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Reaction diffusion Voronoi diagrams: from sensors data to computing.

Vázquez-Otero A, Faigl J, Dormido R, Duro N - Sensors (Basel) (2015)

Bottom Line: The novelty relies on the use of a model configuration that tailors its spatiotemporal dynamics to develop Voronoi diagrams (VD) as a part of the system's natural evolution.The proposed framework is deployed in a solution of related robotic problems, where the generalized VD are used to identify topological places in a grid map of the environment that is created from sensor measurements.The ability of the RD-based computation to integrate external information, like a grid map representing the environment in the model computational grid, permits a direct integration of sensor data into the model dynamics.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences and Automatic Control, UNED, C/ Juan del Rosal, 16, Madrid 28040, Spain. alejandro.vazquez-otero@eli-beams.eu.

ABSTRACT
In this paper, a new method to solve computational problems using reaction diffusion (RD) systems is presented. The novelty relies on the use of a model configuration that tailors its spatiotemporal dynamics to develop Voronoi diagrams (VD) as a part of the system's natural evolution. The proposed framework is deployed in a solution of related robotic problems, where the generalized VD are used to identify topological places in a grid map of the environment that is created from sensor measurements. The ability of the RD-based computation to integrate external information, like a grid map representing the environment in the model computational grid, permits a direct integration of sensor data into the model dynamics. The experimental results indicate that this method exhibits significantly less sensitivity to noisy data than the standard algorithms for determining VD in a grid. In addition, previous drawbacks of the computational algorithms based on RD models, like the generation of volatile solutions by means of excitable waves, are now overcome by final stable states.

No MeSH data available.


Related in: MedlinePlus

The noise sensitivity indicator JPDM Equation (3) in the scaled noisy maps of the potholes environment. (a) potholes, 960 × 960; (b) potholes, 1200 × 1200; (c) potholes, 1360 × 1360; (d) potholes, 1600 × 1600.
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f17-sensors-15-12736: The noise sensitivity indicator JPDM Equation (3) in the scaled noisy maps of the potholes environment. (a) potholes, 960 × 960; (b) potholes, 1200 × 1200; (c) potholes, 1360 × 1360; (d) potholes, 1600 × 1600.

Mentions: The resolution of the grid map may influence both computational techniques to determine the Voronoi diagram, and therefore, its influence has been studied for the potholes, cube and jh environments for which all the maps have been scaled 1.2, 1.5, 1.7 and 2 times. Particular results of the JPDM indicator are shown in Figures 17, 18–19. Examples of the determined skeletons for both algorithms using different map resolutions are shown in Figures 20, 21–22. Although a higher resolution increases the computational burden of both techniques, it can be noticed that the proposed RD-based computation is significantly less sensitive to the change of the resolution, and the number of junction places and leaves remains almost the same according to the change in the thinning algorithm.


Reaction diffusion Voronoi diagrams: from sensors data to computing.

Vázquez-Otero A, Faigl J, Dormido R, Duro N - Sensors (Basel) (2015)

The noise sensitivity indicator JPDM Equation (3) in the scaled noisy maps of the potholes environment. (a) potholes, 960 × 960; (b) potholes, 1200 × 1200; (c) potholes, 1360 × 1360; (d) potholes, 1600 × 1600.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507620&req=5

f17-sensors-15-12736: The noise sensitivity indicator JPDM Equation (3) in the scaled noisy maps of the potholes environment. (a) potholes, 960 × 960; (b) potholes, 1200 × 1200; (c) potholes, 1360 × 1360; (d) potholes, 1600 × 1600.
Mentions: The resolution of the grid map may influence both computational techniques to determine the Voronoi diagram, and therefore, its influence has been studied for the potholes, cube and jh environments for which all the maps have been scaled 1.2, 1.5, 1.7 and 2 times. Particular results of the JPDM indicator are shown in Figures 17, 18–19. Examples of the determined skeletons for both algorithms using different map resolutions are shown in Figures 20, 21–22. Although a higher resolution increases the computational burden of both techniques, it can be noticed that the proposed RD-based computation is significantly less sensitive to the change of the resolution, and the number of junction places and leaves remains almost the same according to the change in the thinning algorithm.

Bottom Line: The novelty relies on the use of a model configuration that tailors its spatiotemporal dynamics to develop Voronoi diagrams (VD) as a part of the system's natural evolution.The proposed framework is deployed in a solution of related robotic problems, where the generalized VD are used to identify topological places in a grid map of the environment that is created from sensor measurements.The ability of the RD-based computation to integrate external information, like a grid map representing the environment in the model computational grid, permits a direct integration of sensor data into the model dynamics.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences and Automatic Control, UNED, C/ Juan del Rosal, 16, Madrid 28040, Spain. alejandro.vazquez-otero@eli-beams.eu.

ABSTRACT
In this paper, a new method to solve computational problems using reaction diffusion (RD) systems is presented. The novelty relies on the use of a model configuration that tailors its spatiotemporal dynamics to develop Voronoi diagrams (VD) as a part of the system's natural evolution. The proposed framework is deployed in a solution of related robotic problems, where the generalized VD are used to identify topological places in a grid map of the environment that is created from sensor measurements. The ability of the RD-based computation to integrate external information, like a grid map representing the environment in the model computational grid, permits a direct integration of sensor data into the model dynamics. The experimental results indicate that this method exhibits significantly less sensitivity to noisy data than the standard algorithms for determining VD in a grid. In addition, previous drawbacks of the computational algorithms based on RD models, like the generation of volatile solutions by means of excitable waves, are now overcome by final stable states.

No MeSH data available.


Related in: MedlinePlus