Evolved transistor array robot controllers

Garvie, Michael, Flascher, Ittai, Philippides, Andrew, Thompson, Adrian and Husbands, Phil (2020) Evolved transistor array robot controllers. Evolutionary Computation, 28 (4). pp. 677-708. ISSN 1063-6560

[img] PDF - Accepted Version
Download (11MB)
[img] PDF - Published Version
Download (1MB)

Abstract

For the first time a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to
analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium.

Item Type: Article
Keywords: robotics, artificial intelligence, evolutionary computation, evolutionary robotics, FPTA, evolvable harware
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 24 Apr 2020 08:54
Last Modified: 12 Jan 2021 14:15
URI: http://sro.sussex.ac.uk/id/eprint/91012

View download statistics for this item

📧 Request an update