用机械轮式小车机器人控制剑道机器人的方法

Chenyang Zhao, T. Shibuya, Naoki Igo, K. Hoshino
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引用次数: 0

摘要

剑道是一种起源于日本的武术形式,即使是老年人或妇女也适用,并且由于在比赛和练习中使用短而易于操作的竹剑,因此有利于促进健康。然而,由于在Tankendo中活跃的人数很少,因此获得与其他人类玩家的实际比赛经验并不容易。在本研究中,作者提出了一种利用机械轮作为运动机构的台车机器人控制Tankendo机器人的方法。虽然这个机器人不能像人一样突然启动或突然停止,但由于它有七个枢轴,可以很好地控制车辆的方向或姿势以及速度。控制命令为“速度”、“行进方向”、“旋转比(曲线曲率)”三种类型,并进行梯形速度控制,防止失去同步。换句话说,在达到控制命令的目标速度之前存在时间延迟。使用具有这些特性的台车机器人,输入的是当前时刻的“机器人状态”(位置、姿态等)和“对手状态”(动作方向、运动速度、与对手的距离等),以及之前几个时刻的“机器人控制命令”。对于输出,使用“速度”,“行进方向”和“旋转比”的最佳参考表是使用强化学习的机器学习的。作者提出了一种机器人控制方法,可以在攻击成为有效攻击之前,在适当的时间移动到适当的位置来防御人类的攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method of Controlling a Tankendo Robot Using a Mecanum Wheel Trolley Robot
Tankendo which is a form of martial arts that originated in Japan is suitable for even aged persons or women and is good for promotion of health since a short and easy to maneuver shinai (bamboo sword) is used during matches as well as during practice. However, since the population of persons active in Tankendo is small, it is not easy to gain experience of actual play against other human players. In this research, the authors propose a method of control of a Tankendo robot that employs a trolley robot that uses a mecanum wheel as the movement mechanism. Although this robot cannot start suddenly or stop suddenly like humans, since it has seven pivots, it is possible to finely control the direction or posture of the vehicle as well as its speed. The control commands are of the three types of “speed”, “direction of travel”, and “rotation ratio (curvature of the curve)”, and, in addition, trapezoidal speed control is being carried out in order to prevent loss of synchronization. In other words, there is a time delay until the target speed of the control command is reached. Using a trolley robot with these characteristics, the inputs are the "robot state" (position, posture, etc.) and the "opponent state" (direction of the shinai, movement speed, distance from the opponent, etc.) at the current instant of time, and the "robot control commands" from several instants of time earlier. For the output, an optimal reference table using "speed," "direction of travel," and " rotation ratio" is machine-learned using reinforcement learning. The authors propose a robot control method that can defend against a human attack by moving to an appropriate position at an appropriate time, just before the attack becomes effective strike.
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