| EPSRC Reference: |
GR/M60675/01 |
| Title: |
EVOLUTION OF SENSORIMOTOR NEURAL NETWORKS TO PERFORM ACTIVE MOTION CAMOUFLAGE |
| Principal Investigator: |
McOwan, Professor PW |
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| Department: |
Computer Science |
| Organisation: |
Queen Mary University of London |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 December 1999 |
Ends: |
30 November 2002 |
Value (£): |
54,941
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| EPSRC Research Topic Classifications: |
| New & Emerging Comp. Paradigms |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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Sensorimeter integration, the mapping of environmental sensory input to the appropriate motor output, is one of the most fundamental questions to be addressed in both biology and robotics. This research will use the genetic algorithm paradigm to evolve computational models of simple, biologically plausible, sensorimeter neural systems, and train them to exhibit active motion camouflage. This naturally occurring stealth behaviour allows a predator to conceal its movement towards its prey by following a trajectory that in effect generates the same optical flow pattern on the prey's retina as would be produced by a stationary point in the environment. This research aims to develop the first explicit neural model for this behaviour. The model will inform the biological community and, in future, should allow the customized transfer of this useful approaching behaviour to other systems, auch as autonomous robots.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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