Achieving intelligence in mobility
| dc.contributor.author | Kaiser, Michael | de |
| dc.date.accessioned | 2004-12-06T12:53:43Z | |
| dc.date.available | 2004-12-06T12:53:43Z | |
| dc.date.created | 1994 | de |
| dc.date.issued | 1999-11-09 | de |
| dc.description.abstract | This paper presents an integrated approach to the application of machine learning tasks that can be observed throughout a number of typical applications of mobile robots and puts those tasks into persepective with respect to both existing and newly developed learning techniques. The actual realization of the approach has been carried out on the two mobile robots PRIAMOS and TESEO, which are both operating in a real office environment. In this context, several experimental results are presented. This paper appeared in: IEEE-Expert: Special Track on Intelligent Robotic Systems, Vol. 10, No. 2, April 1995. | en |
| dc.format.extent | 444945 bytes | |
| dc.format.extent | 629959 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.issn | 0943-4135 | de |
| dc.identifier.uri | http://hdl.handle.net/2003/2597 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-5099 | |
| dc.language.iso | en | de |
| dc.publisher | Universität Dortmund | de |
| dc.relation.ispartofseries | Forschungsberichte des Lehrstuhls VIII, Fachbereich Informatik der Universität Dortmund ; 13 | de |
| dc.subject.ddc | 004 | de |
| dc.title | Achieving intelligence in mobility | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | |
| dcterms.accessRights | open access |