DocumentCode
3487183
Title
Optimal foraging of renewable resources
Author
Enright, J.J. ; Frazzoli, Emilio
Author_Institution
Kiva Syst., UK
fYear
2012
fDate
27-29 June 2012
Firstpage
683
Lastpage
690
Abstract
Consider a team of agents in the plane searching for and visiting target points that appear in a bounded environment, according to a stochastic renewal process with a known absolutely continuous spatial distribution. Agents must detect targets with limited-range onboard sensors. It is desired to minimize the expected waiting time between the appearance of a target point, and the instant it is visited. When the sensing radius is small, the system time is dominated by time spent searching, and it is shown that the optimal policy requires the agents to search a region at a relative frequency proportional to the square root of its renewal rate. On the other hand, when targets appear frequently, the system time is dominated by time spent servicing known targets, and it is shown that the optimal policy requires the agents to service a region at a relative frequency proportional to the cube root of its renewal rate. Furthermore, the presented algorithms in this case recover the optimal performance achieved by agents with full information of the environment. Simulation results verify the theoretical performance of the algorithms.
Keywords
mobile robots; multi-agent systems; multi-robot systems; object detection; renewable energy sources; robot vision; sensors; stochastic processes; absolutely continuous spatial distribution; agents team; bounded environment; limited-range onboard sensors; optimal foraging; optimal policy; plane searching; renewable resources; renewal rate; sensing radius; stochastic renewal process; target detection; time spent searching; time spent servicing; Algorithm design and analysis; Distribution functions; Graphical models; Optimized production technology; Sensors; Tiles; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference (ACC), 2012
Conference_Location
Montreal, QC
ISSN
0743-1619
Print_ISBN
978-1-4577-1095-7
Electronic_ISBN
0743-1619
Type
conf
DOI
10.1109/ACC.2012.6315638
Filename
6315638
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