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State identification for planetary rovers: learning and recognition
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Washington, Richard Aycard, Olivier |
| Copyright Year | 1999 |
| Description | A planetary rover must be able to identify states where it should stop or change its plan. With limited and infrequent communication from ground, the rover must recognize states accurately. However, the sensor data is inherently noisy, so identifying the temporal patterns of data that correspond to interesting or important states becomes a complex problem. In this paper, we present an approach to state identification using second-order Hidden Markov Models. Models are trained automatically on a set of labeled training data; the rover uses those models to identify its state from the observed data. The approach is demonstrated on data from a planetary rover platform. |
| File Size | 500672 |
| Page Count | 6 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20000102369 |
| Archival Resource Key | ark:/13960/t72v7gn5b |
| Language | English |
| Publisher Date | 1999-01-01 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics, Artificial Intelligence And Robotics Markov Processes Identifying Education Planetary Surfaces Roving Vehicles Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
