Paper Info
Title
Automatic Grouping Of Redundant Sensors And Actuators Using Functional And Spatial Connections: Application To Muscle Grouping For 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. This study is applied to muscle grouping of musculoskeletal humanoids Musashi and Kengoro, and its effectiveness is verified.
Year
DOI
Venue
2021
10.1109/LRA.2021.3060715
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Biomimetics, learning from experience, redundant robots
Journal
6
Issue
ISSN
Citations 
2
2377-3766
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Kento Kawaharazuka1713.98
Manabu Nishiura203.04
Yuya Koga304.39
Yusuke Omura404.06
Yasunori Toshimitsu532.77
Yuki Asano63117.24
Kei Okada7534118.08
Koji Kawasaki8710.32
Masayuki Inaba92186410.27