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Event-MILP-Based Task Allocation for Heterogeneous Robotic Sensor Network for Thermosolar Plants

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Abstract

In this paper, an event-Mixed Integer Linear Programming (MILP)-based algorithm is proposed to solve the task allocation problem in a Robotic Sensor Network (RSN). A fleet of two types of vehicles is considered, giving, as a result, a heterogeneous configuration of the network, since each type of vehicle has a nominal velocity and a set of allowed paths to go. The algorithm can be applied to the distributed estimation of the solar irradiance on a parabolic trough thermosolar power plant which can be used to increase the global efficiency of the plant. A simulation environment has been built to test the proposed algorithm, taking into account the behavior of the vehicles and the structure of the solar plant. The algorithm has been compared with traditional methods such as the Optimal Assignment Problem (OAP) using a set of indexes that have been defined to this purpose.

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Data and material has been described in this paper so that results can be reproduced.

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Code is available by request to the first author.

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Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 789051)”.

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 789051)”.

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Authors

Contributions

J.G. Martin: Idea, code, writing of the paper, review and submission. R.A. Garcia: Idea, code, writing of the paper, review. E.F. Camacho: Idea, writing of the paper, review.

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Correspondence to Javier G. Martin.

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The authors declare that they have no conflict of interest.

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J.G. Martin, R.A. Garcia and E.F. Camacho consent to be part of the developed work in this paper.

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Martin, J.G., García, R.A. & Camacho, E.F. Event-MILP-Based Task Allocation for Heterogeneous Robotic Sensor Network for Thermosolar Plants. J Intell Robot Syst 102, 1 (2021). https://doi.org/10.1007/s10846-021-01346-w

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