Abstract
A decentralized, adaptive control law is presented to drive a network of mobile robots to an optimal sensing configuration. The control law is adaptive in that it uses sensor measurements to learn the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. The controller is then improved by introducing a consensus algorithm to propagate sensory information from every robot throughout the network. Convergence and consensus of parameters is proven with a Lyapunovtype proof. The controller with and without consensus is demonstrated in numerical simulations. These techniques are suggestive of broader applications of adaptive control methodologies to decentralized control problems in unknown dynamic environments.
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