Learning hyperparameters in efficient spatial model by robotic sensors

Jinho Jeong; Soo Jeon; Jongeun Choi

doi:10.1115/DSCC2019-9170

Learning hyperparameters in efficient spatial model by robotic sensors

Jinho Jeong, Soo Jeon, Jongeun Choi

Department of Mechanical Engineering

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

Abstract

Recently, a new class of spatial models over a continuum domain that builds on hidden Gaussian Markov Random Fields (GMRFs) was proposed for resource-constrained networked mobile robots dealing with non-stationary physical processes. The hidden GMRF was realized with respect to a proximity graph over a surveillance region. In this paper, we investigate learning strategies based on the maximum likelihood (ML) and the maximum a posteriori (MAP) estimators to find the locational generating points for the spatial model so that mobile robots can efficiently make the prediction. Some promising simulation results and future research directions are discussed.

Original language	English
Title of host publication	Rapid Fire Interactive Presentations
Subtitle of host publication	Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791859162
DOIs	https://doi.org/10.1115/DSCC2019-9170
Publication status	Published - 2019
Event	ASME 2019 Dynamic Systems and Control Conference, DSCC 2019 - Park City, United States Duration: 2019 Oct 8 → 2019 Oct 11

Publication series

Name	ASME 2019 Dynamic Systems and Control Conference, DSCC 2019
Volume	3

Conference

Conference	ASME 2019 Dynamic Systems and Control Conference, DSCC 2019
Country/Territory	United States
City	Park City
Period	19/10/8 → 19/10/11

Bibliographical note

Publisher Copyright:
© 2019 ASME.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Mechanical Engineering
Industrial and Manufacturing Engineering

Access to Document

10.1115/DSCC2019-9170

Cite this

Jeong, J., Jeon, S., & Choi, J. (2019). Learning hyperparameters in efficient spatial model by robotic sensors. In Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles (ASME 2019 Dynamic Systems and Control Conference, DSCC 2019; Vol. 3). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DSCC2019-9170

Jeong, Jinho ; Jeon, Soo ; Choi, Jongeun. / Learning hyperparameters in efficient spatial model by robotic sensors. Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles. American Society of Mechanical Engineers (ASME), 2019. (ASME 2019 Dynamic Systems and Control Conference, DSCC 2019).

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title = "Learning hyperparameters in efficient spatial model by robotic sensors",

abstract = "Recently, a new class of spatial models over a continuum domain that builds on hidden Gaussian Markov Random Fields (GMRFs) was proposed for resource-constrained networked mobile robots dealing with non-stationary physical processes. The hidden GMRF was realized with respect to a proximity graph over a surveillance region. In this paper, we investigate learning strategies based on the maximum likelihood (ML) and the maximum a posteriori (MAP) estimators to find the locational generating points for the spatial model so that mobile robots can efficiently make the prediction. Some promising simulation results and future research directions are discussed.",

author = "Jinho Jeong and Soo Jeon and Jongeun Choi",

note = "Publisher Copyright: {\textcopyright} 2019 ASME.; ASME 2019 Dynamic Systems and Control Conference, DSCC 2019 ; Conference date: 08-10-2019 Through 11-10-2019",

year = "2019",

doi = "10.1115/DSCC2019-9170",

language = "English",

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Jeong, J, Jeon, S & Choi, J 2019, Learning hyperparameters in efficient spatial model by robotic sensors. in Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles. ASME 2019 Dynamic Systems and Control Conference, DSCC 2019, vol. 3, American Society of Mechanical Engineers (ASME), ASME 2019 Dynamic Systems and Control Conference, DSCC 2019, Park City, United States, 19/10/8. https://doi.org/10.1115/DSCC2019-9170

Learning hyperparameters in efficient spatial model by robotic sensors. / Jeong, Jinho; Jeon, Soo; Choi, Jongeun.
Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles. American Society of Mechanical Engineers (ASME), 2019. (ASME 2019 Dynamic Systems and Control Conference, DSCC 2019; Vol. 3).

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

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AB - Recently, a new class of spatial models over a continuum domain that builds on hidden Gaussian Markov Random Fields (GMRFs) was proposed for resource-constrained networked mobile robots dealing with non-stationary physical processes. The hidden GMRF was realized with respect to a proximity graph over a surveillance region. In this paper, we investigate learning strategies based on the maximum likelihood (ML) and the maximum a posteriori (MAP) estimators to find the locational generating points for the spatial model so that mobile robots can efficiently make the prediction. Some promising simulation results and future research directions are discussed.

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Jeong J, Jeon S, Choi J. Learning hyperparameters in efficient spatial model by robotic sensors. In Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles. American Society of Mechanical Engineers (ASME). 2019. (ASME 2019 Dynamic Systems and Control Conference, DSCC 2019). doi: 10.1115/DSCC2019-9170

Learning hyperparameters in efficient spatial model by robotic sensors

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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