Mobile robots are increasingly used in the most diverse spheres of human activities; accordingly, it is essential to ensure their reliable functioning, which in turn determines efficiency. Using appropriate calculations during design, it is possible to increase reliability and reduce the metal consumption of the machine samples being created. It is crucial that such calculations consider the loading modes in which the vehicle is used. The purpose of the presented work is to increase the technical and operational indicators of the electromechanical drive of mobile robots by selecting the input parameters in combination with the appropriate methods and techniques of design and mathematical modelling. In order to achieve the specified goal, the following main tasks of the research are defined: firstly, to improve the model of increasing reliability and reducing the metal consumption of mechanical components of mobile robots; and secondly, to calculate the mechanical components of mobile robots using the proposed model. Providing the necessary margin of strength with a simultaneous reduction in metal density is necessary for improving the electromechanical drive of a mobile robot and improving its characteristics in general. The paper presents a model and developed an algorithm for increasing the reliability and reducing the metal consumption of mechanical components of mobile robots. The method includes geometric, kinematic, dynamic, energy, technical and economic indicators' calculations, as well as strength and stiffness calculations. The calculations were performed for a small mobile robot with an electromechanical transmission, and the results of a study of the reliability and strength characteristics of the shaft of the mobile robotics platform were presented. The case of turning a mobile robot with the realization of the maximum torque, which is transmitted to one of the tracks, is considered. Based on the kinematic scheme of the electric transmission, a solid-state model of one of its elements (the traction star shaft of the crawler motor) was developed, for which, based on the schematized Serensen – Kinasoshvili diagram, the margin of safety was determined. The proposed model has been examined and successfully used to construct the experimental samples of mobile robots.
[1] Aleksandrov, V., Vetlugin, R., & Makarenko, A. (2018). Vzgliady voennykh spetcialistov SShA na boevoe primenenie nazemnykh robotekhnicheskikh kompleksov. Zarubezhnoe voennoe obozrenie, 6, 39-43. [In Russian].
[2] Alves, R. M. F., & Lopes, C. R. (2016). Obstacle avoidance for mobile robots: A hybrid intelligent system based on fuzzy logic and artificial neural network. In Proc. of the 2016 IEEE Intern. Conf. on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada, 24-29 July 2016, 1038-1043.
https://doi.org/10.1109/FUZZ-IEEE.2016.7737802
[3] Bodianskii, Ye. V., et al. (2016). Analiz ta obroblennia potokiv danikh zasobami obchisliuvalnogo intelektu. Monografiia. Lviv: Vid-vo Lviv. politekhniki. [In Ukrainian].
[4] Buchynskyi, M. Y., Gorik, O. V., Cherniavskyi, A. M., & Yakhin, S. V. (2017). Fundamentals of machine creation. Kharkiv: Publishing house "NTMT". [In Ukrainian].
[5] Chen, C. L. P., Yu, D., & Liu, L. (2019). Automatic leader-follower persistent formation control for autonomous surface vehicles. IEEE Access, 7, 12146-12155.
https://doi.org/10.1109/ACCESS.2018.2886202
[6] Denysyuk, P., Teslyuk, V., & Chorna, I. (2018). Development of mobile robot using LIDAR technology based on Arduino controller, 2018 XIV-th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), 240-244.
https://doi.org/10.1109/MEMSTECH.2018.8365742
[7] Dusan, Glavaski, Volf, Mario, & Bonkovic, Mirjana (2009). Robot motion planning using exact cell decomposition and potential field methods. Proceedings of the 9th WSEAS International conference on Simulation, modelling and optimization, World Scientific and Engineering Academy and Society (WSEAS).
[8] Ignatov, A. V., Bogomolov, S. N., & Fedianin, N. D. (2018). K voprosu o razvitii boevykh nazemnykh robototekhnicheskikh kompleksov. Tekhnologiia proizvodstva sistem i kompleksov. Izvestiia TulGU. Tekhnicheskie nauki, 11, 353-358. [In Russian].
[9] Kellman, M., Rivest, F., Pechacek, A., Sohn, L., & Lustig, M. (2017). Barker-Coded node-pore resistive pulse sensing with built-in coincidence correction. 2017 IEEE Intern. Conf. on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 1053-1057.
https://doi.org/10.1109/ICASSP.2017.7952317
[10] Kirkach, B. M., Konokhov, V. I., & Pogorilov, S. Y. (2012). Calculations on fatigue resistance. Kharkiv: NTU "KPI".
[11] Korets, M. S., Tarara, A. M., & Tregub, I. G. (2001). Fundamentals of mechanical engineering. Kyiv, 144.
[12] Matviichuk, K. V., Teslyuk, V. M., & Zelinskyy, A. Ya. (2016). Programming Model of Control Subsystem for Mobile Robotic Technical System. Scientific Bulletin of UNFU, 26(5), 325-333.
https://doi.org/10.15421/40260551
[13] Matviichuk, K., Teslyuk, V., & Teslyuk, T. (2016). Vision system model for mobile robotic systems. Proceeding of the KhIIh International Conference "Perspective Technologies and Methods in MEMS Design", MEMSTECH2016, 20-24 April 2016, Polyana, Lviv, Ukraine, 104-106.
https://doi.org/10.1109/MEMSTECH.2016.7507529
[14] Medina-Santiago, A., Morales-Rosales, L. A., Hernández-Gracidas, C. A., Algredo-Badillo, I., Pano-Azucena, A. D., & Orozco Torres, J. A. (2021). Reactive Obstacle - Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence. Applied Sciences, 11(14), 6468.
https://doi.org/10.3390/app11146468
[15] Mischuk, D. (2013). Review and analysis of robot designs for construction works. Mining, Construction, Road and Land Reclamation Machines, (82), 28-37.
https://doi.org/10.26884/damu.a13827
[16] Palagin, A. V., & Iakovlev, Iu. S. (2017). Osobennosti proektirovaniia kompiuternykh sistem na kristalle PLIS. Matematicheskie mashiny i sistemy, 2, 3-14. [In Russian].
[17] Pavlov, V. M., Kryzhanovskyi, A. S., Borozenets, H. M. (2008). Machine details. Synopsis of lectures. Kyiv: NAU. [In Ukrainian].
[18] Pavlyshche, V. T. (1993). Fundamentals of design and calculation of machine parts. Kyiv: High school. [In Ukrainian].
[19] Pentagon Unmanned Systems Integrated Roadmap 2017-2042 (2018). USNI News. Retrieved from: https://news.usni.org/2018/08/30/pentagon-unmanned-systems-integrated-ro...
[20] Pilsu, Kim, Eunji, Jung, Sua, Bae, Kangsik, Kim & Taikyong, Song, (2016). Barker-sequence-modulated golay coded excitation technique for ultrasound imaging. 2016 IEEE International Ultrasonics Symposium (IUS), Tours, 1-4.
https://doi.org/10.1109/ULTSYM.2016.7728737
[21] Pysarenko, H. S. (1993). Strength of Materials. Kyiv: H. S. Pysarenko, O. L. Kvitka, U. S. Umanskyi. Kyiv: High school. [In Ukrainian].
[22] Stasenko, D. V., Ostrovka, D. V., & Teslyuk, V. M. (2021). Development of an autonomous control system for a mobile robotic system using artificial neural network models. Scientific Bulletin of UNFU, 31(6), 112-117.
https://doi.org/10.36930/40310617
[23] Tsmots, I. G., Teslyuk, V. M., & Vavruk, I. P. (2013). Hardware and software for controlling the movement of a mobile robotic system, in mater. 12th International Conference. The Experience of Designing and Application of CAD Systems in Microelectronics, CADSM2013, Lviv-Polyana, Ukraine, 368.
[24] Tsmots, I. G., Teslyuk, V. M., Opotiak, Yu. V., Parcei, R. V., & Zinko, R. V. (2021). The basic architecture of mobile robotic platform with intelligent motion control system and data transmission protection. Ukrainian Journal of Information Technology, 3(2), 74-80.
https://doi.org/10.23939/ujit2021.02.074
[25] Tsmots, I., Teslyuk, V., & Vavruk, I. (2013). Hardware and software tools for motion control of mobile robotic system. 12th International Conference "The Experience of Designing and Application of CAD Systems in Microelectronics", CADSM 2013, 368 p.
[26] Yang, L., Qi, J., Song, D., Xiao, J., Han, J., & Xia, Y. (2016). Survey of robot 3D path planning algorithms / J Control Sci Eng, 5 p.
https://doi.org/10.1155/2016/7426913
[27] Yusof, Y., Mansor, H. M. A. H., & Ahmad, A. (2016). Formulation of a lightweight hybrid ai algorithm towards self-learning autonomous systems. In Proc. of the 2016 IEEE Confer. on Systems, Process and Control (IC-SPC), Melaka, Malaysia, 16-18 December 2016, 142-147.
https://doi.org/10.1109/SPC.2016.7920719
[28] Zinko, R. V. (2014). Morphological environment for the study of technical systems. Monograph. Lviv: Lviv Polytechnic Publishing House. 386 p. [In Ukrainian].
[29] Zinko, R., Korendiy, V. (2018). Modelling the motion of the drive motor-wheel of an electric vehicle, XVII International Scientific and Technical Conference "Vibrations in Engineering and Technology", Lviv, Ukraine, 56-57.