Controllers based on neuromuscular models hold the promise of energy-efficient and human-like walkers. However, most of them rely on optimizations or cumbersome hand-tuning to find controller parameters which, in turn, are usually working for a specific gait or forward speed only. Consequently, designing neuromuscular controllers for a large variety of gaits is usually challenging and highly sensitive. In this contribution, we propose a neuromuscular controller combining reflexes and a central pattern generator able to generate gaits across a large range of speeds, within a single optimization. Applying this controller to the model of COMAN, a 95 cm tall humanoid robot, we were able to get energy-efficient gaits ranging from 0.4 m/s to 0.9 m/s. This covers normal human walking speeds once scaled to the robot height. In the proposed controller, the robot speed could be continuously commanded within this range by changing three high-level parameters as linear functions of the target speed. This allowed large speed transitions with no additional tuning. By combining reflexes and a central pattern generator, this approach can also predict when the next strike will occur and modulate the step length to step over a hole.
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机译:基于神经肌肉模型的控制器具有节能和类似人的步行者的承诺。但是,它们中的大多数都依靠优化或繁琐的手动调整来找到控制器参数,这些参数通常仅适用于特定步态或前进速度。因此,为多种步态设计神经肌肉控制器通常是具有挑战性且高度敏感的。在此贡献中,我们提出了一种神经肌肉控制器,该控制器将反射和中央模式生成器相结合,能够在单个优化内以很大的速度范围生成步态。将此控制器应用于95厘米高的人形机器人COMAN的模型,我们能够获得0.4 m / s至0.9 m / s的节能步态。一旦覆盖到机器人的高度,这将涵盖正常的人类行走速度。在提出的控制器中,可以通过更改三个高级参数作为目标速度的线性函数,在此范围内连续控制机器人速度。这样就可以进行较大的速度转换,而无需进行其他调整。通过结合反射和中央模式生成器,此方法还可以预测何时会发生下一次打击,并调节步长以跨过孔。
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