Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24–25, 2019, Proceedings

Research Article

A Drone Formation Transformation Approach

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  • @INPROCEEDINGS{10.1007/978-3-030-32388-2_2,
        author={Chenghao Jin and Bing Chen and Feng Hu},
        title={A Drone Formation Transformation Approach},
        proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings},
        proceedings_a={MLICOM},
        year={2019},
        month={10},
        keywords={Drone formation Formation transformation Consistent state OMNET++ platform},
        doi={10.1007/978-3-030-32388-2_2}
    }
    
  • Chenghao Jin
    Bing Chen
    Feng Hu
    Year: 2019
    A Drone Formation Transformation Approach
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-32388-2_2
Chenghao Jin1,*, Bing Chen1,*, Feng Hu1,*
  • 1: Nanjing University of Aeronautics and Astronautics
*Contact email: jinchenghao@nuaa.edu.cn, cb_china@nuaa.edu.cn, huf@nuaa.edu.cn

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.