Neural Fields for Real-Time Navigation of an Omnidirectional Robot

In this paper, we implement a biologically inspired approach for the generation of real-time navigation of a real omnidirectional robot. The approach is based on a so-called neural fields, which are equivalent to continuous recurrent neural networks. Due to its dynamical properties, a neural field produces only one localized peak that indicates the optimum movement direction of the robot. Experimental results support the validity of the approach.

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