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.
|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)|
|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
|Name||ASME 2019 Dynamic Systems and Control Conference, DSCC 2019|
|Conference||ASME 2019 Dynamic Systems and Control Conference, DSCC 2019|
|Period||19/10/8 → 19/10/11|
Bibliographical notePublisher Copyright:
© 2019 ASME.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Mechanical Engineering
- Industrial and Manufacturing Engineering