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ICRA
2008
IEEE

Consensus learning for distributed coverage control

8 years 8 months ago
Consensus learning for distributed coverage control
— A decentralized controller is presented that causes a network of robots to converge to a near optimal sensing configuration, while simultaneously learning the distribution of sensory information in the environment. A consensus (or flocking) term is introduced in the learning law to allow sharing of parameters among neighbors, greatly increasing learning convergence rates. Convergence and consensus is proven using a Lyapunov-type proof. The controller with parameter consensus is shown to perform better than the basic controller in numerical simulations.
Mac Schwager, Jean-Jacques E. Slotine, Daniela Rus
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICRA
Authors Mac Schwager, Jean-Jacques E. Slotine, Daniela Rus
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