Automatic Grouping of Redundant Sensors and Actuators Using Functional and Spatial Connections: Application to Muscle Grouping for Musculoskeletal Humanoids

TL;DR

This paper presents an automated method to group sensors and actuators in robots based on their functional and spatial relationships, demonstrated on a musculoskeletal humanoid to simplify control design.

Contribution

It introduces a novel graph-based approach for automatic grouping of sensors and actuators using functional and spatial data without relying on geometric models.

Findings

Successfully grouped muscles into regions similar to human expert segmentation

Reduced computational complexity for controlling redundant systems

Demonstrated applicability to musculoskeletal humanoids

Abstract

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.