Dirk Schulz

TL;DR: The software architecture of an autonomous, interactive tour-guide robot is presented, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Web-based telepresence and enables robots to operate safely, reliably, and at high speeds in highly dynamic environments.

TL;DR: Bayesian-filter techniques provide a powerful statistical tool to help manage measurement uncertainty and perform multisensor fusion and identity estimation and their application to real-world location-estimation tasks common in pervasive computing is shown.

TL;DR: An interactive tour-guide robot is described, which was successfully exhibited in a Smithsonian museum, and uses learning pervasively at all levels of the software architecture to address issues such as safe navigation in unmodified and dynamic environments, and short-term human-robot interaction.

TL;DR: In this paper, the authors describe the software architecture of an autonomous tour-guideltutor robot, which was recently deployed in the "Deutsches Museum Bonn," were it guided hundreds of visitors through the museum during a six-day deployment period.

Abstract: This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva’s software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes Minerva’s major software components, and provides a comparative analysis of the results obtained in the Smithsonian museum. During two weeks of highly successful operation, the robot interacted with thousands of people, both in the museum and through the Web, traversing more than 44km at speeds of up to 163 cm/sec in the unmodie d museum.