Mobile Robot Position Determining Using Odometry Method

Vladyslav Basiuk; Svitlana Maksymova; Olena Chala; Olha Miliutina

Mobile Robot Position Determining Using Odometry Method

Vladyslav Basiuk

Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine

Svitlana Maksymova

Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine

Olena Chala

Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine

Olha Miliutina

The separate structural subdivision Kharkiv Professional College of Information Technologies of the National Aerospace University named after M. E. Zhukovsky "Kharkiv Aviation Institute"

##semicolon## Mobile robot, Robot control, Odometry, Error accumulation

सार

In this article, the authors propose an analysis of the cumulative error of robot movement. We are considering a two-wheeled robot based on an Arduino board with three ultrasonic rangefinders. The position of the mobile robot is calculated using the odometry method. Experiments were carried out on various coatings. And the calculated position of the robot was compared with the real one. When writing a mobile robot control program, it is proposed to take these results into account.

##submission.citations##

Attar, H., Abu-Jassar, A. T., Yevsieiev, V., Lyashenko, V., Nevliudov, I., & Luhach, A. K. (2022). Zoomorphic mobile robot development for vertical movement based on the geometrical family caterpillar. Computational Intelligence and Neuroscience, 2022.

Maksymova, S., & et al.. (2017). Voice Control for an Industrial Robot as a Combination of Various Robotic Assembly Process Models. Journal of Computer and Communications, 5, 1-15.

Khan, A., & et al.. (2015). Some Effect of Chemical Treatment by Ferric Nitrate Salts on the Structure and Morphology of Coir Fibre Composites. Advances in Materials Physics and Chemistry, 5(1), 39-45.

Attar, H., & et al.. (2022). Control System Development and Implementation of a CNC Laser Engraver for Environmental Use with Remote Imaging. Computational Intelligence and Neuroscience, 2022.

Abu-Jassar, A. T., & et al.. (2022). Electronic user authentication key for access to HMI/SCADA via unsecured internet networks. Computational Intelligence and Neuroscience, 2022.

Nevliudov, I., & et al.. (2020). Development of a cyber design modeling declarative Language for cyber physical production systems. J. Math. Comput. Sci., 11(1), 520-542.

Panigrahi, Prabin Kumar, & et al. (2022). Localization strategies for autonomous mobile robots: A review. Journal of King Saud University-Computer and Information Sciences, 34(8) 6019-6039.

Baker, J. H., & et al.. (2021). Some interesting features of semantic model in Robotic Science. SSRG International Journal of Engineering Trends and Technology, 69(7), 38-44.

Abu-Jassar, A. T., & et al.. (2021). Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems. TEM Journal: Technology, Education, Management, Informatics, 10(4), 1645-1654.

Al-Sharo, Y. M., & et al.. (2021). Neural Networks As A Tool For Pattern Recognition of Fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.

Abu-Jassar, A. T., & et al.. (2023). Access Control to Robotic Systems Based on Biometric: The Generalized Model and its Practical Implementation. International Journal of Intelligent Engineering & Systems, 16(5), 313-328.

Lyashenko, V., Tahseen, A. J. A., Yevsieiev, V., & Maksymova, S. (2023). Automated Monitoring and Visualization System in Production. Int. Res. J. Multidiscip. Technovation, 5(6), 09-18.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2023). Generalized Procedure for Determining the Collision-Free Trajectory for a Robotic Arm. Tikrit Journal of Engineering Sciences, 30(2), 142-151.

Sotnik, S., & et al.. (2020). Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.

Yang, Ping, & et al. (2020). Plane kinematic calibration method for industrial robot based on dynamic measurement of double ball bar. Precision Engineering, 62, 265-272.

Wang, R., & et al. (2020). A point and distance constraint based 6R robot calibration method through machine vision. Robotics and Computer-Integrated Manufacturing, 65, 101959.

Nguyen, H. N., & et al. (2019). A new calibration method for enhancing robot position accuracy by combining a robot model–based identification approach and an artificial neural network–based error compensation technique. Advances in Mechanical Engineering, 11(1), 1687814018822935.

Sellami, S., & Klimchik, A. (2021). A deep learning based robot positioning error compensation. In 2021 International Conference" Nonlinearity, Information and Robotics"(NIR), IEEE. (pp. 1-5).

Wang, W., & et al. (2022). Error compensation of industrial robot based on deep belief network and error similarity. Robotics and Computer-Integrated Manufacturing, 73, 102220.

Cao, C. T., & et al. (2019). A novel indirect calibration approach for robot positioning error compensation based on neural network and hand-eye vision. Applied Sciences, 9(9), 1940.

Woodside, M. R., & et al. (2021). A kinematic error controller for real-time kinematic error correction of industrial robots. Procedia Manufacturing, 53, 705-715.

Sousa, R. B., & et al. (2020). Evolution of odometry calibration methods for ground mobile robots. In 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), IEEE, 294-299.

Lin, P., & et al. (2019). Calibration for odometry of omnidirectional mobile robots based on kinematic correction. In 2019 14th International conference on computer science & education (ICCSE), IEEE, 139-144.

Palacín, J., & et al. (2022). Systematic Odometry Error Evaluation and Correction in a Human-Sized Three-Wheeled Omnidirectional Mobile Robot Using Flower-Shaped Calibration Trajectories. Applied Sciences, 12(5), 2606.

Гіль А., Чала О., Филипенко О. (2021) Промислові iнтерфейси та протоколи передачі даних інтегрованих систем для автоматизованого управління в умовах Industry 4.0 Виробництво & Мехатронні Системи 2021. Матеріали V-ої Міжнародної конференції, Харків, 127-30.

Matarneh R., & et al. (2017). Building Robot Voice Control Training Methodology Using Artificial Neural Net. International Journal of Civil Engineering and Technology, 8(10), 523–532.

Svitlana Maksymova, & Mykhailo Akopov (2023). Selection of Sensors for Building a 3D Model of the Mobile Robot's Environment. Manufacturing & Mechatronic Systems 2023, 33-35.

Максимова С., & Канаєв В. (2023) Розробка підсистеми керування мобільного роботу для орієнтації в виробничому просторі Виробництво & Мехатронні Системи 2023: тези доповідей VII-ої Міжнар. конф. 54–56.