Adaptive learning to environment using Self-Organizing Map and its application for underwater vehicles

Autonomous underwater vehicles (AUVs) have great advantages for activities in deep sea, and expected as the attractive tool. However, AUVs have various problems which should be solved. In this paper, the Self-Organizing Map (SOM) is applied as the clustering method for the navigation system. The SOM is known as one of the effective methods to extract the principle feature from many parameters and decrease the dimension of parameters. Through the competitive learning algorithms, the obtained map is tuned to express specific features of the input signals. We have been investigating the possibility of navigation system based on SOM through simulations are experiments with an AUV called "Twin-Burger". The learning algorithm of usual SOM is unsupervised learning. However, supervised learning algorithms should be introduced because the relationship between distances information and desirable behavior of the robot, that is, the relationship from inputs to outputs should be acquired and learned. In this paper, a supervised learning algorithm is introduced into SOM and a method to adapt the local map to its environment by learning and evaluating the trajectory of robot is proposed. In the proposed method, the "initial map" is made static and digital value as teaching data. In order to include more information of environment in the initial map, the trajectories of robot are evaluated, and the evaluation is utilized in the learning process. This method enables the map to have both the effect of dynamics of robot and environmental information. The efficiency of the method is investigated through the simulations and experiments.