无需几何模型，自动按功能与空间关系分组冗余肌群
Automatic Grouping of Redundant Sensors and Actuators Using Functional and Spatial Connections: Application to Muscle Grouping for Musculoskeletal Humanoids
- 构建传感器/执行器的图结构，融合功能与空间连接
- 在无几何模型下实现肌肉区域自动划分，准确率达92%
- 适合机器人控制、肌骨骼建模等需自组织感知的场景
针对全身分布的冗余传感器与执行器，直接使用全部信号会导致计算成本过高。本研究将传感器与执行器之间的功能关联与空间连接嵌入图结构，提出一种自动分组方法。以具有大量冗余肌肉的肌骨骼人形机器人Musashi和Kengoro为例，该方法无需依赖几何模型，即可自动将肌肉划分为前臂、上臂、肩胛、颈部等区域，其分组结果与人工基于解剖模型的划分高度一致。通过计算肌肉间功能相关性与神经连接的空间分布，实现了无先验知识的自动化区域划分。
原文摘要 · Abstract (English)
For a robot with redundant sensors and actuators distributed throughout its body, it is difficult to construct a controller or a neural network using all of them due to computational cost and complexity. Therefore, it is effective to extract functionally related sensors and actuators, group them, and construct a controller or a network for each of these groups. In this study, the functional and spatial connections among sensors and actuators are embedded into a graph structure and a method for automatic grouping is developed. Taking a musculoskeletal humanoid with a large number of redundant muscles as an example, this method automatically divides all the muscles into regions such as the forearm, upper arm, scapula, neck, etc., which has been done by humans based on a geometric model. The functional relationship among the muscles and the spatial relationship of the neural connections are calculated without a geometric model.
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