Acquisition of visually guided swing motion based on genetic algorithms and neural networks in two-armed bipedal robot
Acquisition of visually guided swing motion based on genetic algorithms and neural networks in two-armed bipedal robot
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基于遗传算法和神经网络的双臂双足机器人视觉引导摆动运动采集
DOI：
10.1109/robot.1997.606734
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
H. Inoue
中科院分区：
文献类型：
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作者：
K. Nagasaka;A. Konno;M. Inaba;H. Inoue
我们描述了通过将 GA（遗传算法）应用于 NN（神经网络）控制器来获取 16 DOF 双臂双足机器人的视觉引导摆动运动的方法。许多研究人员已成功使用进化方法来获取机器人的各种运动，但大多数研究仅通过计算机模拟进行。在这项研究中，我们采用了一个在嘈杂环境中使用的具有复杂身体的真实机器人。在 CRS-CS6400 并行计算机上构建的虚拟世界中检查了进化过程，该虚拟世界模拟了控制系统中的摆动动力学、视觉过程、降噪过程和时间滞后等因素。最初的种群包含 200 个不成功的基因，经过 50 代的人工进化，大约需要几个小时才能创造出成功的个体。利用从上一代最成功的个体中解码出来的神经网络，获得了一个能够成功摆动的真正的双臂双足机器人。
We describe the method in which a visually guided swing motion for a 16 DOF two-armed bipedal robot is acquired by applying a GA (genetic algorithm) to a NN (neural network) controller. The evolutionary approach to the acquisition of various motions for robots has been successfully used by many researchers, but most studies have been carried out only through computer simulations. In this research, we adopt a real robot with a complicated body used in a noisy environment. The evolutionary processes are examined in. A virtual world constructed on a CRS-CS6400 parallel computer which simulates such factors as swing dynamics, visual processes noise reduction processes, and time lags in a control system. It took about and hours for an artificial evolution to create a successfully individual after 50 generations from an initial population of 200 unsuccessful genes. Using the NN decoded from the most successful individual of the last generation, a real two-armed bipedal robot that could swing successfully was obtained.