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

Shiyi Yang, Soo Jeon, Jongeun Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 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 languageEnglish
Title of host publicationMechatronics; 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
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791850701
DOIs
Publication statusPublished - 2016 Jan 1
EventASME 2016 Dynamic Systems and Control Conference, DSCC 2016 - Minneapolis, United States
Duration: 2016 Oct 122016 Oct 14

Publication series

NameASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Volume2

Other

OtherASME 2016 Dynamic Systems and Control Conference, DSCC 2016
CountryUnited States
CityMinneapolis
Period16/10/1216/10/14

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Sampling
Computational efficiency
Costs

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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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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.",
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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 proceedingConference contribution

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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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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). https://doi.org/10.1115/DSCC2016-9815