| Type of Document |
Dissertation |
| Author |
Peng, Jian
|
| Author's Email Address |
jian.peng@vanderbilt.edu |
| URN |
etd-04062004-164409 |
| Title |
Extraction of Salient Features from Sensory-Motor Sequences for Mobile Robot Navigation |
| Degree |
PhD |
| Department |
Electrical Engineering |
| Advisory Committee |
| Advisor Name |
Title |
| Richard Alan Peters, II |
Committee Chair |
| David C. Noelle |
Committee Member |
| Joseph S. Lappin |
Committee Member |
| Kazuhiko Kawamura |
Committee Member |
| Mitch Wilkes |
Committee Member |
|
| Keywords |
- imitation-based learning
- sensory-motor coordination
- computer vision
|
| Date of Defense |
2004-02-24 |
| Availability |
unrestricted |
Abstract
This dissertation presents a method to extract features salient to a mobile robot navigation task in a specific environment. The extraction process is bootstrapped by a human operator’s tele-operation and is based on the sensory-motor coordination principle. Salient feature extraction consists of three steps: tele-operation, offline association, and evaluation. First, the mobile robot is tele-operated in an environment along a path several times. All sensory data and motor drive commands are recorded. Then these recorded sensory-motor sequences are partitioned into episodes according to the changes in the motor commands. Salient features are then extracted by using two statistical criteria: consistency and correlation with the motor commands within an interval around the episode boundaries. Finally, these features are used to drive the robot in the learned environment. Two sets of experiments, in both indoor and outdoor environments, were performed. The results endorsed this methodology.
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(Hours:Minutes:Seconds)
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ISDN (64 Kb) |
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final_electrical.pdf |
3.15 Mb |
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