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dc.contributor.authorGoldhoorn, Alex-
dc.contributor.authorRamisa, Arnau-
dc.contributor.authorLópez de Mántaras, Ramón-
dc.contributor.authorToledo, Ricardo-
dc.identifier.citationArtificial 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.en_US
dc.descriptionThe original publication ia available at http://www.booksonline.iospress.nl/Content/View.aspx?piid=7638en_US
dc.description.abstractSeveral 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.en_US
dc.description.sponsorshipThis 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.en_US
dc.format.extent169070 bytes-
dc.publisherIOS Pressen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectMobile Robot Homingen_US
dc.subjectAverage Landmark Vectoren_US
dc.subjectInvariant Featuresen_US
dc.titleUsing the Average Landmark Vector Method for Robot Homing.en_US
dc.description.peerreviewedPeer revieweden_US
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