基于人类交互神经支配的冗余腱驱动结构拮抗抑制控制，用于肌肉骨骼人形机器人广范围肢体运动
Antagonist Inhibition Control in Redundant Tendon-driven Structures Based on Human Reciprocal Innervation for Wide Range Limb Motion of Musculoskeletal Humanoids
AI总结：
针对腱驱动肌肉骨骼人形机器人几何模型误差导致肌肉张力异常的问题，本文基于人类交互神经支配提出拮抗抑制控制（AIC），并在Kengoro上肢上验证，实现14分钟悬垂和引体向上。
摘要：
解剖学上正确的腱驱动肌肉骨骼人形机器人的身体结构十分复杂，由于在几何模型中表达腱线的复杂路径非常困难，其几何模型与实际机器人之间的差异非常大。如果按照几何模型中的腱线长度来驱动腱驱动肌肉骨骼人形机器人，将会出现非预期的肌肉张力和松弛。在某些情况下，这可能导致实际机器人的损坏。为了解决这一问题，我们关注人类神经系统中的交互神经支配，并基于该反射实现了拮抗抑制控制（AIC）。该控制能够避免由模型误差引起的不必要的内部肌肉张力和腱线松弛，并能够长时间安全地执行广范围运动。为了验证其有效性，我们将AIC应用于腱驱动肌肉骨骼人形机器人Kengoro的上肢，成功实现了悬垂14分钟和引体向上。
英文摘要：
The body structure of an anatomically correct tendon-driven musculoskeletal humanoid is complex, and the difference between its geometric model and the actual robot is very large because expressing the complex routes of tendon wires in a geometric model is very difficult. If we move a tendon-driven musculoskeletal humanoid by the tendon wire lengths of the geometric model, unintended muscle tension and slack will emerge. In some cases, this can lead to the wreckage of the actual robot. To solve this problem, we focused on reciprocal innervation in the human nervous system, and then implemented antagonist inhibition control (AIC) based on the reflex. This control makes it possible to avoid unnecessary internal muscle tension and slack of tendon wires caused by model error, and to perform wide range motion safely for a long time. To verify its effectiveness, we applied AIC to the upper limb of the tendon-driven musculoskeletal humanoid, Kengoro, and succeeded in dangling for 14 minutes and doing pull-ups.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
