2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) 2019
DOI: 10.1109/mrs.2019.8901076
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Decentralized Minimum-Energy Coverage Control for Time-Varying Density Functions

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Cited by 33 publications
(32 citation statements)
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“…Santos et al ( 2019) addressed coverage control with a time varying density function using time-varying CBFs, which is close to the present approach. The contribution of this paper relative to (Santos et al, 2019) is as follows. The controller presented in (Santos et al, 2019) is designed based on the distance between the current robot position and the centroid of the Voronoi cell.…”
Section: Zj(p T)mentioning
confidence: 99%
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“…Santos et al ( 2019) addressed coverage control with a time varying density function using time-varying CBFs, which is close to the present approach. The contribution of this paper relative to (Santos et al, 2019) is as follows. The controller presented in (Santos et al, 2019) is designed based on the distance between the current robot position and the centroid of the Voronoi cell.…”
Section: Zj(p T)mentioning
confidence: 99%
“…Figure 5 shows the time responses of the performance function J for the above two methods, where the blue line shows the performance by the gradient-based controller (Sugimoto et al, 2015), the green line that by (Santos et al, 2019) the yellow line that by the constraint-based controller (18), and the red line illustrates the prescribed performance level c − 4.0. We see that the gradient-based controller (Sugimoto et al, 2015) and (Santos et al, 2019) occasionally fail to meet the desired performance level, namely the value of performance function J goes below c. On the other hand, the constraintbased controller 18) successfully keeps the performance above the level c − 4.0. Figure 6 illustrates the results for n 5, wherein we take c − 2.5 to highlight the differences between the present controller and the other two.…”
Section: Simulationmentioning
confidence: 99%
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“…Often it may be done in places where GPS is not available, and thus each robot must rely on local information to determine its next control action. While there has been much algorithmic progress in sensor coverage over a variety of scenarios in simulation [26][27][28], few of these have actually been tested on physical hardware. For this experiment, we utilize GNNs in order to construct decentralized controllers for the robots.…”
Section: Experiments With Sensor Coverage Using Decentralized Gnn Contmentioning
confidence: 99%