Name
Abstract | ||
|---|---|---|
A critical problem in planning sampling paths for autonomous underwater vehicles is balancing obtaining an accurate scalar field estimation against efficiently utilizing the stored energy capacity of the sampling vehicle. Adaptive sampling approaches can only provide solutions when real-time and a priori environmental data is available. Through utilizing a cost-evaluation function to experimentally evaluate various sampling path strategies for a wide range of scalar fields and sampling densities, it is found that a systematic spiral sampling path strategy is optimal for high-variance scalar fields for all sampling densities and low-variance scalar fields when sampling is sparse. The random spiral sampling path strategy is found to be optimal for low-variance scalar fields when sampling is dense. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICRA.2012.6225231 | 2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) |
Keywords | Field | DocType |
mobile robots,mathematical model,evaluation function,sampling methods,estimation,scalar field,estimating equation,path planning,spirals,systematics | Motion planning,Adaptive sampling,Control theory,Scalar (physics),A priori and a posteriori,Control engineering,Sampling (statistics),Mobile robot,Mathematics,Scalar field,Underwater | Conference |
Volume | Issue | ISSN |
2012 | 1 | 1050-4729 |
Citations | PageRank | References |
1 | 0.37 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Colin Ho | 1 | 14 | 2.09 |
| Andres Mora | 2 | 17 | 2.07 |
| Srikanth Saripalli | 3 | 564 | 60.11 |