太陽熱発電所の日射量予測精度向上のための非類似度に基づくマルチロボット持続的カバレッジ制御
Dissimilarity-Based Persistent Coverage Control of Multi-Robot Systems for Improving Solar Irradiance Prediction Accuracy in Solar Thermal Power Plants
クリギングモデルから導出した非類似度マップを用いて、予測精度向上に必要な領域へ移動センサを誘導する持続的カバレッジ制御アルゴリズムを提案し、移動ロボット実験で有効性を実証した。
著者: Haruki Kawase, Taiga Sugawara, A. Daniel Carnerero
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.