趙太飛，高 鵬，史海泉，李星善

蜂群無(wú)人機(jī)編隊(duì)內(nèi)無(wú)線紫外光協(xié)作避讓算法

趙太飛1,3*，高 鵬1,3，史海泉1,3，李星善2

1西安理工大學(xué)自動(dòng)化與信息工程學(xué)院，陜西 西安 710048;2湖北航天技術(shù)研究院總體設(shè)計(jì)所，湖北 武漢 430040;3陜西省智能協(xié)同網(wǎng)絡(luò)軍民共建重點(diǎn)實(shí)驗(yàn)室，陜西 西安 710048

在戰(zhàn)場(chǎng)復(fù)雜電磁環(huán)境下，保證蜂群無(wú)人機(jī)編隊(duì)機(jī)間飛行安全和編隊(duì)內(nèi)可靠通信尤為重要。本文提出一種蜂群無(wú)人機(jī)編隊(duì)內(nèi)無(wú)線紫外光協(xié)作避讓算法，結(jié)合無(wú)線紫外光覆蓋特點(diǎn)設(shè)計(jì)紫外虛擬圍欄避讓策略，基于增強(qiáng)矢量場(chǎng)直方圖法針對(duì)無(wú)人機(jī)在避讓時(shí)的運(yùn)動(dòng)狀態(tài)的代價(jià)函數(shù)進(jìn)行改進(jìn)，采用無(wú)跡卡爾曼預(yù)測(cè)器預(yù)測(cè)鄰近無(wú)人機(jī)的飛行狀態(tài)。在兩種預(yù)測(cè)場(chǎng)景下的避讓仿真中，結(jié)果表明，與增強(qiáng)矢量場(chǎng)直方圖法進(jìn)行對(duì)比，本文算法的整體運(yùn)動(dòng)軌跡平滑，局部避讓時(shí)無(wú)明顯抖動(dòng)，避讓路徑總長(zhǎng)度平均減少3.46%，總耗時(shí)平均減小18.94%，驗(yàn)證了蜂群無(wú)人機(jī)編隊(duì)內(nèi)無(wú)線紫外光協(xié)作避讓算法的有效性。

蜂群無(wú)人機(jī)；無(wú)線紫外光；虛擬圍欄；協(xié)作避讓；軌跡預(yù)測(cè)；增強(qiáng)矢量場(chǎng)直方圖法

1 引 言

蜂群無(wú)人機(jī)編隊(duì)是由大量載荷不同、類型不同的無(wú)人機(jī)組成，根據(jù)戰(zhàn)場(chǎng)環(huán)境調(diào)整編隊(duì)內(nèi)機(jī)群數(shù)量及隊(duì)形以便執(zhí)行隱秘偵察，重點(diǎn)突防等作戰(zhàn)任務(wù)。蜂群無(wú)人機(jī)編隊(duì)存在無(wú)人機(jī)機(jī)間密度大，對(duì)環(huán)境信息實(shí)時(shí)性要求高的特點(diǎn)[1]。由于各類型的電磁干擾無(wú)處不在，特別是電子對(duì)抗過(guò)程中無(wú)人機(jī)編隊(duì)需要保持無(wú)線電靜默以降低暴露風(fēng)險(xiǎn)，而無(wú)線“日盲”紫外光通信正好能滿足這種通信方式的需求，其優(yōu)勢(shì)主要有背景噪聲小、抗電磁干擾能力強(qiáng)、非直視通信、低功耗、高集成度、易于機(jī)載[2]。因此，采用“日盲”紫外光協(xié)作無(wú)人機(jī)編隊(duì)飛行能為無(wú)人機(jī)編隊(duì)在強(qiáng)電磁干擾環(huán)境中順利執(zhí)行任務(wù)提供有效保障。

路徑規(guī)劃是蜂群無(wú)人機(jī)編隊(duì)順利完成任務(wù)的前提，分為全局路徑規(guī)劃[3-7]和局部避讓算法[8-10]。全局路徑規(guī)劃在已知環(huán)境信息的前提下通過(guò)各類算法實(shí)現(xiàn)規(guī)劃，其優(yōu)勢(shì)在于路徑平滑，避讓效果好，缺點(diǎn)是不能適用于實(shí)時(shí)性高的場(chǎng)景，文獻(xiàn)[5-7]中利用移動(dòng)物體的運(yùn)動(dòng)狀態(tài)預(yù)測(cè)很好地實(shí)現(xiàn)了全局路徑規(guī)劃。局部避讓算法通過(guò)一定避讓條件實(shí)現(xiàn)在線路徑規(guī)劃，其優(yōu)勢(shì)在于適用動(dòng)態(tài)場(chǎng)景，但是其存在局部極小和路徑抖動(dòng)等缺點(diǎn)，較其他局部避讓算法而言，增強(qiáng)矢量場(chǎng)直方圖法易于實(shí)現(xiàn)，局部避讓效果好，得到了廣泛的應(yīng)用。文獻(xiàn)[11-13]通過(guò)結(jié)合全局路徑規(guī)劃算法和局部避讓算法實(shí)現(xiàn)了在動(dòng)態(tài)場(chǎng)景下的高魯棒性的路徑規(guī)劃，大大減小了局部極小、路徑抖動(dòng)等問(wèn)題，也克服了全局路徑規(guī)劃算法動(dòng)態(tài)場(chǎng)景適應(yīng)性的問(wèn)題。本文主要針對(duì)基于無(wú)線紫外光通信的蜂群無(wú)人機(jī)編隊(duì)，利用無(wú)線紫外光構(gòu)建無(wú)人機(jī)安全范圍內(nèi)的環(huán)境直方圖來(lái)避讓和預(yù)警該區(qū)域內(nèi)的鄰近無(wú)人機(jī)，實(shí)現(xiàn)高魯棒性、實(shí)時(shí)性更好無(wú)人機(jī)編隊(duì)內(nèi)的機(jī)間避讓。

2 無(wú)線紫外光自主避讓策略

1) 安全飛行：通過(guò)判斷，在下一個(gè)運(yùn)動(dòng)周期鄰近無(wú)人機(jī)處在鏈路建立區(qū)內(nèi)并且正在遠(yuǎn)離，此時(shí)判定鄰近無(wú)人機(jī)為安全飛行狀態(tài)。

2) 一般危險(xiǎn)：通過(guò)判斷在下一個(gè)運(yùn)動(dòng)周期鄰近無(wú)人機(jī)處于通信區(qū)內(nèi)，但(+2)的周期內(nèi)處于預(yù)警區(qū)，標(biāo)記該無(wú)人機(jī)為潛在危險(xiǎn)。

3) 緊急避讓：通過(guò)判斷在下一個(gè)運(yùn)動(dòng)周期鄰近無(wú)人機(jī)處于預(yù)警區(qū)內(nèi)，當(dāng)前時(shí)刻必須執(zhí)行局部避讓。緊急避讓狀態(tài)下，通過(guò)最佳方向采樣和最佳速度采樣提供局部避讓路徑，再通過(guò)篩選出代價(jià)最小的避讓路徑，最終實(shí)現(xiàn)了機(jī)間自主避讓。

圖1 無(wú)線紫外光虛擬圍欄模型

當(dāng)無(wú)人機(jī)之間互相接收到波長(zhǎng)為2的光信號(hào)時(shí)，雙方開(kāi)始通信；當(dāng)無(wú)人機(jī)之間互相接收到波長(zhǎng)為1的光信號(hào)時(shí)，無(wú)人機(jī)之間開(kāi)始執(zhí)行自保程序。

3 基于無(wú)線紫外光的增強(qiáng)矢量場(chǎng)直方圖法

3.1 無(wú)人機(jī)運(yùn)動(dòng)模型

圖2 無(wú)人機(jī)運(yùn)動(dòng)模型

Fig. 2 UAV motion model

通過(guò)轉(zhuǎn)換關(guān)系可求得在大地坐標(biāo)系下任意無(wú)人機(jī)運(yùn)動(dòng)到點(diǎn)時(shí)的位置信息為

無(wú)人機(jī)運(yùn)動(dòng)時(shí)的速度和位移可由以下公式表示：

3.2 基于無(wú)線紫外光虛擬圍欄的直方圖構(gòu)建

其中：

圖3 無(wú)線紫外光虛擬圍欄直方圖

Fig. 3 Wireless ultraviolet virtual fence histogram

3.3 無(wú)人機(jī)飛行方向的最優(yōu)選擇

虛擬圍欄直方圖本質(zhì)上是一個(gè)無(wú)線紫外光虛擬圍欄覆蓋范圍內(nèi)的可飛行區(qū)域集合，需要通過(guò)一些約束條件篩選出適合飛行的區(qū)域。原有算法通過(guò)設(shè)定可飛行區(qū)域的邊界值，比較二者差值與閾值之間的關(guān)系篩選最佳運(yùn)動(dòng)方向，但是對(duì)于無(wú)人機(jī)來(lái)說(shuō)，這種選擇運(yùn)動(dòng)方向存在一定局限性，下一時(shí)刻對(duì)當(dāng)前時(shí)刻的影響并未考慮，為此，需要通過(guò)預(yù)測(cè)鄰近無(wú)人機(jī)在下一時(shí)刻的飛行狀態(tài)，結(jié)合該狀態(tài)選擇當(dāng)前時(shí)刻無(wú)人機(jī)的運(yùn)動(dòng)方向。而無(wú)人機(jī)系統(tǒng)是非常復(fù)雜的非線性系統(tǒng)[17]，并且利用無(wú)線紫外光設(shè)備提供無(wú)人機(jī)機(jī)間距離、方位角時(shí)也存在誤差。為了提高避讓的精準(zhǔn)度，本文將利用無(wú)跡卡爾曼預(yù)測(cè)器預(yù)測(cè)鄰近無(wú)人機(jī)的飛行軌跡及運(yùn)動(dòng)狀態(tài)以便于運(yùn)動(dòng)方向的選擇。

設(shè)無(wú)人機(jī)作勻變速運(yùn)動(dòng)，由于無(wú)線紫外光設(shè)備及運(yùn)動(dòng)系統(tǒng)自身均含高斯白噪聲，則運(yùn)動(dòng)狀態(tài)方程()和觀測(cè)方程()[18]可表示為

式中：()表示無(wú)人機(jī)系統(tǒng)所包含的高斯白噪聲，其具有協(xié)方差陣。()表示觀測(cè)狀態(tài)下的高斯白噪聲，其具有協(xié)方差陣。

圖4 基于無(wú)線紫外光的避讓流程圖

4 算法仿真結(jié)果與分析

4.1 仿真參數(shù)設(shè)置

避讓算法參數(shù)如表1所示。無(wú)人機(jī)狀態(tài)預(yù)測(cè)時(shí)系統(tǒng)噪聲()具有協(xié)方差，()具有協(xié)方差陣，分別如下：

()和()二者不相關(guān)，采樣次數(shù)=50次，采樣時(shí)間=1 s。表2所示為各個(gè)運(yùn)動(dòng)狀態(tài)預(yù)測(cè)初始參數(shù)。

4.2 避讓算法對(duì)比分析

在算法對(duì)比中，矢量場(chǎng)直方圖避讓算法(VFH+)為局部避讓算法，故存在局部極小的問(wèn)題，并且原算法的路徑鋸齒程度明顯，路徑不平滑。增強(qiáng)矢量場(chǎng)直方圖法(VFH*)為利用A*算法全局搜索關(guān)鍵避讓信息，局部避讓采用矢量場(chǎng)直方圖的避讓算法，該算法雖利用A*算法獲取了全局地圖信息，但是對(duì)于無(wú)人機(jī)蜂群這類高動(dòng)態(tài)應(yīng)用場(chǎng)景存在環(huán)境信息更新不及時(shí)影響避讓效果等問(wèn)題?；诖耍岢隽颂摂M圍欄避讓算法(UAVF)，本算法為考慮當(dāng)前運(yùn)動(dòng)物體的運(yùn)動(dòng)狀態(tài)和運(yùn)動(dòng)物體運(yùn)動(dòng)狀態(tài)預(yù)測(cè)的局部避讓算法。由于VFH+只適合于靜態(tài)障礙物的局部避讓，在此將軌跡預(yù)測(cè)后的位置狀態(tài)離線顯示在地圖中，查看其避讓軌跡狀態(tài)。在場(chǎng)景一下，選取了近前30 s的軌跡，因?yàn)闀?huì)遇發(fā)生在前30 s內(nèi)。在場(chǎng)景二下，選取了近前50 s的軌跡。圖5(a)為場(chǎng)景一中局部避讓軌跡圖，圖5(b)為場(chǎng)景一中避讓軌跡局部放大圖，圖6(a)場(chǎng)景二中局部避讓軌跡圖，圖6(b)為場(chǎng)景二中避讓軌跡局部放大圖。

表1 避讓算法參數(shù)

表2 運(yùn)動(dòng)狀態(tài)初始參數(shù)

圖5 (a) 場(chǎng)景一中局部避讓軌跡；(b) 場(chǎng)景一中避讓軌跡局部放大

圖6 (a) 場(chǎng)景二中局部避讓軌跡；(b) 場(chǎng)景二中避讓軌跡局部放大圖

從圖5(a)中可以觀察出，三類算法均可安全完成局部避讓，并最終到達(dá)目的地。從圖5(b)中可以看出，由于UAVF考慮了自身運(yùn)動(dòng)速度及在避讓時(shí)的機(jī)間距離的冗余，局部避讓時(shí)路徑平滑且轉(zhuǎn)向平緩，機(jī)間距離保持良好，無(wú)明顯軌跡抖動(dòng)。VFH+由于未能提前獲知運(yùn)動(dòng)物體的運(yùn)動(dòng)狀態(tài)，選擇從障礙物的下一個(gè)前進(jìn)方向避讓，在實(shí)際情況中，極有可能在會(huì)遇時(shí)出現(xiàn)碰撞，而且避讓時(shí)出現(xiàn)了明顯的抖動(dòng)。VFH*雖然提前全局搜索適合路徑，并在局部完成避讓，但是從開(kāi)始搜索路徑到避讓，局部路徑較長(zhǎng)，有轉(zhuǎn)向角度。仿真中，VFH*算法共耗時(shí)36.01 s，路徑總長(zhǎng)度570 m；VFH+算法路徑總長(zhǎng)度529 m，共耗時(shí)31.45 s；UAVF算法共耗時(shí)29.23 s，路徑總長(zhǎng)度398 m。相比VFH*算法，總路徑長(zhǎng)度減少3.02%，總耗時(shí)減少18.82%。

圖6(a)表明，三類算法均可安全完成局部避讓，并最終到達(dá)目的地。從圖6(b)可以觀察出，由于VFH+在離線規(guī)劃中未能找到合適避讓方向，故從出發(fā)開(kāi)始選擇繞過(guò)最遠(yuǎn)端物體到達(dá)終點(diǎn)，總耗時(shí)費(fèi)42.97 s，路徑總長(zhǎng)度860 m。VFH*和UAVF均能很好地完成局部避讓，但相比VFH*，UAVF軌跡平滑，轉(zhuǎn)向角度較小。VFH*總耗時(shí)47.48 s，路徑長(zhǎng)度離794 m；UAVF總耗時(shí)38.43 s，路徑總長(zhǎng)度763 m。與VFH*相比，UAVF耗時(shí)減少19.06%，路徑總長(zhǎng)度減少3.90%

4.3 運(yùn)動(dòng)狀態(tài)預(yù)測(cè)性能分析

圖7與圖8中的無(wú)人機(jī)真實(shí)軌跡和預(yù)測(cè)軌跡均為Matlab仿真所得。圖7(a)為場(chǎng)景一中的預(yù)測(cè)軌跡圖，從圖中可以看出，真實(shí)值與預(yù)測(cè)值整體擬合度較高，但是局部相對(duì)誤差依然存在。圖7(b)，7(c)為場(chǎng)景一中的預(yù)測(cè)軌跡局部放大圖，放大比例基本相同，順序?yàn)閺淖笊系接蚁?。圖7(b)是預(yù)測(cè)剛開(kāi)始時(shí)局部放大，可以看出由于采用次數(shù)少，相對(duì)誤差比較明顯。圖7(c)預(yù)測(cè)次數(shù)在25~30左右，相對(duì)誤差減少。綜合兩幅圖可以分析出，由于預(yù)測(cè)次數(shù)的增加，相對(duì)誤差在逐漸減小，最后趨于穩(wěn)定。在場(chǎng)景一的預(yù)測(cè)中，相對(duì)距離誤差最大不超過(guò)6.83 m，相對(duì)速度誤差最大不超過(guò)1.88 m/s，相對(duì)加速度誤差最大不超過(guò)0.17 m/s2。

圖8(a)為場(chǎng)景二中的預(yù)測(cè)軌跡圖，從圖中可以看出，真實(shí)值與預(yù)測(cè)值整體擬合度較高，但是局部相對(duì)誤差明顯。圖8(b)，8(c)為場(chǎng)景二中的軌跡局部放大圖，放大比例基本相同，順序?yàn)樽笙碌接疑稀D8(b)是預(yù)測(cè)剛開(kāi)始時(shí)的局部放大圖，可以看出，由于剛開(kāi)始預(yù)測(cè)，相對(duì)誤差非常明顯，圖8(c)是預(yù)測(cè)次數(shù)在15~25左右，相對(duì)誤差減少幅度大。綜合兩幅局部放大圖可以分析出，隨著預(yù)測(cè)次數(shù)的增加，相對(duì)誤差在減小，最后趨于穩(wěn)定。在場(chǎng)景二的預(yù)測(cè)中，相對(duì)距離誤差最大不超過(guò)8.19 m，相對(duì)速度誤差最大不超過(guò)0.82 m/s，相對(duì)加速度誤差最大不超過(guò)0.11 m/s2。兩次狀態(tài)下的相對(duì)誤差平均值如表3所示。

5 結(jié) 論

本算法考慮了蜂群無(wú)人機(jī)編隊(duì)內(nèi)無(wú)線紫外光隱秘通信的覆蓋特點(diǎn)，提出了無(wú)線紫外光虛擬圍欄避讓策略?；趥鹘y(tǒng)增強(qiáng)矢量場(chǎng)直方圖法，通過(guò)增加速度采樣改進(jìn)代價(jià)函數(shù)，結(jié)合無(wú)跡卡爾曼預(yù)測(cè)器預(yù)測(cè)編隊(duì)內(nèi)其他無(wú)人機(jī)運(yùn)動(dòng)狀態(tài)，實(shí)現(xiàn)了蜂群無(wú)人機(jī)編隊(duì)飛行時(shí)的協(xié)作避讓。仿真結(jié)果表明，與傳統(tǒng)算法相比，本算法在兩種場(chǎng)景中的避讓總路徑長(zhǎng)度平均減少3.46%，總耗時(shí)平均減小18.94%。該算法能夠在未獲取全局地圖的情況下，通過(guò)無(wú)線紫外光設(shè)備及無(wú)人機(jī)運(yùn)動(dòng)狀態(tài)預(yù)測(cè)實(shí)現(xiàn)協(xié)作避讓。

圖7 (a) 場(chǎng)景一中的預(yù)測(cè)軌跡；(b) 第一次局部放大；(c) 第二次局部放大

圖8 (a)場(chǎng)景二中的預(yù)測(cè)軌跡；(b)第一次局部放大；(c) 第二次局部放大

表3 相對(duì)誤差平均值

[1] Yu L, Wei P, Ma Z L,. Analysis on the development of foreign army anti-bee colony drone technology[J]., 2017(12): 26–30.

于力, 魏平, 馬振利, 等. 外軍反蜂群無(wú)人機(jī)技術(shù)發(fā)展分析[J]. 飛航導(dǎo)彈, 2017(12): 26–30.

[2] Luo C. The study of signal processing and design of base band system for non-line-of-sight optical communication[D]. Beijing: University of Chinese Academy of Sciences, 2011.

羅暢. 非視距光通信信號(hào)處理研究與基帶系統(tǒng)設(shè)計(jì)[D]. 北京: 中國(guó)科學(xué)院研究生院, 2011.

[3] Tan Y Y, Li Y, Zhou J,. Path replanning approach for UAV based on A* algorithm in complex environment[J]., 2017, 39(6): 1268–1273.

譚雁英, 李洋, 周軍, 等. 復(fù)雜環(huán)境下基于A*算法的無(wú)人機(jī)路徑再規(guī)劃[J]. 系統(tǒng)工程與電子技術(shù), 2017, 39(6): 1268–1273.

[4] Mac T T, Copot C, Tran D T,. A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization[J]., 2017, 59: 68–76.

[5] Roberge V, Tarbouchi M, Labonte G. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning[J]., 2013, 9(1): 132–141.

[6] Wu Z Y, Li J H, Zuo J M,. Path planning of UAVs based on collision probability and Kalman filter[J]., 2018, 6: 34237–34245.

[7] Chen Y B, Luo G C, Mei Y S,. UAV path planning using artificial potential field method updated by optimal control theory[J]., 2016, 47(6): 1407–1420.

[8] Qu P R, Xue J R, Ma L,. A constrained VFH algorithm for motion planning of autonomous vehicles[C]//, 2015.

[9] Babinec A, Duchoň F, Dekan M,. VFH*TDT (VFH* with time dependent tree): a new laser rangefinder based obstacle avoidance method designed for environment with non-static obstacles[J]., 2014, 62(8): 1098–1115.

[10] Zhang Y Y, Qu D, Ke J,. Dynamic obstacle avoidance for USV based on velocity obstacle and dynamic window method[J]., 2017, 23(1): 1–16.

張洋洋, 瞿棟, 柯俊, 等. 基于速度障礙法和動(dòng)態(tài)窗口法的無(wú)人水面艇動(dòng)態(tài)避障[J]. 上海大學(xué)學(xué)報(bào)(自然科學(xué)版), 2017, 23(1): 1–16.

[11] Ji J, Khajepour A, Melek W W,. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints[J]., 2017, 66(2): 952–964.

[12] Cui R X, Li Y, Yan W S. Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT[J]., 2016, 46(7): 993–1004.

[13] da Silva Arantes J, da Silva Arantes M, Toledo C F M,. Heuristic and genetic algorithm approaches for UAV path planning under critical situation[J]., 2017, 26(1): 1760008.

[14] Zhao T F, Yu X X, Bao H,. Ranging and positioning method using wireless solar blind ultraviolet[J]., 2017, 25(9): 2324–2332.

趙太飛, 余敘敘, 包鶴, 等. 無(wú)線日盲紫外光測(cè)距定位方法[J]. 光學(xué)精密工程, 2017, 25(9): 2324–2332.

[15] Zhang Y L, Zhao C P, Yan H. Path planning for unmanned aerial vehicles using improved bidirectional A* and vector field histogram algorithm[J]., 2019, 19(4): 179–184.

張亞蘭, 趙成萍, 嚴(yán)華. 基于改進(jìn)雙向A*和向量場(chǎng)直方圖算法的無(wú)人機(jī)航路規(guī)劃[J]. 科學(xué)技術(shù)與工程, 2019, 19(4): 179–184.

[16] Sun Y Z, Xiao S D, Pan S F,. VFH algorithm for obstacle avoidance based on Kalman Filter[J]., 2015, 29(4): 373–377, 398.

孫揚(yáng)智, 肖世德, 潘紹飛, 等. 融合卡爾曼濾波的VFH避障算法[J]. 江蘇科技大學(xué)學(xué)報(bào)(自然科學(xué)版), 2015, 29(4): 373–377, 398.

[17] Dai R, Cochran J, Jr. Path planning and state estimation for unmanned aerial vehicles in hostile environments[J]., 2010, 33(2): 595–601.

[18] Guan Y Z, Song C L, Dong H J. Path planning of the free-floating manipulator for capturing a moving target[J]., 2017, 39(6): 803–811.

關(guān)英姿, 宋春林, 董惠娟. 空間自由漂浮機(jī)器人對(duì)運(yùn)動(dòng)目標(biāo)抓捕的路徑規(guī)劃[J]. 機(jī)器人, 2017, 39(6): 803–811.

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

Zhao Taifei1,3*, Gao Peng1,3, Shi Haiquan1,3, Li Xingshan2

1Faculty of Automation and Information Engineering, Xi￠an University of Technology, Xi￠an, Shaanxi 710048, China;2General Design Institute of Hubei Academy of Aerospace Technology, Wuhan, Hubei 430040, China;3Shanxi Civil-Military Integration Key Laboratory of Intelligence Collaborative Networks, Xi￠an, Shaanxi 710048, China

Wireless UV virtual fence model

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.

Citation: Zhao T F, Gao P, Shi H Q,An algorithm for the bee colony drone to use wireless ultraviolet for cooperative obstacle avoidance[J]., 2020, 47(3): 190505

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

Zhao Taifei1,3*, Gao Peng1,3, Shi Haiquan1,3, Li Xingshan2

1School of Automation and Information Engineering, Xi￠an University of Technology, Xi￠an, Shaanxi 710048, China;2General Design Institute of Hubei Academy of Aerospace Technology, Wuhan, Hubei 430040, China;3Shanxi Civil-Military Integration Key Laboratory of Intelligence Collaborative Networks, Xi￠an, Shaanxi 710048, China

For complex battlefield environments, it is especially important to ensure the safety of flight between uninhabited aerial vehicles (UAV) formations and reliable communication within the formation. This paper proposes an algorithm for collaborative avoidance using wireless ultraviolet light between drones in a bee colony drone formation. Combined with the above algorithm and using the characteristics of wireless ultraviolet light coverage, the avoidance strategy of ultraviolet virtual fence is designed. And by enhancing the vector field histogram method to improve the cost function of the state of motion of the drone when performing mutual avoidance. In addition, the algorithm uses the unscented Kalman filter to predict the flight status of nearby uninhabited aerial vehicles. The simulation results show that in the avoidance simulations of the two prediction scenarios, the overall motion trajectory of this algorithm is smoother than that of the enhance vector field histogram method. At the same time, there is no obvious jitter when local avoidance occurs, the total length of the avoidance path is reduced by 3.46% on average, and the total time consumption is reduced by 18.94%. This verifies that the wireless ultraviolet cooperative avoidance algorithm in a bee colony drone formation is effective.

colony drone; wireless ultraviolete; virtual fence; cooperative obstacle avoidance; trajectory prediction; enhanced vector

V279+.2；TN929.1

A

10.12086/oee.2020.190505

: Zhao T F, Gao P, Shi H Q,. An algorithm for the bee colony drone to use wireless ultraviolet for cooperative obstacle avoidance[J]., 2020,47(3): 190505

2019-08-26；

2019-09-26

國(guó)家自然科學(xué)基金資助項(xiàng)目(61971345，U1433110)；陜西省教育廳服務(wù)地方專項(xiàng)計(jì)劃項(xiàng)目(17JF024)；西安市科學(xué)計(jì)劃項(xiàng)目(CXY1835(4))；陜西省重點(diǎn)產(chǎn)業(yè)鏈創(chuàng)新計(jì)劃項(xiàng)目(2017ZDCXL-GY-05-03)；西安市碑林區(qū)科技計(jì)劃項(xiàng)目(GX1921)

趙太飛(1978-)，男，博士，教授，主要從事網(wǎng)絡(luò)通信與自組織網(wǎng)絡(luò)技術(shù)的研究。E-mail：tfz@xaut.edu.cn

趙太飛，高鵬，史海泉，等. 蜂群無(wú)人機(jī)編隊(duì)內(nèi)無(wú)線紫外光協(xié)作避讓算法[J]. 光電工程，2020，47(3): 190505

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)

* E-mail: tfz@xaut.edu.cn