Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation

Shiyi Yang; Soo Jeon; Jongeun Choi

doi:10.1115/DSCC2016-9815

Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation

Shiyi Yang, Soo Jeon, Jongeun Choi

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper proposes an efficient greedy algorithm to estimate the object shape within a limited number of sample data (i.e. the touch-down points). Specifically, we treat the object shape as an implicit surface which is defined as the zero levelset of an unknown function and apply Gaussian process to estimate the surface. The mutual information criteria is utilized to decide which point should be sampled in the next iteration. To expedite the estimation process, we implement sequential Gaussian process for computational efficiency and significantly reduce the computational cost by selecting search area based on the estimated level-set variance. We present some simulation results to compare the performance of the proposed algorithm with the random sampling algorithm and demonstrate the improvements in both speed and accuracy over the random sampling algorithm.

Original language	English
Title of host publication	Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
Publisher	American Society of Mechanical Engineers
ISBN (Electronic)	9780791850701
DOIs	https://doi.org/10.1115/DSCC2016-9815
Publication status	Published - 2016
Event	ASME 2016 Dynamic Systems and Control Conference, DSCC 2016 - Minneapolis, United States Duration: 2016 Oct 12 → 2016 Oct 14

Publication series

Name	ASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Volume	2

Other

Other	ASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Country/Territory	United States
City	Minneapolis
Period	16/10/12 → 16/10/14

Bibliographical note

Publisher Copyright:
Copyright © 2016 by ASME.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Industrial and Manufacturing Engineering
Mechanical Engineering

Access to Document

10.1115/DSCC2016-9815

Cite this

Yang, S., Jeon, S., & Choi, J. (2016). Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation. In Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2016-9815

Yang, Shiyi ; Jeon, Soo ; Choi, Jongeun. / Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation. Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers, 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016).

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title = "Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation",

abstract = "This paper proposes an efficient greedy algorithm to estimate the object shape within a limited number of sample data (i.e. the touch-down points). Specifically, we treat the object shape as an implicit surface which is defined as the zero levelset of an unknown function and apply Gaussian process to estimate the surface. The mutual information criteria is utilized to decide which point should be sampled in the next iteration. To expedite the estimation process, we implement sequential Gaussian process for computational efficiency and significantly reduce the computational cost by selecting search area based on the estimated level-set variance. We present some simulation results to compare the performance of the proposed algorithm with the random sampling algorithm and demonstrate the improvements in both speed and accuracy over the random sampling algorithm.",

author = "Shiyi Yang and Soo Jeon and Jongeun Choi",

note = "Publisher Copyright: Copyright {\textcopyright} 2016 by ASME.; ASME 2016 Dynamic Systems and Control Conference, DSCC 2016 ; Conference date: 12-10-2016 Through 14-10-2016",

year = "2016",

doi = "10.1115/DSCC2016-9815",

language = "English",

series = "ASME 2016 Dynamic Systems and Control Conference, DSCC 2016",

publisher = "American Society of Mechanical Engineers",

booktitle = "Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control",

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Yang, S, Jeon, S & Choi, J 2016, Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation. in Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. ASME 2016 Dynamic Systems and Control Conference, DSCC 2016, vol. 2, American Society of Mechanical Engineers, ASME 2016 Dynamic Systems and Control Conference, DSCC 2016, Minneapolis, United States, 16/10/12. https://doi.org/10.1115/DSCC2016-9815

Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation. / Yang, Shiyi; Jeon, Soo; Choi, Jongeun.
Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers, 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation

AU - Yang, Shiyi

AU - Jeon, Soo

AU - Choi, Jongeun

PY - 2016

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N2 - This paper proposes an efficient greedy algorithm to estimate the object shape within a limited number of sample data (i.e. the touch-down points). Specifically, we treat the object shape as an implicit surface which is defined as the zero levelset of an unknown function and apply Gaussian process to estimate the surface. The mutual information criteria is utilized to decide which point should be sampled in the next iteration. To expedite the estimation process, we implement sequential Gaussian process for computational efficiency and significantly reduce the computational cost by selecting search area based on the estimated level-set variance. We present some simulation results to compare the performance of the proposed algorithm with the random sampling algorithm and demonstrate the improvements in both speed and accuracy over the random sampling algorithm.

AB - This paper proposes an efficient greedy algorithm to estimate the object shape within a limited number of sample data (i.e. the touch-down points). Specifically, we treat the object shape as an implicit surface which is defined as the zero levelset of an unknown function and apply Gaussian process to estimate the surface. The mutual information criteria is utilized to decide which point should be sampled in the next iteration. To expedite the estimation process, we implement sequential Gaussian process for computational efficiency and significantly reduce the computational cost by selecting search area based on the estimated level-set variance. We present some simulation results to compare the performance of the proposed algorithm with the random sampling algorithm and demonstrate the improvements in both speed and accuracy over the random sampling algorithm.

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BT - Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control

PB - American Society of Mechanical Engineers

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ER -

Yang S, Jeon S, Choi J. Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation. In Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers. 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016). doi: 10.1115/DSCC2016-9815

Level-set based greedy algorithm with sequential Gaussian process regression for implicit surface estimation

Abstract

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