New techniques used to improve visual navigation by mobile robots

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Washington, March 31 (ANI): In a new research, certain algorithms used by scientists for research on the analysis of hyperspectral images, have been applied to mobile robots for visual navigation.

With this application, the aim is for the robots to enhance their capacity for spatial orientation and their resources for detecting their surroundings.

The research was carried out by Ivan Villaverde from the Computational Intelligence Group of the Faculty of Informatics at the University of the Basque Country (UPV/EHU).

The researcher studied how the visual navigation of small mobile robots can be improved by applying techniques which have never been tried in robotics previously.

It was principally based on an algebraic system that is used in a hyperspectrometric line of research: lattice computing.

This involves a system based on series of data (instead of numbers) with concrete internal ordering.

As was concluded from the first trials, this technique and certain other ones can be highly useful for enhancing the visual navigation of robots.

Villaverde worked with two types of basic sensors incorporated into mobile robots in order to improve their system of navigation: optic cameras and 3D cameras for range detection.

These are the eyes of the robot, and the researcher focused on three primordial questions so these can see correctly: the location of the robot itself, the capacity of the robot to detect its own movements (being able to fix where it is without recourse to analysing external information), and the capacity to build a map (distances, obstacles, and so on) of surroundings previously unknown to it.

Thus, the previously mentioned lattice computing and certain other innovative techniques were applied to these three questions.

In fact, Villaverde made use of lattice computing, on the one hand, for the self-location of the robot on qualitative maps and, on the other, for the detection of visual markers with optic cameras.

In order to enhance the metric location with 3D cameras, Villaverde applied an innovative hybrid system: combining techniques of evolution and competitive neuronal networks.

Evolution techniques are genetic algorithms and neuronal networks are codes that act like the nervous system in humans.

So, both simulate human mutations and evolution.

The researcher applied these techniques to the 3D cameras and, concretely, to estimating the transformations between 3D views, providing at the same time an estimate of the robot's movement.

As Villaverde outlined in his thesis, he also carried out a basic experiment, confirming that these innovative applications are, effectively, valid for the visual navigation of robots. (ANI)

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