Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots.

Soft Computing (Impact Factor: 1.3). 01/2008; 12(7):715-720. DOI: 10.1007/s00500-007-0245-0
Source: DBLP

ABSTRACT The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning
to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories
if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors
without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle
to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as
the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based
on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.

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