There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

2007 Special Issue: Optimization and applications of echo state networks with leaky- integrator neurons

During the 1990s, numerous worldwide research and development activities have occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R&D activities in the AUV community have increased. However, this is just the beginning for more advanced, yet practical and reliable AUVs. This paper surveys some key areas in current state-of-the-art underwater robotic technologies. It is by no means a complete survey but provides key references for future development. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs.

Design and Control of Autonomous Underwater Robots: A Survey

Training recurrent neural networks is hard. Recently it has however been discovered that it is possible to just construct a random recurrent topology, and only train a single linear readout layer. State-of- the-art performance can easily be achieved with this setup, called Reservoir Computing. The idea can even be broadened by stating that any high di- mensional, driven dynamic system, operated in the correct dynamic regime can be used as a temporal 'kernel' which makes it possible to solve complex tasks using just linear post-processing techniques. This tutorial will give an overview of current research on theory, applica- tion and implementations of Reservoir Computing.

https://biblio.ugent.be/publication/416607/file/447949.pdf

An overview of reservoir computing: theory, applications and implementations

斜向妥当化(Cross-Validation)の必要と方法

A neural network based control system "Self-Organizing Neural-Net-Controller System: SONCS" has been developed as an adaptive control system for Autonomous Underwater Vehicles (AUVs) In this paper, an on-line adaptation method "Imaginary Training" is proposed to improve the time-consuming adaptation process of the original SONCS The Imaginary Training can be realized by a parallel structure which enables the SONCS to adjust the controller network independently of actual operation of the controlled object The SONCS is divided into two separate parts: the Real-World Part where the controlled object is operated according to the objective, and the Imaginary-World Part where the Imaginary Training is carried out In order to adjust the controller network by the Imaginary Training, it is necessary to introduce a forward model network which can generate simulated state variables without involving actual data A neural network "Identification Network" which has a specific structure to simulate the behavior of dynamical systems is proposed as the forward model network The effectiveness of the Imaginary Training is demonstrated by applying to the heading keeping control of an AUV "Twin-Burger" It is shown that the SONCS adjusts the controller network-through on-line processes in parallel with the actual operation >

An on-line adaptation method in a neural network based control system for AUVs

Abstract Artificial Potential Filed (APF) is the most well-known method that is used in mobile robot path planning, however, the shortcoming is that the local minima. To overcome this issue, we present a deadlock free APF based path planning algorithm for mobile robot navigation. The Proposed-APF (P-APF) algorithm searches the goal point in unknown 2D environments. This method is capable of escaping from deadlock and non-reachability problems of mobile robot navigation. In this method, the effective front-face obstacle information associated with the velocity direction is used to modify the Traditional APF (T-APF) algorithm. This modification solves the deadlock problem that the T-APF algorithm often converges to local minima. The proposed algorithm is explained in details and to show the effectiveness of the proposed approach, the simulation experiments were carried out in the MATLAB environment. Furthermore, the numerical analysis of the proposed approach is given to prove a deadlock free motion of the mobile robot.

/pdf/an-artificial-potential-field-based-mobile-robot-navigation-5d3pu9c9dv.pdf

An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

An adaptive neural-net controller system for an underwater vehicle

Describes a neural network system which executes identification of robot dynamics and controller adaptation in parallel with robot control. The system consists of two parts: real-world part and imaginary-world part. The real-world part is a feedback control system for the actual robot. In the imaginary-world part, the model of robot and the controller are adjusted continuously in order to deal with the change of dynamic property caused by disturbance and so on. The system is designed to be suitable for a computer system with parallel processing ability. In the paper, adaptability of the controller system is investigated by heading keeping and path following experiments on the condition that unknown disturbances are given to the robot.

Neural network system for online controller adaptation and its application to underwater robot

With regard to the current sewer pipe inspection technology, all commercial robots are completely tele-operated, usually via a tether cable, by a human operator. In addition, current sewer inspection robots have a poor mobility function to pass any kind of pipe-bends such as curves and junctions so that those robots are only capable to move into the straight pipes. Inspecting the sewage pipes using the state of the arts inspection methods by the current robots is costly, mostly human cost, and not fast enough to check and inspect the amount of sewage pipes will grow stronger than it has actually happened, specially in Japan. In this paper we propose an innovative, fast and robust sewer inspection method by using a passive-active intelligent, fully autonomous, un-tethered robot called "KANTARO" which fits to the pipes within a diameter range of 200-300 millimeters. KANTARO prototype robot, including a novel passive-active intelligent moving mechanism, can move into the straight pipe and pass different kinds of pipe bends without need to any intelligence of the controller or sensor reading. In order to realize a fully autonomous inspection robot, we also developed a small and intelligent 2D laser scanner for detecting of the navigational landmarks such as manholes and pipe joints independently with main computer system, and fusion with a fish eye camera to assist the pipe state and fault detection.

Kazuo Ishii

Papers

An on-line adaptation method in a neural network based control system for AUVs

An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

An adaptive neural-net controller system for an underwater vehicle

Neural network system for online controller adaptation and its application to underwater robot

A New Approach to the Sewer Pipe Inspection: Fully Autonomous Mobile Robot "KANTARO"