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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Martínez-Marín, T. (2007). Learning autonomous behaviours for non-holonomic vehicles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4507 LNCS, pp. 837–846). Springer Verlag. https://doi.org/10.1007/978-3-540-73007-1_101
Mendeley helps you to discover research relevant for your work.