Yoshio Kawauchi

Concept of cellular robotic system (CEBOT) and basic strategies for its realization

This paper presents the concept of Cellular Robotic System (CEBOT), which is one of the self-organizing robotic systems, and describes some basic strategies for its realization. CEBOT is the distributed intelligent system. This system is a Dynamically Reconfigurable Robotic System (DRRS) consisting of a large number of autonomous robotic units called cells. At first, for the determination of CEBOT structure, we describe the reconfiguration methodology for mobile/robotic manipulator systems, which is based on geometric calculation. Because of the important issue that CEBOT can automatically connect and communicate with each other, then we show both the experimental results in automatic connection and separation by CEBOT MarkII designed as a protoype of CEBOT, and the communication experiments in CEBOT MarkII. Finally, we propose the optimal knowledge allocation method based on the information flow of the communication among cells, since the optimal knowledge allocation is considered as one of the most important issues for the distributed intelligent system. In this paper, we describe the ability of CEBOT and show some experimental results.

A principle of distributed decision making of Cellular Robotic System (CEBOT)

The Cellular Robotic System (CEBOT) is studied with the aim of realizing a self-organizing robotic system. A principle of the distributed decision making method for a multi-agents robotic system is reported. Some simple control laws which each unit should obey are proposed. The conditions of parameters of the control laws to keep the total system stable are also discussed. To demonstrate the efficiency of the proposed method, a simple task (carry the object) is carried out by some mobile cells which obey the methods from simulation experiments. The optimal number of cells to execute the task is evaluated and discussed.<<ETX>>

A new approach to production regularity assessment in the oil and chemical industries

Abstract This article presents a new approach to production regularity assessment in the oil and chemical industries. The production regularity is measured by the throughput capacity distribution. A brief survey of some existing techniques is presented, and the structure of the new approach is introduced. The proposed approach is based on analytical methods, i.e. no simulation is necessary. The system modeling is split into three levels: components, basic subsystems, and merged subsystems, and two modeling methods are utilized: Markov modeling and a rule-based method. The main features of the approach are as follows: (1) short calculation time; (2) systems of dependent components can be modeled; (3) maintenance strategies can be modeled; and (4) a variety of system configurations can be modeled. A simple case study is used to demonstrate how the proposed approach can be applied.

Self-organizing intelligence for cellular robotic system 'CEBOT' with genetic knowledge production algorithm

The authors propose a genetic knowledge production algorithm (GKPA) for CEBOT, in order to realize a self-organizing and self-evolutionary knowledge system. They propose a new kind of distributed intelligence system which is based on the concept of CEBOT employing the GKPA. This proposed intelligence system has capabilities of learning, reasoning, and self-organizing. This system consists of various kinds of intelligence units called knowledge-cells. As an example of the applications of the self-organizing intelligence, a robotic manipulator capable of learning and reasoning has been made from the CEBOT concept by installing the intelligence system with GKPA in the manipulator. The knowledge acquisition ability of the manipulator is demonstrated by experimental results.<<ETX>>

Dynamically reconfigurable intelligent system of cellular robotic system (CEBOT) with entropy min/max hybrid algorithm

This paper discusses a framework and the algorithms for a reconfigurable intelligent system, which is capable of knowledge amplification. The proposed intelligent system consists of many kinds of knowledge sources, which have simple levels of data and intelligence together with some blackboards which dynamically organize a hierarchical structure in order to optimize its own performance index. The system has two strategies for reconfiguration of the system structure: one is entropy maximum strategy the other is entropy minimum strategy. The entropy maximum strategy is used when many kinds of tasks are given to the system. The entropy minimum strategy is applied in cases were the system deals with a few tasks only. This paper addresses the fundamental configuration and algorithms which include these two strategies for reconfiguration. The efficiency of the proposed system is shown by some simulations.<<ETX>>

A Strategy Of Self-organization For Cellular Robotic System (CEBOT)

Cellular Robotic SystemCEB0T has been studied to realize a dynamically r econfigurable r obotic system, which can c arry out m any kind of tasks under various environments by the authors. CEBOT consists of a lot of units called cell which has a simple function. To deal with a complex task which can not be carried out by a cell, CEBOT generates various module ,which is composed of some cells. A much complex 5ystem composed of some modules is called structure, which can carry out very complicated tasks. As above mentioned concepts, CEBOT is very similar to a living creature, because tissues and organs are formed by a lot of cells. CEBOT h as to not only create new modules and structures, but also kill some modules and structures, because the number of cells is limited. Therefore it is necessary to study a system which can create and kill new group composed of many units of the system. In this paper, We propose a strategy of self-organization. We call the strategy an Genetic Cell Production Algorithm (GCPA). The GCPA is based on the Genetic Algorithms, which are search algorithms based on the mechanics of natural se1ectiol.r and n atural genetics. We also propose a estimation method for the distributed system, which can be described by relations among subsystems. To simulate the self-organization based on the GCPA, we designed a distributed s ystem, which has a hierarchical cell-structure based on the concept of CEBOT. By using the model of a distributed system, some simulational results and analysis are reported in this paper, to verify the efficiency of the GCPA.

Evolutional Self-organization of Distributed Autonomous Systems

This paper discusses a framework and algorithms for reconfiguration of distributed autonomous systems (hereinafter called DAS). Most of DASs have ill-structured systems which mean nonlinear and complex systems. The proposed method based on Genetic Algorithms (hereinafter called GA) is applicable to optimize such a system. Many parameters of ill-structured systems are hierarchically mapped into parameters of the method as chromosomes in GA. The parameters of the system regarded as chromosomes are updated to increase (or decrease) fitness of each state of the system. As one example, the proposed method is applied to realize the self-organization of intelligent system of cellular robotic system (hereinafter called CEBOT) previously studied by authors. Both hardware and software of CEBOT consists of many functional units called “cells”. In this paper, self-organization of the intelligent system of CEBOT is mainly discussed. The proposed intelligent system consists of many kinds of knowledge sources, which have simple levels of data and intelligence together with some blackboards which dynamically organize a hierarchical structure. It is assumed that each intelligent unit is allowed to locally communicate with the other units to exchange data and information. Therefore, communication load of units can be defined. It is desired that the intelligent system can reorganize in order to maximizing (or minimizing) performance index calculated by using load of each unit. The efficiency of the proposed method is shown by some simulations of maximizing (or minimizing) the performance index of the system during self-organization.

A relation between resource amount and system performance of the cellular robotic system (CEBOT)

The authors have studied the cellular robotic system (CEBOT) with the aim of realizing a self-organizing robotic system. It can be regarded that CEBOT is one of the multi-agents robotic systems studied by many researchers. It is generally considered that one of the features of the multi-agents robot is that the system can carry out many kinds of tasks under various environments due to coordination among many agents. It is assumed that a group of the agents can get higher performance than an ability of single agent. In this paper, the relation between the resource amount, which implies both hardware and software installed into the system, and system performance is discussed. At first, a new concept called function amplification is proposed and defined, the control law of each agent to achieve the function amplification is secondarily discussed. In the last part of this paper, some simulations are reported and discussed to demonstrate the relation between resource amount and system performance.

Numerical analysis of distributed system based on knowledge-module of cellular robotic system 'CEBOT'

Cellular robotic system (CEBOT) consists of many units called cells. CEBOT is required to be able to carry out many different tasks under various surroundings. The authors propose a new kind of distributed intelligent system which is based on the concept of CEBOT. This intelligent system proposed has the abilities of learning, reasoning, and self-organizing. Similar to the hardware structure of CEBOT, this system consists of various kinds of intelligence units called knowledge-cells. By using the knowledge-cells and the proposed algorithms, the system which can generate task procedures is realized. To estimate the efficiency of this system, a search space based on the theory of probability is described.<<ETX>>