Automatic calibration of a spiking head-direction network for representing robot orientation

Stratton, Peter, Milford, Michael, Wiles, Janet and Wyeth, Gordon (2009). Automatic calibration of a spiking head-direction network for representing robot orientation. In: Steve Scheding, Proceedings of the 2009 Australasian Conference on Robotics and Automation. ACRA'09: 2009 Australasian Conference on Robotics and Automation, Sydney, NSW, Australia, (). 2-4 December 2009.

Author Stratton, Peter
Milford, Michael
Wiles, Janet
Wyeth, Gordon
Title of paper Automatic calibration of a spiking head-direction network for representing robot orientation
Conference name ACRA'09: 2009 Australasian Conference on Robotics and Automation
Conference location Sydney, NSW, Australia
Conference dates 2-4 December 2009
Proceedings title Proceedings of the 2009 Australasian Conference on Robotics and Automation
Journal name Proceedings of the 2009 Australasian Conference on Robotics and Automation, ACRA 2009
Place of Publication Sydney, NSW, Australia
Publisher Australian Robotics and Automation Association (ARAA)
Publication Year 2009
Sub-type Fully published paper
ISBN 9780980740400
0980740401
Editor Steve Scheding
Total pages 8
Language eng
Formatted Abstract/Summary
Calibration of movement tracking systems is a difficult problem faced by both animals and robots. The ability to continuously calibrate changing systems is essential for animals as they grow or are injured, and highly desirable for robot control or mapping systems due to the possibility of component wear, modification, damage and their deployment on varied robotic platforms. In this paper we use inspiration from the animal head direction tracking system to implement a self-calibrating, neurally-based robot orientation tracking system. Using real robot data we demonstrate how the system can remove tracking drift and learn to consistently track rotation over a large range of velocities. The neural tracking system provides the first steps towards a fully neural SLAM system with improved practical applicability through selftuning and adaptation.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published under "Human Machine Interfaces & Biomimetics" as Paper 111.

 
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Created: Tue, 01 Mar 2011, 11:18:23 EST by Mrs Barbara Whittaker on behalf of Queensland Brain Institute