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Mobile Robots Navigation Modeling in Known 2D Environment Based on Petri Nets

S. Bartkeviciusa, O. Fiodorovab, A. Knysc, A. Derviniened, G. Dervinisc, V. Raudonisc, A. Lipnickasc, V. Baranauskasc, K. Sarkauskasc, L. Balaseviciusc

a Department of Electrical Power Systems, Kaunas University of Technology, Kaunas, Lithuania;
b Department of Automation, Kaunas University of Technology, Panevezys, Lithuania;
c Department of Automation, Kaunas University of Technology, Kaunas, Lithuania;
d Department of Electronics Engineering, Kaunas University of Technology, Kaunas, Lithuania

* Corresponding Author: V. Baranauskas,

Intelligent Automation & Soft Computing 2018, 24(2), 241-248. https://doi.org/10.1080/10798587.2016.1264695

Abstract

The paper deals with supervised robot navigation in known environments. The navigation task is divided into two parts, where one part of the navigation is done by the supervisor system i.e. the system sets the vector marks on the salient edges of the virtual environment map and guides the robot to reach these marks. Mobile robots have to perform a specific task according to the given paths and solve the local obstacles avoidance individually. The salient point’s detection, vector mark estimation and optimal path calculation are done on the supervisor computer using colored Petri nets. The proposed approach was extended to simulate a flexible manufacturing system consisting of swarm of 17 robots, 17 - warehouses and 17 - manufacturing places. Our experimental investigation showed that simulated mobile robots with proposed supervision system were efficiently moving on the planned path.

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Cite This Article

S. Bartkevicius, . O. Fiodorova, . A. Knys, . A. Derviniene, . G. Dervinis et al., "Mobile robots navigation modeling in known 2d environment based on petri nets," Intelligent Automation & Soft Computing, vol. 24, no.2, pp. 241–248, 2018.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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