Dissimilarity-Based Persistent Coverage Control of Multi-Robot Systems for Improving Solar Irradiance Prediction Accuracy in Solar Thermal Power Plants

Abstract

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.

Figures (6)

• Figure 1: Layout of the plant and spatio-temporal sampling locations of $n$ robots
• Figure 2: Layout of the experiment room and the initial configurations of 4 mobile agents.
• Figure 3: Time-series RMSE comparison (n=4) among (A) the fixed method, (B) the baseline method, and (C) the proposed method.
• Figure 4: Comparison of the estimation performance between (B) the baseline method (left) and (C) the proposed method (right) corresponding to different time steps (top: $t=50$ and bottom: $t=100$). The inner rectangle represents the region $\mathcal{Q}$, while the outer rectangle indicates the area extended by 30 cm both horizontally and vertically. The horizontal axis corresponds to $q_1$, and the vertical axis corresponds to $q_2$. Regarding the agent trajectories (blue lines), open red circles denote past sampling points, and filled red circles indicate the current agent positions. A total of $n \times L = 4 \times 10 = 40$ points are sampled in each field. Agents sometimes appear outside the region $\mathcal{Q}$, which is due to localization errors. (C) incorporates the dissimilarity map into sampling strategies, leading to lower error compared with (B).
• Figure 5: Time-series evolution of the objective function value in (C) the proposed method.