Teaching Localization in Probabilistic Robotics
| Autor: | Fred Martin, Dalphond, J., Tuck, N. |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Scopus-Elsevier |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v26i3.18955 |
| Popis: | In the field of probabilistic robotics, a central problem is to determine a robot’s state given knowledge of a time series of control commands and sensor readings. The effects of control commands and the behavior of sensor devices are both modeled probabilistically. A variety of methods are available for deriving the robot’s belief state, which is a probabilistic representation of the robot’s true state (which cannot be directly known). This paper presents a series of five weekly assignments to teach this material at the advanced undergraduate/graduate level. The theoretical aspect of the work is reinforced by practical implementation exercises using ROS (Robot Operating System), and the Bilibot, an educational robot platform. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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