scholarly journals DeepLabV3+/Efficientnet Hybrid Network-Based Scene Area Judgment for the Mars Unmanned Vehicle System

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8136
Author(s):  
Shuang Hu ◽  
Jin Liu ◽  
Zhiwei Kang
Keyword(s):  
High Accuracy ◽  
Experimental Results ◽  
Hybrid Network ◽  
Ground Vehicles ◽  
Mars Exploration ◽  
Unmanned Vehicle ◽  
Vehicle System ◽  
Safe Area ◽  
Feature Information ◽  
Exploration Mission

Due to the complexity and danger of Mars’s environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. Firstly, DeepLabV3+ is used to extract the feature information of the Mars image due to its high accuracy. Then, the feature information is used as the input for Efficientnet, and the categories of scene areas are obtained, including safe area, report area, and dangerous area. Finally, according to three categories, the Mars unmanned vehicle system performs three operations: pass, report, and send. Experimental results show the effectiveness of the DeepLabV3+/Efficientnet hybrid network in the scene area judgment. Compared with the Efficientnet network, the accuracy of the DeepLabV3+/Efficientnet hybrid network is improved by approximately 18% and reaches 99.84%, which ensures the safety of the exploration mission for the Mars unmanned vehicle system.

scholarly journals Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Experimental Results ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Reconfigurable Robot ◽  
Nvidia Gpu ◽  
Inspection Task

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

scholarly journals High-Accuracy Tracking Using Ultrawideband Signals for Enhanced Safety of Cyclists

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Davide Dardari ◽  
Nicoló Decarli ◽  
Anna Guerra ◽  
Ashraf Al-Rimawi ◽  
Víctor Marín Puchades ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
High Accuracy ◽  
Experimental Results ◽  
Localization Error ◽  
Localization System ◽  
Dynamic Conditions ◽  
The Road ◽  
Road Users

In this paper, an ultrawideband localization system to improve the cyclists’ safety is presented. The architectural solutions proposed consist of tags placed on bikes, whose positions have to be estimated, and anchors, acting as reference nodes, located at intersections and/or on vehicles. The peculiarities of the localization system in terms of accuracy and cost enable its adoption with enhanced risk assessment units situated on the infrastructure/vehicle, depending on the architecture chosen, as well as real-time warning to the road users. Experimental results reveal that the localization error, in both static and dynamic conditions, is below 50 cm in most of the cases.

Identification and Compensation of Geometric and Elastic Errors in Large Manipulators: Application to a High Accuracy Medical Robot

1998 ◽  
Author(s):  
Marco A. Meggiolaro ◽  
Constantinos Mavroidis ◽  
Steven Dubowsky
Keyword(s):  
High Accuracy ◽  
Measured Data ◽  
Experimental Results ◽  
Geometric Errors ◽  
Medical Robot ◽  
Error Sources ◽  
End Effector ◽  
Positioning Errors ◽  
Structural Deformations ◽  
Manufacturing Tolerances

Abstract A method is presented to identify the source of end-effector positioning errors in large manipulators using experimentally measured data. Both errors due to manufacturing tolerances and other geometric errors and elastic structural deformations are identified. These error sources are used to predict, and compensate for, the end-point errors as a function of configuration and measured forces. The method is applied to a new large high accuracy medical robot. Experimental results show that the method is able to effectively correct for the errors in the system.

scholarly journals A Single Target Grasp Detection Network Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Longzhi Zhang ◽  
Dongmei Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Single Object ◽  
Single Target ◽  
High Detection ◽  
Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

Research of stamping of unequal channel bars. Part 5. Methods for calculating the extrusion of channels. 2. Extrusion of non-strengthening material

2021 ◽  
pp. 46-52
Author(s):  
A.L. Vorontsov
Keyword(s):  
Plane Strain ◽  
Theoretical Calculations ◽  
Experimental Studies ◽  
High Accuracy ◽  
Experimental Results ◽  
Die Forging

The results of experimental studies on the extrusion of channels from non-strengthening material are presented. Comparison of theoretical calculations with experimental results showed the high accuracy of the derived formulas. Keywords: die forging, extrusion, punch, matrix, misalignment, plane strain. [email protected]

scholarly journals Traffic marking recognition based on generating antagonistic neural network

2019 ◽  
Vol 136 ◽  
pp. 04076 ◽  
Author(s):  
Shuwei Xu ◽  
Shan Zhang ◽  
Shuwei Xu
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Methods ◽  
Image Detection ◽  
Fast Recognition ◽  
Repeated Sampling ◽  
Backward Propagation ◽  
Traditional Image

This paper presents a method of extracting traffic lines from image images by GAN. Compared with the traditional image detection methods, the counter neural network does not need repeated sampling of Markov chain and adopts the method of backward propagation. Therefore, when detecting the image, GAN do not need to be updated with samples; it can produce better quality samples, express more clearly. Experimental results show that the method has strong generalization ability, fast recognition speed and high accuracy.

Approaches of Ontological Concept Mapping Based on Multi-Strategy

2014 ◽  
Vol 519-520 ◽  
pp. 820-823
Author(s):  
Rong Li ◽  
Lei Liu
Keyword(s):  
Concept Mapping ◽  
High Accuracy ◽  
Experimental Results ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
Ontological Concept

Aiming at solving ontology heterogeneity, in this paper, we propose approaches of ontological concept mapping based on multi-strategy. At first, we use strategies based on concept name, property and taxonomy to calculate similarity. Then mapping results of multi-strategy are obtained. Finally, we present experimental results with high accuracy and point out future research directions.

An Interpolation Procedure for the Minimum Film Thickness in Point Contacts

1990 ◽  
Vol 204 (3) ◽  
pp. 199-206 ◽  
Author(s):  
C J Hooke
Keyword(s):  
Film Thickness ◽  
Contact Geometry ◽  
High Accuracy ◽  
Operating Conditions ◽  
Experimental Results ◽  
Point Contacts ◽  
Design Charts ◽  
Minimum Film Thickness ◽  
Interpolation Procedure

Most engineering point contacts operate in, or close to, the elastic piezoviscous regime. A general interpolation procedure is presented by which the minimum film thickness in any such contact may be estimated. This procedure matches all existing numerical and experimental results with high accuracy. Design charts are provided and these enable the minimum film thickness to be read directly and also allow the effect of changes in contact geometry and operating conditions to be assessed.

scholarly journals Parameter Measurement of Biaxial Braided Composite Preform Based on Phase Congruency

2019 ◽  
Vol 19 (1) ◽  
pp. 8-16
Author(s):  
Zhitao Xiao ◽  
Lei Pei ◽  
Fang Zhang ◽  
Ying Sun ◽  
Lei Geng ◽  
...  
Keyword(s):  
Automatic Measurement ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Block Matching ◽  
Experimental Results ◽  
Corner Detection ◽  
New Method ◽  
Two Dimensional ◽  
Phase Congruency ◽  
Braided Composite

Abstract In this paper, a new method based on phase congruency is proposed to measure pitch lengths and surface braiding angles of two-dimensional biaxial braided composite preforms. Lab space transform and BM3D (block-matching and 3D filter) are used first to preprocess the original acquired images. A corner detection algorithm based on phase congruency is then proposed to detect the corners of the preprocessed images. Pitch lengths and surface braiding angles are finally measured based on the detected corner maps. Experimental results show that our method achieves the automatic measurement of pitch lengths and the surface braiding angles of biaxial braided composite preforms with high accuracy.

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