Hardware design for autonomous robot evolution

Hale, Matthew F.; Angus, Mike; Buchanan, Edgar; Li, Wei; Woolley, Robert; Goff, Leni K. Le; Carlo, Matteo De; Timmis, Jon; Winfield, Alan F.; Hart, Emma; Eiben, Agoston E.; Tyrrell, Andy M.

Abstract

The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition of evolutionary designs from purely simulation environments into the real world creates the possibility for new types of system able to adapt to unknown and changing environments. In this paper, a system for creating robots is introduced in order to allow for their body plans to be designed algorithmically and physically instantiated using the previously introduced Robot Fabricator. This system consists of two types of components. Firstly, skeleton parts are created bespoke for each design by 3D printing, allowing the overall shape of the robot to include almost infinite variety. To allow for the shortcomings of 3D printing, the second type of component are organs which contain components such as motors and sensors, and can be attached to the skeleton to provide particular functions. Specific organ designs are presented, with discussion of the design challenges for evolutionary robotics in hardware. The Robot Fabricator is extended to allow for robots with joints, and some example body plans shown to demonstrate the diversity possible using this system of robot generation.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 IEEE Symposium Series on Computational Intelligence (SSCI)
• Start Date: Dec 1, 2020
• End Date: Dec 4, 2020
• Acceptance Date: Sep 10, 2020
• Online Publication Date: Jan 5, 2021
• Publication Date: Dec 1, 2020
• Deposit Date: Jun 27, 2021
• Pages: 2140-2147
• Book Title: 2020 IEEE Symposium Series on Computational Intelligence (SSCI)
• ISBN: 9781728125466
• DOI: https://doi.org/10.1109/ssci47803.2020.9308204
• Public URL: https://uwe-repository.worktribe.com/output/7494883