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STUDIES ON AUTONOMOUS DECENTRALIZED PRODUCTION SYSTEM MODELED BY MULTIPLE AGENTS USING LEARNING AND ADAPTIVE FUNCTION

使用学习和自适应功能的多智能体建模的自主去中心化生产系统研究

基本信息

批准号：
10650129
负责人：
FURUKAWA Masashi
金额：
$ 1.41万
依托单位：
ASAHIKAWA NATIONAL COLLEGE OF TECHNOLOGY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10650129/
关键词：
AGV Autonomous decentralized system Machine learning Communication protocol Scheduling Adaptation

项目摘要

In modelling an autonomous decentralized production system, which consists of machine tools, transportation equipment, automated warehouses, and data processors, as a multiple agents system, AGV, one of transportation equipment, occupies an important location in the system. AGV in the production system has large freedom because it is movable transportation equipment. This research focuses on acquisition of autonomous behavior in order to transport work-pieces by learning and adapting its dynamic factory environment.During the years 1998-2000, the following topics are studied.(1) Development of scene acquisition method for AGV for use of path acquisition(2) Development of path acquisition method for AGV by use of Q-learning(3) Establishment of communication protocol between AGVs to understand mutual behaviorFor the first problem, SDM is introduced and applied to acquire future scenes in the factory required for AGV to drive itself autonomously. For the second problem, Q-learning is intr … More oduced. By combining SDM and Q-learning, AGV can acquire proper path between given two locations. The research object for the last problem is to acquire AGV collision avoidance knowledge for AGV.To avoid collision between AGVs, it becomes necessary to mutually understand other AGV's behavior - communication protocol. Numerical experiments show Q-learning can acquire proper communication protocols for collision avoidance.For the last year 2001, the following topics are studied.(1) AGV cooperative transportation by pushing a bar-shaped object(2) Collision avoidance with moving object by learningQ-learning is implemented to solve both problems. Numerical experiments in the case that three AGVs push the bar-shaped object are performed. Results show that Q-learning gives AGVs good cooperative behaviours on the first problem. Q-learning taking consider in a time series of situation are developed and implemented. Numerical simulation shows that the proposed method acquires the prediction knowledge to catch up the moving object. Less

在对由机床、运输设备、自动化仓库和数据处理器组成的自主分散生产系统进行建模时，作为一个多智能体系统，AGV作为运输设备之一，在系统中占有重要地位。 AGV在生产系统中由于是可移动的运输设备，具有较大的自由度。本研究的重点是获取自主行为，以便通过学习和适应动态工厂环境来运输工件。在1998-2000年期间，研究了以下主题：（1）开发用于路径获取的AGV场景获取方法（2）开发使用Q-learning的AGV路径获取方法（3）建立AGV之间的通信协议以了解相互行为对于第一个问题，引入了SDM 并应用于获取未来工厂内AGV自动驾驶所需的场景。针对第二个问题，引入了Q-learning。通过结合 SDM 和 Q-learning，AGV 可以获得给定两个位置之间的正确路径。最后一个问题的研究对象是获取AGV的防撞知识。为了避免AGV之间发生碰撞，需要相互了解其他AGV的行为——通信协议。数值实验表明Q-learning能够获得适当的通信协议来避免碰撞。2001年最后一年，研究了以下课题：(1)通过推动条形物体进行AGV协作运输(2)通过学习避免与移动物体碰撞Q-learning解决了这两个问题。进行了3台AGV推动棒状物体的数值实验。结果表明，Q 学习使 AGV 在第一个问题上具有良好的合作行为。开发和实施时考虑了时间序列情况的 Q-learning。数值模拟表明，该方法获得了追赶运动物体的预测知识。较少的