Dimas Hutomo Daud Saputro, Joko Subur, M. Taufiqurrohman
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引用次数: 1

摘要

印度尼西亚机器人大赛(KRI)是一项发展创造力和丰富机器人领域科学技术的活动。在KRI中,有几个分支类别,其中一个是KRPAI(印度尼西亚消防机器人大赛)类别。在KRPAI类别比赛中,机器人的目标是找到一个有火的房间,然后机器人将扑灭房间内的火,并返回起点。这就要求机器人知道自己的实际位置,以便返回到起点。这可以通过添加人工智能(artificial intelligence)人工神经网络来解决。用于学习过程的神经网络模型之一是反向传播。需要注意的是,在人工智能系统中,人工神经网络要想在识别机器人位置时工作得最优,必须先做一个学习过程(learning),才能得到最优的权重值。该学习系统通过输入280个表示28个位置的位置数据样本来实现。学习数据是通过读取机器人与迷宫壁之间的距离以及机器人面对的方向来获得的。为了测试人工神经网络系统,进行了一个运行过程来确定机器人的位置。从140个试验数据中,人工神经网络能够正确识别机器人的位置,准确率为92.85%。仿真结果有望应用于实际的四足机器人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifikasi Posisi Robot Quadpod pada Arena Pertandingan Menggunakan Jaringan Syaraf Tiruan - Algoritma Backpropagation
The Indonesian Robot Contest (KRI) is an event to develop creativity and enrich science and technology in the field of robotics. In KRI, there are several branch categories, one of which is the KRPAI (Indonesian Fire Fighting Robot Contest) category. In the KRPAI category competition, the robot aims to find a room where there is a fire, then the robot will extinguish the fire in the room, and return to the starting point. This requires the robot to know its own actual position so that it can return to the starting point. This can be solved by adding artificial intelligence (Artificial Intelligence) artificial neural networks. One of the neural network models used for the learning process is backpropagation. It should be noted that in artificial intelligence systems, artificial neural networks in order to work optimally in identifying robot positions, it is necessary to do a learning process (learning) first in order to get the optimal weight value. The learning system is carried out by entering 280 position data samples that present 28 positions. Learning data is obtained by reading the distance between the robot and the maze wall and the direction facing the robot. To test the artificial neural network system, a running process is carried out to determine the position of the robot. From 140 trial data conducted, the artificial neural network was able to recognize the position of the robot correctly with an accuracy of 92.85%. The simulation results are expected to be applied to the actual quadpod robot.
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