Abstract
Autonomous Underwater Vehicles (AUVs) are attractive tools to survey earth science and oceanography, however, there exists a lot of problems to be solved such as motion control, acquisition of sensor data, decision-making, navigation without collision, self-localization and so on. In order to realize useful and practical robots, underwater vehicles should take their action by judging the changing condition from their own sensors and actuators, and are desirable to make their behavior, because of features caused by the working environment. We have been investigated the application of brain-inspired technologies such as Neural Networks (NNs), Self-Organizing Map (SOM), etc, into AUVs. A new self-organizing decision making system for AUVs using Modular Network Self-Organizing Map (mnSOM) proposed by Tokunaga et al. is discussed in this paper. The proposed decision making system is developed using recurrent NN type mnSOM. The efficiency of the system is investigated through the simulations.
