| dc.creator |
Goldhoorn, Alex |
|
| dc.creator |
Ramisa, Arnau |
|
| dc.creator |
Lopez de Mantaras, Ramon |
|
| dc.creator |
Toledo, Ricardo |
|
| dc.date |
2008-04-16T13:55:19Z |
|
| dc.date |
2008-04-16T13:55:19Z |
|
| dc.date |
2007 |
|
| dc.date.accessioned |
2017-01-31T01:02:27Z |
|
| dc.date.available |
2017-01-31T01:02:27Z |
|
| dc.identifier |
Artificial Intelligence Research and Development. CCIA'07: 10th International Conference of the ACIA. Andorra, October 25-26. Frontiers in Artificial Intelligence and Applications, Vol. 163. IOS Press. p.p.: 331-338. 2007. |
|
| dc.identifier |
978-1-58603-798-7 |
|
| dc.identifier |
0922-6389 |
|
| dc.identifier |
http://hdl.handle.net/10261/3627 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3627 |
|
| dc.description |
The original publication ia available at
http://www.booksonline.iospress.nl/Content/View.aspx?piid=7638 |
|
| dc.description |
Several methods can be used for a robot to return to a previously visited position. In our approach we use the average landmark vector method to calculate a homing vector which should point the robot to the destination. This approach
was tested in a simulated environment, where panoramic projections of features were used. To evaluate the robustness of the method, several parameters of the simulation were changed such as the length of the walls and the number of features,
and also several disturbance factors were added to the simulation such as noise and occlusion. The simulated robot performed really well. Randomly removing 50% of the features resulted in a mean of 85% successful runs. Even adding more than 100% fake features did not have any significant result on the performance. |
|
| dc.description |
This work has been partially supported by the FI grant from the Generalitat de
Catalunya and the European Social Fund, the MID-CBR project grant TIN2006-15140-
C03-01 and FEDER funds and the Marco Polo Fund of the University of Groningen. |
|
| dc.description |
Peer reviewed |
|
| dc.format |
169070 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
IOS Press |
|
| dc.rights |
openAccess |
|
| dc.subject |
Artificial Intelligence |
|
| dc.subject |
Mobile Robot Homing |
|
| dc.subject |
Average Landmark Vector |
|
| dc.subject |
Invariant Features |
|
| dc.title |
Using the Average Landmark Vector Method for Robot Homing. |
|
| dc.type |
Artículo |
|