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Spatial mapping has been subject of extensive research in the robotics field. Approaches to map building have been proposed, such as topological (Franz et al., 1998), metric (Movarec & Elfes, 1985), and hybrid maps combining these two approaches (Guivant et al., 2004; Kuipers et al., 2004; Bosse et al., 2004; Zivkovic et al., 2005). Diverse issues have arisen as critical to implement practical and robust solutions to the SLAM (Simultaneous Localization And Mapping) problem in real, large and static environments, such as data association, which relates to whether or not two features observed at different points in time correspond to one and the same object or place in the physical world (Hähnel et al., 2003), and perceptual ambiguity that occurs when trying to distinguish between places in the environment that may provide equivalent visual patterns (Frese, 2006). In general terms, relevant aspects of concern are related to the evaluation of SLAM algorithms according to their computational complexity, their approach to the data association problem, and the sort of environment representation built. However, few efforts have been documented relative to the use of spatial representations to navigate directly to designated goal locations (Stentz & Hebert, 1995; Thrun et al., 2000). Even more, we have not found reports of attempts dealing with the “unlearning” process of previously learnt target locations. Therefore, one of the purposes of our research consists on addressing the imbalance between spatial mapping and map exploitation detected in the SLAM literature, as well as the lack of target unlearning research, by taking inspiration from these abilities in animals. Indeed, research cycles involving animal studies, computational modeling, and robotic experimentation, have inspired for many years the understanding of the underlying neurophysiology and neuromechanics of biological systems while also inspiring new robotic architectures and applications. Specifically, the study of behavioral and neurophysiological mechanisms involved in rat spatial cognition provides a basis for the development of computational models of robot mapping and robot experimentation during goal-oriented navigation tasks. These models and robotic architectures offer neurobiologists and neuroethologists alternative platforms to study, analyze and predict spatial cognition based behaviors. This chapter presents a computational system-level model of rat spatial cognition addressing aspects relative to cognitive map generation, adaptation, and exploitation during 27

Robot Topological Mapping and Goal-Oriented Navigation Based on Rat Spatial Cognition