Trajectory Recognition and Navigation Control in the Mobile Robot

2011 ◽  
Vol 464 ◽  
pp. 11-14
Author(s):  
Chun Hui Yang ◽  
Fu Dong Wang
Keyword(s):  
Digital Signal ◽  
Guidance System ◽  
Joint Photographic Expert Group ◽  
Line Extraction ◽  
Robot Trajectory ◽  
Extraction Algorithm ◽  
Stepper Motors ◽  
Pixel Image ◽  
Trajectory Guidance ◽  
The Given

Fast and accurate acquisition of navigation information is the key and premise for robot guidance. In this paper, a robot trajectory guidance system composed of a camera, a Digital Signal Controller and mobile agency driven by stepper motors is given. First the JPEG (Joint Photographic Expert Group) image taken by camera is decoded and turns to correspond pixel image. By binarization process the image is then transformed to a binary image. A fast line extraction algorithm is presented based on Column Elementary Line Segment method. Furthermore the trajectory direction deviation parameters and distance deviation parameters are calculated. In this way the robot is controlled to follow the given track accurately in higher speed.

Research on TV Goniometer Object Extraction Algorithm Based on Threshold Segmentation

2014 ◽  
Vol 889-890 ◽  
pp. 1093-1098
Author(s):  
He Chen ◽  
Nan Li ◽  
Tian Chen Huang ◽  
Rong Xia Duan
Keyword(s):  
Optimal Algorithm ◽  
Detection System ◽  
Detection Algorithm ◽  
Field Of View ◽  
Object Extraction ◽  
Gray Level ◽  
Line Extraction ◽  
Standard Signal ◽  
Extraction Algorithm ◽  
Target Extraction

In the TV goniometer detection system, to play the signal and field of view points line extraction is a key link in the process of parameter detection. Combination of target processing requirements, this article will target extraction algorithm based on gray level threshold and edge detection algorithm is studied, and through the experimental analysis to select the optimal algorithm was applied to the detection of TV goniometer; According to the characteristics of the standard signal and view points, lines, and put forward the corresponding methods of target recognition, and is verified through experiments

Adaptive Line Extraction for Slam Line Based Applications in Unknown Environments

2011 ◽  
Vol 403-408 ◽  
pp. 2057-2064
Keyword(s):  
Mobile Robot ◽  
Laser Scanner ◽  
Line Extraction ◽  
International Conference ◽  
Unknown Environments ◽  
Extraction Algorithm ◽  
Ieee International Conference

Paper has been removed due to plagiarism. The original was published in the Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, February 21 - 26, 2009. Recursive Line Extraction Algorithm from 2D Laser Scanner Applied to Navigation a Mobile Robot, Mohammad Norouzi, Mostafa Yaghobi, Mohammad Rezai Siboni, Mahdi Jadaliha

An adaptive text-line extraction algorithm for printed Arabic documents with diacritics

2020 ◽  
Author(s):  
Khader Mohammad ◽  
Aziz Qaroush ◽  
Mahdi Washha ◽  
Sos Agaian ◽  
Iyad Tumar
Keyword(s):  
Text Line ◽  
Line Extraction ◽  
Extraction Algorithm ◽  
Text Line Extraction

Automatic point cloud feature-line extraction algorithm based on curvature-mutation analysis

2019 ◽  
Vol 27 (5) ◽  
pp. 1218-1228
Author(s):  
陈华伟 CHEN Hua-wei ◽  
袁小翠 YUAN Xiao-cui ◽  
吴禄慎 WU Lu-chen ◽  
王晓辉 WANG Xiao-hui
Keyword(s):  
Mutation Analysis ◽  
Point Cloud ◽  
Line Extraction ◽  
Feature Line ◽  
Extraction Algorithm

Analysis of the Effect of Noise in CosaMP Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 2992-2995
Author(s):  
Zheng Pu Zhang ◽  
Xing Feng Guo ◽  
Bo Tian
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Digital Signal ◽  
Sampling Theorem ◽  
The Novel ◽  
Reconstruction Algorithms ◽  
Research Directions ◽  
Comparison Results ◽  
New Type ◽  
The Given

Compressive sensing is a new type of digital signal processing method. The novel objective of compressive Sensing is to reconstruct a signal accurately and efficiently from far fewer sampling points got by Nyquist sampling theorem. Compressive sensing theory combines the process of sampling and compression to reduce the complexity of signal processing, which is widely used in many fields. so there are wide application prospects in the areas of radar image, wireless sensor network (WSN), radio frequency communication, medical image processing, image device collecting and so on. One of the important tasks in CS is how to recover the signals more accurately and effectively, which is concerned by many researchers. Compressive sensing started late; there are many problems and research directions worthy of our in-depth research. At present, many researchers shove focused on reconstruction algorithms. Reconstruction algorithms are the core of compressive sensing, which are of great significance to reconstructing compressed signals and verifying the accuracy in sampling. These papers introduce CosaMP algorithm; and then study and analyze the Gaussian noise as the main content. Finally, the given signal and random signal, for example, we give a series of comparison results.

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