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A two teraflop swarm

Jones, Simon; Studley, Matthew; Hauert, Sabine; Winfield, Alan F

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Authors

Simon Jones

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Dr Matthew Studley Matthew2.Studley@uwe.ac.uk
Professor of Ethics & Technology/School Director (Research & Enterprise)

Sabine Hauert



Abstract

© 2018 Jones, Studley, Hauert and Winfield. We introduce the Xpuck swarm, a research platform with an aggregate raw processing power in excess of two teraflops. The swarm uses 16 e-puck robots augmented with custom hardware that uses the substantial CPU and GPU processing power available from modern mobile system-on-chip devices. The augmented robots, called Xpucks, have at least an order of magnitude greater performance than previous swarm robotics platforms. The platform enables new experiments that require high individual robot computation and multiple robots. Uses include online evolution or learning of swarm controllers, simulation for answering what-if questions about possible actions, distributed super-computing for mobile platforms, and real-world applications of swarm robotics that requires image processing, or SLAM. The teraflop swarm could also be used to explore swarming in nature by providing platforms with similar computational power as simple insects. We demonstrate the computational capability of the swarm by implementing a fast physics-based robot simulator and using this within a distributed island model evolutionary system, all hosted on the Xpucks.

Citation

Jones, S., Studley, M., Hauert, S., & Winfield, A. F. (2018). A two teraflop swarm. Frontiers in Robotics and AI, 5(FEB), https://doi.org/10.3389/frobt.2018.00011

Journal Article Type Article
Acceptance Date Jan 25, 2018
Publication Date Jan 1, 2018
Deposit Date Feb 20, 2018
Publicly Available Date Feb 20, 2018
Journal Frontiers Robotics AI
Electronic ISSN 2296-9144
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 5
Issue FEB
DOI https://doi.org/10.3389/frobt.2018.00011
Keywords swarm robotics, robot hardware, simulation, evolutionary robotics, behavior trees, distributed evolutionary algorithm, GPGPU, embodied reality modelling
Public URL https://uwe-repository.worktribe.com/output/874730
Publisher URL http://dx.doi.org/10.3389/frobt.2018.00011

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