Description
Soft robots provide an additional measure of safety and compliance over traditionalrigid robots. Generally, control and modelling experiments take place using a
motion capture system for measuring robot configuration. While accurate, motion
capture systems are expensive and require re-calibration whenever the cameras are
adjusted. While advances in soft sensors contribute to a potential solution to sensing
outside of a lab environment, most of these sensing methods require the sensors to
be embedded into the soft robot arm. In this work, a more practical sensing method
is proposed using off-the-shelf sensors and a Robust Extended Kalman Filter based
sensor fusion method. Inertial measurement unit sensors and wire draw sensors are
used to accurately estimate the state of the robot. An explanation for the need for
sensor fusion is included in this work. The sensor fusion state estimate is compared to
a motion capture measurement along with the raw inertial measurement unit reading
to verify the accuracy of the results. The potential for this sensing system is further
validated through Linear Quadratic Gaussian control of the soft robot. The Robust
Extended Kalman Filter based sensor fusion shows an error of less than one degree
when compared to the motion capture system.
Download count: 2
Details
Title
- Robust Extended Kalman Filter Based Sensor Fusion for Soft Robot State Estimation and Control
Contributors
- Stewart, Kyle James (Author)
- Zhang, Wenlong (Thesis advisor)
- Yong, Sze Zheng (Committee member)
- Berman, Spring (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2022
Subjects
Resource Type
Collections this item is in
Note
- Partial requirement for: M.S., Arizona State University, 2022
- Field of study: Mechanical Engineering