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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 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.

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

Example of found skeleton (Voronoi diagram) by the RD-based computational model for the pothole environment: (a) light gray structure; (b) and its one pixel width skeleton shown in red.
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f8-sensors-15-12736: Example of found skeleton (Voronoi diagram) by the RD-based computational model for the pothole environment: (a) light gray structure; (b) and its one pixel width skeleton shown in red.

Mentions: For simplicity and also with regard to the used thinning algorithm [12], we considered the RD-based skeleton as a simple polygon (or a set of simple polygons) and determine a single-pixel width skeleton in this polygon using the thinning algorithm. This allows us to consider the identical procedure to compute the indicators for determination of the skeleton based solely on the thinning algorithm and on the RD-based computational model. Such a skeleton of the determined RD-based Voronoi diagram is shown in Figure 8. An example of superimposed Voronoi diagrams determined in the map with and without the added noise in the environment autolab is shown in Figure 9.


Reaction diffusion Voronoi diagrams: from sensors data to computing.

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

Example of found skeleton (Voronoi diagram) by the RD-based computational model for the pothole environment: (a) light gray structure; (b) and its one pixel width skeleton shown in red.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-15-12736: Example of found skeleton (Voronoi diagram) by the RD-based computational model for the pothole environment: (a) light gray structure; (b) and its one pixel width skeleton shown in red.
Mentions: For simplicity and also with regard to the used thinning algorithm [12], we considered the RD-based skeleton as a simple polygon (or a set of simple polygons) and determine a single-pixel width skeleton in this polygon using the thinning algorithm. This allows us to consider the identical procedure to compute the indicators for determination of the skeleton based solely on the thinning algorithm and on the RD-based computational model. Such a skeleton of the determined RD-based Voronoi diagram is shown in Figure 8. An example of superimposed Voronoi diagrams determined in the map with and without the added noise in the environment autolab is shown in Figure 9.

Bottom Line: 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.

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