Abstract
Collaborative task allocation is a key component of multi-UAV combat system in battlefield environment. The combat effect can be maximized by allocating UAV resources to corresponding targets in an optimal way. Aiming at the problem that the battlefield environment is dynamic and changeable, current online task allocation algorithms mainly consider the rapid deployment of new tasks on the basis of existing assignments, but it is difficult to ensure the maximum payoffs of re-planning results. Based on the distributed auction algorithm, this paper introduces a result update mechanism, which resets some of the original assignments and lets them participate in the auction together with the new tasks, obtaining the re-planning result with maximum payoffs. The simulation results show that compared with other online task allocation algorithms, the introduced algorithm mechanism not only meets the requirement of algorithm timeliness, but also ensures the maximum payoffs of assignments, which is more suitable for dynamic and changeable battlefield environment.
This work was supported by the National Key R&D Program of China under Grant 2017YFF0206201.
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Li, X., Liang, Y. (2022). An Optimal Online Distributed Auction Algorithm for Multi-UAV Task Allocation. In: Shi, X., Bohács, G., Ma, Y., Gong, D., Shang, X. (eds) LISS 2021. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-8656-6_48
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DOI: https://doi.org/10.1007/978-981-16-8656-6_48
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