By Topic

Learning mobile robot control for obstacle avoidance based on motion energy neurons

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Minqi Gao ; Hong Kong Univ. of Sci. & Technol., Hong Kong, China ; Shi, B.E.

Image motion due to self motion is an important cue biological systems use for gathering information about the environment. The motion energy model is commonly used to model the responses of motion selective neurons in the mammalian primary visual cortex. Here, we investigate the hypothesis that these low level responses are directly useful for navigation. This avoids the need for estimating a model of the environment and the delay incurred in computing it. In order to discover the relationship between the neuron responses and the motor control required to avoid obstacles in the environment, we use reinforcement learning to train a robot equipped with infrared depth sensors to avoid objects using the outputs of simulated motion energy neurons by minimizing the long term average of the infrared signals it receives. Our experiments with a Koala robot indicate that the motion energy neuron outputs can effectively trigger obstacle avoidance motions in advance of those triggered by the infrared sensors.

Published in:

Asian Control Conference, 2009. ASCC 2009. 7th

Date of Conference:

27-29 Aug. 2009