The authors show how to pose coverage deployment as L2 matching of a target density by an aggregate sensing function, derive adaptive and directed-consensus control laws, and validate them on physical differential-drive robots.
Optimal Configurations in Coverage Control with Polynomial Costs
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abstract
We revisit the static coverage control problem for placement of vehicles with simple motion on the real line, under the assumption that the cost is a polynomial function of the locations of the vehicles. The main contribution of this paper is to demonstrate the use of tools from numerical algebraic geometry, in particular, a numerical polynomial homotopy continuation method that guarantees to find all solutions of polynomial equations, in order to characterize the \emph{global minima} for the coverage control problem. The results are then compared against a classic distributed approach involving the use of Lloyd descent, which is known to converge only to a local minimum under certain technical conditions.
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Distributed Adaptive Coverage Control of Differential Drive Robotic Sensors
The authors show how to pose coverage deployment as L2 matching of a target density by an aggregate sensing function, derive adaptive and directed-consensus control laws, and validate them on physical differential-drive robots.