Development of Self-emergent Autonomous Adaptation System

在本研究中，为了开发自应急自主适应系统，我们开发了两个自主机器人系统：(1)为了获得四足机器人系统的步态，我们开发了机器人系统的控制器，每条腿分配4个控制器作为异步自治智能体，并对每个智能体应用CSCG （Continuous Space Classifier Generator）。为了评估控制器，我们将机器人系统应用于机器人系统应该移动到光源的任务中，并通过试错验证了机器人系统可以获得步态。(2)为了开发两个AGV的链路连接控制系统，我们开发了每个AGV有两个自治代理的控制系统，并尝试使用强化学习方法获得每个AGV的控制规则。一般来说，由于很难应用像本研究中那样的动态问题，我们将使用SLA（随机学习自动机）的预测器与强化学习相结合。为了评估控制器，我们将控制器应用于两个agv在房间内沿墙壁移动的任务，并使用本文提出的控制器确认两个agv的合作行为。

In this research, to develop self-emergent autonomous adaptation systems, we develop two autonomous robot systems as follows :(1) To obtain a gait of a robot systems with four legs, we developed controllers of the robot systems with four controllers assigned to each legs as an asynchronous autonomous agent, and applied CSCG (Continuous Space Classifier Generator) to each agent. To evaluate the controllers, we applied the robot systems to the task that the robot systems should be moved to a light source, and confirmed that the robot systems can be obtain a gait through trial and error.(2) To develop a control system for two AGVs connected by a link, we developed the control systems with two autonomous agents of each AGV and try to obtain control rules for each AGV by using reinforcement learning. In general, since it is difficult to apply the dynamic problems just like the problem in this research, we combined the predictor by using SLA (Stochastic Learning Automata) with the reinforcement learning. To evaluate the controller, we apply the controller to the task that the two AGVs move along the wall in a room, and confirm the cooperative behavior of the two AGVs by using the controllers proposed in this paper.