Dissimilarity-Based Persistent Coverage Control of Multi-Robot Systems for Improving Solar Irradiance Prediction Accuracy in Solar Thermal Power Plants
This addresses the need for real-time, efficient sensor deployment to enhance forecasting for solar thermal power plant control, representing an incremental advancement in coverage control methods.
The paper tackled the problem of improving solar irradiance prediction accuracy in solar thermal power plants by dynamically positioning mobile sensors, and the result was that the proposed approach achieved more accurate predictions than baselines in experiments with emulated irradiance fields.
Accurate forecasting of future solar irradiance is essential for the effective control of solar thermal power plants. Although various kriging-based methods have been proposed to address the prediction problem, these methods typically do not provide an appropriate sampling strategy to dynamically position mobile sensors for optimizing prediction accuracy in real time, which is critical for achieving accurate forecasts with a minimal number of sensors. This paper introduces a dissimilarity map derived from a kriging model and proposes a persistent coverage control algorithm that effectively guides agents toward regions where additional observations are required to improve prediction performance. By means of experiments using mobile robots, the proposed approach was shown to obtain more accurate predictions than the considered baselines under various emulated irradiance fields.