potential in various applications such as surveillance, search and rescue. To perform safe and efficient navigation, it is vitally important for a UAV to evaluate the environment accurately and promptly. In this work, we present a simulation study for the estimation of foliage distribution as a UAV equipped with biosonar navigates through a forest. Based on a simulated forest environment, foliage echoes are generated by using a bat-inspired bisonar simulator. These biosonar echoes are then used to estimate the spatial distribution of both sparsely and densely distributed tree leaves. While a simple batch processing method is able to estimate sparsely distributed leaf locations well, a wavelet scattering technique coupled with a support vector machine (SVM) classifier is shown to be effective to estimate densely distributed leaves. Our approach is validated by using multiple setups of leaf distributions in the simulated forest environment. Ninety-seven percent accuracy is obtained while estimating thickly distributed foliage.

scholarly journals Environment Classification for Unmanned Aerial Vehicle Using Convolutional Neural Networks

2020 ◽  
Vol 10 (14) ◽  
pp. 4991
Author(s):  
Carlos Villaseñor ◽  
Alberto A. Gallegos ◽  
Javier Gomez-Avila ◽  
Gehová López-González ◽  
Jorge D. Rios ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Computational Cost ◽  
Support Vector ◽  
Normal Amount ◽  
Human Expert ◽  
Aerial Vehicles ◽  
Training Samples ◽  
Aerial Vehicle ◽  
Water Accumulation ◽  
Low Computational Cost

Environment classification is one of the most critical tasks for Unmanned Aerial Vehicles (UAV). Since water accumulation may destabilize UAV, clouds must be detected and avoided. In a previous work presented by the authors, Superpixel Segmentation (SPS) descriptors with low computational cost are used to classify ground, sky, and clouds. In this paper, an enhanced approach to classify the environment in those three classes is presented. The proposed scheme consists of a Convolutional Neural Network (CNN) trained with a dataset generated by both, an human expert and a Support Vector Machine (SVM) to capture context and precise localization. The advantage of using this approach is that the CNN classifies each pixel, instead of a cluster like in SPS, which improves the resolution of the classification, also, is less tedious for the human expert to generate a few training samples instead of the normal amount that it is required. This proposal is implemented for images obtained from video and photographic cameras mounted on a UAV facing in the same direction of the vehicle flight. Experimental results and comparison with other approaches are shown to demonstrate the effectiveness of the algorithm.

scholarly journals Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4416 ◽  
Author(s):  
Baohua Yang ◽  
Mengxuan Wang ◽  
Zhengxia Sha ◽  
Bing Wang ◽  
Jianlin Chen ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Unmanned Aerial Vehicles ◽  
Nitrogen Content ◽  
Spatial Information ◽  
Vegetation Indices ◽  
Scale Transformation ◽  
Support Vector ◽  
Spatial Feature ◽  
Aerial Vehicles ◽  
Wavelet Features

Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R2 = 0.9025, root mean square error (RMSE) = 0.3287) among the three regression algorithms regardless of the use of all the original features or the combination features. Our results implied that our proposed method could improve the estimation accuracy of aboveground nitrogen content of winter wheat from UAVs with consumer digital cameras, which have greater application potential in predicting other growth parameters.

scholarly journals Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1486 ◽  
Author(s):  
Asmamaw Gebrehiwot ◽  
Leila Hashemi-Beni ◽  
Gary Thompson ◽  
Parisa Kordjamshidi ◽  
Thomas Langan
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Image Classification ◽  
Urban Areas ◽  
Human Life ◽  
Confusion Matrix ◽  
Support Vector ◽  
Response Planning ◽  
Aerial Vehicles ◽  
Hazardous Area ◽  
Flooded Areas

Flooding is one of the leading threats of natural disasters to human life and property, especially in densely populated urban areas. Rapid and precise extraction of the flooded areas is key to supporting emergency-response planning and providing damage assessment in both spatial and temporal measurements. Unmanned Aerial Vehicles (UAV) technology has recently been recognized as an efficient photogrammetry data acquisition platform to quickly deliver high-resolution imagery because of its cost-effectiveness, ability to fly at lower altitudes, and ability to enter a hazardous area. Different image classification methods including SVM (Support Vector Machine) have been used for flood extent mapping. In recent years, there has been a significant improvement in remote sensing image classification using Convolutional Neural Networks (CNNs). CNNs have demonstrated excellent performance on various tasks including image classification, feature extraction, and segmentation. CNNs can learn features automatically from large datasets through the organization of multi-layers of neurons and have the ability to implement nonlinear decision functions. This study investigates the potential of CNN approaches to extract flooded areas from UAV imagery. A VGG-based fully convolutional network (FCN-16s) was used in this research. The model was fine-tuned and a k-fold cross-validation was applied to estimate the performance of the model on the new UAV imagery dataset. This approach allowed FCN-16s to be trained on the datasets that contained only one hundred training samples, and resulted in a highly accurate classification. Confusion matrix was calculated to estimate the accuracy of the proposed method. The image segmentation results obtained from FCN-16s were compared from the results obtained from FCN-8s, FCN-32s and SVMs. Experimental results showed that the FCNs could extract flooded areas precisely from UAV images compared to the traditional classifiers such as SVMs. The classification accuracy achieved by FCN-16s, FCN-8s, FCN-32s, and SVM for the water class was 97.52%, 97.8%, 94.20% and 89%, respectively.

scholarly journals Autonomous Vision-Based Aerial Grasping for Rotorcraft Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3410 ◽  
Author(s):  
Lishan Lin ◽  
Yuji Yang ◽  
Hui Cheng ◽  
Xuechen Chen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Region Of Interest ◽  
Target Object ◽  
Robotic Arm ◽  
Support Vector ◽  
Control Laws ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
And Control ◽  
High Computational Efficiency

Autonomous vision-based aerial grasping is an essential and challenging task for aerial manipulation missions. In this paper, we propose a vision-based aerial grasping system for a Rotorcraft Unmanned Aerial Vehicle (UAV) to grasp a target object. The UAV system is equipped with a monocular camera, a 3-DOF robotic arm with a gripper and a Jetson TK1 computer. Efficient and reliable visual detectors and control laws are crucial for autonomous aerial grasping using limited onboard sensing and computational capabilities. To detect and track the target object in real time, an efficient proposal algorithm is presented to reliably estimate the region of interest (ROI), then a correlation filter-based classifier is developed to track the detected object. Moreover, a support vector regression (SVR)-based grasping position detector is proposed to improve the grasp success rate with high computational efficiency. Using the estimated grasping position and the UAV?Äôs states, novel control laws of the UAV and the robotic arm are proposed to perform aerial grasping. Extensive simulations and outdoor flight experiments have been implemented. The experimental results illustrate that the proposed vision-based aerial grasping system can autonomously and reliably grasp the target object while working entirely onboard.

A Gravity Balanced Test Stand for Flight Testing of Small/Micro Unmanned Aerial Vehicles

2008 ◽  
Author(s):  
Qi Lu ◽  
Carlos E. Ortega ◽  
Ou Ma
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Simulation Study ◽  
Test Stand ◽  
Micro Aerial Vehicles ◽  
Flight Testing ◽  
Test Stage ◽  
Safety Test ◽  
Aerial Vehicles ◽  
Innovative Solution

For the purpose of safety, test stands are usually needed for testing unmanned aerial vehicles (UAVs), especially in the early test stage. Although a test stand can prevent the tested vehicle from flying away or crashing, it adds extra burden to the vehicle due to the weight of its moving mechanism. This can be a major problem for micro aerial vehicles (MAVs) because of their tiny lifting or payload capability. This paper presents an innovative solution to this problem by designing a passive gravity balanced test stand. The stand is capable of completely compensating its own weight, so that the tested vehicle will not have the extra burden from the weight of the test stand. To verify the feasibility of the design, a simulation study has been conducted, which demonstrated the benefits of the new design.

Development of thickened semi-synthetic engine oil M-5z / 20 AERO for four-stroke gasoline engines aircraft piston of unmanned aerial vehicles (UAVs)

2020 ◽  
Vol 06 ◽  
pp. 44-47
Author(s):  
A.A. Moykin ◽  
◽  
A.S. Medzhibovsky ◽  
S.A. Kriushin ◽  
M.V. Seleznev ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Engine Oil ◽  
Motor Oil ◽  
Gasoline Engines ◽  
State Research Institute ◽  
Technical Requirements ◽  
Aerial Vehicles ◽  
State Research ◽  
Industrial Batch ◽  
The Russian Federation

Nowadays, the creation of remotely-piloted aerial vehicles for various purposes is regarded as one of the most relevant and promising trends of aircraft development. FAU "25 State Research Institute of Chemmotology of the Ministry of Defense of the Russian Federation" have studied the operation features of aircraft piston engines and developed technical requirements for motor oil for piston four-stroke UAV engines, as well as a new engine oil M-5z/20 AERO in cooperation with NPP KVALITET, LLC. Based on the complex of qualification tests, the stated operational properties of the experimental-industrial batch of M-5z/20 AERO oil are generally confirmed.

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