Exploration vs. Data Refinement via Multiple Mobile Sensors

Abstract

We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework.

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

“…This model relies on wind field inflow sensors and load sensors deployed on up-wind wind turbines. Gaussian Process Regression (GPR) is a probabilistic model that captures and predicts relationships in data by assigning a distribution over functions [49]. They have used GPR model and calibrated it to effectively predict the loads on a wake affected wind turbines.…”

System Reliability Assessment with Imprecise Probabilities

“…Limited samples lead to the presence of epistemic uncertainty over the distributional parameters, which make the traditional research method based on a large number of samples no longer applicable [5][6][7]. Hence, it is of great significance to develop an effective and efficient reliability analysis method for complex systems under limited samples [8,9].…”