Skip to main content

Position Estimator for a Follow Line Robot: Comparison of Least Squares and Machine Learning Approaches

  • Conference paper
  • First Online:
Robotics in Natural Settings (CLAWAR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 530))

Included in the following conference series:

Abstract

Navigation is one of the most important tasks for a mobile robot and the localisation is one of its main requirements. There are several types of localisation solutions such as LiDAR, Radio-frequency and acoustic among others. The well-known line follower has been a solution used for a long time ago and still remains its application, especially in competitions for young researchers that should be captivated to the scientific and technological areas. This paper describes two methodologies to estimate the position of a robot placed on a gradient line and compares them. The Least Squares and the Machine Learning methods are used and the results applied to a real robot allow to validate the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/GSNCodes/ArUCo-Markers-Pose-Estimation-Generation-Python.

  2. 2.

    https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html.

References

  1. M. Pakdaman, M. M. Sanaatiyan and M. R. Ghahroudi, "A line follower robot from design to implementation: Technical issues and problems," 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, pp. 5–9, https://doi.org/10.1109/ICCAE.2010.5451881

  2. Omer Gumus, Murat Topaloglu, Dogan Ozcelik, The Use of Computer Controlled Line Follower Robots in Public Transport, Procedia Computer Science, Volume 102, 2016, Pages 202–208, ISSN 1877–0509

    Google Scholar 

  3. G. Eleftheriou, L. Doitsidis, Z. Zinonos and S. A. Chatzichristofis, "A Fuzzy Rule-Based Control System for Fast Line-Following Robots," 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2020, pp. 388–395, https://doi.org/10.1109/DCOSS49796.2020.00068

  4. J. Lima et al., "An Industry 4.0 Approach for the Robot@Factory Lite Competition," 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2020, pp. 239–244, https://doi.org/10.1109/ICARSC49921.2020.9096164

  5. M. Engin and D. Engin, "Path planning of line follower robot," 2012 5th European DSP Education and Research Conference (EDERC), 2012, pp. 1–5, https://doi.org/10.1109/EDERC.2012.6532213

  6. Chowdhury, N.H., Khushi, D., Rashid, M.M.: Algorithm for Line Follower Robots to Follow Critical Paths with Minimum Number of Sensors. International Journal of Computer (IJC) 24(1), 13–22 (2017)

    Google Scholar 

  7. Evripidou, S., Georgiou, K., Doitsidis, L., Amanatiadis, A.A., Zinonos, Z., Chatzichristofis, S.A.: Educational Robotics: Platforms, Competitions and Expected Learning Outcomes. IEEE Access 8, 219534–219562 (2020). https://doi.org/10.1109/ACCESS.2020.3042555

    Article  Google Scholar 

  8. S. Saadatmand, S. Azizi, M. Kavousi and D. Wunsch, "Autonomous Control of a Line Follower Robot Using a Q-Learning Controller," 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020, pp. 0556–0561, https://doi.org/10.1109/CCWC47524.2020.9031160

  9. B. Li, J. Wu, X. Tan and B. Wang, "ArUco Marker Detection under Occlusion Using Convolutional Neural Network," 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), 2020, pp. 706–711, https://doi.org/10.1109/CACRE50138.2020.9230250

  10. Stigler, Stephen M. The History of Statistics: The Measurement of Uncertainty Before 1900. Cambridge, MA: Belknap Press of Harvard University Press. 1986.ISBN 978-0-674-40340-6

    Google Scholar 

  11. Legendre, Adrien-Marie, Nouvelles méthodes pour la détermination des orbites des comètes [New Methods for the Determination of the Orbits of Comets] (in French), Paris: F. Didot, 1805. hdl:2027/nyp.33433069112559

    Google Scholar 

  12. Åke Björck, Least squares methods, Handbook of Numerical Analysis, Elsevier, Volume 1, 1990, Pages 465–652, ISSN 1570–8659, ISBN 9780444703668, https://doi.org/10.1016/S1570-8659(05)80036-5

  13. Tofallis, C. "Least Squares Percentage Regression". Journal of Modern Applied Statistical Methods. 7: 526–534. 2009. SSRN 1406472 https://doi.org/10.2139/ssrn.1406472

  14. Strutz, T. Data Fitting and Uncertainty : A Practical Introduction to Weighted Least Squares and Beyond (2nd ed.). Springer Vieweg. 2016. ISBN 978-3-658-11455-8

    Google Scholar 

  15. A modern introduction to probability and statistics : understanding why and how. Dekking, Michel, 1946-. London: Springer. 2005. ISBN 978-1-85233-896-1. OCLC 262680588

    Google Scholar 

  16. Gregersen, E.: The Britannica guide to statistics and probability. Britannica Educational Pub. in association with Rosen Educational Services, New York, NY (2011)

    Google Scholar 

  17. Carroll, Raymond J. (1982). "Adapting for Heteroscedasticity in Linear Models". The Annals of Statistics. 10 (4): 1224–1233. JSTOR 2240725. https://doi.org/10.1214/aos/1176345987

  18. Xiao, X., Liu, B., Warnell, G., et al.: Motion planning and control for mobile robot navigation using machine learning: a survey. Auton Robot (2022). https://doi.org/10.1007/s10514-022-10039-8

    Article  Google Scholar 

  19. Mohri, M., Rostamizadeh, A., Talwalkar, A. (2018). Foundations of machine learning. MIT press

    Google Scholar 

  20. Zhao, Y., Zhang, Y.: Comparison of decision tree methods for finding active objects. Advances in Space Research 41(12), 1955–1959 (2008)

    Article  Google Scholar 

  21. Loh, W.Y.: Classification and regression trees. Wiley interdisciplinary reviews: data mining and knowledge discovery 1(1), 14–23 (2011)

    Google Scholar 

  22. Chicco, D., Warrens, M. J., Jurman, G. (2021). In regression analysis evaluation, the coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE. PeerJ. Computer science, 7, e623. https://doi.org/10.7717/peerj-cs.623

Download references

Acknowledgements

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020 and also by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Diogo Matos .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Matos, D. et al. (2023). Position Estimator for a Follow Line Robot: Comparison of Least Squares and Machine Learning Approaches. In: Cascalho, J.M., Tokhi, M.O., Silva, M.F., Mendes, A., Goher, K., Funk, M. (eds) Robotics in Natural Settings. CLAWAR 2022. Lecture Notes in Networks and Systems, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-031-15226-9_41

Download citation

Publish with us

Policies and ethics