海上无人集群联合轨迹设计方法

胡钰林; 吴鹏; 原晓鹏; 李博; 江昊; 罗威

doi:10.11999/JEIT211305

海上无人集群联合轨迹设计方法

doi: 10.11999/JEIT211305

胡钰林^1, ,,
吴鹏¹,
原晓鹏¹,
李博²,
江昊¹,
罗威³

1.
武汉大学电子信息学院武汉 430072
2.
哈尔滨工业大学(威海)信息科学与工程学院威海 264209
3.
中国舰船研究设计中心武汉 430000

基金项目: 国家自然科学基金(62101389)

详细信息

作者简介:
胡钰林：男，1987年生，教授，博士，博士生导师，研究方向为工业物联网、高可靠低时延通信、无人机通信、移动边缘计算等

吴鹏：男，1998年生，硕士生，研究方向为通感一体、无人机通信

原晓鹏：男，1994年生，博士生，研究方向为无人机辅助无线网络、轨迹设计、信息理论和优化技术

李博：男，1983年生，副教授，博士，博士生导师，研究方向为无线通信、空天地网络、海洋信息传感网、飞行自组织网络

江昊：男，1976年生，教授，博士，博士生导师，研究方向为大数据分析与挖掘、智能网络、智能系统、移动边缘计算等

罗威：男，1980年生，高级工程师，博士，研究方向为人工智能

通讯作者:
胡钰林　yulin.hu@whu.edu.cn

中图分类号: TN911; T92
计量
- 文章访问数: 467
- HTML全文浏览量: 453
- PDF下载量: 130
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-21
- 修回日期: 2022-02-11
- 录用日期: 2022-02-19
- 网络出版日期: 2022-02-28
- 刊出日期: 2022-03-28

Joint Trajectory Design for Unmanned Marine Cluster

HU Yulin^{1
, ,},
WU Peng¹,
YUAN Xiaopeng¹,
LI Bo²,
JIANG Hao¹,
LUO Wei³

1.
Electronic and Information School, Wuhan University, Wuhan 430072, China
2.
School of Information Science and Engineering, Harbin Institute of Technology(WH), Weihai 264209, China
3.
China Ship Development and Design Center, Wuhan 430000, China

Funds: The National Natural Science Foundation of China (62101389)

摘要

摘要: 针对海洋大数据收集场景，为提高数据收集效率，该文提出一种无人机和无人船联合数据收集方法。无人船在行驶过程中，通过放飞无人机并行驶到指定地点回收无人机，实现对目标海域内节点数据的高效收集。为最小化无人船和无人机工作时间，该文在无人机集群任务分配的基础上，引入连续悬停飞行(Successive-Hover-and-Fly, SHF)结构以实现低复杂度的联合轨迹优化。待优化问题受限于节点数据量和无人机速度，是难以求解的非凸问题。因此，该文提出了一个高效的连续凸近似技术迭代算法以得到次优解，并通过计算机仿真得以验证。
- 无线通信 /
- 轨迹优化 /
- 任务分配 /
- 凸近似
Abstract: Considering an ocean data collection scenario, to improve data collection efficiency, this paper proposes a joint data collection approach for UAVs and platform. The platform releases UAVs to collect the data and sails to the designated places to recover them, while the UAVs are responsible for the data collection task. To minimize the overall working time of the UAVs and the platform, this paper introduces the Successive-Hover-and-Fly (SHF) structure to achieve a low-complexity joint trajectory optimization on basis of task allocation of the UAV cluster. The formulated problem is difficult to be solved due to the non-convexity, which is constrained by the demanded upload data amount and a maximum UAV speed. To address this problem, an efficient successive convex approximation technique iterative algorithm is proposed to obtain a sub-optimal solution, which is validated by simulation.
- Wireless communication /
- Trajectory optimization /
- Task allocation /
- Convex approximation

HTML全文

图 1 海洋无人机通信系统

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图 2 用户分组

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图 3 单无人机服务场景示例

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图 4 SHF结构下无人机轨迹示例

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图 5 考虑到转折点的SHF结构轨迹示例

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图 6 用户分组示例

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图 7 联合轨迹优化示例

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图 8 不同无人机速度下联合轨迹比较

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图 9 本算法与TSP算法任务时间对比

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表 1 K-means算法流程

初始化　随机选取N个用户作为初始聚类中心
迭代
(1) 　计算其他点到聚类中心的距离，并按照距离最近的原则将　　　他们进行分类，计算目标函数值；
(2) 　更新聚类中心：每个分组中的用户位置的平均值作为新的　　　聚类中心，计算目标函数的值；
(3) 　如果分组结果较上次没有发生变化，则停止迭代；
否则，返回(1)。

下载: 导出CSV

表 2 轨迹迭代算法

初始化　基于TSP初始化轨迹$ ({\boldsymbol{x}}_i^{(0)},{\boldsymbol{y}}_i^{(0)},{\boldsymbol{t}}_i^{(0)}) $和汇合时间$ T_i^{(0)} $，并设置迭代阈值Ε和迭代次数r=0。
迭代
(1) 在迭代点$ ({\boldsymbol{x}}_i^{(r)},{\boldsymbol{y}}_i^{(r)},{\boldsymbol{t}}_i^{(r)}) $对传输数据量凸近似得到$ Q_{i,k}^{(r)}({\boldsymbol{x}}_i^{(r)},{\boldsymbol{y}}_i^{(r)},{\boldsymbol{t}}_i^{(r)}) $；
(2) 通过解问题(P2)得到优化解$ ({\boldsymbol{x}}_i^,{\boldsymbol{y}}_i^,{\boldsymbol{t}}_i^) $，并根据式(11)得到$ T_i^ $；
(3) 如果总任务时间相对于前一次迭代的时间优化小于Ε，停止迭代；
否则，令$ ({\boldsymbol{x}}_i^{(r + 1)},{\boldsymbol{y}}_i^{(r + 1)},{\boldsymbol{t}}_i^{(r + 1)},T_i^{(r + 1)}) = ({\boldsymbol{x}}_i^,{\boldsymbol{y}}_i^,{\boldsymbol{t}}_i^,T_i^) $，r=r+1，回到步骤(1)。

下载: 导出CSV

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