Joint Trajectory Design for Unmanned Marine Cluster
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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。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.
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Key words:
- Wireless communication /
- Trajectory optimization /
- Task allocation /
- Convex approximation
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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)。 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)。 -
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