Abstract
In this paper we propose a generic approach to acquire navigation skills for nonholonomic vehicles in unknown environments. The algorithm uses reinforcement learning to update both the vehicle model and the optimal behaviour at the same time. After the training phase, the vehicle is able to explore the environment through a wall-following behaviour. The vehicle can also reach any goal position by the virtual wall concept. The method does not require function interpolation to obtain a good approximation to the optimal behaviour. The learning time was only a few minutes to acquire the wall-following behaviour. Both simulation and experimental results are reported to show the satisfactory performance of the method.
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
Preview
Unable to display preview. Download preview PDF.
References
Barraquand, J., Latombe, J.C.: On nonholonomic mobile robots and optimal maneuvering. Revue d’Inteligence Artificielle 3(2), 77–103 (1989)
Bertsekas, D.: Neurodynamic Programming. Athena Scientific, Nashua (1996)
Fraichard, T., Ahuactzin, J.M.: Smooth Path Planning for Cars. In: IEEE Int. Conf. on Robot. Autom (ICRA), Seoul, pp. 21–26 (2001)
Hsu, C.S.: A discrete method of optimal control based upon the cell state space concept. Journal of Optimization Theory and Applications 46(4) (1985)
Lamiraux, F., Laumond, J.P.: Smooth Motion Planning for Car-Like Vehicles. IEEE Trans. Robot. Automat. 17(4), 498–502 (2001)
Latombe, J.-C.: Robot Motion Planning. Kluwer Academic, Boston (1991)
Martínez-Marín, T.: Optimal path planning for car-like vehicles in the presence of obstacles. In: Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Shanghai, pp. 1161–1164 (2003)
Martinez-Marin, T., Duckett, T.: Fast Reinforcement Learning for Vision-Guided Mobile Robots. In: IEEE Int. Conf. on Robot. Autom (ICRA), Barcelona (2005)
Moore, A.W., Atkeson, C.G.: Priortized Sweeping: Reinforcement Learning with less data and less time. Machine Learning 13, 103–130 (1993)
Reeds, J.A., Shepp, R.A.: Optimal path for a car that goes both forward and backward. Pacific J. Math. 145(2), 367–393 (1990)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Zufiria, P.J., Martínez-Marín, T.: Improved Optimal Control Methods based upon the Adjoining Cell Mapping Technique. Journal of Optimization Theory and Applications 118(3), 657–680 (2003)
Zufiria, P.J., Guttalu, R.S.: The adjoining cell mapping and its recursive unraveling, Part I: Description of adaptive and recursive algorithms. Nonlinear Dynamics 4, 204–226 (1993)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Marín, T. (2007). Learning Autonomous Behaviours for Non-holonomic Vehicles. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_101
Download citation
DOI: https://doi.org/10.1007/978-3-540-73007-1_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
eBook Packages: Computer ScienceComputer Science (R0)