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SCIENCE CHINA Information Sciences, Volume 62 , Issue 11 : 212204(2019) https://doi.org/10.1007/s11432-018-9887-5

Coordinated flight control of miniature fixed-wing UAV swarms: methods and experiments

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  • ReceivedNov 9, 2018
  • AcceptedMar 29, 2019
  • PublishedSep 19, 2019

Abstract

In this paper, we present our recent advances in both theoretical methods and field experiments for the coordinated control of miniature fixed-wing unmanned aerial vehicle (UAV) swarms. We propose a multi-layered group-based architecture, which is modularized, mission-oriented, and can implement large-scale swarms. To accomplish the desired coordinated formation flight, we present a novel distributed coordinated-control scheme comprising a consensus-based circling rendezvous, a coordinated path-following control for the leader UAVs, and a leader-follower coordinated control for the follower UAVs. The current framework embeds a formation pattern reconfiguration technique. Moreover, we discuss two security solutions (inter-UAV collision avoidance and obstacle avoidance) in the swarm flight problem. The effectiveness of the proposed coordinated control methods was demonstrated in field experiments by deploying up to 21 fixed-wing UAVs.


Acknowledgment

This work was partly supported by National Natural Science Foundation of China (Grant No. 61801494), and Joint Fund of Ministry of Education of China for Equipment Pre-research and Beijing Nova Program (Grant No. 2018047). The authors express their deepest gratitude to the SWARM TEAM of the NUDT. Without their hard work, the flight experiments could not be done.


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  • Figure 3

    (Color online) The multi-layered group-based UAV swarm architecture.

  • Figure 4

    (Color online) The main processes of coordinated formation flight control. (a) Circling rendezvous; (b) hybrid formation control; (c) formation patter reconfiguration.

  • Figure 5

    (Color online) Seven UAVs circling in an ordered alignment. (a) Achieving alignment in the ground station; protectłinebreak (b) the achieved alignment in the field experiment.

  • Figure 6

    Description of the path-following problem: point $p_i$ is the projection of the $i$-th leader UAV onto the directed path $\Gamma_i$.

  • Figure 11

    (Color online) (a) $21$ UAV formation; (b) triangle formation; (c) “Ba-Yi” formation; (d) $2$-row formation; protectłinebreak (e) $2$-column formation; (f) V formation.

  • Figure 12

    Demonstration of the path-following settings and communication topology of the six leader UAVs.

  • Figure 17

    (Color online) State prediction of the UAVs for collision risk judgment.

  • Figure 18

    (Color online) Inter-UAV collision avoidance in flight.

  • Figure 19

    (Color online) Balloon avoidance in flight tests.

  •   

    Algorithm 1 Consensus-based circling rendezvous

    Calculate the angle coordination variable in 1 for each UAV subgroup;

    if all angle coordination variables are sufficiently close to each other, i.e., if Eq. 5 holds, then

    Calculate the desired velocity $V_i^r$ by 3;

    else

    Calculate the desired velocity $V_i^r$ by4;

    end if

  •   

    Algorithm 2 Hybrid formation flight control for UAV $i$

    if UAV $i$ is a leader, then

    Conduct the coordinated path-following algorithm described in Subsection sect. 4.2.1;

    else

    Conduct the leader-follower coordination algorithm in Subsection 4.2.2;

    end if

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