Competitive Learning and its Application in Adaptive Vision for Autonomous Mobile Robots
D.K. McNeill, HOWARD C. CARD · Connection Science · 1999
The task of providing robust vision for autonom ous 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 lear ning algor ithms, known collectively as competitive learning, in order to assess both their theoretical operation and their ability to lear n 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 lear ned knowledge to classify correctly novel visual experiences.This investigation shows that standard competitive lear ning and the DeSieno version of frequency-sensitive competitive lear ning (FSCL) are unsuitable for solving this problem.Soft competitive lear ning , 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 Krishnamur thy version of FSC L is found to be both computationally eý cient and capable of reliably lear ning a suitable solution to the motion identi® cation problem both in simulated tests and in actual hardware-based experiments.