Development of a modular and submersible soft robotic arm system and corresponding learned kinematics models

Null, David

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https://hdl.handle.net/2142/114116 Copy

Description

Title

Development of a modular and submersible soft robotic arm system and corresponding learned kinematics models

Author(s)

Null, David

Issue Date

2021-12-10

Director of Research (if dissertation) or Advisor (if thesis)

Zhang, Yang

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Soft Robotics
• Machine Learning
• Neural Network
• Control Theory
• Underwater Robotics

Abstract

Most soft-body organisms found in nature exist in underwater environments. It is helpful to study the motion and control of soft robots underwater as well. However, a readily available underwater soft robotic system is not available for researchers to use because they are difficult to design, fabricate, and waterproof. Furthermore, submersible robots usually do not have configurable components because of the need for sealed electronics packages. This thesis presents the development of a submersible soft robotic arm which consists of mostly off-the-shelf components and 3D printable parts which can be assembled in a short amount of time. Also, its modular design enables multiple shape configurations and easy swapping of soft actuators. As a first step to exploring machine learning control algorithms on this platform, two deep neural network models were developed, trained, and evaluated to estimate the robot's forward and inverse kinematics. The techniques developed for controlling this underwater soft robotic arm can help advance understanding on how to control soft robotic systems in general.

Graduation Semester

2021-12

Type of Resource

Thesis

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http://hdl.handle.net/2142/114116

Copyright and License Information

Copyright 2021 David Null

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