Martinez-Marin, Tomas and Duckett, Tom (2008) Learning visual docking for non-holonomic autonomous vehicles. In: The Intelligent Vehicles 2008 Symposium (IV08), June 4-6, 2008 , Eindhoven, Netherlands.
|Item Type:||Conference or Workshop Item (Paper)|
|Divisions:||College of Science > School of Computer Science|
|Abstract:||This paper presents a new method of learning visual docking skills for non-holonomic vehicles by direct interaction with the environment. The method is based on a reinforcement algorithm, which speeds up Q-learning by applying memorybased sweeping and enforcing the “adjoining property”, a filtering mechanism to only allow transitions between states that satisfy a fixed distance. The method overcomes some limitations of reinforcement learning techniques when they are employed in applications with continuous non-linear systems, such as car-like vehicles. In particular, a good approximation to the optimal behaviour is obtained by a small look-up table. The algorithm is tested within an image-based visual servoing framework on a docking task. The training time was less than 1 hour on the real vehicle. In experiments, we show the satisfactory performance of the algorithm.|
|Date Deposited:||20 Nov 2008 16:33|
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