Robust Adaptive Coverage Control for Robotic Sensor Networks

This paper presents a distributed control algorithm to drive a group of robots to spread out over an environment and provide adaptive sensor coverage of that environment. The robots use an online learning mechanism to estimate the areas in the environment that require more concentrated sensor coverage, while simultaneously providing this coverage. The algorithm differs from previous approaches in that both provable robustness is emphasized in the learning mechanism, and decentralization over a communication network is emphasized in the control. To achieve a provable bound on the learning error, the robots explore the environment to gather sufficient data. They then switch to a Voronoi-based coverage controller to position themselves for sensing. Robots coordinate their learning, control, and mode switching in a fully distributed fashion over their mesh network. It is proved that the robots approximate the weighting function with a known bounded error, and that they converge to locations that are locally optimal for sensing. Simulations and multiple experiments with six iRobot Create robots are presented to illustrate the performance of the method.

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