Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace
Applying computer vision to mobile robot navigation has been studied more than two decades. The most challenging problems for a vision-based AGV running in a complex workspace involve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably result in incomplete or deform...
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doaj-e9576e99f6d742a7b5a6cf66d59449ab2020-11-25T01:33:28ZengMDPI AGApplied Sciences2076-34172019-10-01919410810.3390/app9194108app9194108Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex WorkspaceXing Wu0Chao Sun1Ting Zou2Haining Xiao3Longjun Wang4Jingjing Zhai5College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaDepartment of Mechanical Engineering, Memorial University of Newfoundland, St. John’s A1B 3X5, CanadaCollege of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224051, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaApplying computer vision to mobile robot navigation has been studied more than two decades. The most challenging problems for a vision-based AGV running in a complex workspace involve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably result in incomplete or deformed path images as well as many fake artifacts. Neither the fixed threshold methods nor the iterative optimal threshold methods can obtain a suitable threshold for the path images acquired on all conditions. It is still an open question to estimate the model parameters of guide paths accurately by distinguishing the actual path pixels from the under- or over- segmentation error points. Hence, an intelligent path recognition approach based on KPCA−BPNN and IPSO−BTGWP is proposed here, in order to resist the interferences from the complex workspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of a path classifier based on KPCA−BPNN. Secondly, an approximation method based on BTGWP was developed for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robust path estimation method based on IPSO was proposed to figure out the path parameters from a set of path pixels surrounded by noise points. Experimental results showed that our approach can effectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in a complex workspace.https://www.mdpi.com/2076-3417/9/19/4108vision guidancepath classificationmodel estimationimage processingcurve approximation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xing Wu Chao Sun Ting Zou Haining Xiao Longjun Wang Jingjing Zhai |
spellingShingle |
Xing Wu Chao Sun Ting Zou Haining Xiao Longjun Wang Jingjing Zhai Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace Applied Sciences vision guidance path classification model estimation image processing curve approximation |
author_facet |
Xing Wu Chao Sun Ting Zou Haining Xiao Longjun Wang Jingjing Zhai |
author_sort |
Xing Wu |
title |
Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace |
title_short |
Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace |
title_full |
Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace |
title_fullStr |
Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace |
title_full_unstemmed |
Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace |
title_sort |
intelligent path recognition against image noises for vision guidance of automated guided vehicles in a complex workspace |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-10-01 |
description |
Applying computer vision to mobile robot navigation has been studied more than two decades. The most challenging problems for a vision-based AGV running in a complex workspace involve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably result in incomplete or deformed path images as well as many fake artifacts. Neither the fixed threshold methods nor the iterative optimal threshold methods can obtain a suitable threshold for the path images acquired on all conditions. It is still an open question to estimate the model parameters of guide paths accurately by distinguishing the actual path pixels from the under- or over- segmentation error points. Hence, an intelligent path recognition approach based on KPCA−BPNN and IPSO−BTGWP is proposed here, in order to resist the interferences from the complex workspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of a path classifier based on KPCA−BPNN. Secondly, an approximation method based on BTGWP was developed for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robust path estimation method based on IPSO was proposed to figure out the path parameters from a set of path pixels surrounded by noise points. Experimental results showed that our approach can effectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in a complex workspace. |
topic |
vision guidance path classification model estimation image processing curve approximation |
url |
https://www.mdpi.com/2076-3417/9/19/4108 |
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