Name
Title | ||
|---|---|---|
Multi-robot coordination through dynamic Voronoi partitioning for informative adaptive sampling in communication-constrained environments. |
Abstract | ||
|---|---|---|
Autonomous underwater vehicles (AUVs) are cost- and time-efficient systems for environmental sampling. Informative adaptive sampling has been shown to be an effective method of sampling a lake or ocean for environmental modeling. In this paper, we focus on multi-robot coordination for informative adaptive sampling. We use a dynamic Voronoi partitioning approach whereby the vehicles, in a decentralized fashion, repeatedly calculate weighted Voronoi partitions for the space. Each vehicle then runs informative adaptive sampling within their partition. The vehicles can request surfacing events to share data between vehicles. Simulation results show that the addition of the coordination with dynamic Voronoi partitioning results in obtaining higher quality models faster. Thus we created a decentralized, multi-robot coordination approach for informative, adaptive sampling of unknown environments. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICRA.2017.7989245 | ICRA |
Field | DocType | Citations |
Adaptive sampling,Effective method,Computer science,Robot kinematics,Control engineering,Vehicle dynamics,Voronoi diagram,Sampling (statistics),Robot,Underwater | Conference | 1 |
PageRank | References | Authors |
0.35 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephanie Kemna | 1 | 21 | 3.25 |
| John Rogers | 2 | 108 | 16.07 |
| Carlos Nieto-Granda | 3 | 50 | 7.37 |
| Stuart H. Young | 4 | 11 | 2.62 |
| Gaurav S. Sukhatme | 5 | 5469 | 548.13 |