Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Simulation Series |
| Herausgeber/-innen | Mohammad S. Obaidat, Helena Szczerbicka, Malamati Louta, George Kartesos, Joaquin Entrialgo, Steven Latre, Jose Saldana |
| Seiten | 106-112 |
| Seitenumfang | 7 |
| Auflage | 10 |
| ISBN (elektronisch) | 9781510838277 |
| Publikationsstatus | Veröffentlicht - 2017 |
| Veranstaltung | 2017 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2017, Part of the 2017 Summer Simulation Multi-Conference, SummerSim 2017 - Bellevue, USA / Vereinigte Staaten Dauer: 9 Juli 2017 → 12 Juli 2017 |
Publikationsreihe
| Name | Simulation Series |
|---|---|
| Nummer | 10 |
| Band | 49 |
| ISSN (Print) | 0735-9276 |
Abstract
The Multi-Agent Flood algorithm is an algorithm to control multiple autonomous agents to explore unknown terrain and to find points of interest in then ewly uncovered territory. It does so by using a mix of indirect and direct communication. The direct communication via radio signals allows for a faster exchange of data between the agents. This layered communication model allows for a robust data transfer that is needed in SAR scenarios, as the algorithm can still use the indirect part of the model if the direct part is not available anymore. Currently the exploration done by the agents is decided randomly without any intelligent input based on the gathered data. The agents will simply try to move along a line until they will hit an obstacle or a point of interest is found. This work introduces ways to steer the autonomous agents based on the collected data, additionally this data will be used to decide which part of the map should be explored next. Another optimization of the performance is also the increased cooperation between the agents, as the agents will also exchange these destination points, thus reducing the number of agents which will explore the same part of the area, leading to a greater spread of the agents through the terrain.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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Simulation Series. Hrsg. / Mohammad S. Obaidat; Helena Szczerbicka; Malamati Louta; George Kartesos; Joaquin Entrialgo; Steven Latre; Jose Saldana. 10. Aufl. 2017. S. 106-112 (Simulation Series; Band 49, Nr. 10).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Combining the multi-agent flood algorithm with frontier-based exploration in search & rescue applications
AU - Blatt, Florian
AU - Szczerbicka, Helena
PY - 2017
Y1 - 2017
N2 - The Multi-Agent Flood algorithm is an algorithm to control multiple autonomous agents to explore unknown terrain and to find points of interest in then ewly uncovered territory. It does so by using a mix of indirect and direct communication. The direct communication via radio signals allows for a faster exchange of data between the agents. This layered communication model allows for a robust data transfer that is needed in SAR scenarios, as the algorithm can still use the indirect part of the model if the direct part is not available anymore. Currently the exploration done by the agents is decided randomly without any intelligent input based on the gathered data. The agents will simply try to move along a line until they will hit an obstacle or a point of interest is found. This work introduces ways to steer the autonomous agents based on the collected data, additionally this data will be used to decide which part of the map should be explored next. Another optimization of the performance is also the increased cooperation between the agents, as the agents will also exchange these destination points, thus reducing the number of agents which will explore the same part of the area, leading to a greater spread of the agents through the terrain.
AB - The Multi-Agent Flood algorithm is an algorithm to control multiple autonomous agents to explore unknown terrain and to find points of interest in then ewly uncovered territory. It does so by using a mix of indirect and direct communication. The direct communication via radio signals allows for a faster exchange of data between the agents. This layered communication model allows for a robust data transfer that is needed in SAR scenarios, as the algorithm can still use the indirect part of the model if the direct part is not available anymore. Currently the exploration done by the agents is decided randomly without any intelligent input based on the gathered data. The agents will simply try to move along a line until they will hit an obstacle or a point of interest is found. This work introduces ways to steer the autonomous agents based on the collected data, additionally this data will be used to decide which part of the map should be explored next. Another optimization of the performance is also the increased cooperation between the agents, as the agents will also exchange these destination points, thus reducing the number of agents which will explore the same part of the area, leading to a greater spread of the agents through the terrain.
KW - Communication
KW - Cooperation
KW - Frontier detection
KW - Multi-agent systems
KW - Search and rescue
UR - http://www.scopus.com/inward/record.url?scp=85029623740&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85029623740
T3 - Simulation Series
SP - 106
EP - 112
BT - Simulation Series
A2 - Obaidat, Mohammad S.
A2 - Szczerbicka, Helena
A2 - Louta, Malamati
A2 - Kartesos, George
A2 - Entrialgo, Joaquin
A2 - Latre, Steven
A2 - Saldana, Jose
T2 - 2017 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2017, Part of the 2017 Summer Simulation Multi-Conference, SummerSim 2017
Y2 - 9 July 2017 through 12 July 2017
ER -