Competitive Learning and its Application in Adaptive Vision for Autonomous Mobile Robots
The task of providing robust vision for autonomous mobile robots is a complex signal processing problem which cannot be solved using traditional deterministic computing techniques. In this article we investigate
four unsupervised neural learning algorithms, known collectively as competitive learning, in order to assess both their theoretical operation and their ability to learn to represent a basic robotic vision
task. This task involves the ability of a modest robotic system to identify the components of basic motion and to generalize upon that learned knowledge to classify correctly novel visual experiences. This
investigation shows that standard competitive learning and the DeSieno version of frequency-sensitive competitive learning (FSCL) are unsuitable for solving this problem. Soft competitive learning, while
capable of producing an appropriate solution, is too computationally expensive in its present form to be used under the constraints of this application. However, the Krishnamurthy version of FSCL is found
to be both computationally efficient and capable of reliably learning a suitable solution to the motion identification problem both in simulated tests and in actual hardware-based experiments.
Keywords: ADAPTIVE VISION; AUTONOMOUS MOBILE ROBOTS; NEURAL COMPUTATION; VISUAL REPRESENTATION
Document Type: Research Article
Publication date: 01 December 1999
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