Abstract
One of the key tasks of mobile robotics is navigation, which for Outdoor-type robots is exacerbated by the functioning in an environment with a priori of unknown characteristics of underlying surfaces. In this paper, for the first time, the learning navigation system for mobile robot based on the group method of data handling (GMDH) is presented. The paper presents the results of training of models both for evaluating the robot’s pose (coordinates and angular orientation) in heterogeneous environment and classification of the type of underlying surfaces. In addition to the direct readings of the on-board sensors, additional parameters (reflecting how the robot perceives the surface terramechanics) were introduced to train the models. The results of testing of the obtained models demonstrate their performance in an essentially heterogeneous environment, when areas of the underlying surfaces are comparable with the robot’s dimensions. This testifies the operability of developed GMDH-based learning system for mobile robot navigation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rogers-Marcovitz, F., George, M., Seegmiller, N., Kelly, A.: Aiding off-road inertial navigation with high performance models of wheel slip. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pp. 215–222. IEEE, Vilamoura, Portugal (2012)
Madhavan, R., Nettleton, E., Nebot, E., Dissanayake, G., Cunningham, J., Durrant-Whyte, H., Corke, P., Roberts, J.: Evaluation of internal navigation sensor suites for underground mining vehicle navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 999–1004. IEEE, Detroit, MI, USA (1999)
Koch, J., Hillenbrand, C., Bems, K.: Inertial navigation for wheeled robots in outdoor terrain. In: Fifth International Workshop on Robot Motion and Control, pp. 169–174. IEEE, Dymaczewo, Poland (2005)
Bingbing, L., Adams, M., Ibañez-Guzmán, J.: Multi-aided inertial navigation for ground vehicles in outdoor uneven environments. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4703–4708. IEEE, Barcelona, Spain (2005)
Liu, Y., Xiong, R., Wang, Y., Huang, H., Xie, X., Liu, X., Zhang, G.: Stereo visual-inertial odometry with multiple kalman filters ensemble. IEEE Trans. Indus. Electron. 63(10), 6205–6216 (2016)
Dupont, E., Collins, E., Coyle, E., Roberts, R.: Terrain classification using vibration sensors: theory and methods. In: New Research on Mobile Robotics, pp. 1–41 (2010)
Ojeda, L., Cruz, D., Reina, G., Borenstein, J.: Current-based slippage detection and odometry correction for mobile robots and planetary rovers. IEEE Trans. Robot. 22(2), 366–378 (2006)
Iagnemma, K., Ward, C.: Classification-based wheel slip detection and detector fusion for mobile robots on outdoor terrain. Auton. Robots 26(1), 33–46 (2009)
Khaleghian, S., Taheri, S.: Terrain classification using intelligent tire. J. Terramech. 71, 15–24 (2017)
Andrakhanov, A.: Technology of autonomous mobile robot control based on the inductive method of self-organization of models. In: Proceedings of the 7th International Symposium “Robotics for Risky Environment – Extreme Robotics”, pp. 361–368. Saint-Petersburg, Russia (2013)
Andrakhanov, A.: Navigation of autonomous mobile robot in homogeneous and heterogeneous environments on basis of GMDH neural networks. In: Proceedings of the 4th International Conference on Inductive Modelling, pp. 133–138. Kiev, Ukraine (2013)
Tyryshkin, A., Andrakhanov, A., Orlov, A.: GMDH-based modified polynomial neural network algorithm.In: GMDH-methodology and Implementation in C (With CD-ROM), Imperial College Press, World Scientific, London (2015). ISBN: 978-1-84816-610-3
Robotino Manual (Order No. 544305). http://www.festo-didactic.com/ov3/media/customers/1100/544305_robotino_deen2.pdf. Accessed 05 June 2017
Ivakhnenko, A., Ivakhnenko, G.: The review of problems solvable by algorithms of the group method of data handling. Pattern Recogn. Image Anal. Adv. Math. Theory Appl. 5(4), 527–535 (1994)
Ivakhnenko, A., Ivakhnenko, G., Mueller, J.: Self-organization of neuronets with active neurons. Int. J. Pattern Recogn. Image Anal. Adv. Math. Theory Appl. 4(4), 177–188 (1994)
Madala, H.R., Ivakhnenko, A.G.: Inductive Learning Algorithms for Complex System Modeling. CRC Press, Boca Raton (1994). ISBN 0-8493-4438-7
Martin, S., Murphy, L., Corke, P.: Building large scale traversability maps using vehicle experience. In: Desai, J.P., Dudek, G., Khatib, O., Kumar, V. (eds.) 13th International Symposium on Experimental Robotics 2012, STAR, vol. 88, pp. 891–905. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Andrakhanov, A., Belyaev, A. (2018). GMDH-Based Learning System for Mobile Robot Navigation in Heterogeneous Environment. In: Shakhovska, N., Stepashko, V. (eds) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-70581-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-70581-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70580-4
Online ISBN: 978-3-319-70581-1
eBook Packages: EngineeringEngineering (R0)