<title>Adaptive fuzzy approach to modeling of operational space for autonomous mobile robots</title>

Abstract

Robots operating in an unstructured environment need high level of modeling of their operational space in order to plan a suitable path from an initial position to a desired goal. From this perspective, operational space modeling seems to be crucial to ensure a sufficient level of autonomy. In order to compile the information from various sources, we propose a fuzzy approach to evaluate each unit region on a grid map by a certain value of transition cost. This value expresses the cost of movement over the unit region: the higher the value, the more expensive the movement through the region in terms of energy, time, danger, etc. The approach for modeling, proposed in this paper, employs fuzzy granulation of information on various terrain features and their combination based on a fuzzy neural network. In order to adapt to the changing environmental conditions, and to improve the validity of constructed cost maps on-line, the system can be endowed with learning abilities. The learning subsystem would change parameters of the fuzzy neural network based decision system by reinforcements derived from comparisons of the actual cost of transition with the cost obtained from the model.