scholarly journals Experimental studies of a convolutional neural network for application in the navigation system of a mobile robot

2018 ◽  
Vol 145 ◽  
pp. 611-616 ◽  
Author(s):  
Nikita S. Verbitsky ◽  
Eugene V. Chepin ◽  
Alexander A. Gridnev
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Convolutional Neural Network ◽  
Navigation System ◽  
Experimental Studies

scholarly journals An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

2019 ◽  
Vol 9 (14) ◽  
pp. 2808 ◽  
Author(s):  
Yahui Peng ◽  
Xiaochen Liu ◽  
Chong Shen ◽  
Haoqian Huang ◽  
Donghua Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Navigation System ◽  
Moving Objects ◽  
Harmful Effect ◽  
Improved Method ◽  
Vision Navigation ◽  
Vehicle Velocity ◽  
Flow Algorithm

Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.

scholarly journals Deep learning for extracting micro-fracture: Pixel-level detection by convolutional neural network

2020 ◽  
Vol 205 ◽  
pp. 03007
Author(s):  
Yejin Kim ◽  
Seong Jun Ha ◽  
Tae sup Yun
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Frequency Noise ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Hydraulic Stimulation ◽  
Roughness Analysis

Hydraulic stimulation has been a key technique in enhanced geothermal systems (EGS) and the recovery of unconventional hydrocarbon resources to artificially generate fractures in a rock formation. Previous experimental studies present that the pattern and aperture of generated fractures vary as the fracking pressure propagation. The recent development of three-dimensional X-ray computed tomography allows visualizing the fractures for further analysing the morphological features of fractures. However, the generated fracture consists of a few pixels (e.g., 1-3 pixels) so that the accurate and quantitative extract of micro-fracture is highly challenging. Also, the high-frequency noise around the fracture and the weak contrast across the fracture makes the application of conventional segmentation methods limited. In this study, we adopted an encoder-decoder network with a convolutional neural network (CNN) based on deep learning method for the fast and precise detection of micro-fractures. The conventional image processing methods fail to extract the continuous fractures and overestimate the fracture thickness and aperture values while the CNN-based approach successfully detects the barely seen fractures. The reconstruction of the 3D fracture surface and quantitative roughness analysis of fracture surfaces extracted by different methods enables comparison of sensitivity (or robustness) to noise between each method.

scholarly journals A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2477 ◽  
Author(s):  
Kamal M. Othman ◽  
Ahmad B. Rad
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Navigation System ◽  
Social Robot ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Robotic Navigation ◽  
Monocular Camera ◽  
Simulation Results ◽  
Novel Algorithm

In this paper, we propose a novel algorithm to detect a door and its orientation in indoor settings from the view of a social robot equipped with only a monocular camera. The challenge is to achieve this goal with only a 2D image from a monocular camera. The proposed system is designed through the integration of several modules, each of which serves a special purpose. The detection of the door is addressed by training a convolutional neural network (CNN) model on a new dataset for Social Robot Indoor Navigation (SRIN). The direction of the door (from the robot’s observation) is achieved by three other modules: Depth module, Pixel-Selection module, and Pixel2Angle module, respectively. We include simulation results and real-time experiments to demonstrate the performance of the algorithm. The outcome of this study could be beneficial in any robotic navigation system for indoor environments.

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