Tag Archives: neural network

xxx A. Nemeikšis and V. Osadčuks
Development of intelligent system of mobile robot movement planning in unknown dynamic environment by means of multi-agent system
Abstract |

Development of intelligent system of mobile robot movement planning in unknown dynamic environment by means of multi-agent system

A. Nemeikšis and V. Osadčuks*

Latvia University of Life Sciences and Technologies, Faculty of Engineering, 5 J. Cakstes blvd., Jelgava LV-3001, Latvia
*Correspondence: nemeiksis.andrius@llu.lv, vitalijs.osadcuks@llu.lv

Abstract:

Through the ages the world has conceived the projects which are aimed at creating diverse models of robots that would be beneficial for exploration of different dangerous surfaces where human participation is excluded. Therefore, the main task of the study of this article is to develop the researches, the object of which is mobile robot movement in unfamiliar environment, based on multi agent apparatus system and neural networks. The aim of the research is to develop methods for creating intellectual systems for planning mobile robot movement in unfamiliar environment applying the methods of multi agent apparatus and neural networks ensuring the robot executes the planned and adjusted on the way safe trajectory in an environment with unknown obstacles. Accordingly, the entire study of the article is based on a two-stage process. The first stage involves determination of distance between the robot and the obstacles in its operating area as well as classification of the possible location of obstacles, based on the information received from distance sensors, using the model of multilayer neural networks. During the second stage bypassing obstacles, wall tracking, movement-to-destination as well as speed management agents are developed. As the result of the study, a method was suggested for creating neural network model for classification of environment into agents and their consistent switching, which, according to the classification table compiled, involves all the possible locations of obstacles occurring on the robot’s movement trajectory and allows reducing the number of unfamiliar environment situations that are necessary to identify.

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