| dc.creator | Petereit, Janko | |
| dc.date.accessioned | 2021-01-21T18:04:30Z | |
| dc.date.available | 2021-01-21T18:04:30Z | |
| dc.date.created | 2016 | |
| dc.identifier.isbn | 978-3-731-50580-8 | |
| dc.identifier.issn | 1863-6489 | |
| dc.identifier.other | https://www.ksp.kit.edu/9783731505808 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12010/16835 | |
| dc.format.extent | 282 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | eng | spa |
| dc.publisher | KIT Scientific Publishing | spa |
| dc.subject | Ciencias de la computación | spa |
| dc.title | Adaptive State | spa |
| dc.subject.lemb | Robots móviles | spa |
| dc.subject.lemb | Conducción autónoma | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.local | Abierto (Texto Completo) | spa |
| dc.identifier.doi | 10.5445/KSP/1000058693 | |
| dc.description.abstractenglish | Mobile robot motion planning in unstructured dynamic environments is a challenging task. Thus, often suboptimal methods are employed which perform global path planning and local obstacle avoidance separately. This work introduces a holistic planning algorithm which is based on the concept of state | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_2f33 | spa |
| dc.rights.creativecommons | https://creativecommons.org/licenses/by-sa/4.0/legalcode | |
