Robust laser positioning in a mobile robot machine vision system

Authors

  • Alexander Gurko Kharkiv National Automobile and Highway University, Ukraine https://orcid.org/0000-0001-9905-8584
  • Oleg Sergiyenko Engineering Institute of Autonomous University of Baja California, Mexico, Blvd. Benito Juarez y Calle de la Normal, s/n, Col. Insurgentes Este, 21280, Mexicali, Baja California, Mexico, Ukraine https://orcid.org/0000-0002-0623-6976
  • Lars Lindner Engineering Institute of Autonomous University of Baja California, Mexico, Blvd. Benito Juarez y Calle de la Normal, s/n, Col. Insurgentes Este, 21280, Mexicali, Baja California, Mexico, Mexico https://orcid.org/0000-0002-0623-6976

DOI:

https://doi.org/10.30977/VEIT.2021.20.0.03

Keywords:

DC motor, laser positioning, machine vision system, robust control

Abstract

Problem. Laser scanning devices are widely used in Machine Vision Systems (MVS) of an autonomous mobile robot for solving SLAM problems. One of the concerns with MVS operation is the ability to detect relatively small obstacles. This requires scanning a limited sector within the field of view or even focusing on a specific point of space. The accuracy of the laser beam positioning is hampered by various kinds of uncertainties both due to the model simplifying and using inaccurate values of its parameters, as well as lacking information about perturbations. Goal. This paper presents the improvement of the MVS, described in previous works of the authors, by robust control of the DC motor, which represents the Positioning Laser drive. Methodology. For this purpose, a DC motor model is built, taking into account the parametric uncertainty. A robust digital PD controller for laser positioning is designed, and a comparative evaluation of the robust properties of the obtained control system with a classical one is carried out. The PWM signal formation by the microcontroller and processes in the H-bridge are also taken into account. Results. The obtained digital controller meets the transient process and accuracy requirements and combines the simplicity of a classic controller with a weak sensitivity to the parametric uncertainties of the drive model. Originality. The originality of the paper is in its focus on the MVS of the autonomous mobile robot developed by the authors. Practical value. The implementation of the MVS with the proposed controller will increase the reliability of obstacles detection within a robot field of view and the accuracy of environment mapping.

Author Biographies

Alexander Gurko, Kharkiv National Automobile and Highway University

Professor, Dr. of Sc, professor of the Automation and Computer-Integrated Technologies Department

Oleg Sergiyenko, Engineering Institute of Autonomous University of Baja California, Mexico, Blvd. Benito Juarez y Calle de la Normal, s/n, Col. Insurgentes Este, 21280, Mexicali, Baja California, Mexico

Associate Professor, Dr. of Sc.

Lars Lindner, Engineering Institute of Autonomous University of Baja California, Mexico, Blvd. Benito Juarez y Calle de la Normal, s/n, Col. Insurgentes Este, 21280, Mexicali, Baja California, Mexico

PhD

References

Bresson G., Alsayed Z., Yu Li, and Glaser S., (2017). Simultaneous localization and mapping: a survey of current trends in autonomous driving. IEEE Transactions on Intelligent Vehicles, 20, 194–220. DOI: 10.1109/TIV.2017.2749181.

Li Z., Yang C., Su C.Y., Deng J., and Zhang W.. (2016). Vision-based model predictive control for steering of a nonholonomic mobile robot. IEEE Trans. on Control Systems Technology, 24(2), 553–564. DOI: 10.1109/TCST.2015.2454484.

N. Shalal, Low T., McCarthy C., and Hancock N.. (2015). Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part A: Tree detection. Computers and Electronics in Agriculture, 119, 254–266. DOI: 10.1016/j.compag.2015.09.025.

Su Z., et al. (2017). Global localization of a mobile robot using lidar and visual features. Proc. ROBIO, Macao, China, 2377–2383. DOI: 10.1109/ROBIO.2017.8324775.

Kumar G.A., et al. A LiDAR and IMU integrated indoor navigation system for UAVs and its application in real-time pipeline classification. (2017). Sensors, 17(6), 1268 [Online]. Available: http://dx.doi.org/10.3390/s17061268.

Kumar P., et al. An algorithm for automated estimation of road roughness from mobile laser scanning data. The Photogrammetric Record, 30(149), 30–45, 2015. DOI: 10.1111/phor.12090.

Song C., Wang X., and Cui N. (2018). Mixed-Degree Cubature H∞ Information Filter-Based Visual-Inertial Odometry. Applied Sciences, 9(1), 56. [Online]. Available: http://dx.doi.org/10.3390/app9010056

Huang K. and Stachniss C. (2018). Joint ego-motion estimation using a laser scanner and a monocular camera through relative orientation estimation and 1-DoF ICP. Proc. IROS, Madrid, Spain, 671–677. DOI: 10.1109/IROS.2018.8593965

Ivanov M., et al. (2019). Mobile Robot Path Planning Using Continuous Laser Scanning. Optoelectronics in Machine Vision-Based Theories and Applications, IGI Global, 338–372. [Online] Available: DOI: 10.4018/978-1-5225-5751-7.ch012.

Wen S., et al. (2018). Camera recognition and laser detection based on EKF-SLAM in the autonomous navigation of humanoid robot. Journal of Intelligent & Robotic Systems, 92(2), 256–277. DOI: https://doi.org/10.1007/s10846-017-0712-5.

Zhao P., et al. (2018). Panoramic Image and Three-Axis Laser Scanner Integrated Approach for Indoor 3D Mapping. Remote Sensing, 10(8), 1269. [Online]. Available: http://dx.doi.org/10.3390/rs10081269

Basaca-Preciado L. C., et al. (2014). Optical 3D laser measurement system for navigation of autonomous mobile robot. Optics and Lasers in Engineering, 54, 159–169. DOI: 10.1016/j.optlaseng.2013.08.005.

Lindner L., et al. (2016). Mobile robot vision system using continuous laser scanning for industrial application. Industrial Robot, 43(4), 360–369. DOI: 10.1108/IR-01-2016-0048.

Lindner L., et al. (2017). Machine vision system errors for unmanned aerial vehicle navigation. Proc. ISIE, Edinburgh, UK, 1615–1620. DOI: 10.1109/ISIE.2017.8001488.

Reyes-García M., et al. (2018). Reduction of angular position error of a machine vision system using the digital controller LM629. Proc. IECON 2018, Washington, D.C., USA, 3200–3205.

Lindner L., et al. (2016). UAV remote laser scanner improvement by continuous scanning using DC motors. IECON 2016, Florence, Italy, 371–376. DOI: 10.1109/IECON.2016.7793316.

Tamaki K., et al. (1986). Microprocessor-based robust control of a DC servo motor. IEEE Control Systems Magazine, 6(5), 30–36, 1986. DOI: 10.1109/MCS.1986.1105133.

Fallahi M. and Azadi S. (2009). Robust control of DC motor using fuzzy sliding mode control with PID compensator. IMECS, Hong Kong, China, 2, 5. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.148. 9267&rep=rep1&type=pdf

Eker I. (2006). Sliding mode control with PID sliding surface and experimental application to an electromechanical plant. ISA Trans., 45(1), 109–118. DOI: 10.1016/S0019-0578(07)60070-6.

Březina L. and Březina T. (2011). H-infinity controller design for a DC motor model with uncertain parameters. Engineering Mechanics, 18, (5-6), 271–279.

Dey N., Mondal U., and Mondal D. (2016). Design of a H-infinity robust controller for a DC servo motor system. Proc. ICICPI, Kolkata, India, 27–31. DOI: 10.1109/ICICPI.2016.7859667.

Nguyen B. H., Ngo H. B., and Ryu J. H. (2009). Novel robust control algorithm of DC motors. Proc. URAI, Gwangju, Korea, 119–122.

Dorf R. C. and Bishop R. H. (2010). Modern Control Systems, 12th ed., Prentice Hall.

Гурко А.Г. (2019). Робастное управление приводом лазера системы технического зрения. Радиоэлектроника, информатика, управление, 1, 238–246. Gurko A. (2019). Robastnoe upravlenie privodom lazera sistemyi tehnicheskogo zreniya. [Robust control of laser actuator for technical vision system]. Radio Electronics Computer Science Control, 1, 238–246. DOI: 10.15588/1607-3274-2019-1-22.

Gu D.-W., Petkov P. H., and Konstantinov M.M. (2013). Robust control design with MATLAB, 2nd ed., Springer Science & Business Media.

Maxon RE-max Catalog. (2013). Maxon Motor, Sachseln, Switzerland. [Online]. Available: http://storkdrives.com/wp-content/uploads/2013/

/maxon-RE-max-catalog-data1.pdf

Garcia-Cruz X. M., et al. (2014). Optimization of 3D laser scanning speed by use of combined variable step. Optics and Lasers in Engineering, 54, 141–151. DOI: 10.1016/j.optlaseng.2013.08.011

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Published

2021-11-30

How to Cite

Gurko, A., Sergiyenko, O. ., & Lindner, L. (2021). Robust laser positioning in a mobile robot machine vision system. Vehicle and Electronics. Innovative Technologies, (20), 27–36. https://doi.org/10.30977/VEIT.2021.20.0.03

Issue

No. 20 (2021)

Section

INTELLIGENT TRANSPORT MANAGEMENT SYSTEMS. SYNERGETIC SYSTEMS OF ECO-VEHICLES

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