Theory and application of probabilistic techniques for autonomous mobile robotics. Topics include probabilistic state estimation and decision making for mobile robots stochastic representations of the environment dynamic models and sensor models for mobile robots algorithms for mapping and localization planning and control in the presence of uncertainty cooperative operation of multiple mobile robots mobile sensor networks application to autonomous marine (underwater and floating), ground, and air vehicles.
Doctor of Philosophy (PhD) in Mechanical Engineering
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| content serial | Description |
|---|
| 1 | Introduction to autonomous vehicle. |
| 2 | Theories of probabilistic techniques for autonomous mobile robotics. |
| 3 | Probabilistic state estimation. |
| 4 | Decision making for mobile robots. |
| 5 | Stochastic representations of the environment |
| 6 | Sensor models for mobile robots |
| 7 | Sensor models for mobile robots |
| 8 | Algorithms for mapping and localization. |
| 9 | Algorithms for mapping and localization. |
| 10 | Planning and control in the presence of uncertainty |
| 11 | Planning and control in the presence of uncertainty |
| 12 | Cooperative operation of multiple mobile robots. |
| 13 | Mobile sensor networks. |
| 14 | Applications. |
| 15 | Applications. |
| 16 | Final Examination |
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