Self-organising fuzzy decision trees for robot navigation: An online learning approach

Abstract

Proposes a hybrid technique for intelligent robot navigation based on incremental decision trees (ITI-2.8) and incorporating fuzzy logic for flexible control. The robot perception is decomposed into a hierarchy of simpler virtual environments, termed worlds. Training examples generated from the robot's past rewarded experiences are exposed to ITI-2.8 in an incremental manner and online to evolve an array of fuzzy associative memories (FAM), each representing a unique world. That is, generated FAMs, which are structurally nonlinear (in contrast to ordinary FAMs), are engineered online and from inception to store and access fuzzy control rule spaces representing different perceptions. Each decision tree is encoded in one FAM and is local to a certain perception. The fundamental strengths of the algorithm in building online FAMs, is its incremental nature and automatically generating fuzzy training vectors without human intervention. Fuzziness is integrated to provide suitable reasoning in the face of inherent uncertainty in the sensory input data and to merge conflicting behaviours to generate smooth trajectories. Global navigation is achieved by activating a hierarchy of local FAMs.