Name
Abstract | ||
|---|---|---|
A planetary rover must be able to identify stateswhere it should stop or change its plan. With limitedand infrequent communication from ground, the rovermust recognize states accurately. However, the sensordata is inherently noisy, so identifying the temporalpatterns of data that correspond to interesting or importantstates becomes a complex problem. In this paper,we present an approach to state identication usingsecond-order Hidden Markov Models. Models aretrained automatically on a... |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/ROBOT.2000.844756 | ICRA |
Keywords | Field | DocType |
hidden Markov models,learning (artificial intelligence),mobile robots,pattern recognition,planetary rovers,state estimation,second-order hidden Markov models,state identification,temporal patterns | Training set,Markov process,Artificial intelligence,Engineering,Hidden Markov model,Planetary rover,Machine learning,Mobile robot | Conference |
Volume | Issue | ISSN |
2 | 1 | 1050-4729 |
Citations | PageRank | References |
3 | 0.58 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Aycard | 1 | 309 | 26.57 |
| Richard Washington | 2 | 54 | 4.04 |