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

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，作为多智能体系统的运输设备之一的AGV在系统中占有重要地位。AGV作为一种可移动的运输设备，在生产系统中具有较大的自由度。本研究的重点是通过学习和适应动态工厂环境来获得自主行为，从而实现工件的运输。在1998-2000年期间，研究了以下主题。(1)基于路径获取的AGV场景获取方法的开发(2)基于Q-learning的AGV路径获取方法的开发(3)AGV之间通信协议的建立以了解相互行为。针对第一个问题，引入SDM并应用于AGV自动驾驶所需的工厂未来场景的获取。对于第二个问题，我们引入了Q-learning。通过SDM和q -学习的结合，AGV可以获取给定两个位置之间的合适路径。最后一个问题的研究目标是获取AGV的避碰知识。为了避免AGV之间的碰撞，有必要相互了解其他AGV的行为-通信协议。数值实验表明，q -学习可以获得适合避碰的通信协议。在2001年的最后一年，研究了以下主题。(1) AGV通过推动条形物体协同运输(2)通过学习实现与运动物体的避碰（q -learning），解决这两个问题。进行了三辆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。Q-learning在考虑时间序列的情况下被开发和实现。数值仿真结果表明，该方法能够获取预测知识，对运动目标进行跟踪。少

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