Name
Title | ||
|---|---|---|
High Dexterity Docking of an UUV by Fast Determination of the Area Manipulability Measure of the Arm Using ANN. |
Abstract | ||
|---|---|---|
The scope of this paper is to contribute towards the advancements in the autonomy and in the optimal planning of the intervention mission of an Unmanned Underwater Vehicle (UUV). Particularly, an approach is proposed for the automatic and fast determination of a high dexterity docking location. A feed forward back propagation Artificial Neural Network (ANN) is trained to calculate directly a dexterity index for the manipulator of the UUV defined in the task area and called Area Manipulability Measure (AMM). The main advantage of this procedure is the very fast determination of the AMM using the ANN compared with the very long analytical calculation of the AMM, which is used only for the training. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3182/20120905-3-HR-2030.00040 | IFAC Proceedings Volumes |
Keywords | Field | DocType |
Autonomous mobile robots,Neural Networks,Manipulation,Genetic algorithms,Inverse kinematic problem | Docking (dog),Simulation,Feed forward back propagation,Manipulator,Optimal planning,Engineering,Artificial neural network,Genetic algorithm,Underwater,Unmanned underwater vehicle | Conference |
Volume | Issue | ISSN |
45 | 22 | 1474-6670 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panagiotis Sotiropoulos | 1 | 1 | 2.05 |
| Nikos A. Aspragathos | 2 | 243 | 37.69 |
| Franck Geffard | 3 | 0 | 0.34 |