Name
Abstract | ||
|---|---|---|
Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system’s dynamics. This work addresses the problem of identifying multiple contexts of an AUV model. We build a simulation model of the robot from experimental data, and use it to fill in the missing data and generate different model contexts. We implement an architecture based on long-short-term-memory (LSTM) networks to learn the different contexts directly from the data. We show that the LSTM network can achieve high classification accuracy compared to baseline methods, showing robustness against noise and scaling efficiently on large datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/AUV.2018.8729823 | 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV) |
Keywords | Field | DocType |
Context modeling,Data models,Vehicle dynamics,Support vector machines,Manipulator dynamics,Biological system modeling | Architecture,Experimental data,Robustness (computer science),Control engineering,Artificial intelligence,Missing data,Engineering,Robot,Scaling,Robotics,Machine learning,Underwater | Journal |
Volume | Citations | PageRank |
abs/1809.06179 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bilal Wehbe | 1 | 0 | 1.01 |
| Octavio Arriaga | 2 | 0 | 0.34 |
| M. M. Krell | 3 | 45 | 7.53 |
| Frank Kirchner | 4 | 143 | 24.53 |