通过模仿人体拮抗肌抑制机制，实现绳索驱动类人机器人的稳定长时运动。
Antagonist Inhibition Control in Redundant Tendon-driven Structures Based on Human Reciprocal Innervation for Wide Range Limb Motion of Musculoskeletal Humanoids
- 基于人体互惠性神经控制，设计拮抗肌抑制控制器
- 在Kengoro机器人上实现14分钟悬垂与引体向上
- 适合需要高精度、长时稳定运动的类人机器人研究
构造一个解剖学上准确的绳索驱动类人机器人结构非常复杂，其几何模型与实际机器人之间存在巨大差异，因为难以在几何模型中准确表达绳索线缆的复杂路径。若仅根据几何模型中的绳索长度来驱动机器人，将导致非预期的肌肉张力和松弛，严重时可能造成实际机器人损坏。为解决此问题，本文借鉴人体神经系统中的互惠性神经控制机制，提出了基于反射的拮抗肌抑制控制（AIC）。该控制可有效避免因模型误差引起的内部肌肉张力与绳索松弛，从而实现长时间安全的全范围运动。为验证有效性，将AIC应用于绳索驱动类人机器人Kengoro的上肢，成功实现了持续14分钟的悬垂及引体向上动作。
原文摘要 · Abstract (English)
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.
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