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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

Average numbers of the leaves detected in the noisy maps created for the particular value of the noise level σ. (a) environment autolab; (b) environment jh; (c) environment cube; (d) environment potholes.
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f11-sensors-15-12736: Average numbers of the leaves detected in the noisy maps created for the particular value of the noise level σ. (a) environment autolab; (b) environment jh; (c) environment cube; (d) environment potholes.

Mentions: The proposed statistical indicators of the noise sensitivity, i.e., JPDM Equation (3), and the average number of the detected leaves are depicted in Figures 10 and 11, respectively. The results show that the proposed RD-based computation of the Voronoi diagram is less sensitive to the added noise, as the number of junction places is usually lower than the number of junction places for the map without the noise. The lowest value of the JPDM is –100% for the cube environment, where none of the junction places have been detected in the environment, see Figures 12 and 13. On the other hand, the thinning algorithm is a more sensitive to the noise as the number junction places is quickly increasing, and for the autolab, jh and potholes environments, the maximal number of the junction places is for the noise level σ = 4; see the example in Figure 14.


Reaction diffusion Voronoi diagrams: from sensors data to computing.

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

Average numbers of the leaves detected in the noisy maps created for the particular value of the noise level σ. (a) environment autolab; (b) environment jh; (c) environment cube; (d) environment potholes.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-15-12736: Average numbers of the leaves detected in the noisy maps created for the particular value of the noise level σ. (a) environment autolab; (b) environment jh; (c) environment cube; (d) environment potholes.
Mentions: The proposed statistical indicators of the noise sensitivity, i.e., JPDM Equation (3), and the average number of the detected leaves are depicted in Figures 10 and 11, respectively. The results show that the proposed RD-based computation of the Voronoi diagram is less sensitive to the added noise, as the number of junction places is usually lower than the number of junction places for the map without the noise. The lowest value of the JPDM is –100% for the cube environment, where none of the junction places have been detected in the environment, see Figures 12 and 13. On the other hand, the thinning algorithm is a more sensitive to the noise as the number junction places is quickly increasing, and for the autolab, jh and potholes environments, the maximal number of the junction places is for the noise level σ = 4; see the example in Figure 14.

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