Abstract
Swarms of Unmanned Aerial Vehicles (UAV) have been foreseen by multiple organizations to serve an important role in future air-based warfare and civilian operations. UAVs are less expensive than their piloted counterparts, provide greater flexibilities and remove the need for on-board pilot support. Efficient control of swarms opens a set of new challenges, such as automatic UAV coordination, efficient swarm monitoring and dynamic mission planning. In this paper, we investigate the problem of dynamic mission planning for a UAV swarm. An agent-based control framework is proposed, which employs a control agent for task assignment and multiple UAV agents for local task scheduling. A prototype simulation framework is implemented as a proof-of-concept. Experimentation with the framework suggests the effectiveness of swarm control using several mission planning mechanisms.
| Original language | English |
|---|---|
| Title of host publication | Simulation Series |
| Pages | 7-14 |
| Number of pages | 8 |
| Volume | 45 |
| Edition | 1 |
| State | Published - May 3 2013 |
| Externally published | Yes |
| Event | Agent-Directed Simulation Symposium, ADS 2013, Part of the 2013 Spring Simulation Multiconference, SpringSim 2013 - San Diego, CA, United States Duration: Apr 7 2013 → Apr 10 2013 |
Other
| Other | Agent-Directed Simulation Symposium, ADS 2013, Part of the 2013 Spring Simulation Multiconference, SpringSim 2013 |
|---|---|
| Country | United States |
| City | San Diego, CA |
| Period | 4/7/13 → 4/10/13 |
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Keywords
- Agent-based simulation
- Mission planning
- UAV swarm
ASJC Scopus subject areas
- Computer Networks and Communications
Cite this
Agent-based simulation for UAV swarm mission planning and execution. / Wei, Yi; Madey, Gregory R.; Blake, M. Brian.
Simulation Series. Vol. 45 1. ed. 2013. p. 7-14.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Agent-based simulation for UAV swarm mission planning and execution
AU - Wei, Yi
AU - Madey, Gregory R.
AU - Blake, M. Brian
PY - 2013/5/3
Y1 - 2013/5/3
N2 - Swarms of Unmanned Aerial Vehicles (UAV) have been foreseen by multiple organizations to serve an important role in future air-based warfare and civilian operations. UAVs are less expensive than their piloted counterparts, provide greater flexibilities and remove the need for on-board pilot support. Efficient control of swarms opens a set of new challenges, such as automatic UAV coordination, efficient swarm monitoring and dynamic mission planning. In this paper, we investigate the problem of dynamic mission planning for a UAV swarm. An agent-based control framework is proposed, which employs a control agent for task assignment and multiple UAV agents for local task scheduling. A prototype simulation framework is implemented as a proof-of-concept. Experimentation with the framework suggests the effectiveness of swarm control using several mission planning mechanisms.
AB - Swarms of Unmanned Aerial Vehicles (UAV) have been foreseen by multiple organizations to serve an important role in future air-based warfare and civilian operations. UAVs are less expensive than their piloted counterparts, provide greater flexibilities and remove the need for on-board pilot support. Efficient control of swarms opens a set of new challenges, such as automatic UAV coordination, efficient swarm monitoring and dynamic mission planning. In this paper, we investigate the problem of dynamic mission planning for a UAV swarm. An agent-based control framework is proposed, which employs a control agent for task assignment and multiple UAV agents for local task scheduling. A prototype simulation framework is implemented as a proof-of-concept. Experimentation with the framework suggests the effectiveness of swarm control using several mission planning mechanisms.
KW - Agent-based simulation
KW - Mission planning
KW - UAV swarm
UR - http://www.scopus.com/inward/record.url?scp=84876845815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876845815&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781627480291
VL - 45
SP - 7
EP - 14
BT - Simulation Series
ER -