Graduate School of Engineering Science, Osaka University, Osaka, Japan.
Graduate School of Engineering, Kyoto University, Kyoto, Japan.
Bioinspir Biomim. 2024 Nov 7;20(1). doi: 10.1088/1748-3190/ad839e.
A central pattern generator (CPG)-based controller enhances the adaptability of quadrupedal locomotion, for example, by controlling the trunk posture. The conventional CPG-based controllers with attitude control often utilized the posture angle as feedback information. However, if the robot's body is as soft as a musculoskeletal structure, it can detect the over-tilting of the trunk based on proprioceptive information of the muscles. In general, proprioceptive information such as muscle tension changes more rapidly than posture angle information. Therefore, a feedback loop based on proprioceptive information has great potential to respond to sudden disturbances that occur during locomotion over uneven terrain. In this research, we proposed a CPG-based controller utilizing the tension of soft pneumatic artificial muscles (PAMs). Musculoskeletal quadruped robots driven by PAMs are so soft, which prevents over-tilting of the trunk because the soft leg acts like a suspension. In addition, tension, one of the proprioceptive information of PAMs, exhibits high sensitivity to changes in trunk posture because the soft body's motion easily is affected by over-tilting of the trunk. To validate the efficacy of the proposed controller, we conducted numerical simulations with a simple quadruped model and experiments with a musculoskeletal quadruped robot. As a result, the tension feedback is not effective for posture stabilization on flat terrain whereas it is effective on uneven terrain. Moreover, the tension feedback improved the running velocity over uneven terrain. These results will enhance the locomotion capability of musculoskeletal quadruped robots, advancing their practical application.
基于中央模式发生器（CPG）的控制器增强了四足运动的适应性，例如通过控制躯干姿势。具有姿态控制的传统基于 CPG 的控制器通常使用姿态角作为反馈信息。然而，如果机器人的身体像肌肉骨骼结构一样柔软，它可以基于肌肉的本体感受信息检测到躯干的过度倾斜。通常，肌肉张力等本体感受信息的变化比姿态角信息更快。因此，基于本体感受信息的反馈回路具有很大的潜力来应对在不平坦地形上运动时突然出现的干扰。在这项研究中，我们提出了一种基于 CPG 的控制器，该控制器利用软气动人工肌肉（PAM）的张力。由 PAM 驱动的肌肉骨骼四足机器人非常柔软，因为软腿像悬架一样，可以防止躯干过度倾斜。此外，PAM 的本体感受信息之一的张力对躯干姿势变化表现出高度敏感性，因为软体的运动很容易受到躯干过度倾斜的影响。为了验证所提出的控制器的有效性，我们使用简单的四足模型进行了数值模拟，并使用肌肉骨骼四足机器人进行了实验。结果表明，在平坦地形上，张力反馈对于稳定姿态无效，而在不平坦地形上则有效。此外，张力反馈提高了在不平坦地形上的运行速度。这些结果将增强肌肉骨骼四足机器人的运动能力，推进其实际应用。
