Learning hyperparameters in efficient spatial model by robotic sensors

Jinho Jeong, Soo Jeon, Jongeun Choi

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationRapid Fire Interactive Presentations
Subtitle of host publicationAdvances 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
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791859162
DOIs
Publication statusPublished - 2019
EventASME 2019 Dynamic Systems and Control Conference, DSCC 2019 - Park City, United States
Duration: 2019 Oct 82019 Oct 11

Publication series

NameASME 2019 Dynamic Systems and Control Conference, DSCC 2019
Volume3

Conference

ConferenceASME 2019 Dynamic Systems and Control Conference, DSCC 2019
CountryUnited States
CityPark City
Period19/10/819/10/11

Bibliographical note

Funding Information:
The work of Jinho Jeong and Jongeun Choi was supported in part by the National Research Foundation of Korea (NRF) under Grant 2018R1A4A1025986 and 2018R1A2B6008063 funded by the Korea government (MSIT). This work was also supported by the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Ministry of Trade, Industry and Energy(10073129). (Corresponding author: Jonguen Choi.)

All Science Journal Classification (ASJC) codes

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

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