Convolutional Vision Transformer as a Path Following Controller for Omnidirectional Robots

Abstract

A novel deep neural network (DNN) based controller for omnidirectional robots is proposed. The controller decomposes the prescribed reference path, corresponding to a fixed prediction horizon, into multiple paths of shorter horizons. This implicitly enforces a Hankel structure in the input and consequently also on the output. Taking advantage of this, a convolutional vision transformer model is used to realize the controller which is then trained to predict state and controls over multiple prediction horizons. Model training is performed in a self-supervised manner using a synthetic dataset. The proposed controller is shown to be more efficient than a model designed for a single prediction horizon. In comparison to a model predictive controller, the proposed approach exhibits competitive performance in path following tasks and is three times faster on average for the same prediction length.