Predictive receding-horizon multi-robot task allocation applied to the mapping of direct normal irradiance in a thermosolar power plant

Abstract

This article considers a robotic sensor network that measures the loss of irradiance in a thermosolar power plant due to moving clouds. To this end, a receding-horizon predictive algorithm is proposed for multi-robot task allocation. Despite the high nonlinearity of the problem, the experiments carried out varying the horizon size show that the proposed method has a good performance with small horizons and tasks moving in similar directions, outperforming a previously published approach based on genetic algorithms. Finally, realistic simulations performed on a solar plant implemented in Robot Operating System/Gazebo prove the feasibility of the proposed method and its potential to provide significant performance gains with a much lower investment than an equivalent fixed sensor network.