Vision-based heading angle estimation for an autonomous mobile robots navigation
- Format
- Article
- Status
- publishedVersion
- Description
Autonomous mobile robots navigation and control systems are still hugely important in real time robotic applications. When moving towards fully autonomous navigation, guidance plays a vital task for successful autonomous navigation. In this paper, the authors propose real time guidance fuzzy logic application based on edge and color information surrounding the road surface by using omnidirectional cameras. Autonomous navigation systems must be able to recognize feature descriptors from both edge and color information. Firstly, it was proposed to extract the longest segments of lines from the above mentioned methods. Secondly, RANdom SAmple Consensus (RANSAC) curve fitting method was implemented for detecting the best curve fitting given the data set of points for each line segment. Thirdly, the set of intersection points for each pair of curves were extracted. Fourthly, the Density-based spatial clustering of applications with noise (DBSCAN) method was used in estimating the vanishing point (VP). Finally, to control the mobile robot in an unknown environment, a fuzzy logic controller facilitated by the VP was implemented. Preliminary results were gathered and tested on a group of consecutive frames undertaken at the University of Ulsan (UoU) to prove their effectiveness.
Autonomous mobile robots navigation and control systems are still hugely important in real time robotic applications. When moving towards fully autonomous navigation, guidance plays a vital task for successful autonomous navigation. In this paper, the authors propose real time guidance fuzzy logic application based on edge and color information surrounding the road surface by using omnidirectional cameras. Autonomous navigation systems must be able to recognize feature descriptors from both edge and color information. Firstly, it was proposed to extract the longest segments of lines from the above mentioned methods. Secondly, RANdom SAmple Consensus (RANSAC) curve fitting method was implemented for detecting the best curve fitting given the data set of points for each line segment. Thirdly, the set of intersection points for each pair of curves were extracted. Fourthly, the Density-based spatial clustering of applications with noise (DBSCAN) method was used in estimating the vanishing point (VP). Finally, to control the mobile robot in an unknown environment, a fuzzy logic controller facilitated by the VP was implemented. Preliminary results were gathered and tested on a group of consecutive frames undertaken at the University of Ulsan (UoU) to prove their effectiveness.
- Publication Year
- 2018
- Language
- eng
- Topic
- Navigation
Image segmentation
Robot sensing systems
Mobile robots
Image color analysis
Cameras
Navigation
Image segmentation
Robot sensing systems
Mobile robots
Image color analysis
Cameras
- Repository
- RI de Documento Digitales de Acceso Abierto de la UTP
- Get full text
- https://ieeexplore.ieee.org/abstract/document/6864917/
http://ridda2.utp.ac.pa/handle/123456789/5089
- Rights
- embargoedAccess
- License