In allusion to the complex route characteristics of the irregular shape and the fuzzy feature for the mobile robot vision navigation in unstructured environment, this paper proposed a method based on fuzzy-rough set theory for unstructured path recognition and visual guidance. Firstly, we established an adaptive charge-coupled device (CCD) image definition automatic control algorithm to capture the high definition image of navigation area, and based on that a fuzzy-rough set model(F-R model) for unstructured path recognition is developed, which on the one hand by means of the rough set method the target and background and uncertainty area are predefined according to the gray features of the image itself, on the other hand the iterative relative fuzzy connectedness (IRFC) image ROI delineation algorithm is fused with the rough set method to reclassify the uncertain region and delineate the boundary of robot navigation path and non navigation region. By establishing a fusion F-R model, the seeds location and path identification can be automatically realized in unknown unstructured path region without the environmental prior knowledge. The experimental results showed that the proposed method is of practical significance to improve the ability of autonomous exploration of mobile robots in unstructured environment. Currently, the algorithm and running speed need to be further optimized for fast path recognition of robot navigation, which can lay the foundation for vision based high speed mobile robot navigation.
Liming Zhao, Jing Chen, Yi Zhang, Chuan Ye, Xiaodong Xu, and Hong Xiao, "Research on method of vision navigation for mobile robot in unstructured environment
," Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 106791A (Presented at SPIE Photonics Europe: April 26, 2018; Published: 24 May 2018); https://doi.org/10.1117/12.2306583.
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