Name
Abstract | ||
|---|---|---|
A robot audio localization system is presented that combines the outputs of multiple adaptive filter models of the Cerebellum to calibrate a robot's audio map for various acoustic environments. The system is inspired by the MOdular Selection for Identification and Control (MOSAIC) framework. This study extends our previous work that used multiple cerebellar models to determine the acoustic environ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LRA.2018.2850447 | IEEE Robotics and Automation Letters |
Keywords | Field | DocType |
Robots,Adaptation models,Context modeling,Brain modeling,Acoustics,Predictive models,Calibration | Computer vision,Control engineering,Localization system,Adaptive filter,Artificial intelligence,Modular design,Engineering,Robot,Calibration,Acoustic source localization | Journal |
Volume | Issue | ISSN |
3 | 4 | 2377-3766 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark D. Baxendale | 1 | 0 | 0.34 |
| Martin J. Pearson | 2 | 215 | 26.34 |
| Mokhtar Nibouche | 3 | 54 | 3.39 |
| Emanuele Lindo Secco | 4 | 71 | 10.43 |
| Anthony G. Pipe | 5 | 255 | 39.08 |