Abstract
This paper presents a novel class of self-organizing autonomous sensing agents that form a swarm and learn the static field of interest through noisy measurements from neighbors for gradient climbing. In particular, each sensing agent maintains its own smooth map which estimates the field. It updates its map using measurements from itself and its neighbors and simultaneously moves toward a maximum of the field using the gradient of its map. The proposed cooperatively learning control consists of motion coordination based on the recursive spatial estimation of an unknown field of interest with measurement noise. The convergence properties of the proposed coordination algorithm are analyzed using the ODE approach and verified by a simulation study.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC |
| Pages | 3139-3144 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2007 Dec 1 |
| Event | 46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States Duration: 2007 Dec 12 → 2007 Dec 14 |
Other
| Other | 46th IEEE Conference on Decision and Control 2007, CDC |
|---|---|
| Country | United States |
| City | New Orleans, LA |
| Period | 07/12/12 → 07/12/14 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modelling and Simulation
- Control and Optimization
Cite this
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Cooperatively learning mobile agents for gradient climbing. / Choi, Jongeun; Oh, Songhwai; Horowitz, Roberto.
Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC. 2007. p. 3139-3144 4434061.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Cooperatively learning mobile agents for gradient climbing
AU - Choi, Jongeun
AU - Oh, Songhwai
AU - Horowitz, Roberto
PY - 2007/12/1
Y1 - 2007/12/1
N2 - This paper presents a novel class of self-organizing autonomous sensing agents that form a swarm and learn the static field of interest through noisy measurements from neighbors for gradient climbing. In particular, each sensing agent maintains its own smooth map which estimates the field. It updates its map using measurements from itself and its neighbors and simultaneously moves toward a maximum of the field using the gradient of its map. The proposed cooperatively learning control consists of motion coordination based on the recursive spatial estimation of an unknown field of interest with measurement noise. The convergence properties of the proposed coordination algorithm are analyzed using the ODE approach and verified by a simulation study.
AB - This paper presents a novel class of self-organizing autonomous sensing agents that form a swarm and learn the static field of interest through noisy measurements from neighbors for gradient climbing. In particular, each sensing agent maintains its own smooth map which estimates the field. It updates its map using measurements from itself and its neighbors and simultaneously moves toward a maximum of the field using the gradient of its map. The proposed cooperatively learning control consists of motion coordination based on the recursive spatial estimation of an unknown field of interest with measurement noise. The convergence properties of the proposed coordination algorithm are analyzed using the ODE approach and verified by a simulation study.
UR - http://www.scopus.com/inward/record.url?scp=52449124591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=52449124591&partnerID=8YFLogxK
U2 - 10.1109/CDC.2007.4434061
DO - 10.1109/CDC.2007.4434061
M3 - Conference contribution
SN - 1424414989
SN - 9781424414987
SP - 3139
EP - 3144
BT - Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
ER -