Predictive receding-horizon multi-robot task allocation applied to the mapping of direct normal irradiance in a thermosolar power plant
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 carr...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/183412 |
| Acceso en línea: | https://hdl.handle.net/11441/183412 https://doi.org/10.1016/j.solener.2023.111911 |
| Access Level: | acceso abierto |
| Palabra clave: | Multi-robot system Task planning Predictive control Sensor network Direct normal irradiance Spatially distributed estimation Thermosolar plant |
| Sumario: | 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. |
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