An integrated architecture for motion-control and path-planning

Abstract

We consider the problem of learning how to control a plant with nonlinear control characteristics and solving the path-planning problem at the same time. The solution is based on a path-planning model that designates a speed field to be tracked, the speed field being the gradient of the equilibrium solution of a diffusionlike process which is simulated on an artificial neural network by spreading activation. The relaxed diffusion field serves as the input to the interneurons which detect the strength of activity flow in between neighboring discretizing neurons. These neurons then emit the control signals to control neurons which are linear elements. The interneuron to control-neuron connections are trained by a variant of Hebb's rule during control. The proposed method, whose most attractive feature is that it integrates reactive path-planning and continuous motion control in a natural fashion, can be used for learning redundant control problems. © 1998 John Wiley & Sons, Inc. 15: 1–15, 1998