﻿ 面向海上目标搜索任务的多无人机协同航路优化
 海洋科学  2018, Vol. 42 Issue (1): 147-152 PDF
http://dx.doi.org/10.11759/hykx20171011014

#### 文章信息

YAO Peng, QI Sheng-bo, XIE Ze-xiao. 2018.

Cooperative path optimization of multi-UAVs when searching for maritime targets

Marina Sciences, 42(1): 147-152.
http://dx.doi.org/10.11759/hykx20171011014

### 文章历史

Cooperative path optimization of multi-UAVs when searching for maritime targets
YAO Peng, QI Sheng-bo, XIE Ze-xiao
College of Engineering, Ocean University of China, Qingdao 266100, China
Abstract: In this paper, we solve the cooperative path optimization problem of multiple unmanned aerial vehicles (UAVs) when searching for maritime targets. First, we analyze the problem in detail by modeling its basic elements. Next, based on updated results from the detection process, we use the consensus theory with a state predictor to fuse the UAV target probability maps. Lastly, we use distributed model predictive control (DMPC) to optimize the UAV searching routes and simultaneously introduce local and future searching rewards. The simulation results indicate that the searching efficiency of our proposed method is higher than that achieved by current techniques.
Key words: unmanned aerial vehicles (UAVs)    searching maritime target    path optimization    consensus theory with state predictor    distributed model predictive control (DMPC)

1 面向海上目标搜索任务的多无人机协同航路优化问题描述

 $\begin{gathered} {{\dot x}_i} = {v_i}\cos {\psi _i} \hfill \\ {{\dot y}_i} = {v_i}\cos {\psi _i} \hfill \\ {{\dot \psi }_i} = \frac{{{\rm{g}}\tan {\phi _i}}}{{{v_i}}} \hfill \\ \end{gathered}$ (1)

 $\boldsymbol{u}_i^*[k:k + N-1]{\rm{ = arg max }}{J_i}$ (7)

 ${\rm{ }}{J_i}{\rm{ = }}{\lambda _{\rm{m}}}{J_{\rm{m}}} + {\lambda _{\rm{f}}}{J_{\rm{f}}} + {\lambda _{\rm{u}}}{J_{\rm{u}}}$ (8)

 ${D_{1:k}}{\rm{ = }}1 - \prod\limits_{t = 1}^k {{{\bar D}_t}}$ (9)

 ${\bar D_k}{\rm{ = }}\sum\limits_{m = 1}^M {\left( {Q(\boldsymbol{x}_k^m|{z_{1:k}})(1 - p({z_k}|\boldsymbol{x}_k^m))} \right)}$ (10)

 ${J_{\rm{m}}}{\rm{ = }}{D_{1:k + N}} - {D_{1:k}}$ (11)

 图 2 未来预期搜索区域 Fig. 2 Predictive detection region
 ${J_{\rm{f}}}{\rm{ = }}\sum\limits_{\forall \boldsymbol{x}_k^m \in S}^{} {\left( {Q(\boldsymbol{x}_k^m\left| {z_{1:k}^{}} \right.)p(z_k^{}\left| {\boldsymbol{x}_k^m} \right.)} \right)}$ (12)

 ${J_{\rm{u}}}{\rm{ = }}{\lambda _{\rm{u}}}\sum\limits_{t = k}^{k + N - 1} {\left\| {u(t) - u(t - 1)} \right\|}$ (13)

4 仿真结果

 图 3 先验目标概率图 Fig. 3 Prior target probability map

 图 4 基于本文方法的规划结果 Fig. 4 Planned results by our method a.无人机航路; b.更新的目标概率图 a. UAV path; b. Updated target probability map

 图 5 基于传统DMPC的规划结果 Fig. 5 Planned results by traditional DMPC a.无人机航路; b.更新的目标概率图 a. UAV path; b. Updated target probability map

 图 6 基于不同方法的搜索收益曲线 Fig. 6 Searching payoff curve by different methods
5 结论

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