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Development of an Autonomous Robotics Platform for Road Marks Painting Using Laser Simulator and Sensor Fusion Technique

Published online by Cambridge University Press:  02 October 2020

Mohammed A. H. Ali Open the ORCID record for Mohammed A. H. Ali [Opens in a new window] ,
Musa Mailah ,
Khaja Moiduddin ,
Wadea Ameen  and
Hisham Alkhalefah
Mohammed A. H. Ali*
Affiliation:
Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia
Musa Mailah
Affiliation:
Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
Khaja Moiduddin
Affiliation:
Advanced Manufacturing Institute, King Saud University, Riyadh 11451, Saudi Arabia
Wadea Ameen
Affiliation:
Advanced Manufacturing Institute, King Saud University, Riyadh 11451, Saudi Arabia
Hisham Alkhalefah
Affiliation:
Advanced Manufacturing Institute, King Saud University, Riyadh 11451, Saudi Arabia
*
*Corresponding author. E-mail: [email protected]
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Summary

The design and experimental works of an autonomous robotic platform for road marks painting are presented in this paper as the first autonomous system of its kind. The whole system involves two main sub-systems, namely: an autonomous mobile robot navigation system which is used for recognizing the roads and estimating the position of road marks, and automatic road marks painting system that is attached to the mobile robot platform to control the spray of the paint on the road’s surface. The experimental results show the capability of the proposed system to perform the task of autonomous road marks painting with accuracy of ±10 cm.

Keywords

Autonomous navigation systemRoad marks paintingLaser simulator (LS)Laser range finder (LRF)Wi-Fi cameraOdometrySensor fusion
Type
Articles
Information
Robotica , Volume 39 , Issue 3 , March 2021 , pp. 535 - 556
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Design and Construction Standards: Marking Pavements Guidelines. City of Edmonton (April 2012).Google Scholar
Top, Marks, A Guide To The Road Marking Industry in UK (Road Safety Marking Association Department (RSMA), UK, 2008).Google Scholar
Guideline Pavements, Marking, Part D: Materials and Equipments (Department Of Transportation and Main Roads. Queensland. Australia, June 2013).Google Scholar
Witt, A., Smith, R. and Visser, A., “Durability and Cost Effectiveness of Road Marking Paint,” In: South African Transport Conference. Conference Planners, South Africa, 17–20 July (2000).Google Scholar
Blaauw, G. and Padmos, P., “Night-time visibility of various types of road markings: A paper on durability including conditions of rain, fog and dew,” SAE Technical Paper Series 820412 (Society of Automotive Engineers, Warrendale, 1982).Google Scholar
Havell, D. F., “Improving the Skid Resistance and Cost Effectiveness of Road Marking Materials,” In: Annual Transportation Convention, Johannesburg, 25–29 July (1983).Google Scholar
Crisman, J. D. and Thorpe, C. E., “SCARF: A color vision system that tracks roads and intersections,” IEEE Trans. Robot. Autom. 9(1), 49–58 (1993).CrossRefGoogle Scholar
Kluge, K. and Thorpe, C., “The YARF system for vision-based road following,” Math. Comput. Model. 22(4–7), 213–233 (1995).CrossRefGoogle Scholar
Enkelmann, W., Struck, G. and Geisler, J., ROMA – A System for Model-Based Analysis of Road Markings (Fraunhofer-Institut fur Informations- und Datenverarbeitung (IITB) Karlsruhe, Germany, 1995).Google Scholar
Jochem, T. M., Pomerleau, D. A. and Thorpe, C. E., “Vision-based neural network road and intersection detection and traversal,” Technical Report: 8186-7108 (The Robotics Institute, Carnegie Mellon University, Pittsburgh PA 15213, 1995).Google Scholar
Kim, S.-H., Roh, C.-W., Kang, S.-C. and Park, M.-Y., “Outdoor Navigation of a Mobile Robot Using Differential GPS and Curb Detection,” In: IEEE International Conference on Robotics and Automation, Roma, Italy, 10–14 April (2007).Google Scholar
Lee, D., Son, S., Yang, K., Park, J. and Lee, H., “Sensor Fusion Localization System for Outdoor Mobile Robot,” In: ICROS-SICE International Joint Conference Fukuoka International Congress Center, Japan, August 18–21 (2009).Google Scholar
Matsushita, Y. and Miura, J., “On-line road boundary modeling with multiple sensory features, flexible road model and particle filter,” Robot. Auton. Syst. 59(5), 274–284 (2011).CrossRefGoogle Scholar
Georgiev, A. and Allen, P. K., “Localization methods for a mobile robot in urban environments,” IEEE Trans. Robot. 20(5), 851 (2004).CrossRefGoogle Scholar
Ali, M. A. H., Mailah, M., Hing, T. H., “Implementation of laser simulator search graph for detection and path planning in roundabout environments,” WSEAS Trans. Sig. Process. 10(2014), 118–126 (2014).Google Scholar
Ali, M. A. H., Mailah, M., Hing, T. H., “A Novel Approach for Visibility Search Graph Based Path Planning,” In: 13th International Conference on Robotics, Control and Manufacturing Systems, Kuala-lumpur, 2–4 April (2013) pp. 44–49.Google Scholar
Ali, M. A. H., Mailah, M., Hing, T. H., “Path Navigation of Mobile Robot in a Road Roundabout Setting,” In: 1st International Conference on Systems, Control (2012).Google Scholar
Ali, M. A. H., Mailah, M., Yussof, W. A. B., Hamedon, Z. B., “Sensors Fusion Based Online Mapping and Features Extraction of Mobile Robot in the Road Following and Roundabout,” In: IOP Conference Series: Materials Science, vol. 114 (2016).CrossRefGoogle Scholar
Ali, M. A. H. and Mailah, M., “Path planning and control of mobile robot in road environments using sensors fusion and active force control,” IEEE Trans. Veh. Technol. 68(3), 2176–2195 (2019).CrossRefGoogle Scholar
Ali, M. A. H., Mailah, M. and Hing, T. H., “Path planning of mobile robot for autonomous navigation of road roundabout intersection,” Int. J. Mech. 6(4), 203–211 (2012).Google Scholar
Ali, M. A. H., Yusoff, W. A. B. W., Hamedon, Z. B. and Yusssof, Z. B. M., “Global Mobile Robot Path Planning Using Laser Simulator,” In: Robotics and Manufacturing Automation (ROMA) (2016) pp. 1–6.Google Scholar
Ali, M. A. H. and Mailah, M., “Laser simulator a novel search graph-based path planning approach,” Int. J. Adv. Robot. Syst. 15(5), 1–16 (2018).CrossRefGoogle Scholar
Ali, M. A. H. and Lun, A. K., “A cascading fuzzy logic with image processing algorithm-based defect detection for automatic visual inspection of industrial cylindrical object’s surface,” Int. J. Adv. Manuf. Technol. 102(1–4), 81–94 (2018). https://doi.org/10.1007/s00170-018-3171-7.CrossRefGoogle Scholar
Nattharith, P. and Guzel, M. S., “Machine vision and fuzzy logic-based navigation control of a goal-oriented mobile robot,” Adap. Behav. 24(3), 168–180 (2016).CrossRefGoogle Scholar
Arkin, R. C., “Motor schema—Based mobile robot navigation,” Int. J. Robot. Res. 8(4), 92–110 (1989).CrossRefGoogle Scholar
Guzel, M. S., Ajabshir, V. B., Nattharith, P., Gezer, E. C. and Can, S., “A novel framework for multi-agent systems using a decentralized strategy,” Robotica 37(4), 691–707 (2019).CrossRefGoogle Scholar
White Drive Products, USA. Drive Wheel Motor Torque Calculations (2005).Google Scholar
Oklahoma Department of Transportation. Appendix D: Cost Estimate Details-Indian Nations Council of Government, 2nd West Street, Suite 800, OK 74103 (2015)Google Scholar
Road Safety Marking Association Department. Top Marks: A Guide to the Road Marking Industry in UK. (RSMA, UK, 2008).Google Scholar