Name
Abstract | ||
|---|---|---|
Safe mobility in rough terrain is important for high-risk missions. In order to achieve stability and mobility precision it is necessary to have a good knowledge of the terrain properties. This work treats the problem of outdoor classification terrain analyzing proprioceptives sensor data, current measure, wheel speeds and slippage. The use of principal component analysis (PCA) reduces the space dimension of acquired data to a lower dimension to classificate in which terrain the robot is moving. This paper presents experimental results to validate the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3233/978-1-60750-061-2-19 | CCIA |
Keywords | Field | DocType |
outdoor classification terrain,proprioceptives sensor data,current measure,terrain classification,terrain property,outdoor mobile robot,mobility precision,space dimension,lower dimension,rough terrain,safe mobility,acquired data,principal component analysis,mobile robot | Terrain classification,Computer vision,Computer science,Terrain,Slippage,Artificial intelligence,Robot,Mobile robot,Principal component analysis | Conference |
Volume | ISSN | Citations |
202 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Caballero Parga | 1 | 0 | 0.34 |
| Albert Figueras | 2 | 0 | 0.34 |
| Santi Esteva | 3 | 0 | 0.68 |
| Rafael Hesse | 4 | 0 | 0.34 |