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Programming and Computer Software

, Volume 45, Issue 8, pp 557–569 | Cite as

Software Advances using n-agents Wireless Communication Integration for Optimization of Surrounding Recognition and Robotic Group Dead Reckoning

  • M. IvanovEmail author
  • O. SergiyenkoEmail author
  • V. TyrsaEmail author
  • L. LindnerEmail author
  • J. C. Rodriguez-QuiñonezEmail author
  • W. Flores-FuentesEmail author
  • M. Rivas-Lopez
  • D. Hernández-BalbuenaEmail author
  • J. I. Nieto HipólitoEmail author
Article
  • 7 Downloads

Abstract

Nowadays artificial intelligence and swarm robotics become wide spread and take their approach in civil tasks. The main purpose of the article is to show the influence of common knowledge about surroundings sharing in the robotic group navigation problem by implementing the data transferring within the group. Methodology provided in article reviews a set of tasks implementation of which improves the results of robotic group navigation. The main questions for the research are the problems of robotics vision, path planning, data storing and data exchange. Article describes the structure of real-time laser technical vision system as the main environment-sensing tool for robots. The vision system uses dynamic triangulation principle. Article provides examples of obtained data, distance-based methods for resolution and speed control. According to the data obtained by provided vision system were decided to use matrix-based approach for robots path planning, it inflows the tasks of surroundings discretization, and trajectory approximation. Two network structure types for data transferring are compared. Authors are proposing a methodology for dynamic network forming based on leader changing system. For the confirmation of theory were developed an application of robotic group modeling. Obtained results show that common knowledge sharing between robots in-group can significantly decrease individual trajectories length.

Notes

ACKNOWLEDGMENTS

We want to extend our gratitude to the Universidad Autónoma de Baja California, Instituto de Ingeniería and the CONACYT for providing the resources that made this research possible.

REFERENCES

  1. 1.
    Eskridge, B., Valle, E., and Schlupp, I., Emergence of leadership within a homogeneous group, PLoS One, 2015, vol. 10, no. 7.CrossRefGoogle Scholar
  2. 2.
    Pshikhopov, V., Medvedev, M., Kolesnikov, A., Fedorenko, R., and Boris, G., Decentralized control of a group of homogeneous vehicles in obstructed environment, J. Control Sci. Eng., vol. 2016, art. ID 7192371.Google Scholar
  3. 3.
    Sergiyenko, O., Ivanov, M., Tyrsa, V., Rivas-Lopez, M., Hernandez-Balbuena, D., Flores-Fuentes, W., Rodriguez-Quinonez, J.C., Nieto-Hipolito, J.I., Hernandez, W., and Tchernykh, A., Data transferring model determination in robotic group, Rob. Auton. Syst., 2016, vol. 83, pp. 251–260.CrossRefGoogle Scholar
  4. 4.
    Sergiyenko, O.Yu., Ivanov, M.V., Kartashov, V.M., Tyrsa, V.V., Hernandez-Balbuena, D., and Nieto-Hipolito, J.I., “Transferring model in robotic group, Proc. 25th IEEE Int. Symp. on Industrial Electronics (ISIE), Santa Clara, 2016.Google Scholar
  5. 5.
    Grymin, D.J., Neas, C.B., and Farhood, M., “A hierarchical approach for primitive based motion planning and control of autonomous vehicles, Rob. Auton. Syst., 2014, vol. 62, no. 2, pp. 214–228.CrossRefGoogle Scholar
  6. 6.
    Kovacs, B., Szayer, G., Tajti, F., Burdelis, M., and Korondi, P., A novel potential field method for path planning of mobile robots by adapting animal motion attributes, Rob. Auton. Syst., 2016, vol. 82, pp. 24–34.CrossRefGoogle Scholar
  7. 7.
    Bobkov, V.A., Ron’shin, Y.I., Kudryashov, A.P., and Mashentsev, V.Y., 3D SLAM from stereoimages, Program. Comput. Software, vol. 40, no. 4, pp. 159–165.CrossRefGoogle Scholar
  8. 8.
    Bobkov, V.A., Kudryashov, A.P., and Mel’man, S.V., On the recovery of motion of dynamic objects from stereo images, Program. Comput. Software, vol. 44, no. 3, pp. 148–158.CrossRefGoogle Scholar
  9. 9.
    Kamaev, A.N., Sukhenko, V.A., and Karmanov, D.A., Constructing and visualizing three-dimensional sea bottom models to test AUV machine vision systems, Program. Comput. Software, vol. 43, no. 3, pp. 184–195.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Vilao, C.O., Perico, D.H., Silva, I.J., Homem, T.P.D., Tonidandel, F., and Bianchi, R.A.C., A single camera vision system for a humanoid robot, Proc. Joint Conf. on Robotics: SBRLARS Robotics Symp. and Robocontrol, Sao Carlos, 2014.Google Scholar
  11. 11.
    Katalinic, B., Gryaznov, N., and Lopota, A., Computer vision for mobile on-ground robotics, Proc. Eng., 2015, vol. 100, pp. 1376–1380.CrossRefGoogle Scholar
  12. 12.
    Achtelik, M.C. and Scaramuzza, D., Vision-controlled micro flying robots: from system design to autonomous navigation and mapping in GPS-denied environments, IEEE Rob. Autom. Mag., 2014, vol. 21, no. 3, pp. 26–40.Google Scholar
  13. 13.
    Pashchenko, N.F., Zipa, K.S., and Ignatenko, A.V., An algorithm for the visualization of stereo images simultaneously captured with different exposures, Program. Comput. Software, 2017, vol. 43, no. 4, pp. 250–257.CrossRefGoogle Scholar
  14. 14.
    Alenya Ribas, G., Foix Salmeron, S., and Torras Genis, C., ToF cameras for active vision in robotics, Sens. Actuators, A, 2014, vol. 218, pp. 10–22.CrossRefGoogle Scholar
  15. 15.
    Mikhaylichenko, A.A. and Kleshchenkov, A.B., Approach to non-contact measurement of geometric parameters of large-sized objects, Program. Comput. Software, 2018, vol. 44, no. 4, pp. 271–277.CrossRefGoogle Scholar
  16. 16.
    Basaca-Preciado, L.C., Sergiyenko, O.Y., Rodriguez-Quinonez, J.C., Garcia, X., Tyrsa, V.V., Rivas-Lopez, M., Hernandez-Balbuena, D., Mercorelli, P., Podrygalo, M., Gurko, A., Tabakova, I., and Starostenko, O., Optical 3D laser measurement system for navigation of autonomous mobile robot, Opt. Lasers Eng., 2014, vol. 54, pp. 159–169.CrossRefGoogle Scholar
  17. 17.
    Sergiyenko, O., Hernandez, W., Tyrsa, V., Cruz, L.D., Starostenko, O., and Pena-Cabrera, M., Remote sensor for spatial measurements by using optical scanning, MDPI Sens., 2009, vol. 9, no. 7, pp. 5477–5492.CrossRefGoogle Scholar
  18. 18.
    Garcia, X., Sergiyenko, O., Tyrsa, V., Rivas-Lopez, M., D. Hernandez-Balbuena, Rodriguez-Quinonez, J.C., Basaca-Preciado, L.C., and Mercorelli, P., Optimization of 3D laser scanning speed by use of combined variable step, Opt. Lasers Eng., 2014, vol. 54, pp. 141–151.CrossRefGoogle Scholar
  19. 19.
    Hocking, J., Unity in Action: Multiplatform Game Development in C# with Unity 5, New York: Manning, 2015.Google Scholar
  20. 20.
    Ali, A.A., Rashid, A.T., Frasca, M., and Fortuna, L., An algorithm for multi-robot collision-free navigation based on shortest distance, Rob. Auton. Syst., 2016, vol. 75, pp. 119–128.CrossRefGoogle Scholar
  21. 21.
    Trianni, V., Tuci, E., Ampatzis, C., and Dorigo, M., Evolutionary swarm robotics: a theoretical and methodological itinerary from individual neurocontrollers to collective behaviors, in The Horizons of Evolutionary Robotics, Cambridge, Mass: MIT Press, 2014.Google Scholar
  22. 22.
    Vincent, R., Morisset, B., Agno, A., Eriksen, M., and Ortiz, C., Centibots: large-scale autonomous robotic search and rescue experiment, Proc. 2nd Int. Joint Topical Meeting on Emergency Preparedness & Response and Robotics & Remote Systems, Red Hook, NY: Curran Assoc., 2008.Google Scholar
  23. 23.
    Munoz, P., Maria, R.-M.D., and Barrero, D.F., Unified framework for path-planning and task-planning for autonomous robots, Rob. Auton. Syst., 2016, vol. 82, pp. 1–14.CrossRefGoogle Scholar
  24. 24.
    Bezier, P.E., How Renault Uses Numerical Control for Car Body Design and Tooling, Detroit: Soc. Autom. Eng., 1968.Google Scholar
  25. 25.
    Han, L., Yashiro, H., Nejad, T., Do, Q., and Mita, S., Bezier curve based path planning for autonomous vehicle in urban environment, Proc. IEEE Intelligent Vehicles Symp., La Jolla, CA, 2010.Google Scholar
  26. 26.
    Kawabata, K., Ma, L., Xue, J., Zhu, C., and Zheng, N., A path generation for automated vehicle based on Bezier curve and viapoints, Rob. Auton. Syst., 2015, vol. 74, pp. 243–252.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Universidad Autonoma de Baja California (UABC), Instituto de IngenieríaMexicaliMexico
  2. 2.Universidad Autonoma de Baja California (UABC), Facultad de IngenieríaMexicaliMexico
  3. 3.Universidad Autonoma de Baja California (UABC), Facultad de Ingenieria, Arquitectura y DiseñoEnsenadaMexico

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