Becoming incrementally reactive: on-line learning of an evolving decision tree array for robot navigation

Authors:
G. H. Shah Hamzei;D. J. Mulvaney;I. Sillitoe
Affiliations:
Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, Leics LE11 3TU (UK). E-mail: G.H.Shah-Hamzei@lboro.ac.uk;Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, Leics LE11 3TU (UK). E-mail: D.J.Mulvaney@lboro.ac.uk;Department of Technology, University of Borås, 501 15 Borås (Sweden). E-mail: Ian@adm.ing.hb.se
Venue:
Robotica
Year:
1999

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Abstract

This paper proposes a novel hierarchical multi-layer decision tree for representing reactive robot navigation knowledge. In this representation, the perception space is decomposed into a hierarchical set of worlds reflecting environments which are homogeneous in nature and which vary in complexity in an ordered manner. Each world is used to produce a corresponding decision tree which is trained incrementally. The instantaneous perception of the robot is used to select an appropriate rule from the decision tree and a sequence of rule activations form the complete trajectory. The ability to keep the knowledge complexity manageable and under control is an important aspect of the technique.