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Paper ThB07.6

Kunze, Lars (Technische Universität München), Beetz, Michael (Technische Universität München), Saito, Manabu (University of Tokyo), Azuma, Haseru (The Univ. of Tokyo), Okada, Kei (The University of Tokyo), Inaba, Masayuki (The University of Tokyo)

Searching Objects in Large-Scale Indoor Environments: A Decision-Theoretic Approach

Scheduled for presentation during the Regular Session "AI Reasoning Methods" (ThB07), Thursday, May 17, 2012, 11:45−12:00, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on April 23, 2014

Keywords AI Reasoning Methods, Domestic Robots, Search and Rescue Robots

Abstract

Many of today's mobile robots are supposed to perform everyday manipulation tasks autonomously. However, in large-scale environments, a task-related object might be out of the robot's reach. Hence, the robot first has to search for the object in its environment before it can perform the task. In this paper, we present a decision-theoretic approach for searching objects in large-scale environments using probabilistic environment models and utilities associated with object locations. We demonstrate the feasibility of our approach by integrating it into a robot system and by conducting experiments where the robot is supposed to search different objects with various strategies in the context of fetch-and-delivery tasks within a multi-level building.

 

 

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