Abstract
Several approaches have been developed over time aiming to improve the localization aspects, especially in mobile robotics. Besides the more traditional techniques, mainly based on analytical models, artificial intelligence has emerged as an interesting alternative. The current study proposes to explore the machine learning model structure optimization for pose estimation, using the RobotAtFactory 4.0 competition as the main context. Using a Bayesian Optimization-based framework, the parameters of a Multi-Layer Perceptron (MLP) model, trained to estimate the components of the 2D pose (x, y, and \(\theta \)) of the robot were optimized in four different scenarios of the same context. The results obtained showed a quality improvement of up to 60% on the estimation when compared with the modes without any optimization. Another aspect observed was the different optimizations found for each model, even in the same scenario. An additional interesting result was the possibility of the reuse of optimization between scenarios, presenting an interesting approach to reduce time and computational resources.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Available in https://github.com/P33a/SimTwo.
- 2.
- 3.
- 4.
- 5.
- 6.
References
Braun, J., et al.: A robot localization proposal for the RobotAtFactory 4.0: a novel robotics competition within the industry 4.0 concept. Front. Robot. AI 9 (2022). https://doi.org/10.3389/frobt.2022.1023590
Klein L.C., et al.: A machine learning approach to robot localization using fiducial markers in RobotAtFactory 4.0 competition. Sensors. 23(6), 3128 (2023). https://doi.org/10.3390/s23063128 Hidden to the revision
Klein, L.C., et al.: Using machine learning approaches to localization in an embedded system on robotatfactory 4.0 competition: a case study. In: 2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Tomar, Portugal, 2023, pp. 69–74 (2023). https://doi.org/10.1109/ICARSC58346.2023.10129619. Hidden to the revision
Klein, L.C., et al.: Deep learning-based localization approach for autonomous robots in the robotatfactory 4.0 competition. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds.) Optimization, Learning Algorithms and Applications. OL2A 2023. CCIS, vol. 1982. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-53036-4_13 Hidden to the revision
Braun, J., Júnior, A.O., Berger, G.S., Lima, J., Pereira, A.I., Costa, P.: RobotAtFactory 4.0: a ROS framework for the SimTwo simulator. In: 2022 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 205–210. IEEE, April 2022
Bengio, Y., Goodfellow, I., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Huang, S., Dissanayake, G.: Robot Localization: An Introduction, pp. 1–10. Wiley Encyclopedia of Electrical and Electronics Engineering, Hoboken (1999)
Grewal, M.S., Weill, L.R., Andrews, A.P.: Global Positioning Systems, Inertial Navigation, and Integration. John Wiley & Sons, Hoboken (2007)
Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960). https://doi.org/10.1115/1.3662552
Welch, G., Bishop, G.A.: An Introduction to the Kalman Filter, vol. 1, no. 4, pp. 1–16. EBooks, New York (1995). http://academic.csuohio.edu/simond/reduce/ijar.pdf
Fox, D., Burgard, W., Thrun, S.: Markov localization for mobile robots in dynamic environments. J. Artif. Intell. Res. 11, 391–427 (1999)
Gordon, N.J., Salmond, D.J., Smith, A.F.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEE Proceedings F (Radar and Signal Processing), vol. 140, no. 2, pp. 107–113. IET Digital Library, April 1993
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson, London (2016)
Kumari, V.S.R., Kumar, P.R.: Optimization of multi-layer perceptron neural network using genetic algorithm for arrhythmia classification. Communications 3(5), 150–157 (2015)
Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2 (NIPS’12), pp. 2951–2959. Curran Associates Inc., Red Hook, NY, USA (2012)
Mockus, J., Tiesis, V., Zilinskas, A.: The application of Bayesian methods for seeking the extremum. Towards Glob. Optim. 2, 117–129 (1978)
Costa, P., Gonçalves, J., Lima, J., Malheiros, P.: Simtwo realistic simulator: a tool for the development and validation of robot software. Theory Appl. Math. Comput. Sci. 1(1), 17–33 (2011)
Brochu, E., Cora, V.M., De Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning (2010). arXiv preprint arXiv:1012.2599
Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 25 (2012)
Sousa, R., et al.: A Robotic Framework for the Robot@Factory 4.0 Competition (2024). https://doi.org/10.13140/RG.2.2.29701.77283
Ferreira, T., Braun, J., Lima, J., Pinto, V.H., Santos, M., Costa, P.: Robot at factory 4.0: an auto-referee proposal based on artificial vision. In: Iberian Robotics Conference, pp. 475–487. Springer International Publishing, Cham, November 2022
Acknowledgments
F. Martins also thanks the Sensors and Smart Systems group, Research Centre in Bio-based Economy of Hanze University of Applied Sciences.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Klein, L.C. et al. (2024). Optimization of Machine Learning Models Applied to Robot Localization in the RobotAtFactory 4.0 Competition. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2024. Communications in Computer and Information Science, vol 2280. Springer, Cham. https://doi.org/10.1007/978-3-031-77426-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-77426-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-77425-6
Online ISBN: 978-3-031-77426-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)