Shuhei Nishida

An online adaptation control system using mnSOM

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) and Self-Organizing Map (SOM) into AUVs. A new controller system for AUVs using Modular Network SOM (mnSOM) proposed by Tokunaga et al. is discussed in this paper. The proposed system is developed using recurrent NN type mnSOM. The efficiency of the system is investigated through the simulations.

An Adaptive Neural Network Control System using mnSOM

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) and self-organizing map (SOM) into AUVs. A new controller system for AUVs using modular network SOM (mnSOM) proposed by Tokunaga et al. is discussed in this paper. The proposed system is developed using recurrent NN type mnSOM. The efficiency of the system is investigated through the simulations.

An Adaptive Controller System Using mnSOM (2nd Report: Implementation into an Autonomous Underwater Robot)

Autonomous underwater vehicles (AUVs) have great advantages for activities in deep oceans [1], and are expected as the attractive tool for underwater development or investigation near future. However, AUVs have various problems which should be solved such as motion control, acquisition of sensors’ information, behavioral decision, navigation without collision, self-localization and so on. Therefore, the AUVs should be autonomous and adaptive to their environment.

A Navigation System for an Underwater Vehicle Using a Self-Organizing Map

Abstract Underwater vehicles are expected as attractive tools in the deepocean. 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. Development of navigation system, which can navigate vehicles without the collision to the obstacle, is one of the most important problems. Self-Organizing Map(SOM) is appliedto robots system, because of the capability of clustering and feature extraction. The efficiency of the system is investigated through simulations and experiments with an underwater vehicle “Twin-Burger”.