DOI: https://doi.org/10.15587/1729-4061.2019.164337

Localization and navigation of mobile robots in an environment with variable properties

Sergey Udovenko, Anton Sorokin

Abstract


A method of localization and navigation of a mobile robot in an environment with variable properties in conditions of limited possibilities was proposed for remote control which provides for a possibility of switching the mode of robot control to a state of autonomous navigation. The method is based on combined application of a fuzzy model and an RL-algorithm that makes it possible to improve the set of fuzzy rules using the signal of reinforcement.

Improvement of the method of localization of mobile objects using iBeacon and NFC technologies in a space with known maps of premises was proposed which enables reduction of the number of transmitters necessary for localization.

The method of identification of mobile object movement routes was modified with the use of the modified Jump Point Search algorithm. Essence of the modification consists in the use of the algorithm of Manhattan distance between coordinates of the route points. This makes it possible to reduce impact of individual surges on the results of calculations compared with the basic algorithm.

The obtained results can be used in mobile robot control systems in an environment with variable properties at limited possibilities for remote control. The results of testing the proposed methods and corresponding computational procedures confirm their performance and prospects of practical application. Application of the above approach makes it possible to take into account obstacle configurations and adjust the navigation strategy to improve the system quality (in 95 % of the test experiments, the mobile robot reached the target in an environment with various types of obstacles)

Keywords


mobile object; localization; autonomous navigation; fuzzy controller; control with reinforcement learning

References


Ventorim, B. G., Dal Poz, W. R. (2016). Performance Evaluation of GPS and GLONASS Systems, Combined and Individually, in Precise Point Positioning. Boletim de Ciencias Geodesicas, 22 (2), 265–281. doi: https://doi.org/10.1590/s1982-21702016000200015

Kaemarungsi, K., Krishnamurthy, P. (2004). Modeling of indoor positioning systems based on location fingerprinting. IEEE INFOCOM 2004. doi: https://doi.org/10.1109/infcom.2004.1356988

Dhillon, S. S., Chakrabarty, K. (2003). Sensor placement for effective coverage and surveillance in distributed sensor networks. 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003. doi: https://doi.org/10.1109/wcnc.2003.1200627

Fahimi, F. (2009). Autonomous Robots. Modeling, Path Planning and Control. Springer, 348. doi: https://doi.org/10.1007/978-0-387-09538-7

Zashcholkin, K. V., Kalinichenko, V. V., Ulchenko, N. O. (2013). Realization of complex means of navigation of autonomous mobile robot. Elektrotekhnichni ta kompiuterni systemy, 9, 102–109.

Chernonozhkin, V. A., Polovko, S. A. (2008). Local navigation system for surface mobile robots. Nauchno-tekhnicheskiy vestnik informacionnyh tekhnologiy, mekhaniki i optiki, 57, 13–22.

Ersson, T., Hu, X. (2010). Path Planning and Navigation of Mobile Robots in Unknown Environments. IEEE Journal of Robotics and Automation, 6, 212–228.

Vossiek, M., Wiebking, L., Gulden, P., Wieghardt, J., Hoffmann, C., Heide, P. (2003). Wireless local positioning. IEEE Microwave Magazine, 4 (4), 77–86. doi: https://doi.org/10.1109/mmw.2003.1266069

Matveev, A. S., Hoy, M. C., Savkin, A. V. (2015). A globally converging algorithm for reactive robot navigation among moving and deforming obstacles. Automatica, 54, 292–304. doi: https://doi.org/10.1016/j.automatica.2015.02.012

Bobtsov, A. A., Dobriborsci, D., Kapitonov, A. A. (2017). Navigation and control system for mobile robot. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 17 (2), 365–367. doi: https://doi.org/10.17586/2226-1494-2017-17-2-365-367

Udovenko, S. G., Sorokin, A. R. (2015). Hybrid method of filtration in the tasks of localization of mobile robots. Systemy obrobky informatsiyi, 10, 248–254.

Cherroun, L., Boumehraz, M. (2012). Designing of Goal Seeking and Obstacle Avoidance Behaviors for a Mobile Robot Using Fuzzy Techniques. Journal of Automation and Systems Engineering (JASE), 6 (4), 164–171.

Hryshko, A. A., Udovenko, S. G., Chalaya, L. E. (2012). Hybrid machine learning methods in dynamic objects control systems. Bionics of Intelligense, 1 (78), 78–84.


GOST Style Citations


Ventorim B. G., Dal Poz W. R. Performance Evaluation of GPS and GLONASS Systems, Combined and Individually, in Precise Point Positioning // Boletim de Ciencias Geodesicas. 2016. Vol. 22, Issue 2. P. 265–281. doi: https://doi.org/10.1590/s1982-21702016000200015 

Kaemarungsi K., Krishnamurthy P. Modeling of indoor positioning systems based on location fingerprinting // IEEE INFOCOM 2004. 2004. doi: https://doi.org/10.1109/infcom.2004.1356988 

Dhillon S. S., Chakrabarty K. Sensor placement for effective coverage and surveillance in distributed sensor networks // 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003. 2003. doi: https://doi.org/10.1109/wcnc.2003.1200627 

Fahimi F. Autonomous Robots. Modeling, Path Planning and Control. Springer, 2009. 348 p. doi: https://doi.org/10.1007/978-0-387-09538-7 

Zashcholkin K. V., Kalinichenko V. V., Ulchenko N. O. Realization of complex means of navigation of autonomous mobile robot // Elektrotekhnichni ta kompiuterni systemy. 2013. Issue 9. P. 102–109.

Chernonozhkin V. A., Polovko S. A. Local navigation system for surface mobile robots // Nauchno-tekhnicheskiy vestnik informacionnyh tekhnologiy, mekhaniki i optiki. 2008. Issue 57. P. 13–22.

Ersson T., Hu X. Path Planning and Navigation of Mobile Robots in Unknown Environments // IEEE Journal of Robotics and Automation. 2010. Issue 6. P. 212–228.

Wireless local positioning / Vossiek M., Wiebking L., Gulden P., Wieghardt J., Hoffmann C., Heide P. // IEEE Microwave Magazine. 2003. Vol. 4, Issue 4. P. 77–86. doi: https://doi.org/10.1109/mmw.2003.1266069 

Matveev A. S., Hoy M. C., Savkin A. V. A globally converging algorithm for reactive robot navigation among moving and deforming obstacles // Automatica. 2015. Vol. 54. P. 292–304. doi: https://doi.org/10.1016/j.automatica.2015.02.012 

Bobtsov A. A., Dobriborsci D., Kapitonov A. A. Navigation and control system for mobile robot // Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2017. Vol. 17, Issue 2. Р. 365–367. doi: https://doi.org/10.17586/2226-1494-2017-17-2-365-367 

Udovenko S. G., Sorokin A. R. Hybrid method of filtration in the tasks of localization of mobile robots // Systemy obrobky informatsiyi. 2015. Issue 10. P. 248–254.

Cherroun L., Boumehraz M. Designing of Goal Seeking and Obstacle Avoidance Behaviors for a Mobile Robot Using Fuzzy Techniques // Journal of Automation and Systems Engineering (JASE). 2012. Vol. 6, Issue 4. Р. 164–171.

Hryshko A. A., Udovenko S. G., Chalaya L. E. Hybrid machine learning methods in dynamic objects control systems // Bionics of Intelligense. 2012. Issue 1 (78). P. 78–84.







Copyright (c) 2019 Sergey Udovenko, Anton Sorokin

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061