Name
Abstract | ||
|---|---|---|
This paper presents a multi-AUV state-estimator that can determine the 3D position of a tagged fish. In addition to angle measurements, the state-estimator also incorporates distance and depth measurements. These additional sensor measurements allow for greater accuracy in the position estimates. A newly developed motion model that better accounts for multiple hypotheses of the motion of a tagged fish is used to increase the robustness of the state-estimator. A series of multi-AUV shark tracks were conducted at Santa Catalina Island, California over the span of four days to demonstrate the ability of the state-estimator to determine the 3D position of a tagged leopard shark. Additional experiments in which the AUVs tracked a tagged boat of known location were conducted to quantify the performance of the presented state-estimator. Experimental results demonstrate a three-fold decrease in mean state-estimation error compared to previous works. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IROS.2014.6943046 | IROS |
Keywords | Field | DocType |
motion model,distance measurement,angle measurement,santa catalina island,california,motion control,marine engineering,state estimation,autonomous underwater vehicle,mobile robots,tagged leopard shark,multi-robot systems,autonomous underwater vehicles,3d position determination,multiauv shark tracks,position estimation,tagged fish,depth measurement,multiauv state estimator,position control,sensor measurements | Computer vision,State estimator,Computer science,Multiple hypotheses,Robustness (computer science),Hydrophone,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
2153-0858 | 3 | 0.46 |
References | Authors | |
12 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yukun Lin | 1 | 5 | 0.92 |
| Hannah Kastein | 2 | 3 | 0.46 |
| Taylor Peterson | 3 | 3 | 0.46 |
| Connor White | 4 | 5 | 0.92 |
| Christopher G. Lowe | 5 | 25 | 3.30 |
| Christopher M Clark | 6 | 376 | 29.76 |