Abstract
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<p>In this paper we propose a simple … <p>In this paper we propose a simple method for low-level navigation for autonomous mobile robots, employing an artificial neural network. Both corridor following and obstacle avoidance in indoor environments are managed by the same network. Raw grayscale images of size 32 x 23 pixels are processed one at a time by a feed-forward neural network. The output signals from the network directly control the motor control system of the robot. The feed-forward network is trained using the RPROP algorithm. Experiments in both familiar and unfamiliar environments are reported.</p>iliar environments are reported.</p>
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Author
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Magnus Jonsson +
, Per-Arne Wiberg +
, Nicholas Wickström +
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Diva
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http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239973
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EndPage
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111 +
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HostPublication
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Proc. International Workshop on Mechatronical Computer Systems for Perception and Action (MCPA'97), Pisa, Italy, Feb. 10-12 +
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PublicationType
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Conference Paper +
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StartPage
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105 +
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Title
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Vision-based low-level navigation using a feed-forward neural network +
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Year
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1997 +
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Has queryThis property is a special property in this wiki.
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Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
, Publications:Vision-based low-level navigation using a feed-forward neural network +
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Categories |
Publication +
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Modification dateThis property is a special property in this wiki.
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26 June 2014 03:43:01 +
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