图 书 馆
双足步行的机器学习Machine Learning for Bipedal Walking |
|
| 课程网址: | http://videolectures.net/aaai2011_remy_bipedal/ |
| 主讲教师: | Travis Brown; Sekou Remy |
| 开课单位: | 圣母大学 |
| 开课时间: | 2011-12-19 |
| 课程语种: | 英语 |
| 中文简介: |  双足机器人的运动给控件设计人员带来了重大挑战。控制这些系统的运动方程通常是混合的或由于间歇的地面接触而切换的，即使在最简单的情况下，它也包含许多耦合的非线性微分方程。这些属性使传统的控制技术难以应用。在本文中，通过将前馈神经网络，遗传算法和传统的PD控制相结合，创建了一个5链接平面两足动物机器人的替代控制器。神经网络使用某些状态变量作为输入，并以与HZD定性相似的方式基于当前状态生成所需的目标关节状态。然后，PD控制器尝试强制机器人进入此配置。以这种方式，神经网络根据状态变量的某种组合来指定时不变轨迹。一种改进的遗传算法被用于发展成功的系统神经控制器。 |
| 课程简介: | Bipedal robotic locomotion presents a significant challenge to the controls designer. The equations of motion governing these systems are generally hybrid or switched due to intermittent ground contact and consist of numerous coupled non-linear differential equations even in the simplest case. These attributes make traditional control techniques difficult to apply. In this paper, an alternative controller for a 5-link planar biped robot is created through a combination of feedforward neural networks, genetic algorithms and traditional PD control. The neural network uses certain state variables as input and generates a desired target joint state based on the current state in a manner qualitatively similar to HZD. A PD controller than attempts to force the robot into this configuration. In this way the neural network specifies a time invariant trajectory as a function of some combination of state variables. A modified genetic algorithm is used to evolve successful neural controllers for the system. |
| 关 键 词: | 双足机器人; 机器学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-30：zyk |
| 最后编审: | 2021-03-10：zyk |
| 阅读次数: | 55 |