IMU Sensor Based Omnidirectional Robot Localization and Rotary Encoder

Setyo Budi Marwanto, Riky Dwi Puriyanto

Abstract


Localization is a technique to determine the position of the robot in an environment. Robot positioning is a basic problem when designing a mobile robot. If the robot does not know its position, then the next robot action will be difficult to determine. To be able to determine the position of the omnidirectional robot requires good speed control on the DC motor. In omnidirectional robots, positioning is through the use of a rotary encoder sensor to count the movement of the omni robot at X and Y coordinates and the IMU sensor to maintain the direction of the robot facing. PID control is also applied to control the rotational speed of each DC motor on the robot wheel. Odometry is the method used in this study. The odometry system aims to estimate the position relative to the initial position of the omni robot to estimate the change in position from time to time. The final result of this research is the application of the odometry method based on a rotary encoder and IMU sensor can produce an effective and stable robot motion and can move in all directions (holonomic) by maintaining the robot's facing direction. The results of the test form simple motions such as forward, backward, right side, and left side motion, as well as forming a box trajectory that has a position error that is not large and quite accurate. The average error value at coordinate X is 1.44 cm and at coordinate Y is 1.67 cm.

Keywords


Localization; Omnidirectional; Odometry

Full Text:

PDF

References


B. Miodrag, M. Lukić, J. Bajić, B. Dakić and M. Vukadinović, “Hardware realization of autonomous robot localization system,” 2012 Proceedings of the 35th International Convention MIPRO, pp. 146-150, 2012, https://ieeexplore.ieee.org/document/6240630.

Kok Seng Chong and L. Kleeman, “Accurate odometry and error modelling for a mobile robot,” Proceedings of International Conference on Robotics and Automation, pp. 2783-2788 vol.4, 1997, https://doi.org/10.1109/ROBOT.1997.606708.

R. H. Abiyev, I. S. Günsel, N. Akkaya, E. Aytac, A. Çaǧman, and S. Abizada, “Fuzzy control of omnidirectional robot,” Procedia Comput. Sci., vol. 120, no. 10, pp. 608–616, 2017, https://doi.org/10.1016/j.procs.2017.11.286.

N. Ushimi and Y. Aoba, “Development of a two-wheels caster type of odometer with a dual shaft for Omnidirectional Mobile Robots,” Procedia Eng., vol. 41, pp. 163–169, 2012, https://doi.org/10.1016/j.proeng.2012.07.157.

W. Li, C. Yang, Y. Jiang, X. Liu, and C. Y. Su, “Motion planning for omnidirectional wheeled mobile robot by potential field method,” J. Adv. Transp., vol. 2017, no. 2, pp. 1–11, 2017, https://doi.org/10.1155/2017/4961383.

F. Tajti, G. Szayer, B. Kovács, P. Barna, and P. Korondi, “Optical flow based odometry for mobile robots supported by multiple sensors and sensor fusion,” Automatika, vol. 57, no. 1, pp. 201–211, 2017, https://doi.org/10.7305/automatika.2016.07.886.

A. A. Santoso Gunawan et al., "Line Follower Smart Trolley System V2 using RFID," 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI), pp. 17-21, 2021, https://doi.org/10.1109/ICCSAI53272.2021.9609710.

J. A. Batlle and A. Barjau, “Holonomy in mobile robots,” Rob. Auton. Syst., vol. 57, no. 4, pp. 433–440, 2009, https://doi.org/10.1016/j.robot.2008.06.001.

A. F. Tehrani, A. M. Doosthosseini, H. R. Moballegh, P. Amini, and M. M. DaneshPanah, “A new odometry system to reduce asymmetric errors for omnidirectional mobile robots,” RoboCup 2003: Robot Soccer World Cup VII, vol. 3020, pp. 600–610, 2004, https://doi.org/10.1007/978-3-540-25940-4_57.

H. Y. Lin and C. H. He, “Mobile robot self-localization using omnidirectional vision with feature matching from real and virtual spaces,” Appl. Sci., vol. 11, no. 8, pp. 1–14, 2021, https://doi.org/10.3390/app11083360.

H. Min, Y. Lin, S. Wang, F. Wu, and X. Shen, “Path planning of mobile robot by mixing experience with modified artificial potential field method,” Adv. Mech. Eng., vol. 7, no. 12, pp. 1–17, 2015, https://doi.org/10.1177/1687814015619276.

J. Inthiam and C. Deelertpaiboon, “Self-localization and navigation of holonomic mobile robot using omni-directional wheel odometry,” TENCON 2014 - 2014 IEEE Region 10 Conference, pp. 1-5, 2014, https://doi.org/10.1109/TENCON.2014.7022281.

M. Taufiqqurohman and N. F. Sari, “Odometry Method and Rotary Encoder for Wheeled Soccer Robot,” IOP Conf. Ser. Mater. Sci. Eng., vol. 407, no. 1, 2018, https://doi.org/10.1088/1757-899X/407/1/012103.

A. Martinelli, “The accuracy on the parameter estimation of an odometry system of a mobile robot,” Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), vol. 2, pp. 1378-1383, 2002, https://doi.org/10.1109/ROBOT.2002.1014735.

D. Tak, A. Jain, P. S. Savnani and D. Akash Mecwan, “Path Tracing in Holonomic Drive System with Reduced Overshoot using Rotary Encoders,” 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 343-348, 2020, https://doi.org/10.1109/SPIN48934.2020.9071196.

J. Moreno et al., “Design, implementation and validation of the three-wheel holonomic motion system of the assistant personal robot (APR),” Sensors, vol. 16, no. 10, pp. 1–21, 2017, https://doi.org/10.3390/s16101658.

I. Hassani, I. Maalej, and C. Rekik, “Holomonic Control of a Robot with an Omni-Directional Drive,” Math. Probl. Eng., vol. 3, no. 11, pp. 1–7, 2006, https://doi.org/10.1155/2018/2163278.

C. M. S. Francisco G Rossomando, “Identification and control of nonlinear dynamics of a mobile robot in discrete time using an adaptive technique based on neural PID,” Neural Comput. Appl., vol. 26, pp. 1179–1191, 2015, https://doi.org/10.1007/s00521-014-1805-8.

T. Gujarathi and K. Bhole, “Gait Analysis Using Imu Sensor,” 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-5, 2019, https://doi.org/10.1109/ICCCNT45670.2019.8944545.

Y. Jiang and S. Yin, “Recent Advances in Key-Performance-Indicator Oriented Prognosis and Diagnosis With a MATLAB Toolbox: DB-KIT,” IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 2849-2858, 2019, https://doi.org/10.1109/TII.2018.2875067.




DOI: https://doi.org/10.59247/csol.v1i2.39

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Setyo Budi Marwanto, Riky Dwi Puriyanto

 
Control Systems and Optimization Letters
ISSN: 2985-6116
Website: https://ejournal.csol.or.id/index.php/csol
Email: alfian_maarif@ieee.org
Publisher: Peneliti Teknologi Teknik Indonesia
Address: Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia