Paper Info
Title
Online Self-Body Image Acquisition Considering Changes In Muscle Routes Caused By Softness Of Body Tissue For Tendon-Driven Musculoskeletal Humanoids
Abstract
Tendon-driven musculoskeletal humanoids have many benefits in terms of the flexible spine, multiple degrees of freedom, and variable stiffness. At the same time, because of its body complexity, there are problems in controllability. First, due to the large difference between the actual robot and its geometric model, it cannot move as intended and large internal muscle tension may emerge. Second, movements which do not appear as changes in muscle lengths may emerge, because of the muscle route changes caused by softness of body tissue. To solve these problems, we construct two models: ideal joint-muscle model and muscle-route change model, using a neural network. We initialize these models by a man-made geometric model and update them online using the sensor information of the actual robot. We validate that the tendon-driven musculoskeletal humanoid Kengoro is able to obtain a correct self-body image through several experiments.
Year
DOI
Venue
2018
10.1109/IROS.2018.8593428
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Controllability,Computer science,Stiffness,Geometric modeling,Control engineering,Solid modeling,Artificial neural network,Robot,Tendon,Humanoid robot
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Kento Kawaharazuka1713.98
Shogo Makino264.57
Masaya Kawamura353.17
Ayaka Fujii402.03
Yuki Asano53117.24
Kei Okada6534118.08
Masayuki Inaba72186410.27