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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

Determined skeletons and topological maps with highlighted junction places and leaves in the map potholes with the noise level σ = 4: (a) RD-based Voronoi diagram; (b) and its corresponding skeleton superimposed on the skeleton determined in the noise-less map (in red); (c) Pruned GVG representing skeleton of the map determined by the thinning algorithm [12]; (d) and the corresponding skeleton superimposed on the skeleton determined in the noise-less map.
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f16-sensors-15-12736: Determined skeletons and topological maps with highlighted junction places and leaves in the map potholes with the noise level σ = 4: (a) RD-based Voronoi diagram; (b) and its corresponding skeleton superimposed on the skeleton determined in the noise-less map (in red); (c) Pruned GVG representing skeleton of the map determined by the thinning algorithm [12]; (d) and the corresponding skeleton superimposed on the skeleton determined in the noise-less map.

Mentions: The number of leaves significantly changes with the noise level for the thinning algorithm, while for the proposed RD-based computation of the skeleton, it changes only slightly and typically decreases with a higher level of the noise σ, as can be seen in Figure 11. The only exception is the environment potholes, where the initial skeleton for σ = 0 does not contain any leaves; see Figure 15, while with increasing σ, some parts become unreachable for the robot (wavefront propagation), which is visualized in Figure 16.


Reaction diffusion Voronoi diagrams: from sensors data to computing.

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

Determined skeletons and topological maps with highlighted junction places and leaves in the map potholes with the noise level σ = 4: (a) RD-based Voronoi diagram; (b) and its corresponding skeleton superimposed on the skeleton determined in the noise-less map (in red); (c) Pruned GVG representing skeleton of the map determined by the thinning algorithm [12]; (d) and the corresponding skeleton superimposed on the skeleton determined in the noise-less map.
© Copyright Policy
Related In: Results  -  Collection

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

f16-sensors-15-12736: Determined skeletons and topological maps with highlighted junction places and leaves in the map potholes with the noise level σ = 4: (a) RD-based Voronoi diagram; (b) and its corresponding skeleton superimposed on the skeleton determined in the noise-less map (in red); (c) Pruned GVG representing skeleton of the map determined by the thinning algorithm [12]; (d) and the corresponding skeleton superimposed on the skeleton determined in the noise-less map.
Mentions: The number of leaves significantly changes with the noise level for the thinning algorithm, while for the proposed RD-based computation of the skeleton, it changes only slightly and typically decreases with a higher level of the noise σ, as can be seen in Figure 11. The only exception is the environment potholes, where the initial skeleton for σ = 0 does not contain any leaves; see Figure 15, while with increasing σ, some parts become unreachable for the robot (wavefront propagation), which is visualized in Figure 16.

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