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KI - Künstliche Intelligenz
Published in: KI - Künstliche Intelligenz 2/2011

01-05-2011 | Projekt

Offroad Navigation Using Adaptable Motion Patterns

Authors: Frank Hoeller, Timo Röhling, Dirk Schulz

Published in: KI - Künstliche Intelligenz | Issue 2/2011

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Abstract

We present a navigation system which is able to steer an electronically controlled ground vehicle to given destinations considering all obstacles in its vicinity. The approach is designed for vehicles without a velocity controlled drive-train, making it especially useful for typical remote-controlled vehicles. The vehicle is controlled by sets of commands, each set representing a specific maneuver. These sets are combined in a tree-building procedure to form trajectories towards the given destination. While the sets of commands are executed the vehicle’s behavior is measured to refine the prediction used for path generation. This enables the approach to adapt to surface alterations. We tested our system using a 400 kg EOD robot in an outdoor environment.
previous article Autonomous Off-Road Navigation for MuCAR-3
next article Behaviour-Based Off-Road Robot Navigation

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Literature
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Brock O, Khatib O (1999) High-speed navigation using the global dynamic window approach. In: Proc of the IEEE international conference on robotics & automation (ICRA)
2.
Bruch M, Gilbreath G, Muelhauser J (2002) Accurate waypoint navigation using non-differential GPS. In: Proc of AUVSI unmanned systems
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Brunner M, Schulz D, Cremers AB (2010) Position estimation of mobile robots considering characteristic terrain model. In: International conference on informatics in control, automation and robotics (ICINCO), June 2010
4.
Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom Mag 4(1):23–33 CrossRef
5.
Hoeller F, Roehling T, Schulz D (2010) Offroad navigation using adaptable motion patterns. In: International conference on informatics in control, automation and robotics (ICINCO), June 2010
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Neumann G, Maass W, Peters J (2009) Learning complex motions by sequencing simpler motion templates. In: Proc of the international conference on machine learning (ICML)
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Pivtoraiko M, Knepper RA, Kelly A (2009) Differentially constrained mobile robot motion planning in state lattices. J Field Robot 26(3):308–333 CrossRef
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v. Hundelshausen F, Himmelsbach M, Hecker F, Mueller A, Wuensche H-J (2008) Driving with tentacles: Integral structures for sensing and motion. J Field Robot 25(9):640–673 CrossRefMATH
Metadata
Title
Offroad Navigation Using Adaptable Motion Patterns
Authors
Frank Hoeller
Timo Röhling
Dirk Schulz
Publication date
01-05-2011
Publisher
Springer-Verlag
Published in
KI - Künstliche Intelligenz / Issue 2/2011
Print ISSN: 0933-1875
Electronic ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-011-0099-6

Other articles of this Issue 2/2011

KI - Künstliche Intelligenz 2/2011 Go to the issue

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Übersichtsbeitrag

Off-road Robotics—An Overview

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Autonomous Off-Road Navigation for MuCAR-3

Editorial

Themenheft Offroad-Robotik

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Sensor-Fusion Based Real-Time 3D Outdoor Scene Reconstruction and Analysis on a Moving Mobile Outdoor Robot

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