蜂群无人机编队内无线紫外光协作避让算法

赵太飞,高鹏,史海泉,等. 蜂群无人机编队内无线紫外光协作避让算法[J]. 光电工程,2020,47(3):190505. doi: 10.12086/oee.2020.190505
引用本文: 赵太飞,高鹏,史海泉,等. 蜂群无人机编队内无线紫外光协作避让算法[J]. 光电工程,2020,47(3):190505. doi: 10.12086/oee.2020.190505
Zhao T F, Gao P, Shi H Q, et al. An algorithm for the bee colony drone to use wireless ultraviolet for cooperative obstacle avoidance[J]. Opto-Electron Eng, 2020, 47(3): 190505. doi: 10.12086/oee.2020.190505
Citation: Zhao T F, Gao P, Shi H Q, et al. An algorithm for the bee colony drone to use wireless ultraviolet for cooperative obstacle avoidance[J]. Opto-Electron Eng, 2020, 47(3): 190505. doi: 10.12086/oee.2020.190505

蜂群无人机编队内无线紫外光协作避让算法

  • 基金项目:
    国家自然科学基金资助项目(61971345,U1433110);陕西省教育厅服务地方专项计划项目(17JF024);西安市科学计划项目(CXY1835(4));陕西省重点产业链创新计划项目(2017ZDCXL-GY-05-03);西安市碑林区科技计划项目(GX1921)
详细信息
    作者简介:
    通讯作者: 赵太飞, E-mail: tfz@xaut.edu.cn
  • 中图分类号: V279+.2; TN929.1

An algorithm for the bee colony drone to use wireless ultraviolet for cooperative obstacle avoidance

  • Fund Project: Supported by National Natural Science Foundation of China (61971345, U1433110), Shaanxi Provincial Department of Education Service Local Special Project (17JF024), Xi'an Science Project (CXY1835(4)), Shaanxi Provincial Key Industry Chain Innovation Project (2017ZDCXL-GY-05-03), and Xi'an Beilin District Science and Technology Plan Project (GX1921)
More Information
  • 在战场复杂电磁环境下,保证蜂群无人机编队机间飞行安全和编队内可靠通信尤为重要。本文提出一种蜂群无人机编队内无线紫外光协作避让算法,结合无线紫外光覆盖特点设计紫外虚拟围栏避让策略,基于增强矢量场直方图法针对无人机在避让时的运动状态的代价函数进行改进,采用无迹卡尔曼预测器预测邻近无人机的飞行状态。在两种预测场景下的避让仿真中,结果表明,与增强矢量场直方图法进行对比,本文算法的整体运动轨迹平滑,局部避让时无明显抖动,避让路径总长度平均减少3.46%,总耗时平均减小18.94%,验证了蜂群无人机编队内无线紫外光协作避让算法的有效性。

  • Overview: Uninhabited aerial vehicles (UAVs) are widely used not only in civil fields such as power inspection and environmental monitoring, but also in military applications such as reconnaissance, surveillance and confusion. The drone "bee colony" is composed of a group of small unmanned aerial vehicles that work together independently. It has excellent features such as low cost, high damage resistance, good sensing ability, strong collaboration ability and functional distribution, which can improve the efficiency of completing task. In the complex electromagnetic environment of the battlefield, it is especially important to ensure the flight safety between the formation of the drone group and the reliable communication within the formation. The advantages of wireless ultraviolet communication mainly include small background noise, strong anti-electromagnetic interference capability, all-weather non-direct view communication, low power consumption, high integration, easy to load, etc., which can meet the communication requirements in this environment.

    This paper proposes an algorithm for collaborative avoidance using wireless ultraviolet light between drones in a bee colony drone formation. Through combining avoidance algorithm with the characteristics of wireless ultraviolet light coverage, a wireless ultraviolet virtual fence avoidance strategy is proposed. Considering the relationship between the enhanced vector field histogram method and its own motion state to improve the cost function and verify the effectiveness of the avoidance algorithm. The unscented Kalman filter predictor is used to predict the flight state of the adjacent drone in order to achieve safe and efficient avoidance. Through computer simulation in two prediction scenarios, the results show that the improved enhanced vector field histogram method has smooth overall motion trajectory and good avoidance effect. Compared with the original algorithm, this algorithm has no obvious jitter when it is partially avoided, the turning arc is large and there is no sharp turn. It is more suitable for the actual application and reduces the path length and time consumption. In summary, in the complex battlefield environment, the bee swarm drone can not only use airborne wireless ultraviolet equipment to achieve stable network communication, it can also use improved enhanced vector field methods based on wireless ultraviolet light to enable efficient avoidance between drones in a bee colony drone formation.

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  • 图 1  无线紫外光虚拟围栏模型

    Figure 1.  Wireless UV virtual fence model

    图 2  无人机运动模型

    Figure 2.  UAV motion model

    图 3  无线紫外光虚拟围栏直方图

    Figure 3.  Wireless ultraviolet virtual fence histogram

    图 4  基于无线紫外光的避让流程图

    Figure 4.  Avoidance flow chart based on wireless ultraviolet

    图 5  (a) 场景一中局部避让轨迹;(b)场景一中避让轨迹局部放大

    Figure 5.  (a) Local avoidance track in the scene 1; (b) Partial enlargement of the avoidance trajectory in the scene 1

    图 6  (a) 场景二中局部避让轨迹;(b)场景二中避让轨迹局部放大图

    Figure 6.  (a) Local evasive trajectory in scene 2; (b) Partial enlargement of the escaping trajectory in scene 2

    图 7  (a) 场景一中的预测轨迹;(b)第一次局部放大;(c)第二次局部放大

    Figure 7.  (a) Predicted trajectory in scene 1; (b) First partial enlargement; (c) Second partial enlargement

    图 8  (a) 场景二中的预测轨迹;(b)第一次局部放大;(c)第二次局部放大

    Figure 8.  (a) Predicted trajectory in scene 2; (b) First partial enlargement; (c) Second partial enlargement

    表 1  避让算法参数

    Table 1.  Avoidance algorithm parameters

    参数 取值
    速度区间v/(m/s) (0, 10)
    加速度区间a/(m/s2) (-6, 6)
    转向角速度区间vw/(rad/s) (0.1, 4)
    初始航向角 n/2
    参数a 1.5
    权重系数μ1 6
    权重系数μ2 2
    权重系数μ3 1
    权重系数μ4 1
    下载: 导出CSV

    表 2  运动状态初始参数

    Table 2.  Initial state of motion

    起始点位置/m (10, 400) (400, 10)
    X方向初始速度/(m/s) 1 3
    Y方向初始速度/(m/s) -7 30
    X方向初始加速度/(m/s2) 0.6 0.2
    Y方向初始加速度/(m/s2) -0.8 -0.5
    下载: 导出CSV

    表 3  相对误差平均值

    Table 3.  Relative error average

    参量 场景一 场景二
    相对距离平均误差/m 3.070165894 2.707479523
    相对加速度平均误差/(m/s2) 0.050127791 0.068594285
    相对速度平均误差/(m/s) 0.610216267 0.570158205
    下载: 导出CSV
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收稿日期:  2019-08-26
修回日期:  2019-09-26
刊出日期:  2020-03-01

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