As autonomous vehicle development continues at growing speeds, so does the need to optimize, diagnose, and test various elements of autonomous systems under different conditions. Since such processes should be carried out in parallel, it may result in bottlenecks in development and increased complexity. The trend for Digital Twins offers a promising option for the diagnosis and testing to be carried out separately from the physical devices, incl. autonomous vehicles in the virtual world. The idea of intercommunication between virtual and physical twins provides possibilities to estimate risks, drawbacks, physical damages to the vehicle’s drive systems, and the physical vehicleʼs critical conditions. Although providing communications between these systems arises at the speed that will be adequate to represent the physical vehicle in the virtual world correctly, it is still a trendy topic. This paper aims to demonstrate the enhancement of communications by using the Robot Operating System (ROS) as a middleware interface between two twinning systems by the example of the autonomous vehicleʼs propulsion drive. Data gathered from the physical and virtual worlds can be exchanged in the middle to allow for continuous training and optimization of the propulsion drive model, which would lead to more efficient path planning and energy-efficient drive of the autonomous vehicle itself. Additionally, a comparative analysis of ROS and its next version ROS2 is provided, discussing their differences and outlining drawbacks.
1. AhmadiAhangar, R., Rosin, A., Niaki, A. N., Palu, I. and Korõtko, T. A review on real-time simulation and analysis methods of microgrids. Int. Trans. Electr. Energy Syst., 2019, 29(11), e12106.
2. Venkatesan, S., Manickavasagam, K., Tengenkai, N. and Vijayalakshmi, N. Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin. IET Electr. Power Appl., 2019, 13(9), 1328–1335.
3. Turner, G. Soaring through virtual aviation: The role of VR in aerospace manufacturing. Manufacturing Global, 2020.
4. Gevorkov, L., Rassõlkin, A., Kallaste, A. and Vaimann, T. Simulink based model of electric drive for throttle valve in pumping application. In Proceedings of the 2018 19th International Scientific Conference on Electric Power Engineering (EPE), Brno, Czech Republic, May 16–18, 2018. IEEE, 1–4.
5. Rasheed, I., Asad, B., Khaliq, H. S., Khan, M. H., Bukhari, S. Z. H. and Bukhari, S. N.-U.-H. Fast numerical techniques based analysis of electromagnetic problems using MATLAB. In Proceedings of the 2014 12th International Conference on Frontiers of Information Technology, Islamabad, Pakistan, December 17–19, 2014. IEEE, 2015, 115–120.
6. Kousi, N., Gkournelos, C., Aivaliotis, S., Giannoulis, C., Michalos, G. and Makris, S. Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. Procedia Manuf., 2019, 28, 121–126.
7. Kuts, V., Sarkans, M., Otto, T., Tähemaa, T. and Bondarenko, Y. Digital Twin: Concept of hybrid programming for industrial robots – use case. In Proceedings of the ASME 2019 International Mechanical Engineering Congress and Exposition, vol. 2B, Salt Lake City, UT, USA, November 11–14, 2019.
8. Kuts. V., Modoni, G. E., Otto, T., Sacco, M., Tähemaa, T., Bondarenko, Y. and Wang, R. Synchronizing physical factory and its digital twin through an IIoT middleware: a case study. Proc. Est. Acad. Sci., 2019, 68(4), 364–370.
9. Rassõlkin, A., Vaimann, T., Kallaste, A. and Kuts, V. Digital twin for propulsion drive of autonomous electric vehicle. In Proceedings of the 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga, Latvia, October 7–9, 2019. IEEE, 2020, 1–4.
10. Khaled, N., Pattel, B. and Siddiqui, A. Digital Twin development and cloud deployment for a Hybrid Electric Vehicle. In Digital Twin Development and Deployment on the Cloud. Academic Press, Cambridge, MA, 2020.
11. Sell, R., Coatanéa, E. and Christophe, F. Important aspects of early design in mechatronic. In Proceedings of the 6th International DAAAM Baltic Conference Industrial Engineering, Tallinn, Estonia, April 24–26, 2008.
12. Rassõlkin, A., Rjabtšikov, V., Vaimann, T., Kallaste, A., Kuts, V. and Partyshev, A. Digital Twin of an electrical motor based on empirical performance model. In Proceedings of the 2020 XI International Conference on Electrical Power Drive Systems (ICEPDS), St Petersburg, Russia, October 4–7, 2020. IEEE, 1–4.
13. Sita, E., Horváth, C. M., Thomessen, T., Korondi, P. and Pipe, A. G. ROS-Unity3D based system for monitoring of an industrial robotic process. In Proceedings of the 2017 IEEE/SICE International Symposium on System Integration, Taipei, Taiwan, December 11–14, 2017. IEEE, 2018, 1047–1052.
14. Noetic Ninjemys: The Last Official ROS 1 Release. Open Robotics.
https://www.openrobotics.org/blog/2020/5/23/noetic-ninjemys-the-last-official-ros-1-release (accessed 2021-05-29).
15. Thomas, D. Changes between ROS 1 and ROS 2. ROS 2 Design. http://design.ros2.org/articles/changes.html (accessed 2021-05-26).
16. Maruyama, Y., Kato, S. and Azumi, T. Exploring the performance of ROS2. In Proceedings of the 13th International Conference on Embedded Software (EMSOFT), Pittsburgh, PA, USA, October 1–7, 2016. ACM, 1–10.