Abstract
In this paper, a centralized mission planner is presented. The planner employs a genetic algorithm for the optimization of the temporal planning problem. With the knowledge of agents’ specification and capabilities, as well as constraints and parameters for each task, the planner can produce plans that utilize multi-agent tasks, concurrency on agent level, and heterogeneous agents. Numerous optimization criteria that can be of use to the mission operator are tested on the same mission data set. Promising results and effectiveness of this approach are presented in the case study section.
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Acknowledgment
The research leading to the presented results has been undertaken within the SWARMs European project (Smart and Networking Underwater Robots in Cooperation Meshes), under Grant Agreement n. 662107-SWARMs-ECSEL-2014-1, which is partially supported by the ECSEL JU and the VINNOVA.
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Miloradović, B., Çürüklü, B., Ekström, M. (2017). A Genetic Mission Planner for Solving Temporal Multi-agent Problems with Concurrent Tasks. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_51
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DOI: https://doi.org/10.1007/978-3-319-61833-3_51
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