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Robust Extended Kalman Filter Based Sensor Fusion for Soft Robot State Estimation and Control.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Robust Extended Kalman Filter Based Sensor Fusion for Soft Robot State Estimation and Control./
作者:	Stewart, Kyle.
面頁冊數:	1 online resource (57 pages)
附註:	Source: Masters Abstracts International, Volume: 83-11.
Contained By:	Masters Abstracts International83-11.
標題:	Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29165738click for full text (PQDT)
ISBN:	9798802704257

Robust Extended Kalman Filter Based Sensor Fusion for Soft Robot State Estimation and Control.
Stewart, Kyle.

Robust Extended Kalman Filter Based Sensor Fusion for Soft Robot State Estimation and Control. - 1 online resource (57 pages)

Source: Masters Abstracts International, Volume: 83-11.

Thesis (M.S.)--Arizona State University, 2022.

Includes bibliographical references

Soft robots provide an additional measure of safety and compliance over traditional rigid 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798802704257Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Extended Kalman FilterIndex Terms--Genre/Form:

542853
Electronic books.

Robust Extended Kalman Filter Based Sensor Fusion for Soft Robot State Estimation and Control.
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500 $a Source: Masters Abstracts International, Volume: 83-11.
500 $a Advisor: Zhang, Wenlong.
502 $a Thesis (M.S.)--Arizona State University, 2022.
504 $a Includes bibliographical references
520 $a Soft robots provide an additional measure of safety and compliance over traditional rigid 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Robotics. $3 519753
650 4 $a Remote sensing. $3 535394
650 4 $a Electrical engineering. $3 649834
653 $a Extended Kalman Filter
653 $a Robot Sensing
653 $a Sensor Filter
653 $a Sensor Fusion
653 $a Soft Robot
653 $a State Estimation
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