title: Spatial abstraction for autonomous robot navigation.
abstract: Optimal navigation for a simulated robot relies on a detailed map and explicit path planning, an approach problematic for real-world robots that are subject to noise and error. This paper reports on autonomous robots that rely on local spatial perception, learning, and commonsense rationales instead. Despite realistic actuator error, learned spatial abstractions form a model that supports effective travel.
authors.affiliation: Department of Computer Science, Hunter College of The City University of New York, New York, NY, USA, susan.epstein@hunter.cuny.edu.
authors.forename: Susan L
authors.initials: SL
authors.lastname: Epstein
authors.forename: Anoop
authors.initials: A
authors.lastname: Aroor
authors.forename: Matthew
authors.initials: M
authors.lastname: Evanusa
authors.forename: Elizabeth I
authors.initials: EI
authors.lastname: Sklar
authors.forename: Simon
authors.initials: S
authors.lastname: Parsons
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D012371:Robotics; D013028:Space Perception; D065854:Spatial Navigation / Q000502:physiology*