Papers by Author: Tomislav Stipančić

Abstract: The objective of this paper is to discuss the probabilistic part of the model for robot group control applied in industrial applications. The proposed model is based on well-known concepts of Ubiquitous Computing [1] and enables contextual perception of a working environment. Compared with classical industrial robots, usually preprogrammed for a limited number of operations / actions, the system based on this model can react in uncertain situations and scenarios. The model combines ontology to describe the specific domain of interest and decision–making mechanisms based on Bayesian Networks (BN) to enable the work of a single robot without human intervention by learning Behavioral Patterns (BP) of other robots in the group.

Abstract: In this paper, an integration of Honey bees mating algorithm (HBMA) and adaptive resonance theory neural network (ART1) for efficient path planning of a mobile robot in a static environment is presented. The robot must find shortest route from given origin to the target position. Moreover, it should be able to memorize the environment and, if it faces known world, execute already learned trajectory found by HBMA solver, or solve the world and memorize the trajectory for the given environment. This is done using Adaptive Resonance Theory based neural network. This way simulated robot is able to navigate through environment and to continuously increase its knowledge.