The Sensory Ego-Sphere (SES) is an interface for a robot that serves to mediate information between sensors and cognition. The SES can be visualized as a sphere centered on the coordinate frame of the robot, spatially indexed by polar and azimuthal angles. Internally, the SES is a graph with a fixed number of edges that partitions surrounding space and contains localized sensor information from the robot. This paper describes the SES and gives the results of implementing the SES on multiple robots, both humanoid and mobile, to support essential functions such as a localized short-term memory, spatio-temporal sensory-motor event detection, attentional processing, data sharing, and ego-centric navigation.

Dedicated to: Kevin Fleming, who always encouraged and supported me, Jimmy Fleming, who shared so much happiness, and to Anne Fleming, who is always listening and supportive ii ACKNOWLEDGEMENTS Thanks to Dr. Kawamura for his guidance, to Dr. Wilkes for many hours of help, instruction, feedback, to Katherine Achim for much help and support and to everyone at the CIS lab, for all the help iii TABLE OF CONTENTS iv Page DEDICATION.................................................................................................................... ii

Abstract—Hand drawn sketches are natural means by which a high level description of an environment can be provided. They can be exploited to impart coarse prior information about the scene to a robot, thereby enabling it to perform autonomous navigation and exploration when a full metrical description of the scene is not available beforehand. In this paper, we present a navigation system supplemented by a tablet interface that allows a user to sketch a rough map of an indoor environment and a desired trajectory for the robot to follow. We propose a novel theoretical framework for sketch interpretation based upon the manifold formalism in which associations between the sketch and the real world are modeled as local deformation of a suitable metric manifold. We also present empirical results from experimental evaluations of our approach in real world scenarios both from the perspective of the navigation capability and the usability of the interface. I.

*The authors assert equal contribution and joint first authorship. Abstract—Metric maps provide a reliable basis for mobile robot navigation. However, such maps are in general quite resource expensive and do not scale very well. Aiming for a highly scalable map, we adopt theories of insect navigation to develop an algorithm which builds a topological map for global navigation. Similar to insect conduct, positions in space are memorized as snapshots, which are unique configurations of landmarks. Unlike conventional snapshot approaches, we do not simply store the landmarks as a set, but we build a landmark tree which enables us to easily free memory in case of a continuously growing map while still preserving the dominant information. The resulting navigation is not sensor specific and solely relies on the directions of arbitrary landmarks. The generated map enables a mobile robot to navigate between defined locations and let it retrace a previously pursued path. Finally, we verify the reliability of the Landmark-Tree Map (LT-Map) concept and its robustness on memory limitations. Index Terms—map, topological, navigation, landmarks, tree, bio-inspired, efficient, scalable