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A Drone Formation Transformation Approach

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Abstract

In the process of performing fixed-wing drone formations, it is usually necessary to perform a variety of formations according to mission requirements or environmental changes. However, performing such formation transformation during formation flight will face many technical challenges. In this paper, we first present a Six-Tuple State Coherence (STSC) model for fixed-wing drone formations, and based on this model, the definition of drone formation transformation is given. Moreover, a drone formation change algorithm (DFCA) is proposed. When a new formation is needed, the master node first adopts the centralized Hungarian algorithm to determine the location allocation scheme of the new formation, and then each node calculates and executes dubins paths distributedly to maintain the consistency of the formation yaw angle, and finally adjusts the speed of the nodes to ensure the formation of STSC. The prototype system conforming to DFCA algorithm is implemented on OMNET++ platform, and numerous simulation experiments are carried out. The experimental results show the feasibility of the DFCA algorithm and show that it can control the drone formation transformation at a lower cost.

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Acknowledgments

This work was supported in part by the National Key Research and Development Program of China, under Grant 2017YFB0802303, in part by the National Natural Science Foundation of China, under Grant 61672283.

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Correspondence to Bing Chen .

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Jin, C., Chen, B., Hu, F. (2019). A Drone Formation Transformation Approach. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-32388-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32387-5

  • Online ISBN: 978-3-030-32388-2

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