In this paper, region exploration path planning algorithm is proposed. In order for a mobile robot to perform this task, appropriate measures with the shape of the working environment, which may be intricate or curved, is necessary. In addition, a robot must be able to react and be flexible when confronted with obstacles. With this algorithm, these challenges can be met by approximately expressing the working environment in grid points and regenerating the path using one that was planned beforehand. Simulations are used to demonstrate proposed exploration path planning and re-planning algorithm.

In this paper, we propose cooperative exploration method for mobile robots in a working area. The method will generate and share the minimal-cost path for each mobile robot. Each mobile robot goes through several observation points to accomplish the exploration. The observation points should be arranged at a fixed distance from at least on corresponding observation point. In addition, the number of observation points and the lengths of paths for each mobile robot are to be minimized. The proposed method should have the efficiency in computational cost concurrently with the adaptability to dynamic environmental changes.

This is a review paper on Mobiligence research. In order to solve the mechanism of intelligence with which higher organisms can behave even in unknown environment, and to discover design principle of artificial systems that have such intelligence, a new concept called Mobiligence is introduced based on assumption that the intelligence must be emerged through the interaction of the system with the environment caused by its mobility. The research on the Mobiligence is characterized by embodied plasticity, co-embodiment with environment, and internal generation of abductive information. The framework of the research on Mobiligence is presented in this paper as well as the originality of the research.

This paper offers a proposal for an algorithm of controlling a mobile robot that searches for and rearranges objects with unknown locations and shape. In this paper, we divide the task into two parts: exploration task and rearrangement task. The algorithms for each part of the task are presented with respect to the effectiveness of the path length and computational cost. Additionally integration algorithm that effectively combines exploration and rearrangement is presented. Experiments with a real robot are conducted to demonstrate the effectiveness of the proposed algorithm.

In this paper, we describe a novel self-localization algorithm. Self-localization methods are required for lowering the computational cost and handling vague sensor data. Thus, we propose to use only the uniform distribution to represent probability distributions in Monte Carlo localization, and name this method a uniform Monte Carlo localization (Uniform MCL). We manifest the low computational cost and robustness of Uniform MCL in the environment of RoboCup Sony legged robot league.

Learning approach is effective to design a agent that takes action autonomously. We propose to adopt teaching approach to design autonomous agents for RoboCup soccer tasks. Our teaching system consists of the ceiling CCD camera, SONY legged robots, and a PC for the teaching terminal. As a first step of the teaching process, we realized the self-localization of the robot using BP algorithm of feed forward neural networks.

Complicated tasks are often difficult to be expressed as single reward systems. In the human learning process, the relation between sensory inputs and action out-puts can be understood to have been acquired before-hand using an internal multidimensional reward system. We introduce reinforcement learning under multidimensional evaluation. The internal reward system includes both immediate evaluation and delayed rewards. The proposed architecture of the learning system is as a two layered Q-Learning system, which is combined with dynamic cell structure. We assume in the pushing task by a manipulator that information from touch sensors and motion detector of the vision system are available. The simulation showed that the acquired knowledge in the lower layer greatly helps to learn the pushing task.

Some kinds of spatiotemporal pattern generator systems are expressed by evolution equations. Such evolution equations are composed of many dynamic units which behave from only its local information. This is one example to coordinate many subsystems which observe the system from inside and to generate a global order. This paper shows how to treat these evolution equations and how to apply it to network systems. First, a continuous media system which is expressed by a gradient system in function space is considered. One of the simplest potential functionals derives a reaction diffusion equation which decreases the value of potential functional monotonously. The similar result is found in graph space. That is, a reaction diffusion equation on a graph decreases the value of a potential functional monotonously. This means that a network of dynamic units which observe only their connecting units' states can generate a global order in the same way of continuous media. This theory can treat some internal dynamic network system which should coordinate without some kinds of central controllers.

The concept of an autonomous decentralized system can be applied to recognizing various complicated and skillful actions performed by humans. This concept is the basis for a proposed pattern recognition system, which can be regarded as an associative memory system modeled by a reaction-diffusion equation system. This paper improves upon this pattern recognition system using the property of a dispersive wave instead of diffusion. This improvement has three advantages: acceleration of recognition, spread of similar regions and human-like hysteresis in recognition. Computer simulation has shown that propagation of a dispersive wave is faster than that of diffusion, and that the dispersive wave system exhibits the above advantageous properties of recognition.

Visual information processing in biological systems is performed simultaneously and in parallel at each neural unit. That is, a global task with no supervisor is achieved by the interaction among many subsystems. In this sense, the visual process shares the common functional structure with 'autonomous decentralized systems' which have attracted much attention in the field of system engineering. We attempt to design a pictorial pattern recognition system based on the concept of the autonomous decentralized system. The local interaction among subsystems is described by reaction-diffusion equations, in which each subsystem utilizes only the neighboring information around itself.>

The authors consider a synthesis approach to autonomous decentralized systems in which the functional order of the entire system is generated by cooperative interaction among its subsystems, each of which has the autonomy to control a part of the state of the system, and its application to pattern generators of animal locomotion. Biological locomotive rhythms, their generators, and gait patterns of quadrupeds are reviewed briefly. A design principle for autonomous coordination of many oscillators is proposed. Using these results, a gait pattern generator that suitably changes gait patterns for its moving speed is synthesized. It is shown, using computer simulations, that the system generates and changes patterns suitable for its moving speed, i.e., walk, trot, and gallop.>