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

2026-03-26

AI Summary

This work addresses the lack of dynamic sensor deployment strategies for real-time solar irradiance forecasting, which hinders high-accuracy prediction under limited sensor resources. To overcome this limitation, the authors propose an active sensing framework that integrates a Kriging model with a disparity map to guide multi-robot systems toward regions offering the highest information gain. A novel persistent coverage control algorithm is developed to coordinate the robots’ movements and optimize spatial sampling over time. This study presents the first integration of disparity maps with multi-robot coverage control specifically tailored to enhance irradiance prediction performance. Experimental results across diverse simulated irradiance fields demonstrate that the proposed approach significantly outperforms baseline methods, substantially improving the accuracy of solar irradiance forecasts for concentrated solar power plants.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Active Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• solar irradiance prediction
• multi-robot systems
• persistent coverage control
• kriging
• mobile sensors

Innovation

• dissimilarity-based coverage
• persistent coverage control
• kriging model
• multi-robot system
• solar irradiance prediction