Integration of Reactive Utilitarian Navigation and Topological Modeling

Abstract

This chapter describes a hybrid autonomous navigation system for mobile robots. The control architecture proposed is highly modular and is based on the concept of behavior, which is a generalization of the usual reactive interpretation of this term. The proposed navigation system involves a straightforward integration of reactive and deliberative modules, enabling global, model-based navigation and local, adaptive navigation. At the local navigation level, we introduce the concept of utilitarian navigation, which models low-level robot navigation as a functional optimization process. Thanks to this innovative perspective, we have been able to implement low-level tasks, like collision avoidance and sensory source search and evasion, which have been integrated into the hybrid navigation system. At the global navigation level, two fundamental problems are considered: (1) map or model building and (2) route planning. Fuzzy Petri nets (FPN) are used to construct topological maps. A minimum cost algorithm of the FPN propagation has been implemented for route planning and execution. This chapter also discusses the experimental work carried out with realistic simulations, as well as with a holonomic prototype built by the authors and a Nomad-200 mobile platform.