Despite the appealing concept of central pattern generator (CPG)-based control for bipedal walking robots, there is currently no systematic methodology for the design of a CPG-based controller. To remedy this oversight, we attempted to apply the Tegotae approach, which is a Japanese concept describing how well a perceived reaction, i.e., sensory information, matches an expectation, i.e., an intended motor command, in the design of localized controllers in the CPG-based bipedal walking model. To this end, we developed a Tegotae function that quantifies the Tegotae concept. This function allowed us to systematically incorporate decentralized controllers into our bipedal walking model design. We designed a two-dimensional bipedal walking model using Tegotae functions that was subsequently implemented in simulations to verify the validity of the proposed design scheme. We found that our model can walk on both flat and uneven terrains, and confirmed that the application of the Tegotae functions in all of the joint controllers results in excellent adaptability to environmental changes.

中文翻译：

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基于 Tegotae 的控制在双足行走中产生自适应肢体间和肢体内协调

尽管基于中央模式生成器 (CPG) 的双足行走机器人控制概念很有吸引力，但目前还没有用于设计基于 CPG 的控制器的系统方法。为了弥补这一疏忽，我们尝试应用 Tegotae 方法，这是一个日本概念，描述在 CPG 中的本地控制器设计中感知反应（即感觉信息）与期望（即预期的运动命令）的匹配程度基于双足行走的模型。为此，我们开发了一个 Tegotae 函数来量化 Tegotae 概念。该功能使我们能够系统地将分散控制器纳入我们的双足行走模型设计中。我们使用 Tegotae 函数设计了一个二维双足步行模型，随后在仿真中实现该模型，以验证所提出设计方案的有效性。我们发现我们的模型可以在平坦和不平坦的地形上行走，并证实 Tegotae 功能在所有联合控制器中的应用都对环境变化具有出色的适应性。