Nonuniform Line Coverage From Noisy Scalar Measurements

Abstract

Abstract-We study the problem of distributed coverage control in a network of mobile agents arranged on a line. The goal is to design distributed dynamics for the agents to achieve optimal coverage positions with respect to a scalar density field that measures the relative importance of each point on the line. Unlike previous work, which implicitly assumed the agents know this density field, we only assume that each agent can access noisy samples of the field at points close to its current location. We provide a simple randomized protocol wherein every agent samples the scalar field at three nearby points at each step and which guarantees convergence to the optimal positions. We further analyze the convergence time of this protocol and show that, under suitable assumptions, the squared distance to the optimal coverage configuration decays as O(1/t) with the number of iterations t, where the constant scales polynomially with the number of agents n. We illustrate these results with simulations.

Design of Stochastic Robotic Swarms for Target Performance Metrics in Boundary Coverage Tasks

“…In contrast to this work, we address a static stochastic coverage scenario in which the encountered object or region is large compared to the robots. Other related work has addressed the specific problem of optimal mobile sensor deployment along a line with respect to a scalar density field, possibly in the presence of measurement noise [31], sensor failures [32], and packet loss [33]. x 0 = 0…”

2D Voronoi Coverage Control with Gaussian Density Functions by Line Integration

“…Lee et al have proposed the centroidal Voronoi coverage control with time-varying density functions [19]. Davison et al have extended the results of the Voronoi coverage control with an Euclidean metric to the case with non-Euclidean metric [20]. Miah et al have considered Voronoi coverage control with intermittent communications [21].…”