Obstacle detection for mobile vehicle using neural network and fuzzy logic

Huaijiang Sun; Jingyu Yang

doi:10.1117/12.441696

20 September 2001 Obstacle detection for mobile vehicle using neural network and fuzzy logic

Huaijiang Sun, Jingyu Yang

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441696
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

In our mobile vehicle project, sensors for environment modeling are a CCD color camera and two line-scan laser range finders. The CCD color camera is used to detect road edges. The two line-scan laser range finders are used to detect obstacles. Only two line-scan laser range finders increase processing speed, but there are blind zones for low obstacles, especially near the vehicle. In this paper, neural network and fuzzy logic are used to cluster and fuse obstacle points provided by two line-scan laser range finders. There is an assumption that obstacles missed by laser radar in some instant must be detected previously. A circle Adaptive Resonance neural network algorithm is used to incrementally cluster obstacle points provided by laser range finders into candidate obstacles. Every candidate obstacle is expressed by a circle, and is assigned a belief by a fuzzy logic system. Inputs of the fuzzy logic system are radius and number of points. Fuzzy rules are provided by human and can be fine-tuned with training data. The final true obstacle is the nearest one chosen from candidate obstacles whose beliefs exceed a threshold. Experiment results indicate that our mobile vehicle can safely follow road and avoid obstacles.

Citation Download Citation

Huaijiang Sun and Jingyu Yang "Obstacle detection for mobile vehicle using neural network and fuzzy logic", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441696

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