We have recently introduced a neural network mobile robot controller
(NETMORC). This controller, based on previously developed neural
network models of biological sensory-motor control, autonomously
learns the forward and inverse odometry of a differential drive robot
through an unsupervised learning-by-doing cycle. After an initial
learning phase, the controller can move the robot to an arbitrary
stationary or moving target while compensating for noise and other
forms of disturbance, such as wheel slippage or changes in the robot's
plant. In addition, the forward odometric map allows the robot to
reach targets in the absence of sensory feedback. The controller
is also able to adapt in response to long-term changes in the robot's
plant, such as a change in the radius of the wheels. In this article
we review the NETMORC architecture and describe its simplified algorithmic
implementation, we present new, quantitative results on NETMORC's
performance and adaptability under noise-free and noisy conditions,
we compare NETMORC's performance on a trajectory-following task with
the performance of an alternative controller, and we describe preliminary
results on the hardware implementation of NETMORC with the mobile
robot ROBUTER
%0 Journal Article
%1 Gaudiano:1996
%A Gaudiano, P.
%A Zalama, E.
%A Coronado, J. L.
%D 1996
%J Systems, Man and Cybernetics, Part B, IEEE Transactions on
%K imported
%P 485-496
%T An unsupervised neural network for low-level control of a wheeledmobile
robot: noise resistance, stability, and hardware implementation
%V 26
%X We have recently introduced a neural network mobile robot controller
(NETMORC). This controller, based on previously developed neural
network models of biological sensory-motor control, autonomously
learns the forward and inverse odometry of a differential drive robot
through an unsupervised learning-by-doing cycle. After an initial
learning phase, the controller can move the robot to an arbitrary
stationary or moving target while compensating for noise and other
forms of disturbance, such as wheel slippage or changes in the robot's
plant. In addition, the forward odometric map allows the robot to
reach targets in the absence of sensory feedback. The controller
is also able to adapt in response to long-term changes in the robot's
plant, such as a change in the radius of the wheels. In this article
we review the NETMORC architecture and describe its simplified algorithmic
implementation, we present new, quantitative results on NETMORC's
performance and adaptability under noise-free and noisy conditions,
we compare NETMORC's performance on a trajectory-following task with
the performance of an alternative controller, and we describe preliminary
results on the hardware implementation of NETMORC with the mobile
robot ROBUTER
@article{Gaudiano:1996,
abstract = {We have recently introduced a neural network mobile robot controller
(NETMORC). This controller, based on previously developed neural
network models of biological sensory-motor control, autonomously
learns the forward and inverse odometry of a differential drive robot
through an unsupervised learning-by-doing cycle. After an initial
learning phase, the controller can move the robot to an arbitrary
stationary or moving target while compensating for noise and other
forms of disturbance, such as wheel slippage or changes in the robot's
plant. In addition, the forward odometric map allows the robot to
reach targets in the absence of sensory feedback. The controller
is also able to adapt in response to long-term changes in the robot's
plant, such as a change in the radius of the wheels. In this article
we review the NETMORC architecture and describe its simplified algorithmic
implementation, we present new, quantitative results on NETMORC's
performance and adaptability under noise-free and noisy conditions,
we compare NETMORC's performance on a trajectory-following task with
the performance of an alternative controller, and we describe preliminary
results on the hardware implementation of NETMORC with the mobile
robot ROBUTER},
added-at = {2009-06-26T15:25:19.000+0200},
author = {Gaudiano, P. and Zalama, E. and Coronado, J. L.},
biburl = {https://www.bibsonomy.org/bibtex/290f17b74e04fb53a5b20ea2fcf2d794b/butz},
description = {diverse cognitive systems bib},
interhash = {cd30d58f6f6f53201408921528f6f5de},
intrahash = {90f17b74e04fb53a5b20ea2fcf2d794b},
journal = {Systems, Man and Cybernetics, Part B, IEEE Transactions on},
keywords = {imported},
owner = {butz},
pages = {485-496},
timestamp = {2009-06-26T15:25:30.000+0200},
title = {An unsupervised neural network for low-level control of a wheeledmobile
robot: noise resistance, stability, and hardware implementation},
volume = 26,
year = 1996
}