Static Shape Control of Soft Continuum Robots Using Deep Visual Inverse Kinematic Models

Soft continuum robots are highly flexible and adaptable, making them ideal for unstructured environments such as the human body and agriculture. However, their high compliance and maneuverability make them difficult to model, sense, and control. Current control strategies focus on Cartesian space co...

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Vydáno v:IEEE transactions on robotics Ročník 39; číslo 4; s. 1 - 16
Hlavní autoři: Almanzor, Elijah, Ye, Fan, Shi, Jialei, Thuruthel, Thomas George, Wurdemann, Helge A., Iida, Fumiya
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Actuators
Artificial neural networks
Closed loops
Computational modeling
End effectors
Feedback
Inverse kinematics
Kinematics
Machine learning
Medical robotics
Robot control
robot kinematics
robot learning
Robot sensing systems
Robots
Robust control
Shape
Shape control
soft robotics
Task analysis
ISSN:1552-3098, 1941-0468
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Shrnutí:Soft continuum robots are highly flexible and adaptable, making them ideal for unstructured environments such as the human body and agriculture. However, their high compliance and maneuverability make them difficult to model, sense, and control. Current control strategies focus on Cartesian space control of the end-effector, but few works have explored full-body control. This study presents a novel image-based deep learning approach for closed-loop kinematic shape control of soft continuum robots. The method combines a local inverse kinematics formulation in the image space with deep convolutional neural networks for accurate shape control that is robust to feedback noise and mechanical changes in the continuum arm. The shape controller is fast and straightforward to implement; it takes only a few hours to generate training data, train the network, and deploy, requiring only a web camera for feedback. This method offers an intuitive and user-friendly way to control the robot's 3-D shape and configuration through teleoperation using only 2-D hand-drawn images of the desired target state without the need for further user instruction or consideration of the robot's kinematics.
Bibliografie:ObjectType-Article-1
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2023.3275375