Biologically Inspired Vision and Control for an Autonomous Flying Vehicle
| dc.contributor.author | Garratt, Matthew Adam | en_US |
| dc.date.accessioned | 2010-02-22T02:59:05Z | en_US |
| dc.date.accessioned | 2011-01-04T02:35:22Z | |
| dc.date.available | 2010-02-22T02:59:05Z | en_US |
| dc.date.available | 2011-01-04T02:35:22Z | |
| dc.date.issued | 2007 | |
| dc.description.abstract | This thesis makes a number of new contributions to control and sensing for unmanned vehicles. I begin by developing a non-linear simulation of a small unmanned helicopter and then proceed to develop new algorithms for control and sensing using the simulation. The work is field-tested in successful flight trials of biologically inspired vision and neural network control for an unstable rotorcraft. The techniques are more robust and more easily implemented on a small flying vehicle than previously attempted methods. ¶ ... | en_US |
| dc.identifier.other | b23509600 | |
| dc.identifier.uri | http://hdl.handle.net/1885/49285 | |
| dc.language.iso | en | en_US |
| dc.rights.uri | The Australian National University | en_US |
| dc.subject | Helicopter | en_US |
| dc.subject | optic flow | en_US |
| dc.subject | neural network | en_US |
| dc.subject | terrain following | en_US |
| dc.subject | sensor fusion | en_US |
| dc.title | Biologically Inspired Vision and Control for an Autonomous Flying Vehicle | en_US |
| dc.type | Thesis (PhD) | en_US |
| dcterms.valid | 2008 | en_US |
| local.contributor.affiliation | Visual Sciences, Research School of Biological Sciences | en_US |
| local.contributor.affiliation | The Australian National University | en_US |
| local.description.refereed | yes | en_US |
| local.identifier.doi | 10.25911/5d7a2c48c1521 | |
| local.mintdoi | mint | |
| local.type.degree | Doctor of Philosophy (PhD) | en_US |