Probabilistic Approach to Robot Group Control

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.

[1]  Hubert K. Rampersad,et al.  Integrated and Simultaneous Design for Robotic Assembly , 1995 .

[2]  Marko Svaco,et al.  Autonomous Planning Framework for Distributed Multiagent Robotic Systems , 2011, DoCEIS.

[3]  Petar Ćurković,et al.  Swarm-Based Approach to Path Planning Using Honey-Bees Mating Algorithm and ART Neural Network , 2009 .

[4]  Kathryn B. Laskey,et al.  Bayesian ontologies in AI systems , 2006 .

[5]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[6]  Niels. Lohse,et al.  Towards an ontology framework for the integrated design of modular assembly systems , 2006 .

[7]  Holger Knublauch,et al.  The Protégé OWL Plugin: An Open Development Environment for Semantic Web Applications , 2004, SEMWEB.

[8]  Petar Ćurković,et al.  ROBUST AUTONOMOUS ASSEMBLY IN ENVIRONMENT WITH RELATIVELY HIGH LEVEL OF UNCERTAINTY , 2008 .

[9]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[10]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[11]  D. L. McGuinness.  The Description Logic Handbook: Configuration , 2007 .

[12]  Bojan Jerbic,et al.  Self-adaptive Vision System , 2010, DoCEIS.

[13]  Valeriy Vyatkin,et al.  A deployment of an ontology-based reconfiguration agent for intelligent mechatronic systems , 2007, 2008 IEEE International Symposium on Industrial Electronics.

[14]  A. Siadat,et al.  MASON: A Proposal For An Ontology Of Manufacturing Domain , 2006, IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06).

[15]  Petar Ćurković,et al.  Hybridization of adaptive genetic algorithm and ART 1 neural architecture for efficient path planning of a mobile robot , 2008 .

[16]  Asma S. Larik,et al.  Efforts to blend ontology with Bayesian networks: An overview , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).