Technology Category
Intelligent Robots: Multimodal Perception & Sensor FusionPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Active Learning
Dissimilarity-Based Persistent Coverage Control of Multi-Robot Systems for Improving Solar Irradiance Prediction Accuracy in Solar Thermal Power Plants
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
solar irradiance prediction
multi-robot systems
persistent coverage control
kriging
mobile sensors
Innovation
Methods, ideas, or system contributions that make the work stand out.
dissimilarity-based coverage
persistent coverage control
kriging model
multi-robot system
solar irradiance prediction
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