Automatic neural-based pattern classification of motion behaviors in autonomous robots

Abstract

This paper addresses the problem of providing autonomous robots with a system that allows them to classify the motion behavior patterns of groups of robots present in their surroundings. It is a first step in the development of a cognitive model that can detect and understand the events occurring in the environment that are not due to the robot's own actions. The recognition of motion patterns must be achieved from the input data acquired by the robot through its camera during real time operation and, consequently, it can be addressed as a high dimensional dynamic pattern classification problem. Artificial Neural Networks (ANN) have been widely used in this type of classification problems, where a preprocessing stage is typically introduced in order to reduce dimensionality. In this stage, the processing window size and the dimensional transformation parameters must be selected according to specific domain knowledge, and they remain fixed during the ANN classification process. Such an approach is not applicable here as there is no prior information on the number of robots present or the dimensional reduction level required to describe the possible robot motion behaviors. Consequently, this work proposes a hybrid approach based on the application of a classification system called ANPAC (Automatic Neural-based Pattern Classifier) that uses a variable size ANN to perform the classification and an advisor module to adjust the preprocessing parameters and, consequently, the size of the ANN, depending on the learning results of the network. The components and operation of ANPAC are described in depth and illustrated using an example related to the recognition of behavior patterns in the motion of flocks.