一个神经机械解决方案,用于可调节的机器人合规性和准确性
Ignacio Abadía1, Alice Bruel2, Grégoire Courtine3
1Research Center for Information and Communication Technologies, Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain.
这项研究引入了一种用于机器人的新型大脑肌肉控制器,模仿人类神经机械. 它通过整合肌肉模型和小脑网络来实现可适应的机器人运动行为,以在各种环境中提高性能.
科学领域:
- 机器人和神经科学 机器人和神经科学
- 生物机械工程 生物机械工程
- 控制系统 控制系统
背景情况:
- 机器人需要适应性的运动行为来实现现实世界的互动.
- 人类的运动控制整合了中枢神经系统和生物力学 (神经力学).
- 小脑和肌肉的共同收缩是人类运动适应和度控制的关键.
研究的目的:
- 开发一个机器人控制器,模仿人类神经机械,以调节运动行为.
- 将肌肉粘性弹性,共收缩和小脑适应性整合到一个统一的控制解决方案中.
- 在非结构化环境中提高机器人的适应性和强度.
主要方法:
- 提出了一个大脑肌肉控制器,将肌肉模型与粘性弹性和协同收缩相结合.
- 在没有先前的分析解决方案的情况下,纳入一个小脑网络来进行运动适应.
- 实现了反控制循环,使用扭矩命令来驱动机器人.
主要成果:
- 控制器成功启用了可调节的机器人电机行为.
- 已经证明,缩调制可以调节机器人的刚性和精度.
- 该系统证明了对有效载荷干扰和在未知的地形上运行的稳定性.
结论:
- 拟议的神经机械启发的控制器扩大了机器人的运动谱.
- 小脑适应和肌肉合收缩对于提高机器人的性能和适应性是有效的.
- 这种方法为更具多功能性和类似人类的机器人运动提供了途径.
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