Abstract
We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications like search and rescue, environmental monitoring, and task allocation. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests. We design quorum based stochastic control policies that enable the team of agents to distribute themselves among multiple candidate sites in a specified ratio, and compare our results to the linear stochastic policies described in (Halasz et al., in Proceedings of the International Conference on Intelligent Robots and Systems (IROS'07), pp. 2320-2325, 2007). We show how our quorum model consistently performs better than the linear models while minimizing computational requirements and now it can be implemented without the use of inter-agent wireless communication.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 121-141 |
| Number of pages | 21 |
| Journal | Swarm Intelligence |
| Volume | 2 |
| Issue number | 2-4 |
| DOIs | |
| State | Published - 2008 |
| Externally published | Yes |
Fingerprint
Keywords
- Bio-inspired control
- Decentralized control
- Robot swarms
- Task allocation
ASJC Scopus subject areas
- Ecological Modeling
- Computer Science(all)
Cite this
Biologically inspired redistribution of a swarm of robots among multiple sites. / Hsieh, M. Ani; Halász, Ádám; Berman, Spring; Kumar, Vijay.
In: Swarm Intelligence, Vol. 2, No. 2-4, 2008, p. 121-141.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Biologically inspired redistribution of a swarm of robots among multiple sites
AU - Hsieh, M. Ani
AU - Halász, Ádám
AU - Berman, Spring
AU - Kumar, Vijay
PY - 2008
Y1 - 2008
N2 - We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications like search and rescue, environmental monitoring, and task allocation. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests. We design quorum based stochastic control policies that enable the team of agents to distribute themselves among multiple candidate sites in a specified ratio, and compare our results to the linear stochastic policies described in (Halasz et al., in Proceedings of the International Conference on Intelligent Robots and Systems (IROS'07), pp. 2320-2325, 2007). We show how our quorum model consistently performs better than the linear models while minimizing computational requirements and now it can be implemented without the use of inter-agent wireless communication.
AB - We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications like search and rescue, environmental monitoring, and task allocation. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests. We design quorum based stochastic control policies that enable the team of agents to distribute themselves among multiple candidate sites in a specified ratio, and compare our results to the linear stochastic policies described in (Halasz et al., in Proceedings of the International Conference on Intelligent Robots and Systems (IROS'07), pp. 2320-2325, 2007). We show how our quorum model consistently performs better than the linear models while minimizing computational requirements and now it can be implemented without the use of inter-agent wireless communication.
KW - Bio-inspired control
KW - Decentralized control
KW - Robot swarms
KW - Task allocation
UR - http://www.scopus.com/inward/record.url?scp=54249101629&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=54249101629&partnerID=8YFLogxK
U2 - 10.1007/s11721-008-0019-z
DO - 10.1007/s11721-008-0019-z
M3 - Article
VL - 2
SP - 121
EP - 141
JO - Swarm Intelligence
JF - Swarm Intelligence
SN - 1935-3812
IS - 2-4
ER -