Millward, B., Maddock, S. orcid.org/0000-0003-3179-0263 and Mangan, M. orcid.org/0000-0002-0293-8874 (2020) Towards insect inspired visual sensors for robots. In: Fox, C., Duckett, T. and Richards, A., (eds.) UKRAS20 Conference: “Robots into the real world” Proceedings. UKRAS20 Conference: “Robots into the real world”, 2020-04, Online conference. EPSRC UK-RAS Network , pp. 140-141.
Abstract
Flying insects display a repertoire of complex behaviours that are facilitated by their non-standard visual system that if understood would offer solutions for weight- and power- constrained robotic platforms such as micro unmanned aerial vehicles (MUAVs). Crucial to this goal is revealing the specific features of insect eyes that engineered solutions would benefit from possessing, however progress in exploration of the design space has been limited by challenges in accurately replicating insect vision. Here we propose that emerging ray-tracing technologies are ideally placed to realise the high-fidelity replication of the insect visual perspective in a rapid, modular and adaptive framework allowing development of technical specifications for a new class of bio-inspired sensor. A proof-of-principle insect eye renderer is shown and insights into research directions it affords discussed.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. This is an author-produced version of a paper subsequently published in UKRAS20 Conference: “Robots into the real world” Proceedings. |
Keywords: | Novel sensing; Artificial Intelligence and Robotics; Bioinspired; Vision; Rendering |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 May 2020 08:59 |
Last Modified: | 15 May 2020 09:33 |
Published Version: | https://www.ukras.org/publications/ras-proceedings... |
Status: | Published |
Publisher: | EPSRC UK-RAS Network |
Refereed: | Yes |
Identification Number: | 10.31256/do2ik3h |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160806 |