LiU Electronic Press
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381 kb
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Author:
Dornhege, Christian (University of Freiburg) (Foundations of Artificial Intelligence)
Kleiner, Alexander (University of Freiburg) (Collaborative Robotics)
Title:
Fully Autonomous Planning and Obstacle Negotiation on Rough Terrain Using Behavior Maps
Publication type:
Conference paper (Refereed)
Language:
English
In:
In Video Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)
Conference:
Conference on Intelligent Robots and Systems (IROS)
Pages:
2561-2562
Year of publ.:
2007
URI:
urn:nbn:se:liu:diva-72545
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72545
ISBN:
978-1-4244-0912-9
Subject category:
Robotics
Computer Science
Project:
Artificial Intelligence & Integrated Computer Systems
Abstract(en) :

To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.

Available from:
2011-11-28
Created:
2011-11-28
Last updated:
2013-08-08
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381 kb
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