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This paper considers deployment problems where a mobile robotic network mustoptimize its configuration in a distributed way in order to minimize asteady-state cost function that depends on the spatial distribution of certainprobabilistic events of interest. Moreover, it is assumed that the eventlocation distribution is a priori unknown, and can only be progressivelyinferred from the observation of the actual event occurrences. Three classes ofproblems are discussed in detail: coverage control problems, spatialpartitioning problems, and dynamic vehicle routing problems. In each case,distributed stochastic gradient algorithms optimizing the performance objectiveare presented. The stochastic gradient view simplifies and generalizespreviously proposed solutions, and is applicable to new complex scenarios, suchas adaptive coverage involving heterogeneous agents. Remarkably, thesealgorithms often take the form of simple distributed rules that could beimplemented on resource-limited platforms.

Adaptive Algorithms for Coverage Control and Space Partitioning in Mobile Robotic Networks