By Topic

Evolving neural network controllers to produce leg cycles for gait generation

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
$33 $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)
G. B. Parker ; Comput. Sci., Connecticut Coll., New London, CT, USA ; Zhiyi Li

The generation of gaits for hexapod locomotion controllers can be divided into two main parts: the cyclic action of a single leg (leg cycles) and the coordination of all legs to combine individual leg cycles to produce forward movement. In this paper, we use a genetic algorithm (GA) to evolve the structure of an artificial neural network (NN) that produces leg cycles in a hexapod robot. The movement of the robot's leg is controlled by a horizontal servo and vertical servo. The servos are controlled by a NN that generates a cycle of pulses. With minimal restrictions on the structure of the NN a GA is used to find the parameters of neurons and the connections between them. The pulse sequences generated by the evolved NNs resulted in leg cycles that produced efficient forward movement.

Published in:

Automation Congress, 2002 Proceedings of the 5th Biannual World  (Volume:14 )

Date of Conference:

2002