A set of regression models for controlling a trolley equipped with a robot manipulator integrated into the technological process
Автор: Kholodilin I.Yu., Grigorev M.A., Kushnarev V.A., Savosteenko N.V., Spitsin D.V., Osipov O.I.
Журнал: Вестник Южно-Уральского государственного университета. Серия: Энергетика @vestnik-susu-power
Рубрика: Электротехнические комплексы и системы
Статья в выпуске: 4 т.24, 2024 года.
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The paper focuses on the problem of virtual environment systems and mobile robot simulators. The simulator utilizes the Robotics System Toolbox and Robotics Toolbox which help it to enhance pedagogical experience of computer science courses. Thanks to the mobile robot equipped with the fisheye camera and structured light, this simulator, created using Unity, can be used in learning navigation techniques taking place inside the indoor environment. An interactive indoor environment with different obstacles is also included in the virtual environment. The main goal of the simulator is to motivate students to keep studying robotics and consequently improve the quality of engineering education. We believe that integrating these tools into the educational process will increase students’ interest in the subject and as a result, students will get a valuable practical experience. This paper not only demonstrates the compatibility of the proposed simulator with Matlab toolboxes, but also presents comparison analysis between considered toolboxes.
Virtual environment systems, mobile robot simulators, robotics system toolbox, robotics toolbox, navigation techniques, pedagogical experience, simulation platform
Короткий адрес: https://sciup.org/147247633
IDR: 147247633 | DOI: 10.14529/power240406
Список литературы A set of regression models for controlling a trolley equipped with a robot manipulator integrated into the technological process
- A. Lenskiy, H. Junho, K. Dongyun, and P. Junsu, “Educational platform for learning programming via controlling mobile robots,” in Proc. International Conference on Data and Software Engineering, 2014, pp. 1-4.
- M. Sadiku, P. Adebo, and S. Musa, “Online Teaching and Learning,” International Journals of Advanced Research in Computer Science and Software Engineering, vol. 8, no.2, pp. 73-75, Feb. 2018.
- A. S. Alves Gomes, J. F. Da Silva and L. R. De Lima Teixeira, “Educational Robotics in Times of Pandemic: Challenges and Possibilities,” 2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, Natal, Brazil, 2020, pp. 1-5.
- L. Ma, H. Bai, Q. Dai, and H. Wang, “Practice and Thinking of Online Teaching During Epidemic Period *,” in Proc. 15th International Conference on Computer Science & Education, 2020, pp. 568-571.
- J. Hu, and B. Zhang, “Application of SalesForce Platform in Online Teaching in Colleges and Universities under Epidemic Situation,” in Proc. International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, 2020, pp. 276-279.
- L. Kexin, Q. Yi, S. Xiaoou, and L. Yan, “Future Education Trend Learned From the Covid-19 Pandemic: Take ≪Artificial Intelligence≫ Online Course As an Example,” in Proc. International Conference on Artificial Intelligence and Education, 2020, pp. 108-111.
- S. E. Bazarov, I. Y. Kholodilin, A. S. Nesterov, and A. V. Sokhina, “Applying Augmented Reality in practical classes for engineering students,” in Proc. IOP Conf. Ser.: Earth Environ. Sci. 87, 2017.
- Robotics System Toolbox. [Online]. Available: https://www.mathworks.com/help/robotics/ (accessed Feb. 6, 2021).
- P. I. Corke, “A Robotics Toolbox for Matlab,” IEEE Robotics and Automation Magazine, 1996.
- A. Afzal, D. Katz, C. Goues, and C. Timperley, “A Study on the Challenges of Using Robotics Simulators for Testing,” Apr. 2020. [Online]. Available: https://arxiv.org/pdf/2004.07368.pdf (accessed Feb. 8, 2021).
- I. Kholodilin, Y. Li, and Q. Wang, “Omnidirectional Vision System With Laser Illumination in a Flexible Configuration and Its Calibration by One Single Snapshot,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 11, pp. 9105-9118, Nov. 2020.
- D. Scaramuzza, A. Martinelli, and R. Siegwart, “A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structuredfrom Motion,” In Proc. IEEE International Conference on Computer Vision Systems, Jan. 2006.
- S. Saxena, S. Jain, S. Tripathi, and K. Gupta, “Comparative Analysis of Image Segmentation Techniques,” Advances in Communication and Computational Technology: Springer, Singapore, 2021, vol 668, pp. 317-331.
- D. Scaramuzza, “OCamCalib: Omnidirectional Camera Calibration Toolbox for Matlab”. [Online]. Available: https://www.sites.google.com/site/scarabotix/ocamcalib-toolbox (accessed Feb. 12, 2021).
