Development of Self-emergent Autonomous Adaptation System

在本研究中，为了开发自我涌现的自主适应系统，我们开发了如下两个自主机器人系统：（1）为了获得四条腿的机器人系统的步态，我们开发了机器人系统的控制器，每个腿分配有四个控制器作为异步自主代理，并将CSCG（连续空间分类器生成器）应用于每个代理。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，并确认机器人系统可以通过试错来获得步态。(2)为了开发一个控制系统的两个AGV连接的链接，我们开发了控制系统，每个AGV的两个自主代理，并尝试获得控制规则，为每个AGV使用强化学习。在一般情况下，因为它是很难应用的动态问题，就像在这项研究中的问题，我们结合使用SLA（随机学习自动机）与强化学习的预测。为了验证该控制器的有效性，我们将其应用于两个AGV在房间内沿沿着移动的任务中，验证了本文所设计的控制器对两个AGV的协同行为的有效性。

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.