Name
Abstract | ||
|---|---|---|
This paper explores the possibility of using WiFi localization techniques for autonomous free-flying robots on the International Space Station (ISS). We have collected signal strength samples from the ISS, built the WiFi map using Gaussian processes, implemented a localizer based on particle filters, and evaluated the performance. Our results show the average error of 1.59 meters, which is accurate enough to identify which ISS module the robot is currently in. However, we found that most errors occurred in some specific modules under the current WiFi settings. This paper describes the challenges of applying WiFi localization techniques to the ISS and suggests several approaches to achieve better performance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/INTELES.2014.7008981 | IES |
Keywords | DocType | Citations |
particle filtering (numerical methods),wifi localization techniques,wifi settings,autonomous aerial vehicles,iss module,signal strength samples,gaussian processes,international space station,wireless lan,wifi map,particle filters,autonomous free-flying robots,training data,robots,mathematical model | Conference | 1 |
PageRank | References | Authors |
0.43 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jongwoon Yoo | 1 | 1 | 0.43 |
| Taemn Kim | 2 | 382 | 28.18 |
| Christopher Provencher | 3 | 1 | 0.43 |
| Terrence Fong | 4 | 1 | 1.11 |