Abstract
Detection and recognition of obstacles are the most important concerns for fish robots to avoid collision for path planning as well as natural and smooth movements. The more information about obstacle shapes we obtain, the better control of fish robots we can apply. The method employing only simple distance measuring sensors without cameras is proposed. We use three fixed IR sensors and one IR sensor, which is mounted on a motor shaft to scan a certain range of foreground from the head of a fish robot. The fish robot’s ability to recognize the features of an obstacle is improved to avoid collision based on the fuzzy neural networks. Evident features such as obstacles’ sizes and angles are obtained from the scanned data by a simple distance sensor through neural network training algorithms. Experimental results show the successful path control of the fish robot without hitting on obstacles.
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© 2007 Springer-Verlag Berlin Heidelberg
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Na, S.Y., Shin, D., Kim, J.Y., Baek, SJ., Min, S.H. (2007). Obstacle Recognition and Collision Avoidance of a Fish Robot Based on Fuzzy Neural Networks. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_38
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DOI: https://doi.org/10.1007/978-3-540-71441-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71440-8
Online ISBN: 978-3-540-71441-5
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