無(wú)人機(jī)

·編者按·

無(wú)人駕駛飛機(jī)（Unmanned Aerial Vehicle，UAV），簡(jiǎn)稱“無(wú)人機(jī)”，是具有自主程序控制、可進(jìn)行無(wú)線遙控飛行的空中飛行器，可與遙控人員協(xié)作完成半自主控制，也可在無(wú)人駕駛、控制的狀態(tài)下自主操作.無(wú)人機(jī)主要包括飛機(jī)機(jī)體、飛控系統(tǒng)、數(shù)據(jù)鏈系統(tǒng)、發(fā)射回收系統(tǒng)、電源系統(tǒng)等，其相關(guān)技術(shù)涉及隱身、飛行控制、動(dòng)力、數(shù)據(jù)鏈、發(fā)射與回收等方面.無(wú)人機(jī)系統(tǒng)的主要特點(diǎn)有：（1）機(jī)體靈活性好，體積小、重量輕.（2）可擔(dān)負(fù)多載荷任務(wù)并進(jìn)行遠(yuǎn)距離、長(zhǎng)時(shí)間續(xù)航.（3）隱身性能好，生存能力強(qiáng)，費(fèi)用低廉.（4）安全系數(shù)高，自主控制能力強(qiáng).

第一架無(wú)人機(jī)是1917年由英國(guó)研制成功的.早期的無(wú)人機(jī)是在退役飛機(jī)基礎(chǔ)上改裝而來(lái)的，只能設(shè)置固定航線，無(wú)法人工干預(yù)和自主反應(yīng)，主要用于炮兵輔助靶標(biāo)演練.20世紀(jì)60年代以后，隨著無(wú)線電、自動(dòng)控制、計(jì)算機(jī)等技術(shù)的發(fā)展，無(wú)人機(jī)不再完全依賴地面控制，可以靈活執(zhí)行多種任務(wù)，并應(yīng)用于戰(zhàn)爭(zhēng)行動(dòng)中.到20世紀(jì)90年代以來(lái)，信息化技術(shù)、衛(wèi)星通信技術(shù)、高效空氣動(dòng)力技術(shù)等相關(guān)技術(shù)迅猛發(fā)展，無(wú)人機(jī)的性能不斷提升、功能不斷擴(kuò)展，應(yīng)用領(lǐng)域也越來(lái)越廣泛.目前，全世界范圍內(nèi)已掀起了無(wú)人機(jī)研制的熱潮，共有57個(gè)國(guó)家研制和發(fā)展無(wú)人機(jī)，種類多達(dá)1000多種，其中，己成為無(wú)人機(jī)產(chǎn)品的有400多種，這些無(wú)人機(jī)主要應(yīng)用于軍事領(lǐng)域，并逐步向民用領(lǐng)域擴(kuò)展.

有關(guān)無(wú)人機(jī)的研究，美國(guó)占據(jù)了制高點(diǎn).迄今為止，美國(guó)己開(kāi)發(fā)出高、中、低空，大、中、小型上百種無(wú)人機(jī)，覆蓋了情報(bào)/監(jiān)視/偵察、電子對(duì)抗、通信中繼、攻擊作戰(zhàn)等任務(wù)領(lǐng)域，其無(wú)人機(jī)產(chǎn)品在研制水平、性能指標(biāo)、技術(shù)成熟度方面都居于世界前列.我國(guó)無(wú)人機(jī)的研究起步于20世紀(jì)50年代，在90年代取得實(shí)質(zhì)性進(jìn)展，經(jīng)過(guò)不懈努力，其性能不斷提高，現(xiàn)己形成較為完善的無(wú)人機(jī)體系，各種類型、各種功能的無(wú)人機(jī)己投入使用.但從整體水平來(lái)看，我國(guó)無(wú)人機(jī)研究水平與美國(guó)和以色列等無(wú)人機(jī)強(qiáng)國(guó)相比差距比較明顯.存在的問(wèn)題主要有：（1）雖然我國(guó)無(wú)人機(jī)種類多，但重復(fù)投資、低水平重復(fù)、高端無(wú)人機(jī)發(fā)展依然較落后.（2）我國(guó)發(fā)動(dòng)機(jī)研制基礎(chǔ)較為薄弱，無(wú)人機(jī)在特定的高空低雷諾、大過(guò)載等飛行條件下，對(duì)發(fā)動(dòng)機(jī)也提出了特殊的要求.（3）網(wǎng)絡(luò)化通信問(wèn)題.

戰(zhàn)爭(zhēng)對(duì)武器裝備的高射程、高精度、零傷亡、高重復(fù)利用率以及隱蔽性等特征提出更高要求，無(wú)人機(jī)成為不可或缺的主戰(zhàn)裝備.隨著無(wú)人機(jī)飛行平臺(tái)和載荷設(shè)備的逐步完善，無(wú)人機(jī)在軍事和民用領(lǐng)域的運(yùn)用越來(lái)越多，特別是在軍事領(lǐng)域中，無(wú)人機(jī)的起到的作用越來(lái)越大.隨著科技的不斷發(fā)展，現(xiàn)代化的戰(zhàn)爭(zhēng)環(huán)境日益復(fù)雜，僅僅依靠單架無(wú)人機(jī)將無(wú)法完成任務(wù)，多無(wú)人機(jī)在協(xié)同目標(biāo)搜索、目標(biāo)打擊等方面具有單個(gè)無(wú)人機(jī)無(wú)可比擬的高效性和實(shí)時(shí)性，在信息化、網(wǎng)絡(luò)化、體系化背景下，多機(jī)協(xié)同作戰(zhàn)必將是未來(lái)戰(zhàn)爭(zhēng)中一種主要的作戰(zhàn)方式，成為無(wú)人機(jī)發(fā)展的主流和趨勢(shì).

本專題得到了段海濱教授（北京航空航天大學(xué)自動(dòng)化科學(xué)與電氣工程學(xué)院）的大力支持.

·熱點(diǎn)數(shù)據(jù)排行·

截至2015年5月25日，中國(guó)知網(wǎng)（CNKI）和Web of Science（WOS）的數(shù)據(jù)報(bào)告顯示，以“無(wú)人駕駛飛機(jī)或無(wú)人機(jī)（Unmanned Aerial Vehicle，UAV）”為詞條可以檢索到的期刊文獻(xiàn)分別為1565與2976條，本專題將相關(guān)數(shù)據(jù)按照：研究機(jī)構(gòu)發(fā)文數(shù)、作者發(fā)文數(shù)、期刊發(fā)文數(shù)、被引用頻次進(jìn)行排行，結(jié)果如下.

無(wú)人機(jī)

·編者按·

無(wú)人駕駛飛機(jī)（Unmanned Aerial Vehicle，UAV），簡(jiǎn)稱“無(wú)人機(jī)”，是具有自主程序控制、可進(jìn)行無(wú)線遙控飛行的空中飛行器，可與遙控人員協(xié)作完成半自主控制，也可在無(wú)人駕駛、控制的狀態(tài)下自主操作.無(wú)人機(jī)主要包括飛機(jī)機(jī)體、飛控系統(tǒng)、數(shù)據(jù)鏈系統(tǒng)、發(fā)射回收系統(tǒng)、電源系統(tǒng)等，其相關(guān)技術(shù)涉及隱身、飛行控制、動(dòng)力、數(shù)據(jù)鏈、發(fā)射與回收等方面.無(wú)人機(jī)系統(tǒng)的主要特點(diǎn)有：（1）機(jī)體靈活性好，體積小、重量輕.（2）可擔(dān)負(fù)多載荷任務(wù)并進(jìn)行遠(yuǎn)距離、長(zhǎng)時(shí)間續(xù)航.（3）隱身性能好，生存能力強(qiáng)，費(fèi)用低廉.（4）安全系數(shù)高，自主控制能力強(qiáng).

第一架無(wú)人機(jī)是1917年由英國(guó)研制成功的.早期的無(wú)人機(jī)是在退役飛機(jī)基礎(chǔ)上改裝而來(lái)的，只能設(shè)置固定航線，無(wú)法人工干預(yù)和自主反應(yīng)，主要用于炮兵輔助靶標(biāo)演練.20世紀(jì)60年代以后，隨著無(wú)線電、自動(dòng)控制、計(jì)算機(jī)等技術(shù)的發(fā)展，無(wú)人機(jī)不再完全依賴地面控制，可以靈活執(zhí)行多種任務(wù)，并應(yīng)用于戰(zhàn)爭(zhēng)行動(dòng)中.到20世紀(jì)90年代以來(lái)，信息化技術(shù)、衛(wèi)星通信技術(shù)、高效空氣動(dòng)力技術(shù)等相關(guān)技術(shù)迅猛發(fā)展，無(wú)人機(jī)的性能不斷提升、功能不斷擴(kuò)展，應(yīng)用領(lǐng)域也越來(lái)越廣泛.目前，全世界范圍內(nèi)已掀起了無(wú)人機(jī)研制的熱潮，共有57個(gè)國(guó)家研制和發(fā)展無(wú)人機(jī)，種類多達(dá)1000多種，其中，己成為無(wú)人機(jī)產(chǎn)品的有400多種，這些無(wú)人機(jī)主要應(yīng)用于軍事領(lǐng)域，并逐步向民用領(lǐng)域擴(kuò)展.

有關(guān)無(wú)人機(jī)的研究，美國(guó)占據(jù)了制高點(diǎn).迄今為止，美國(guó)己開(kāi)發(fā)出高、中、低空，大、中、小型上百種無(wú)人機(jī)，覆蓋了情報(bào)/監(jiān)視/偵察、電子對(duì)抗、通信中繼、攻擊作戰(zhàn)等任務(wù)領(lǐng)域，其無(wú)人機(jī)產(chǎn)品在研制水平、性能指標(biāo)、技術(shù)成熟度方面都居于世界前列.我國(guó)無(wú)人機(jī)的研究起步于20世紀(jì)50年代，在90年代取得實(shí)質(zhì)性進(jìn)展，經(jīng)過(guò)不懈努力，其性能不斷提高，現(xiàn)己形成較為完善的無(wú)人機(jī)體系，各種類型、各種功能的無(wú)人機(jī)己投入使用.但從整體水平來(lái)看，我國(guó)無(wú)人機(jī)研究水平與美國(guó)和以色列等無(wú)人機(jī)強(qiáng)國(guó)相比差距比較明顯.存在的問(wèn)題主要有：（1）雖然我國(guó)無(wú)人機(jī)種類多，但重復(fù)投資、低水平重復(fù)、高端無(wú)人機(jī)發(fā)展依然較落后.（2）我國(guó)發(fā)動(dòng)機(jī)研制基礎(chǔ)較為薄弱，無(wú)人機(jī)在特定的高空低雷諾、大過(guò)載等飛行條件下，對(duì)發(fā)動(dòng)機(jī)也提出了特殊的要求.（3）網(wǎng)絡(luò)化通信問(wèn)題.

戰(zhàn)爭(zhēng)對(duì)武器裝備的高射程、高精度、零傷亡、高重復(fù)利用率以及隱蔽性等特征提出更高要求，無(wú)人機(jī)成為不可或缺的主戰(zhàn)裝備.隨著無(wú)人機(jī)飛行平臺(tái)和載荷設(shè)備的逐步完善，無(wú)人機(jī)在軍事和民用領(lǐng)域的運(yùn)用越來(lái)越多，特別是在軍事領(lǐng)域中，無(wú)人機(jī)的起到的作用越來(lái)越大.隨著科技的不斷發(fā)展，現(xiàn)代化的戰(zhàn)爭(zhēng)環(huán)境日益復(fù)雜，僅僅依靠單架無(wú)人機(jī)將無(wú)法完成任務(wù)，多無(wú)人機(jī)在協(xié)同目標(biāo)搜索、目標(biāo)打擊等方面具有單個(gè)無(wú)人機(jī)無(wú)可比擬的高效性和實(shí)時(shí)性，在信息化、網(wǎng)絡(luò)化、體系化背景下，多機(jī)協(xié)同作戰(zhàn)必將是未來(lái)戰(zhàn)爭(zhēng)中一種主要的作戰(zhàn)方式，成為無(wú)人機(jī)發(fā)展的主流和趨勢(shì).

本專題得到了段海濱教授（北京航空航天大學(xué)自動(dòng)化科學(xué)與電氣工程學(xué)院）的大力支持.

·熱點(diǎn)數(shù)據(jù)排行·

截至2015年5月25日，中國(guó)知網(wǎng)（CNKI）和Web of Science（WOS）的數(shù)據(jù)報(bào)告顯示，以“無(wú)人駕駛飛機(jī)或無(wú)人機(jī)（Unmanned Aerial Vehicle，UAV）”為詞條可以檢索到的期刊文獻(xiàn)分別為1565與2976條，本專題將相關(guān)數(shù)據(jù)按照：研究機(jī)構(gòu)發(fā)文數(shù)、作者發(fā)文數(shù)、期刊發(fā)文數(shù)、被引用頻次進(jìn)行排行，結(jié)果如下.

研究機(jī)構(gòu)發(fā)文數(shù)量排名（CNKI）

研究機(jī)構(gòu)發(fā)文數(shù)量排名（WOS）

作者發(fā)文數(shù)量排名（CNKI）

作者發(fā)文數(shù)量排名（WOS）

期刊發(fā)文數(shù)量排名（CNKI）

期刊發(fā)文數(shù)量排名（WOS）

根據(jù)中國(guó)知網(wǎng)（CNKI）數(shù)據(jù)報(bào)告，以無(wú)人機(jī)和無(wú)人駕駛飛機(jī)為詞條可以檢索到的高被引論文排行結(jié)果如下.

根據(jù)Web of Science統(tǒng)計(jì)數(shù)據(jù)，以無(wú)人機(jī)或無(wú)人駕駛飛機(jī)（Unmanned Aerial Vehicle，UAV）為詞條可以檢索到的高被引論文排行結(jié)果如下.

國(guó)外數(shù)據(jù)庫(kù)高被引論文排行

·經(jīng)典文獻(xiàn)推薦·

基于Web of Science檢索結(jié)果，利用Histcite軟件選取LCS（Local Citation Score，本地引用次數(shù)）TOP 30文獻(xiàn)作為節(jié)點(diǎn)進(jìn)行分析，得到本領(lǐng)域推薦的經(jīng)典文獻(xiàn)如下.

Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle

Berni，Jose A.J； Zarco-Tejada，Pablo J； Suarez，Lola； et al.

來(lái)源出版物：IEEE Transactions on Geoscience and Remote Sensing，2009，47（3）: 722-738

Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots

Lelong，Camille C.D； Burger，Philippe； Jubelin，Guillaume； et al.

來(lái)源出版物：SENSORS，2008，8（5）: 3557-3585聯(lián)系郵箱：Lelong，Camille C.D； camille.lelong@cirad.fr

An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle（UAV）Imagery，Based on Structure from Motion（SfM）Point Clouds

Turner，Darren； Lucieer，Arko； Watson，Christopher

來(lái)源出版物：Remote Sensing，2012，4（5）: 1392-1410

Acquisition，Orthorectification，and Object-based Classification of Unmanned Aerial Vehicle（UAV）Imagery for Rangeland Monitoring

Laliberte，Andrea S； Herrick，Jeffrey E； Rango，Albert； et al.

來(lái)源出版物：Photogrammetric Engineering and Remote Sensing，2010，76（6）: 661-672

聯(lián)系郵箱：Laliberte，Andrea S； alaliber@nmsu.edu

Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

Rosnell，Tomi； Honkavaara，Eija

來(lái)源出版物：SENSORS，2012，12（1）: 453-480聯(lián)系郵箱：Rosnell，Tomi； tomi.rosnell@fgi.fi

·推薦綜述·

基于仿生智能的無(wú)人作戰(zhàn)飛機(jī)控制技術(shù)發(fā)展新思路*

段海濱1，邵山2，蘇丙未3，張雷4

1. 北京航空航天大學(xué)自動(dòng)化科學(xué)與電氣工程學(xué)院，飛行器控制一體化技術(shù)國(guó)防科技重點(diǎn)實(shí)驗(yàn)室，北京 100191

2. 沈陽(yáng)飛機(jī)設(shè)計(jì)研究所，飛行控制部，沈陽(yáng) 110035

3. 北京臨近空間飛行器系統(tǒng)工程研究所，總體二室，北京 100076

4. 空軍裝備研究院，綜合計(jì)劃處，北京 100085

1引言

未來(lái)信息技術(shù)的一個(gè)必然發(fā)展趨勢(shì)是智能化，而這種智能化實(shí)現(xiàn)的有效途徑應(yīng)是向自然界中多種形式的智能行為學(xué)習(xí)和模擬.自然界中的許多自適應(yīng)優(yōu)化現(xiàn)象不斷給人以啟示：生物體和自然生態(tài)系統(tǒng)可通過(guò)自身的演化就使許多在人類看起來(lái)高度復(fù)雜的優(yōu)化問(wèn)題得到完美的解決［1］.近些年來(lái)，一些與經(jīng)典的數(shù)學(xué)規(guī)劃原理截然不同的、試圖通過(guò)模擬自然生態(tài)系統(tǒng)機(jī)制以求解復(fù)雜問(wèn)題的仿生智能（bio-inspired intelligence，BI）方法相繼出現(xiàn)（如遺傳算法、蟻群優(yōu)化、微粒群優(yōu)化、人工免疫、人工蜂群優(yōu)化、文化進(jìn)化、情感計(jì)算、DNA計(jì)算等），大大豐富了現(xiàn)代優(yōu)化技術(shù)，也為那些傳統(tǒng)最優(yōu)化技術(shù)難以處理的組合優(yōu)化問(wèn)題提供了切實(shí)可行的解決方案［2］.伴隨著模擬自然與生物機(jī)理為特征的仿生智能計(jì)算時(shí)代的悄然興起，一些仿生智能技術(shù)已在經(jīng)典NP-C問(wèn)題的求解和實(shí)際應(yīng)用中顯示出強(qiáng)大的生命力和進(jìn)一步發(fā)展的潛力.

仿生智能技術(shù)具有較強(qiáng)的魯棒性、優(yōu)良的分布式計(jì)算機(jī)制、易于與其它方法相結(jié)合等優(yōu)點(diǎn).盡管大部分算法的嚴(yán)格理論基礎(chǔ)尚未奠定［3］，國(guó)內(nèi)外的有關(guān)研究還處于實(shí)驗(yàn)探索和初步應(yīng)用階段，但是目前人們對(duì)仿生智能的研究已經(jīng)拓展到了多個(gè)應(yīng)用領(lǐng)域，由解決一維靜態(tài)優(yōu)化問(wèn)題發(fā)展到解決多維動(dòng)態(tài)組合優(yōu)化問(wèn)題，而且在仿生智能的硬件實(shí)現(xiàn)上也取得了很多突破性進(jìn)展，從而使仿生智能展現(xiàn)出勃勃生機(jī)和廣闊的發(fā)展前景.

無(wú)人作戰(zhàn)飛機(jī)（unmanned combat aerial vehicle，UCAV）是一種有動(dòng)力、可控制、能攜帶多種任務(wù)設(shè)備、執(zhí)行多種任務(wù)，并能重復(fù)使用的無(wú)人作戰(zhàn)飛行器，也是一種充分利用信息技術(shù)革命成果而發(fā)展的高性能信息化武器裝備平臺(tái).無(wú)人作戰(zhàn)飛機(jī)不僅可廣泛用于通訊、氣象、災(zāi)害監(jiān)測(cè)、農(nóng)業(yè)、地質(zhì)、交通等多個(gè)民用領(lǐng)域，而且還可應(yīng)用于智能監(jiān)控和偵察、人工干擾、誘餌、網(wǎng)點(diǎn)通信、對(duì)敵防空壓制、攻擊機(jī)/巡航導(dǎo)彈防御、目標(biāo)攻擊、空空作戰(zhàn)、邊境巡邏等軍事領(lǐng)域.

無(wú)人作戰(zhàn)飛機(jī)系統(tǒng)的每個(gè)組成部分都是一個(gè)高技術(shù)的復(fù)雜子系統(tǒng)，而且各個(gè)組成部分相互之間具有很強(qiáng)的依賴性和協(xié)調(diào)性.因此，高技術(shù)密集性和“系統(tǒng)之系統(tǒng)（system of systems）”是無(wú)人作戰(zhàn)飛機(jī)系統(tǒng)的兩個(gè)重要特征.無(wú)人作戰(zhàn)飛機(jī)不僅在民用方面大顯身手，而其所具有的重量輕、低成本、零傷亡、機(jī)動(dòng)性高、隱蔽性好和適應(yīng)性強(qiáng)等特點(diǎn)，使其可適應(yīng)長(zhǎng)時(shí)間、大縱深作戰(zhàn)，甚至可成為空中作戰(zhàn)武器平臺(tái)，支持海、陸、空、天、電五維一體的未來(lái)高技術(shù)戰(zhàn)爭(zhēng).因此，由于無(wú)人作戰(zhàn)飛機(jī)在未來(lái)軍事斗爭(zhēng)中具有突出的地位和作用，近幾年來(lái)各軍兵種對(duì)無(wú)人機(jī)系統(tǒng)的需求十分旺盛，目前已有多種型號(hào)的無(wú)人作戰(zhàn)飛機(jī)列入了“殺手锏”裝備發(fā)展計(jì)劃.當(dāng)前，以美國(guó)為首的西方各國(guó)都投入大量的人力、財(cái)力和物力進(jìn)行無(wú)人作戰(zhàn)飛機(jī)的研究，美、英、法、德等國(guó)相繼拋出其無(wú)人機(jī)研究方案和技術(shù)驗(yàn)證報(bào)告.美國(guó)國(guó)防高級(jí)研究計(jì)劃局分別與空軍、海軍聯(lián)合研制X-45A空軍無(wú)人作戰(zhàn)飛機(jī)和X-47A海軍無(wú)人作戰(zhàn)飛機(jī).美國(guó)國(guó)防部在2005年8月8日發(fā)布的無(wú)人機(jī)路線圖《無(wú)人飛行器系統(tǒng)路線圖2005—2030》中，把無(wú)人作戰(zhàn)飛機(jī)置于優(yōu)先發(fā)展的位置（圖1）［4］.2007年12月18日，美國(guó)國(guó)防部又發(fā)布了《無(wú)人飛行器系統(tǒng)路線圖2007—2032》［5］，與2005版的主要區(qū)別是對(duì)無(wú)人系統(tǒng)收集的數(shù)據(jù)進(jìn)行更加完善的標(biāo)記來(lái)改進(jìn)作戰(zhàn)指揮員之間偵查、監(jiān)視和情報(bào)任務(wù)的傳播方式.

瑞典、意大利、西班牙、希臘、瑞士、比利時(shí)和法國(guó)聯(lián)合開(kāi)發(fā)“神經(jīng)元”無(wú)人機(jī)原型機(jī).俄羅斯正加大無(wú)人作戰(zhàn)飛機(jī)的投資和研發(fā)力度，在高空長(zhǎng)航時(shí)戰(zhàn)略偵察無(wú)人機(jī)、無(wú)人作戰(zhàn)飛機(jī)和先進(jìn)旋翼無(wú)人機(jī)等技術(shù)領(lǐng)域努力追趕美國(guó).除了軍事強(qiáng)國(guó)以外，一些第三世界國(guó)家和軍事不發(fā)達(dá)國(guó)家也紛紛涉足無(wú)人機(jī)領(lǐng)域，在全球興起一股研制無(wú)人作戰(zhàn)飛機(jī)的熱潮.但是我國(guó)目前現(xiàn)有的無(wú)人作戰(zhàn)飛機(jī)裝備和技術(shù)能力與世界先進(jìn)國(guó)家相比在許多方面還有很大差距，以其軍事應(yīng)用為目的的新思想、新概念、新原理等基礎(chǔ)性研究和應(yīng)用研究還比較薄弱.

多無(wú)人作戰(zhàn)飛機(jī)協(xié)同控制是無(wú)人作戰(zhàn)飛機(jī)系統(tǒng)的核心內(nèi)容和關(guān)鍵技術(shù)之一，其內(nèi)容主要包括多機(jī)協(xié)同編隊(duì)飛行及編隊(duì)重構(gòu)、態(tài)勢(shì)評(píng)估及定位、任務(wù)分配、航路規(guī)劃及重規(guī)劃、通信問(wèn)題、信息融合等方面［6］.西方國(guó)家雖然在無(wú)人作戰(zhàn)飛機(jī)的控制與實(shí)現(xiàn)技術(shù)上處于國(guó)際領(lǐng)先地位，但是他們?cè)诙酂o(wú)人作戰(zhàn)飛機(jī)協(xié)同控制技術(shù)方面仍然存在很多研究空白，特別是采用仿生智能技術(shù)對(duì)多無(wú)人作戰(zhàn)飛機(jī)協(xié)同控制問(wèn)題進(jìn)行研究，目前還處于起步階段，因此，我們國(guó)家在這個(gè)時(shí)候?qū)@一新興的多學(xué)科交叉領(lǐng)域進(jìn)行立項(xiàng)并深入研究顯得十分必要.

目前在高智能化自主無(wú)人作戰(zhàn)飛機(jī)控制方面存在的主要問(wèn)題就是無(wú)人作戰(zhàn)飛機(jī)的自主控制性問(wèn)題，這也是目前困擾各國(guó)軍工科研人員的頭等難題，目前的芯片處理速度還不夠快，軟件尤其是高度自適應(yīng)的軟件也沒(méi)有，導(dǎo)致目前無(wú)人作戰(zhàn)飛機(jī)的智能化存在處理速度慢、可靠性低等瓶頸問(wèn)題［7］，總的來(lái)說(shuō)，無(wú)人戰(zhàn)機(jī)的高智能自主控制是一項(xiàng)艱巨的工程，只有在容錯(cuò)技術(shù)、行為智能和自適應(yīng)推理系統(tǒng)（如神經(jīng)網(wǎng)絡(luò)）取得突破的前提下，無(wú)人作戰(zhàn)飛機(jī)的智能化才能成為現(xiàn)實(shí).而人工腦（artificial brain）、群體智能、仿生硬件等交叉學(xué)科領(lǐng)域的仿生智能新技術(shù)為無(wú)人作戰(zhàn)飛機(jī)的高度智能化工程實(shí)現(xiàn)提供了可行的技術(shù)途徑.此外，多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制也是一個(gè)新興的戰(zhàn)略性研究領(lǐng)域.

仿生智能涌現(xiàn)過(guò)程中所體現(xiàn)出的動(dòng)態(tài)性、自組織性、協(xié)同性、強(qiáng)魯棒性等特點(diǎn)與復(fù)雜戰(zhàn)場(chǎng)環(huán)境下對(duì)無(wú)人作戰(zhàn)飛機(jī)控制的許多要求是相符的.本文在闡述仿生智能和無(wú)人作戰(zhàn)飛機(jī)基本概念原理的基礎(chǔ)上，從基于人工腦的無(wú)人作戰(zhàn)飛機(jī)高智能化自主控制技術(shù)、基于群體智能理論的多無(wú)人作戰(zhàn)飛機(jī)協(xié)同控制技術(shù)、基于群智能-Bayesian網(wǎng)絡(luò)的復(fù)雜作戰(zhàn)態(tài)勢(shì)評(píng)估技術(shù)、基于仿生硬件的無(wú)人作戰(zhàn)飛機(jī)高智能化自主控制技術(shù)、網(wǎng)絡(luò)環(huán)境下基于元啟發(fā)式（meta-heuristic）智能的多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制等方面提出了無(wú)人作戰(zhàn)飛機(jī)控制技術(shù)發(fā)展的新思路，這些技術(shù)對(duì)于極大提高作戰(zhàn)任務(wù)的有效性以及無(wú)人作戰(zhàn)飛機(jī)的生存概率具有重要的意義，同時(shí)也為無(wú)人作戰(zhàn)平臺(tái)的智能化、綜合化和先進(jìn)化提供新的突破方向和切實(shí)可行的技術(shù)途徑.

2發(fā)展思路

圖2給出了本論文所提出的基于仿生智能的無(wú)人作戰(zhàn)飛機(jī)控制技術(shù)發(fā)展新思路框.

2.1基于人工腦的無(wú)人作戰(zhàn)飛機(jī)高智能化自主控制技術(shù)

自主控制領(lǐng)域的人工腦主要是指發(fā)展類似人腦有認(rèn)知能力的硬件的研究［8］.人工腦融入了進(jìn)化的思想，通過(guò)學(xué)習(xí)具有前期記憶的能力，可以對(duì)外界環(huán)境“認(rèn)知”、“思考”、“決定”，形成對(duì)外界環(huán)境進(jìn)行反應(yīng)的“行為”，如圖3所示.

人工腦結(jié)合人工智能及控制工程理論，從人工生命的觀點(diǎn)，用計(jì)算機(jī)作為手段再現(xiàn)腦的思維決策過(guò)程，用“人工腦”控制器能夠使無(wú)人作戰(zhàn)飛機(jī)具有更高的智能化.人工腦采取了兩方面的實(shí)現(xiàn)方式：類似生命的模型（life-like modeling）和社會(huì)模型（social modeling），包括傳統(tǒng)的用于神經(jīng)系統(tǒng)的學(xué)習(xí)模型，如人工神經(jīng)網(wǎng)絡(luò).人工腦控制器主要完成兩方面的功能：一是控制的功能，它控制無(wú)人作戰(zhàn)飛機(jī)的各種動(dòng)作；另一個(gè)是學(xué)習(xí)的功能，首先掌握基本的生存知識(shí)和技能，然后從外界環(huán)境中學(xué)習(xí)到相關(guān)的具體知識(shí).當(dāng)然，在控制無(wú)人作戰(zhàn)飛機(jī)飛行運(yùn)動(dòng)過(guò)程中也要學(xué)習(xí)和獲得知識(shí).

人工腦無(wú)人作戰(zhàn)飛機(jī)是在無(wú)人作戰(zhàn)飛機(jī)控制系統(tǒng)中應(yīng)用人工生命方面的理論、方法.這里所謂的“人工生命”的理論、方法和技術(shù)包括有關(guān)“人工腦”、“人工感官”、“人工器官”、“人工人”及“人工動(dòng)物”的理論、方法和技術(shù).人工腦無(wú)人作戰(zhàn)飛機(jī)控制系統(tǒng)的基本體系結(jié)構(gòu)見(jiàn)圖4所示.

（1）基于人工腦的“智能控制器”：用計(jì)算機(jī)軟、硬件或光機(jī)電材料研究、開(kāi)發(fā)的各種模擬“自然腦”的腦模型作為未來(lái)無(wú)人作戰(zhàn)飛機(jī)的控制核心.由于人工腦具有高水平的“人工思維智能”，所以基于人工腦的智能控制器的無(wú)人作戰(zhàn)飛機(jī)具有很強(qiáng)的自主性.

（2）基于人工感官的反饋測(cè)量裝置：人工感官是指模擬人或動(dòng)物感覺(jué)器官的技術(shù)模型，基于人工感官的反饋裝置是由多種智能傳感器形成多媒體集成的人工感官系統(tǒng)，使得無(wú)人作戰(zhàn)飛機(jī)具有相應(yīng)的視覺(jué)、聽(tīng)覺(jué)，以及多感官信息融合、多模式數(shù)據(jù)挖掘等功能.

（3）基于人工器官的控制執(zhí)行機(jī)構(gòu)：人工器官是指模擬人體或動(dòng)物的效應(yīng)器官的技術(shù)模型，如人工手臂、人工腿腳、人工心臟等.無(wú)人作戰(zhàn)飛機(jī)上安裝上基于人工器官的執(zhí)行機(jī)構(gòu)，可以模擬人體的手腳、心臟的控制機(jī)理和調(diào)節(jié)功能，在執(zhí)行人工腦控制器下達(dá)的命令過(guò)程中實(shí)現(xiàn)雙向調(diào)節(jié)、協(xié)調(diào)控制.

2.2基于群體智能理論的多無(wú)人作戰(zhàn)飛機(jī)協(xié)同控制技術(shù)

群體智能起源于科學(xué)家對(duì)群體性昆蟲的觀察和研究.從蜜蜂、螞蟻等昆蟲的成蟲來(lái)看，它們的智商并不高，也沒(méi)有誰(shuí)在指揮，但它們卻能協(xié)同工作，建起堅(jiān)固、漂亮的巢穴，集中食物、撫養(yǎng)子女，依靠群體的能力，發(fā)揮了超出個(gè)體的智能，這就叫群體智能.在計(jì)算智能領(lǐng)域，群體智能是指任何啟發(fā)于群居性昆蟲群體和其它動(dòng)物群體的集體行為而設(shè)計(jì)的算法和分布式問(wèn)題解決裝置.群體智能包括基于群體的智能進(jìn)化算法，如遺傳算法、蟻群優(yōu)化法、粒子群優(yōu)化等.如果把這種理論應(yīng)用到無(wú)人駕駛飛機(jī)方面，比如每架無(wú)人作戰(zhàn)飛機(jī)和導(dǎo)彈差不多，只裝備低性能傳感器和簡(jiǎn)單程序，但是讓這種簡(jiǎn)易的無(wú)人作戰(zhàn)飛機(jī)大量出動(dòng)，相互協(xié)作，攻擊目標(biāo).盡管每個(gè)傳感器的探測(cè)能力比較低，但是群體最前面的無(wú)人作戰(zhàn)飛機(jī)會(huì)近距離地探測(cè)目標(biāo)，并立刻把探測(cè)到的信息傳遞給后面的無(wú)人作戰(zhàn)飛機(jī)，后面的無(wú)人作戰(zhàn)飛機(jī)就會(huì)對(duì)目標(biāo)群起而攻之.對(duì)于有人機(jī)來(lái)說(shuō)，這是極難對(duì)付的.人的頭腦再機(jī)敏也無(wú)法同時(shí)對(duì)付10架、20架無(wú)人作戰(zhàn)飛機(jī)的攻擊，圖5給出了群體智能的一般流程［1］.

群體智能的特點(diǎn)和優(yōu)點(diǎn)主要有［1］:

（1）群體中相互合作的個(gè)體是分布式的，這樣更能夠適應(yīng)當(dāng)前網(wǎng)絡(luò)環(huán)境下的工作狀態(tài)；

（2）沒(méi)有中心的控制與數(shù)據(jù)，這樣的系統(tǒng)更具有魯棒性，不會(huì)由于某一個(gè)或者某幾個(gè)個(gè)體的故障而影響整個(gè)問(wèn)題的求解；

（3）可以不通過(guò)個(gè)體之間直接通信而是通過(guò)非直接通信進(jìn)行合作，這樣的系統(tǒng)具有更好的可擴(kuò)充性；

（4）由于系統(tǒng)中個(gè)體的增加而增加的系統(tǒng)的通信開(kāi)銷在這里十分小.系統(tǒng)中每個(gè)個(gè)體的能力十分簡(jiǎn)單，這樣每個(gè)個(gè)體的執(zhí)行時(shí)間比較短，并且實(shí)現(xiàn)也比較簡(jiǎn)單，具有簡(jiǎn)單性.

群體智能的上述優(yōu)點(diǎn)已使其成為信息處理領(lǐng)域的一個(gè)重要研究方向.群體智能以其分布性、簡(jiǎn)單性、靈活性和健壯性在組合優(yōu)化問(wèn)題、知識(shí)發(fā)現(xiàn)、通信網(wǎng)絡(luò)、機(jī)器人等研究領(lǐng)域顯示出的潛力和優(yōu)勢(shì)，使得群體智能成為無(wú)人作戰(zhàn)飛機(jī)控制領(lǐng)域一個(gè)研究新熱點(diǎn).據(jù)媒體報(bào)道，美國(guó)五角大樓正在資助一個(gè)稱為蟲群戰(zhàn)略（swarm strategy）的群體智能研究項(xiàng)目，研究?jī)?nèi)容主要涉及用群體智能等技術(shù)來(lái)指揮協(xié)調(diào)無(wú)人飛行器和地面車輛的運(yùn)動(dòng).

群體智能理論在未來(lái)多無(wú)人作戰(zhàn)飛機(jī)領(lǐng)域的研究和應(yīng)用可主要集中在下述幾個(gè)方面：

（1）基于分布式多智能體（agent）的多無(wú)人作戰(zhàn)飛機(jī)協(xié)同控制；

（2）基于群體智能的多無(wú)人作戰(zhàn)飛機(jī)的協(xié)同航路規(guī)劃及其重規(guī)劃；

（3）基于群體智能的多無(wú)人作戰(zhàn)飛機(jī)編隊(duì)控制及其重構(gòu)；

（4）基于群體智能的多無(wú)人作戰(zhàn)飛機(jī)對(duì)地攻擊態(tài)勢(shì)評(píng)估及協(xié)同目標(biāo)分配；

（5）基于群體智能的多無(wú)人作戰(zhàn)飛機(jī)協(xié)同目標(biāo)攻擊.

2.3基于群智能-Bayesian網(wǎng)絡(luò)的復(fù)雜作戰(zhàn)態(tài)勢(shì)評(píng)估技術(shù)

由于復(fù)雜作戰(zhàn)態(tài)勢(shì)評(píng)估的輸入數(shù)據(jù)和知識(shí)庫(kù)數(shù)據(jù)都含有不確定性，解決它需要從不完全、不確定的或者不精確的知識(shí)和信息中做出推理，完成對(duì)當(dāng)前戰(zhàn)場(chǎng)態(tài)勢(shì)的解釋，所以，不確定、不完全的知識(shí)如何表示，如何推理是實(shí)現(xiàn)態(tài)勢(shì)評(píng)估所必須完成的關(guān)鍵所在.基于概率知識(shí)表達(dá)的Bayesian網(wǎng)絡(luò)成為人工智能中非精確知識(shí)表達(dá)與推理領(lǐng)域近十幾年來(lái)研究的熱點(diǎn)，Bayesian網(wǎng)絡(luò)（bayesian network，BN）是一種用網(wǎng)絡(luò)拓?fù)鋪?lái)表示客觀隨機(jī)事件的因果關(guān)系，以Bayesian概率理論為推理基礎(chǔ)的處理不確定性因素的工具［9］.Bayesian網(wǎng)絡(luò)具有很嚴(yán)密的數(shù)學(xué)依據(jù)和理論基礎(chǔ)，其思想符合人類思考、理解、學(xué)習(xí)和抽象的過(guò)程，而且其網(wǎng)絡(luò)結(jié)構(gòu)能夠很好的響應(yīng)結(jié)構(gòu)的改變.在不確定性問(wèn)題的處理上，Bayesian網(wǎng)絡(luò)顯得非?？煽亢陀行?Bayesian網(wǎng)絡(luò)的計(jì)算模型化已經(jīng)成為建設(shè)決策知識(shí)系統(tǒng)的一個(gè)重要部分，并被稱為智能軟件，廣泛應(yīng)用于“診斷與故障檢測(cè)、醫(yī)療診斷、交通管理、軍事目標(biāo)自動(dòng)識(shí)別、數(shù)據(jù)挖掘、作戰(zhàn)意圖自動(dòng)估計(jì)、信息融合”等方面.在使用Bayesian網(wǎng)絡(luò)進(jìn)行事件推理之前，首先應(yīng)確定Bayesian網(wǎng)絡(luò)結(jié)構(gòu)，即根據(jù)數(shù)據(jù)進(jìn)行Bayesian網(wǎng)絡(luò)學(xué)習(xí).而Bayesian網(wǎng)絡(luò)結(jié)構(gòu)的確定是一個(gè)被證明了的NP-hard問(wèn)題，使用啟發(fā)式算法是解決NP-hard問(wèn)題很好的途徑.可將Bayesian算法和群智能優(yōu)化結(jié)合，使用群智能元啟發(fā)方式指導(dǎo)了搜索過(guò)程.

每群智能個(gè)體使用增量式的方法構(gòu)筑網(wǎng)絡(luò)，如圖6所示，定義G0為初始狀態(tài)，下標(biāo)0表示G為空?qǐng)D［10］，Gh表示當(dāng)前狀態(tài)有h條邊，智能個(gè)體每一步就是從當(dāng)前某狀態(tài)Gh增加一條有向邊 xi←xj到新的狀態(tài)Gh+1，即Gh+1= Gh∪｛xi←xj｝，在終止?fàn)顟B(tài)時(shí)，已找不到新的邊使G所對(duì)應(yīng)的Bayesian網(wǎng)絡(luò)打分值更高.

上述思路有效地利用了問(wèn)題域的啟發(fā)信息，同時(shí)很好地發(fā)揮了智能群之間簡(jiǎn)單卻有效的相互協(xié)作作用，適合解決大規(guī)模的Bayesian網(wǎng)絡(luò)學(xué)習(xí)問(wèn)題，從而實(shí)現(xiàn)無(wú)人作戰(zhàn)飛機(jī)對(duì)復(fù)雜作戰(zhàn)態(tài)勢(shì)的有效評(píng)估.

2.4基于仿生硬件的無(wú)人作戰(zhàn)飛機(jī)高智能化自主控制技術(shù)

仿生硬件早期也稱為演化硬件（evolvable hardware，EHW）［3］，是一種能根據(jù)外部環(huán)境的變化而自主地、動(dòng)態(tài)地改變自身的結(jié)構(gòu)和行為以適應(yīng)其生存環(huán)境的硬件電路，它可以像生物一樣具有硬件自適應(yīng)、自組織和自修復(fù)特性.早在20世紀(jì)60年代，計(jì)算機(jī)之父Neumann［11］就提出了研制具有自繁殖與自修復(fù)能力通用機(jī)器的偉大構(gòu)想.1992年，日本學(xué)者Garis［12］和瑞士聯(lián)邦工學(xué)院的科學(xué)家們同時(shí)提出了將FPGA的結(jié)構(gòu)可重配置特性與進(jìn)化算法相結(jié)合的方案，標(biāo)志著仿生硬件這一新興研究領(lǐng)域的正式誕生.隨后仿生硬件的研究得到了迅猛發(fā)展，很多國(guó)內(nèi)外學(xué)者投身其中.同時(shí)，鑒于這一新興研究領(lǐng)域可望在空間探索和國(guó)防應(yīng)用中產(chǎn)生巨大影響，美國(guó)航空航天局（national aeronautics and space administration，NASA）和國(guó)防部（department of defense，DoD）對(duì)仿生硬件也表現(xiàn)出極大的興趣［13］，并于1999年召開(kāi)了首屆NASA/DoD仿生硬件專題討論會(huì)，之后每年都要舉行一次類似的討論會(huì)，以促進(jìn)仿生硬件的理論研究和應(yīng)用開(kāi)發(fā)，其研究目標(biāo)是研制用于航天飛機(jī)、宇宙飛船、空間探測(cè)器、人造衛(wèi)星、戰(zhàn)略飛機(jī)、核潛艇的自適應(yīng)與自修復(fù)電子系統(tǒng)和測(cè)控系統(tǒng).

仿生群體智能在無(wú)人作戰(zhàn)飛機(jī)控制方面的硬件實(shí)現(xiàn)需要應(yīng)用到仿生硬件.仿生硬件模擬自然進(jìn)化過(guò)程，將仿生優(yōu)化算法的思想用于硬件物理結(jié)構(gòu)的設(shè)計(jì)，特別是電子系統(tǒng)的設(shè)計(jì).仿生硬件的硬件基礎(chǔ)是可編程邏輯器件，可用如下公式來(lái)描述該定義［14］:仿生硬件（EHW）=仿生算法+可編程邏輯器件（FPGA）.

通過(guò)對(duì)交替邏輯與路由結(jié)構(gòu)之間的選擇，可將FPGA視為軟件與硬件系統(tǒng)之間聯(lián)系的橋梁.利用目前流行的專業(yè)軟件可快速地完成許多傳統(tǒng)電路的設(shè)計(jì)，從而避免了傳統(tǒng)電路設(shè)計(jì)中一些不必要的開(kāi)支，并能快速糾正設(shè)計(jì)中所出現(xiàn)的一些錯(cuò)誤.將電路配置到FPGA芯片上并切換到運(yùn)行模式，而該模式的內(nèi)在并行性可保證其高效的快速性能.FPGA技術(shù)加速了系統(tǒng)的開(kāi)發(fā)研制進(jìn)程，這項(xiàng)技術(shù)不僅適合于快速成型，而且還可替代中小規(guī)模生產(chǎn)中的邏輯門陣列.基于仿生智能的FPGA設(shè)計(jì)結(jié)構(gòu)如圖7所示［15］.

由圖7可見(jiàn)，該仿生硬件結(jié)構(gòu)主要包括三個(gè)基本模塊，即群體模塊、發(fā)生器模塊和評(píng)價(jià)模塊.基于群體智能的無(wú)人作戰(zhàn)飛機(jī)仿生硬件主要有如下特點(diǎn).

（1）硬件自組織

基于仿生進(jìn)化機(jī)理的仿生硬件無(wú)須人員干預(yù)，可實(shí)現(xiàn)硬件的自組織、自動(dòng)化設(shè)計(jì)［16］，能通過(guò)硬件自身的在線進(jìn)化過(guò)程來(lái)獲得具備預(yù)期功能的電路和無(wú)人作戰(zhàn)飛機(jī)系統(tǒng)結(jié)構(gòu).利用仿生硬件能實(shí)現(xiàn)無(wú)人作戰(zhàn)飛機(jī)硬件的標(biāo)準(zhǔn)化、可重用性、多用途以及通用化.

（2）硬件自適應(yīng)

仿生硬件能滿足復(fù)雜空戰(zhàn)環(huán)境變化對(duì)硬件所要求的結(jié)構(gòu)與功能自適應(yīng)性，而目前傳統(tǒng)的硬件電路無(wú)法實(shí)現(xiàn)這種結(jié)構(gòu)自適應(yīng).無(wú)人作戰(zhàn)飛機(jī)仿生硬件通過(guò)電路結(jié)構(gòu)與參數(shù)的在線自適應(yīng)調(diào)整，可有效解決如因作戰(zhàn)損失、空戰(zhàn)環(huán)境改變而引起無(wú)人作戰(zhàn)飛機(jī)控制電路性能變化的問(wèn)題，也可組成高速并行信息處理的自適應(yīng)控制硬件系統(tǒng).

（3）硬件自修復(fù)

目前無(wú)人作戰(zhàn)飛機(jī)電子系統(tǒng)的結(jié)構(gòu)越來(lái)越復(fù)雜，系統(tǒng)集成化是必然趨勢(shì).若高度集成化的無(wú)人作戰(zhàn)飛機(jī)嵌入式電子系統(tǒng)在運(yùn)行中出現(xiàn)故障，傳統(tǒng)的容錯(cuò)與系統(tǒng)功能恢復(fù)方法難以實(shí)現(xiàn)，而且這種板級(jí)部件冗余容錯(cuò)系統(tǒng)結(jié)構(gòu)復(fù)雜，其重構(gòu)算法繁瑣，硬件體積較大，成本較高，容錯(cuò)能力與系統(tǒng)可靠性有限；而仿生硬件可以彌補(bǔ)這些不足.

（4）執(zhí)行速度快

無(wú)人作戰(zhàn)飛機(jī)仿生硬件自適應(yīng)的結(jié)果是新的硬件結(jié)構(gòu)自身，因此，與其它基于軟件的自適應(yīng)系統(tǒng)相比，仿生硬件能得到顯著的加速，而這一優(yōu)點(diǎn)正是復(fù)雜作戰(zhàn)環(huán)境下無(wú)人作戰(zhàn)飛機(jī)控制系統(tǒng)所需要的.

2.5網(wǎng)絡(luò)環(huán)境下基于元啟發(fā)式智能的多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制技術(shù)

元啟發(fā)式智能主要是指一類通用型的啟發(fā)式智能方法，這類方法的優(yōu)化機(jī)理不過(guò)分依賴待解問(wèn)題的結(jié)構(gòu)信息，并可與各類控制方法進(jìn)行有效結(jié)合，還可應(yīng)用到眾多類別的復(fù)雜組合優(yōu)化問(wèn)題中.目前元啟發(fā)式智能已成為仿生智能交叉學(xué)科中一個(gè)非?；钴S的前沿性研究領(lǐng)域.

無(wú)人作戰(zhàn)車是一種有動(dòng)力、可控制、能攜帶多種任務(wù)設(shè)備、執(zhí)行多種任務(wù)，并能重復(fù)使用的地面移動(dòng)式武裝機(jī)器人［17］.網(wǎng)絡(luò)環(huán)境下多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制技術(shù)是一個(gè)新的研究領(lǐng)域，該技術(shù)可以拓寬無(wú)人作戰(zhàn)飛機(jī)和無(wú)人作戰(zhàn)車的應(yīng)用范圍，提高其偵察、搜救及執(zhí)行其它任務(wù)的效率.但是其在自主性、動(dòng)態(tài)性、協(xié)同性等方面所表現(xiàn)出的高要求使得常規(guī)的協(xié)同控制方法已顯得無(wú)能為力，而且該技術(shù)中滲透著許多強(qiáng)耦合、多約束的復(fù)雜控制與決策問(wèn)題，而這些問(wèn)題通常具有組合爆炸的搜索空間［18］.常規(guī)的基于數(shù)學(xué)規(guī)劃方法已顯得無(wú)能為力，而基于元啟發(fā)式智能的分布式協(xié)同控制方法則是一種有前途的發(fā)展方向和主要技術(shù)途徑.

Chaimowicz等人［19］把多無(wú)人機(jī)/無(wú)人車異構(gòu)協(xié)同形象地比喻成“空中牧羊犬”，并提出了一種基于機(jī)器視覺(jué)的“空中牧羊犬”分層遞階框架.圖8給出了由美國(guó)海軍研究院McCook［20］所繪制的一種多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)協(xié)同掩護(hù)軍隊(duì)自動(dòng)運(yùn)輸?shù)娜蝿?wù)想定.

由圖8可見(jiàn)，多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制的目的是控制和協(xié)調(diào)一組或幾組無(wú)人作戰(zhàn)飛機(jī)及無(wú)人作戰(zhàn)車之間的行為，以執(zhí)行和完成相互協(xié)調(diào)的特定任務(wù).西方國(guó)家（特別是美國(guó)）雖然在多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制與實(shí)現(xiàn)技術(shù)上處于國(guó)際領(lǐng)先地位，但他們?cè)谠擃I(lǐng)域仍然存在很多研究空白，特別是采用元啟發(fā)式智能對(duì)多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制問(wèn)題進(jìn)行研究，目前還處于起步階段.

網(wǎng)絡(luò)環(huán)境下多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制中將利用元啟發(fā)式智能的如下特點(diǎn)：

（1）在元啟發(fā)式智能涌現(xiàn)的過(guò)程中，新的信息會(huì)很快被加入到環(huán)境中，而由于啟發(fā)式信息更新機(jī)制的存在，舊的信息會(huì)不斷被丟失，體現(xiàn)出一種動(dòng)態(tài)特性；

（2）群體所表現(xiàn)出來(lái)的復(fù)雜行為是通過(guò)簡(jiǎn)單個(gè)體的交互表現(xiàn)出高度的智能，因此具有自組織性；

（3）由于許多元啟發(fā)智能體在環(huán)境中感受散布的信息同時(shí)，沒(méi)有中心的控制與數(shù)據(jù)，這使得不同的智能體會(huì)有不同的選擇策略，具有分布并行性；

（4）最優(yōu)方案是通過(guò)眾多智能體的群體合作被搜索得到的，這一過(guò)程具有協(xié)同性；

（5）元啟發(fā)式智能中的每個(gè)個(gè)體只能感知局部信息，個(gè)體的能力所遵循的規(guī)則非常簡(jiǎn)單，同時(shí)系統(tǒng)用于通信的開(kāi)銷較少，所以元啟發(fā)式智能的實(shí)現(xiàn)簡(jiǎn)單，易于擴(kuò)充；

（6）單個(gè)智能個(gè)體之間、群體之間以及與環(huán)境之間的相互作用、相互影響、相互協(xié)作，使智能群體可以完成非常復(fù)雜的任務(wù)，不會(huì)由于某一個(gè)或幾個(gè)智能個(gè)體的故障而影響對(duì)整個(gè)問(wèn)題的求解，這種適應(yīng)性表現(xiàn)為元啟發(fā)式智能的魯棒性.

元啟發(fā)式智能在網(wǎng)絡(luò)環(huán)境下多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)分布協(xié)同控制領(lǐng)域的研究和應(yīng)用可主要集中在下述幾個(gè)方面：

（1）基于元啟發(fā)式智能的多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)群集運(yùn)動(dòng)技術(shù)；

（2）基于元啟發(fā)式智能的多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車可視化導(dǎo)航技術(shù)；

（3）基于元啟發(fā)式智能的多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車“追逃”技術(shù)；

（4）基于元啟發(fā)式智能的多無(wú)人作戰(zhàn)飛機(jī)/無(wú)人作戰(zhàn)車異構(gòu)任務(wù)鏈技術(shù).

3結(jié)束語(yǔ)

近年來(lái)，仿生智能技術(shù)有了驚人的快速發(fā)展，而無(wú)人作戰(zhàn)飛機(jī)控制技術(shù)的設(shè)計(jì)與實(shí)現(xiàn)基本上仍遵循一種固定的模式.今天，當(dāng)大家爭(zhēng)相研制高性能無(wú)人作戰(zhàn)飛機(jī)的時(shí)候，就感到常規(guī)的設(shè)計(jì)與實(shí)現(xiàn)技術(shù)難以滿足新型無(wú)人作戰(zhàn)飛機(jī)的發(fā)展需求.新型無(wú)人作戰(zhàn)飛機(jī)強(qiáng)調(diào)要高、精、尖，特別在智能化方面有很高的要求，各種技術(shù)上也要?jiǎng)?chuàng)新，從而帶來(lái)研制上的高難度和高風(fēng)險(xiǎn).目前由于受到固有的技術(shù)思想的羈絆，使得研制出來(lái)的新型無(wú)人作戰(zhàn)飛機(jī)中智能的成份并不多，難以實(shí)現(xiàn)跨越式發(fā)展.而仿生智能技術(shù)則可極大提高無(wú)人作戰(zhàn)飛機(jī)的智能性、作戰(zhàn)任務(wù)的有效性以及無(wú)人機(jī)的生存概率.本文所提出的發(fā)展思路可為新型無(wú)人作戰(zhàn)飛機(jī)的智能化、綜合化和先進(jìn)化提供新的突破方向和可行的技術(shù)途徑.

·高被引論文摘要·

被引頻次：106

無(wú)人機(jī)航空遙感系統(tǒng)關(guān)鍵技術(shù)研究

晏磊，呂書強(qiáng)，趙紅穎，等

簡(jiǎn)要分析了在我國(guó)發(fā)展無(wú)人機(jī)航空遙感系統(tǒng)的必要性和可行性，針對(duì)我國(guó)某型民用無(wú)人機(jī)，設(shè)計(jì)了無(wú)人機(jī)航空遙感信息平臺(tái)的總體技術(shù)框架.在此基礎(chǔ)上，對(duì)構(gòu)建無(wú)人機(jī)航空遙感信息平臺(tái)所涉及的關(guān)鍵技術(shù)進(jìn)行了分析.指出高精度的組合導(dǎo)航、不同遙感傳感器設(shè)備的集成、遙感數(shù)據(jù)機(jī)上處理與壓縮以及遙感數(shù)據(jù)近實(shí)時(shí)傳輸?shù)仁菍?shí)現(xiàn)無(wú)人機(jī)航空遙感信息平臺(tái)的關(guān)鍵技術(shù).

航空遙感；無(wú)人機(jī)；信息平臺(tái)

來(lái)源出版物：武漢大學(xué)學(xué)報(bào)（工學(xué)版），2004，37（6）: 67-70

被引頻次：101

無(wú)人機(jī)遙感監(jiān)測(cè)系統(tǒng)研究

崔紅霞，林宗堅(jiān)，孫杰

為了滿足遙感應(yīng)用對(duì)大比例尺、高分辨率的低空數(shù)字航空影像的需求，中國(guó)測(cè)繪科學(xué)研究院研制了無(wú)人機(jī)遙感監(jiān)測(cè)系統(tǒng)UAVRS II.該系統(tǒng)運(yùn)用了以下關(guān)鍵技術(shù)：①以面陣CCD數(shù)碼相機(jī)、單軸穩(wěn)定平臺(tái)作為機(jī)載遙感設(shè)備；②研制了遙感設(shè)備控制系統(tǒng)，依據(jù)實(shí)時(shí)的傳感器參數(shù)，控制遙感設(shè)備的姿態(tài)和曝光間隔；③地面監(jiān)控系統(tǒng)是該遙感系統(tǒng)的重要組成部分，集3S技術(shù)和相關(guān)技術(shù)于一體；④利用一個(gè)兩步法的相機(jī)檢校方法對(duì)數(shù)字相機(jī)實(shí)施了標(biāo)定；⑤利用大傾角空中三角測(cè)量、半自動(dòng)匹配等技術(shù)開(kāi)發(fā)了影像后處理軟件.實(shí)驗(yàn)結(jié)果表明以該系統(tǒng)實(shí)施的航空攝影和數(shù)據(jù)后處理獲得了預(yù)期的效果.

無(wú)人機(jī)；3S集成技術(shù)；遙感設(shè)備自動(dòng)控制系統(tǒng)；大傾角空中三角測(cè)量；相機(jī)檢校

來(lái)源出版物：測(cè)繪通報(bào)，2005，（5）: 11-14

被引頻次：96

無(wú)人機(jī)的發(fā)展現(xiàn)狀與展望

淳于江民，張珩

回顧了無(wú)人機(jī)的發(fā)展歷程，并闡述了無(wú)人機(jī)的系統(tǒng)結(jié)構(gòu)、分類、用途及其關(guān)鍵技術(shù)，就主要機(jī)型做了簡(jiǎn)要的介紹，最后對(duì)無(wú)人機(jī)發(fā)展中亟待解決的問(wèn)題及趨勢(shì)做了詳細(xì)的分析.

無(wú)人機(jī)；發(fā)展現(xiàn)狀；趨勢(shì)

來(lái)源出版物：飛航導(dǎo)彈，2005，（2）: 23-27

被引頻次：96

一種無(wú)人機(jī)路徑規(guī)劃算法研究

符小衛(wèi)，高曉光

指出了飛行器航跡規(guī)劃與路徑規(guī)劃的區(qū)別；提出了一種給定威脅分布下的無(wú)人機(jī)路徑規(guī)劃算法.根據(jù)威脅分布情況構(gòu)造無(wú)人機(jī)可能飛行的航路集，用voronoi圖表示出來(lái)，采用Dijkstra算法搜索威脅分布圖，求解粗略最短路徑.在粗略最短路徑的基礎(chǔ)上，應(yīng)用三次樣條曲線和序列二次規(guī)劃的方法求解最優(yōu)路徑.用Matlab進(jìn)行仿真驗(yàn)證，證明了算法的有效性.

無(wú)人機(jī)；路徑規(guī)劃；voronoi圖；Dijkstra算法；三次樣條曲線；序列二次規(guī)劃

來(lái)源出版物：系統(tǒng)仿真學(xué)報(bào)，2004，16（1）: 20-34

被引頻次：94

國(guó)外無(wú)人機(jī)自主飛行控制研究

唐強(qiáng)，朱志強(qiáng)，王建元

無(wú)人機(jī)自主飛行控制的研究屬于飛行控制的前沿問(wèn)題，其目的是實(shí)現(xiàn)無(wú)人機(jī)的自主飛行控制、決策和管理.由于其高度的復(fù)雜性和智能性，在理論和工程實(shí)際上尚處于起步階段.結(jié)合近年來(lái)國(guó)外的發(fā)展?fàn)顩r和一些主要的研究成果，對(duì)無(wú)人機(jī)的自主飛行控制的研究進(jìn)行了概述.首先介紹了自主控制的概念，然后分別探討了無(wú)人機(jī)自主飛行控制中幾個(gè)相關(guān)的關(guān)鍵問(wèn)題，主要包括飛行中規(guī)劃與重規(guī)劃，自主飛行控制的分層結(jié)構(gòu)，以及無(wú)人機(jī)自主著陸等問(wèn)題，最后對(duì)未來(lái)的發(fā)展方向和面臨的挑戰(zhàn)進(jìn)行了展望.

無(wú)人機(jī)；自主飛行控制；規(guī)劃；分層結(jié)構(gòu)；自主著陸

來(lái)源出版物：系統(tǒng)工程與電子技術(shù)，2004，26（3）: 418-422

被引頻次：79

非量測(cè)數(shù)碼相機(jī)的畸變差檢測(cè)研究

崔紅霞，孫杰，林宗堅(jiān)，等

本文主要研究利用數(shù)字畸變模型和附加參數(shù)的光束法平差對(duì)非量測(cè)數(shù)碼相機(jī)進(jìn)行內(nèi)方位元素和畸變差的測(cè)定.分別運(yùn)用兩種檢校方法對(duì)UAVRS Ⅱ無(wú)人機(jī)遙感系統(tǒng)機(jī)載非量測(cè)數(shù)碼相機(jī)進(jìn)行檢校實(shí)驗(yàn)，并采用統(tǒng)一比例尺的方法進(jìn)行精度比較.實(shí)驗(yàn)結(jié)果表明兩種方法檢校精度接近，證明了該面陣數(shù)碼相機(jī)的畸變基本符合系統(tǒng)畸變規(guī)律，只存在極少量的隨機(jī)畸變，也進(jìn)一步證明了利用數(shù)字畸變模型糾正數(shù)碼相機(jī)畸變差的可行性.

相機(jī)檢校；畸變；無(wú)人機(jī)；光束法平差

來(lái)源出版物：測(cè)繪科學(xué)，2005，30（1）: 105-112聯(lián)系郵箱：崔紅霞，lnchx316@sohu.com

被引頻次：79

基于雙圓特征的無(wú)人機(jī)著陸位置姿態(tài)視覺(jué)測(cè)量方法

張廣軍，周富強(qiáng)

提出了一種無(wú)人機(jī)自主著陸位置姿態(tài)的單目視覺(jué)測(cè)量方法，建立了機(jī)載攝像機(jī)的運(yùn)動(dòng)和投影模型.設(shè)計(jì)了新型雙圓圖案著陸平面靶標(biāo)，采用雙圓的8個(gè)公切點(diǎn)，產(chǎn)生21個(gè)具有透視投影不變性的特征點(diǎn)，并提出了在復(fù)雜背景中全自動(dòng)雙圓特征的圖像提取新方法及標(biāo)記特征點(diǎn)的方案，實(shí)驗(yàn)表明，768×576像素大小的圖像，特征提取及標(biāo)記耗時(shí)小于9 ms.仿真試驗(yàn)表明，攝像機(jī)距離靶標(biāo)10 m左右，噪聲偏差達(dá)到1.5像素時(shí)，單軸位置RMS誤差小于6 cm，單軸姿態(tài)RMS誤差小于0.7°，所提出的算法具有很強(qiáng)的抗噪聲能力，能夠滿足無(wú)人機(jī)自主著陸位置姿態(tài)實(shí)時(shí)測(cè)量的要求.

無(wú)人機(jī)；位置；姿態(tài)；特征點(diǎn)；透視投影

來(lái)源出版物：航空學(xué)報(bào)，2005，26（3）: 344-348

被引頻次：70

基于遺傳算法的無(wú)人機(jī)航路規(guī)劃

馬云紅，周德云

無(wú)人機(jī)在執(zhí)行任務(wù)時(shí)需要裝載根據(jù)戰(zhàn)場(chǎng)環(huán)境預(yù)先規(guī)劃好的最優(yōu)路徑，路徑的優(yōu)劣直接決定了無(wú)人機(jī)的作戰(zhàn)效率.采用遺傳算法進(jìn)行無(wú)人機(jī)路徑優(yōu)化，算法利用極坐標(biāo)描述威脅位置和航路點(diǎn)，將路徑編碼由二維縮減至一維，降低了搜索空間，提高了優(yōu)化效率.對(duì)算法進(jìn)行了相應(yīng)的仿真，仿真結(jié)果表明，編碼方式大大提高了優(yōu)化效率，得到的航路有效地規(guī)避了威脅.

無(wú)人機(jī)；航路規(guī)劃；遺傳算法；編碼方式

來(lái)源出版物：電光與控制，2005，12（5）: 24-27

被引頻次：67

無(wú)人機(jī)發(fā)展現(xiàn)狀及相關(guān)技術(shù)

鄒湘伏，何清華，賀繼林

較詳細(xì)地論述了無(wú)人機(jī)的發(fā)展現(xiàn)狀以及無(wú)人機(jī)技術(shù)要素，并從無(wú)人機(jī)技術(shù)發(fā)展的角度分析了無(wú)人機(jī)的發(fā)展趨勢(shì).

無(wú)人機(jī)；無(wú)人機(jī)技術(shù)；發(fā)展趨勢(shì)

來(lái)源出版物：飛航導(dǎo)彈，2006（10）: 9-14

被引頻次：60

基于VORONOI圖的無(wú)人機(jī)航跡規(guī)劃

趙文婷，彭俊毅

無(wú)人機(jī)的航跡規(guī)劃對(duì)發(fā)揮無(wú)人機(jī)的自主飛行和執(zhí)行任務(wù)中起著至關(guān)重要的作用.在分析VORONOI圖的性質(zhì)基礎(chǔ)上，以無(wú)人機(jī)仿真環(huán)境中作戰(zhàn)想定與任務(wù)規(guī)劃成員為背景研究基于 VORONOI圖的航跡規(guī)劃技術(shù).重點(diǎn)討論了如何根據(jù)威脅分布的VORONOI圖建模，計(jì)算航路代價(jià)，航跡搜索和航跡優(yōu)化.最后總結(jié)了算法的優(yōu)缺點(diǎn)，提出算法的改進(jìn)新方法.

無(wú)人機(jī)；VORONOI圖；航跡規(guī)劃；航路代價(jià)

來(lái)源出版物：系統(tǒng)仿真學(xué)報(bào)，2006，18（S2）: 159-165

被引頻次：171

Decentralized overlapping control of a formation of unmanned aerial vehicles

Stipanovic，DM； Inalhan，G； Teo，R； et al.

來(lái)源出版物：Automatica，2004，40（8）: 1285-1296

被引頻次：146

Visually guided landing of an unmanned aerial vehicle

Saripalli，S； Montgomery，JF； Sukhatme，GS； et al.

來(lái)源出版物：IEEE Transactions on Robotics and Automation，2003，19（3）: 371-380

被引頻次：138

Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle

Berni，Jose A.J； Zarco-Tejada，Pablo J； Suarez，Lola； et al.

被引頻次：96

Evolutionary algorithm based offline/online path planner for UAV navigation

Nikolos，IK； Valavanis，KP； Tsourveloudis，NC； et al.

來(lái)源出版物：IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics，2003，33（6）: 898-912

被引頻次：96

A practical visual servo control for an unmanned aerial vehicle

Guenard，Nicolas； Hamel，Tarek； Mahony，Robert

來(lái)源出版物：IEEE Transactions on Robotics，2008，24（2）: 331-340聯(lián)系郵箱：Guenard，Nicolas； guenardn@zoe.cea.fr

被引頻次：84

Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support

Herwitz，SR； Johnson，LF； Dunagan，SE； et al.

來(lái)源出版物：Computers and Electronics in Agriculture，2004，44（1）: 49-61聯(lián)系郵箱：Herwitz，SR； sherwitz@mail.arc.nasa.gov

被引頻次：72

Output Feedback Control of a Quadrotor UAV Using Neural Networks

Dierks，Travis； Jagannathan，Sarangapani

來(lái)源出版物：IEEE Transactions on Neural Networks，2010，21（1）: 50-66

被引頻次：68

Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle（UAV）Imagery

Laliberte，Andrea S； Rango，Albert

來(lái)源出版物：IEEE Transactions on Geoscience and Remote Sensing，2009，47（3）: 761-770聯(lián)系郵箱：Laliberte，AS； alaliber@nmsu.edu

被引頻次：62

A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance

Goerzen，C； Kong，Z； Mettler，B

來(lái)源出版物：Journal of Intelligent & Robotic Systems，2010，57（1-4）: 65-100

被引頻次：55

Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots

Lelong，Camille C.D； Burger，Philippe； Jubelin，Guillaume； et al.

·推薦論文摘要·

應(yīng)急響應(yīng)中PixelGrid無(wú)人機(jī)遙感數(shù)據(jù)處理的關(guān)鍵技術(shù)與應(yīng)用

賈嬌，艾海濱，張力，等

隨著數(shù)字?jǐn)z影測(cè)量技術(shù)的發(fā)展，低空無(wú)人機(jī)攝影方式得到了很大的重視.在災(zāi)害應(yīng)急領(lǐng)域，可幫助獲得受災(zāi)地區(qū)高質(zhì)量的正射影像圖、DEM和全景圖等產(chǎn)品，及時(shí)獲取災(zāi)區(qū)的準(zhǔn)確信息，為救災(zāi)減災(zāi)提供方便.考慮到無(wú)人機(jī)攝影不同于常規(guī)攝影方式，需要在提高大量數(shù)據(jù)的處理速度的前提下，保證處理成果的精度符合應(yīng)用要求.以高分辨率遙感影像一體化測(cè)圖系統(tǒng)（Pixel Grid）為依托，介紹對(duì)災(zāi)情發(fā)生后獲得的影像數(shù)據(jù)進(jìn)行快速處理的相關(guān)方法、實(shí)現(xiàn)技術(shù)及應(yīng)用.

數(shù)字?jǐn)z影測(cè)量；無(wú)人機(jī)；應(yīng)急響應(yīng)；Pixel Grid；數(shù)據(jù)處理

來(lái)源出版物：測(cè)繪通報(bào)，2013，（5）: 62-65

基于啟發(fā)式自適應(yīng)離散差分進(jìn)化算法的多UCAV協(xié)同干擾空戰(zhàn)決策

薛羽，莊毅，張友益，等

研究了多無(wú)人作戰(zhàn)飛機(jī)（UCAV）協(xié)同干擾空戰(zhàn)決策（MUCJAD）問(wèn)題，在干擾效能評(píng)估指標(biāo)量化方法的基礎(chǔ)上為該問(wèn)題建立了優(yōu)化模型.為有效求解該模型，提出一種啟發(fā)式自適應(yīng)離散差分進(jìn)化（H-SDDE）算法.在H-SDDE算法中，設(shè)計(jì)了包含4種候選解產(chǎn)生策略的候選策略池，引入了候選解產(chǎn)生策略及其參數(shù)的自適應(yīng)學(xué)習(xí)過(guò)程.此外，結(jié)合實(shí)際問(wèn)題為算法設(shè)計(jì)了基于威脅度的擴(kuò)展型整數(shù)編碼方案、基于威脅度的啟發(fā)式個(gè)體調(diào)整操作、基于約束滿足的個(gè)體修復(fù)操作.在12個(gè)測(cè)試實(shí)例上進(jìn)行了仿真驗(yàn)證，結(jié)果表明，H-SDDE算法與其他同類算法相比在求解質(zhì)量和求解速度上具有明顯優(yōu)勢(shì)，能夠更好地發(fā)揮多UCAV協(xié)同干擾整體效果.

無(wú)人機(jī)；協(xié)同干擾；決策；啟發(fā)式算法；自適應(yīng)；優(yōu)化；差分進(jìn)化

來(lái)源出版物：航空學(xué)報(bào)，2013，34（2）: 343-351聯(lián)系郵箱：莊毅，zhuangyi@nuaa.edu.cn

2012年世界軍用無(wú)人機(jī)發(fā)展動(dòng)向及評(píng)述

張翼麟，張紹芳，李鵬飛

根據(jù)2012年世界主要國(guó)家和地區(qū)軍用無(wú)人機(jī)的發(fā)展，梳理總結(jié)了美國(guó)、歐洲、以色列、俄羅斯、印度等國(guó)家和地區(qū)軍用無(wú)人機(jī)的發(fā)展動(dòng)向，并在此基礎(chǔ)上對(duì)2012年全球軍用無(wú)人機(jī)的發(fā)展特點(diǎn)進(jìn)行了簡(jiǎn)要評(píng)述.

無(wú)人機(jī)；路線圖；X-47B；神經(jīng)元無(wú)人機(jī)；美國(guó)

來(lái)源出版物：飛航導(dǎo)彈，2013（7）: 69-72

多無(wú)人機(jī)分布式協(xié)同異構(gòu)任務(wù)分配

邸斌，周銳，丁全心

研究異構(gòu)無(wú)人機(jī)對(duì)不同類型目標(biāo)執(zhí)行偵察、打擊和評(píng)估任務(wù)的協(xié)同任務(wù)分配問(wèn)題.采用信息論中熵的變化量對(duì)偵察與評(píng)估任務(wù)中所獲取的信息量進(jìn)行度量，將無(wú)人機(jī)對(duì)不同類型目標(biāo)的打擊能力抽象為對(duì)目標(biāo)的毀傷概率，并考慮各個(gè)任務(wù)之間的相互關(guān)聯(lián)，建立異構(gòu)多無(wú)人機(jī)協(xié)同任務(wù)分配模型.設(shè)計(jì)了基于相鄰局部通信的分布式拍賣算法，實(shí)現(xiàn)了多無(wú)人機(jī)協(xié)同任務(wù)分配問(wèn)題的優(yōu)化求解.仿真結(jié)果表明了所建模型的合理性和求解方法的有效性.

無(wú)人機(jī)；任務(wù)分配；分布式拍賣；信息論；熵

來(lái)源出版物：控制與決策，2013，28（2）: 274-278

基于輔助信息的無(wú)人機(jī)圖像批處理三維重建方法

郭復(fù)勝，高偉

隨著我國(guó)低空空域?qū)γ裼玫拈_(kāi)放，無(wú)人機(jī)（Unmanned aerial vehicles，UAVs）的應(yīng)用將是一個(gè)巨大的潛在市場(chǎng).目前，如何對(duì)輕便的無(wú)人機(jī)獲取的圖像進(jìn)行全自動(dòng)處理，是一項(xiàng)急需解決的瓶頸技術(shù).本文將探索如何將近年來(lái)在視頻、圖像領(lǐng)域獲得巨大成功的三維重建技術(shù)應(yīng)用到無(wú)人機(jī)圖像處理領(lǐng)域，對(duì)無(wú)人機(jī)圖像進(jìn)行全自動(dòng)的大場(chǎng)景三維重建.本文首先給出了經(jīng)典增量式三維重建方法Bundler在無(wú)人機(jī)圖像處理中存在的問(wèn)題，然后通過(guò)分析無(wú)人機(jī)圖像的輔助信息的特點(diǎn)，提出了一種基于批處理重建（Batch reconstruction）框架下的魯棒無(wú)人機(jī)圖像三維重建方法.多組無(wú)人機(jī)圖像三維重建實(shí)驗(yàn)表明：本文提出的方法在算法魯棒性、三維重建效率與精度等方面都具有很好的結(jié)果.

三維重建；無(wú)人機(jī)；批處理重建；輔助信息

來(lái)源出版物：自動(dòng)化學(xué)報(bào)，2013，39（6）: 834-845

基于仿鷹眼視覺(jué)的無(wú)人機(jī)自主空中加油

段海濱，張奇夫，鄧亦敏，等

自主空中加油是提高無(wú)人機(jī)作戰(zhàn)能力的重要技術(shù)支撐，而鷹在所有的動(dòng)物中以視覺(jué)最為敏銳而著稱.針對(duì)無(wú)人機(jī)自主空中加油中的精確對(duì)接需求，設(shè)計(jì)了基于仿鷹眼視覺(jué)原理的無(wú)人受油機(jī)視覺(jué)測(cè)量方法及半物理試驗(yàn)平臺(tái).采用鷹眼視覺(jué)注意機(jī)制提取虛擬視景中的興趣區(qū)域，剔除噪聲和干擾.在對(duì)受油機(jī)上的顏色標(biāo)志點(diǎn)進(jìn)行識(shí)別的基礎(chǔ)上采用P3P算法，對(duì)加油機(jī)和受油機(jī)之間的相對(duì)位姿進(jìn)行精確估計(jì)，并設(shè)計(jì)了基于比例-積分-微分的硬式加油伸縮管控制律.基于上述技術(shù)，在實(shí)驗(yàn)室環(huán)境下設(shè)計(jì)開(kāi)發(fā)了無(wú)人機(jī)自主空中加油半物理仿真平臺(tái).實(shí)驗(yàn)結(jié)果表明:所設(shè)計(jì)的仿鷹眼視覺(jué)測(cè)量方法和自主控制律能滿足無(wú)人機(jī)自主空中加油的性能要求，具有較強(qiáng)的實(shí)時(shí)性、準(zhǔn)確性和魯棒性.

無(wú)人機(jī)；自主空中加油；鷹眼視覺(jué)；位姿估計(jì)

來(lái)源出版物：儀器儀表學(xué)報(bào)，2014，35（7）: 1451-1458聯(lián)系郵箱：段海濱，hbduan@buaa.edu.cn

無(wú)人機(jī)遙感系統(tǒng)的研究進(jìn)展與應(yīng)用前景

李德仁，李明

闡述了無(wú)人機(jī)遙感興起的背景.從無(wú)人飛行平臺(tái)、飛行姿態(tài)控制與導(dǎo)航、數(shù)據(jù)傳輸與存儲(chǔ)、數(shù)據(jù)處理、傳感器技術(shù)、空域使用政策等方面探討了發(fā)展無(wú)人機(jī)遙感系統(tǒng)的基礎(chǔ)、問(wèn)題、研究進(jìn)展和趨勢(shì).通過(guò)描述無(wú)人機(jī)遙感系統(tǒng)在大量相關(guān)行業(yè)領(lǐng)域的應(yīng)用與實(shí)踐，點(diǎn)出了發(fā)展無(wú)人機(jī)遙感系統(tǒng)的必要性與意義所在.最后，從科技、政策等方面給出了發(fā)展無(wú)人機(jī)遙感技術(shù)和產(chǎn)業(yè)的建議.

無(wú)人機(jī)；無(wú)人機(jī)遙感；無(wú)人機(jī)遙感系統(tǒng)

來(lái)源出版物：武漢大學(xué)學(xué)報(bào)（信息科學(xué)版），2014，39（5）: 505-513聯(lián)系郵箱：李明，lisouming@whu.edu.cn

無(wú)人機(jī)多偵察載荷協(xié)同偵察效能評(píng)估

張旺，申洋，陳偉

多偵察載荷協(xié)同偵察已經(jīng)成為當(dāng)前和未來(lái)無(wú)人偵察機(jī)主流工作方式.根據(jù)電子信號(hào)偵察設(shè)備、合成孔徑雷達(dá)（SAR）和長(zhǎng)焦距傾斜CCD相機(jī)3種機(jī)載偵察載荷的偵察特點(diǎn)，分析了影響機(jī)載多偵察載荷協(xié)同偵察效能的因素和指標(biāo)，在各分項(xiàng)評(píng)估模型的基礎(chǔ)上采用線性加權(quán)算法建立了無(wú)人機(jī)多偵察載荷的協(xié)同偵察效能模型.最后分別在不同強(qiáng)度的電子對(duì)抗環(huán)境下，對(duì)以上3種機(jī)載偵察載荷兩兩協(xié)同的偵察效能進(jìn)行了計(jì)算和分析，驗(yàn)證了模型的可用性.

無(wú)人機(jī)；偵察載荷；協(xié)同偵察效能；效能評(píng)估

來(lái)源出版物：電光與控制，2014，21（3）:1-4

偵察打擊一體化無(wú)人機(jī)關(guān)鍵技術(shù)及其發(fā)展趨勢(shì)分析

馮卉，毛紅保，吳天愛(ài)

針對(duì)偵察打擊一體化無(wú)人機(jī)的“發(fā)現(xiàn)即摧毀”能力，概述了偵察打擊一體化無(wú)人機(jī)的發(fā)展現(xiàn)狀；分析了其目標(biāo)搜索與任務(wù)規(guī)劃、機(jī)載武器投放規(guī)劃、攻擊航跡快速生成、一站多機(jī)控制等關(guān)鍵技術(shù)；預(yù)測(cè)了偵察打擊一體化無(wú)人機(jī)未來(lái)發(fā)展趨勢(shì)；提出了偵察打擊一體化無(wú)人機(jī)基于體系作戰(zhàn)、實(shí)戰(zhàn)檢驗(yàn)和完善情報(bào)體系的發(fā)展建議.

無(wú)人機(jī)；偵察打擊一體化；關(guān)鍵技術(shù)

來(lái)源出版物：飛航導(dǎo)彈，2014，（3）: 42-46

基于改進(jìn)快速擴(kuò)展隨機(jī)樹方法的隱身無(wú)人機(jī)突防航跡規(guī)劃

莫松，黃俊，鄭征，等

針對(duì)隱身無(wú)人機(jī)在日趨嚴(yán)密的雷達(dá)防御系統(tǒng)下的生存問(wèn)題，提出了基于改進(jìn)快速擴(kuò)展隨機(jī)樹的隱身突防航跡規(guī)劃方法.本文首先對(duì)隱身突防航跡規(guī)劃中無(wú)人機(jī)的動(dòng)態(tài)雷達(dá)散射截面積和雷達(dá)的發(fā)現(xiàn)準(zhǔn)則這兩個(gè)關(guān)鍵問(wèn)題進(jìn)行了分析和建模，然后針對(duì)現(xiàn)有算法在解決隱身飛機(jī)航跡規(guī)劃問(wèn)題時(shí)的不足，設(shè)計(jì)了改進(jìn)快速擴(kuò)展隨機(jī)樹算法，將無(wú)人機(jī)的雷達(dá)散射截面積隨姿態(tài)變化的情況考慮到新節(jié)點(diǎn)生成中，并且結(jié)合滾動(dòng)時(shí)域策略計(jì)算時(shí)域范圍內(nèi)所有節(jié)點(diǎn)的瞬時(shí)發(fā)現(xiàn)概率均值，以判斷新節(jié)點(diǎn)可行性.仿真結(jié)果和對(duì)比研究表明，算法的改進(jìn)策略能夠處理隱身突防航跡規(guī)劃的兩個(gè)特性，并且可在復(fù)雜環(huán)境下快速生成更優(yōu)的突防路徑.

無(wú)人機(jī)（UAV）；路徑規(guī)劃；快速擴(kuò)展隨機(jī)樹（RRT）；雷達(dá)散射截面（RCS）

來(lái)源出版物：控制理論與應(yīng)用，2014，31（3）: 375-385聯(lián)系郵箱：莫松，neomo112@gmail.com

無(wú)人機(jī)自動(dòng)防撞沖突檢測(cè)與優(yōu)化控制方法

許云紅，周銳，夏潔，等

研究了無(wú)人機(jī)防撞沖突檢測(cè)與威脅級(jí)別評(píng)估方法，建立了航向角控制、速度控制、高度控制以及航向角與速度組合控制等多種不同的防撞控制策略；建立了自動(dòng)防撞多目標(biāo)代價(jià)函數(shù)模型以及多目標(biāo)最優(yōu)決策方法.仿真結(jié)果證明了該方法的有效性和較好的實(shí)用性.

無(wú)人機(jī)；自動(dòng)防撞；威脅評(píng)估；最優(yōu)決策

來(lái)源出版物：電光與控制，2014，21（1）: 1-6

輕小型無(wú)人機(jī)航攝技術(shù)現(xiàn)狀及發(fā)展趨勢(shì)

畢凱，李英成，丁曉波，等

輕小型無(wú)人機(jī)因其獲取影像機(jī)動(dòng)靈活、影像分辨率高、成本低等優(yōu)勢(shì)，成為傳統(tǒng)航空攝影測(cè)量手段的有效補(bǔ)充，已在測(cè)繪地理信息、防災(zāi)減災(zāi)、反恐維穩(wěn)、農(nóng)業(yè)估產(chǎn)、水利電力工程建設(shè)、鐵路、公路帶狀選線工程，以及西部1∶5萬(wàn)地形圖空白區(qū)測(cè)圖工程、遠(yuǎn)離大陸的島礁測(cè)繪等諸多領(lǐng)域發(fā)揮了積極作用.本文針對(duì)當(dāng)前民用輕小型無(wú)人機(jī)航攝系統(tǒng)開(kāi)展航空攝影和影像攝影測(cè)量處理的情況，從航空攝影航線設(shè)計(jì)、搭載傳感器情況、航攝質(zhì)量快速檢查、像控測(cè)量工作及影像攝影測(cè)量處理等方面闡述了現(xiàn)階段民用輕小型無(wú)人機(jī)航空攝影及影像攝影測(cè)量處理的現(xiàn)狀，分析了現(xiàn)階段輕小型無(wú)人機(jī)航攝技術(shù)存在的問(wèn)題，對(duì)下一階段輕小型無(wú)人機(jī)航攝技術(shù)的發(fā)展提出了建議.

無(wú)人機(jī)；非量測(cè)型相機(jī)；小像幅航空攝影；航線設(shè)計(jì)；質(zhì)量檢查；像控測(cè)量；發(fā)展趨勢(shì)

來(lái)源出版物：測(cè)繪通報(bào)，2015（3）: 27-31聯(lián)系郵箱：畢凱，24677958@qq.com

基于Laguerre圖的自優(yōu)化A-Star無(wú)人機(jī)航路規(guī)劃算法

魏瑞軒，許卓凡，王樹磊，等

為了降低無(wú)人機(jī)航路規(guī)劃的運(yùn)算量，減少規(guī)劃時(shí)間，確保算法對(duì)于任意形狀威脅區(qū)域和地形的適應(yīng)性以及所規(guī)劃航路的準(zhǔn)確性，提出了一種新穎的LA-Star算法用于無(wú)人機(jī)航路規(guī)劃.首先把威脅區(qū)域和禁飛區(qū)域簡(jiǎn)化為圓形，利用Laguerre圖算法進(jìn)行航路預(yù)規(guī)劃，在此基礎(chǔ)上簡(jiǎn)化二次規(guī)劃空間的范圍，之后恢復(fù)威脅區(qū)域和禁飛區(qū)域的真實(shí)形狀，在簡(jiǎn)化后的規(guī)劃空間內(nèi)使用改進(jìn)A-Star算法實(shí)施二次航路規(guī)劃，最后對(duì)生成的航路進(jìn)行自優(yōu)化處理.仿真結(jié)果證明了LA-Star算法滿足航路規(guī)劃的實(shí)時(shí)性和準(zhǔn)確性要求.

無(wú)人機(jī)；航路規(guī)劃；LA-Star算法；Laguerre圖；A-Star算法

來(lái)源出版物：系統(tǒng)工程與電子技術(shù)，2015，37（3）: 577-582

無(wú)人機(jī)編隊(duì)機(jī)動(dòng)飛行時(shí)的隊(duì)形保持反饋控制

邵壯，祝小平，周洲，等

為提高編隊(duì)大機(jī)動(dòng)時(shí)的隊(duì)形保持能力，采用虛擬結(jié)構(gòu)編隊(duì)方法，基于李稚普諾夫直接法設(shè)計(jì)獨(dú)立的非線性隊(duì)形保持控制器，并在此基礎(chǔ)上采用非線性模型預(yù)側(cè)控制方法設(shè)計(jì)含隊(duì)形反饋的編隊(duì)軌跡跟蹤器.通過(guò)在代價(jià)函數(shù)中引入隊(duì)形誤差代價(jià)來(lái)實(shí)現(xiàn)隊(duì)形反饋控制策略，并采用動(dòng)態(tài)參數(shù)實(shí)現(xiàn)編隊(duì)隊(duì)形保持和沿參考軌跡飛行之間的自適應(yīng)切換，各無(wú)人機(jī)通過(guò)滾動(dòng)求解有限時(shí)域優(yōu)化問(wèn)題得到虛擬結(jié)構(gòu)的控制指令.仿真結(jié)果表明，相較于無(wú)隊(duì)形反饋的情況，所設(shè)計(jì)的含隊(duì)形反饋軌跡跟蹤器能夠顯著地降低編隊(duì)大機(jī)動(dòng)時(shí)的隊(duì)形保持誤差.

無(wú)人機(jī)；編隊(duì)飛行；隊(duì)形反饋；虛擬結(jié)構(gòu)；非線性模型預(yù)側(cè)控制

來(lái)源出版物：西北工業(yè)大學(xué)學(xué)報(bào)，2015，33（1）: 26-32

無(wú)人機(jī)機(jī)載激光雷達(dá)系統(tǒng)航帶拼接方法研究

趙大偉，裴海龍，丁潔，等

為了減少機(jī)載激光雷達(dá)（Li DAR）系統(tǒng)中系統(tǒng)誤差和隨機(jī)誤差造成的航帶間三維（3D）空間偏移，提高數(shù)據(jù)精度，選取基于數(shù)據(jù)驅(qū)動(dòng)的“六參數(shù)”航帶平差方法，實(shí)現(xiàn)無(wú)人機(jī)機(jī)載激光雷達(dá)系統(tǒng)的航帶拼接.在分析了機(jī)載激光掃描系統(tǒng)的數(shù)據(jù)特征的基礎(chǔ)上利用改進(jìn)的3D正態(tài)分布變換（3D-NDT）進(jìn)行航帶配準(zhǔn)，得到航帶間的變換關(guān)系參數(shù).通過(guò)具體實(shí)驗(yàn)對(duì)常見(jiàn)的迭代最近點(diǎn)（ICP）算法與3D正態(tài)分布變換算法進(jìn)行比較，驗(yàn)證了該方法實(shí)現(xiàn)航帶拼接，具有速度快、精度高、穩(wěn)健性好等特點(diǎn)，非常適合于工程實(shí)際應(yīng)用.

激光光學(xué)；激光雷達(dá)；航帶平差；正態(tài)分布變換；點(diǎn)云配準(zhǔn)

來(lái)源出版物：中國(guó)激光，2015，42（1）: 0114002聯(lián)系郵箱：裴海龍，auhlpei@scut.edu.cn

無(wú)人機(jī)航跡角的非線性增益遞歸滑?？刂?/p>

孫秀霞，劉希，徐嵩，等

針對(duì)固定翼無(wú)人機(jī)（unmanned aerial vehicles，UAVs）自主著陸過(guò)程中的航跡角跟蹤控制問(wèn)題，提出了一種非線性增益遞歸滑?？刂品椒?通過(guò)引入一個(gè)新的非線性增益函數(shù)，并設(shè)計(jì)含有積分項(xiàng)的遞歸滑模面，在保證航跡角跟蹤控制精度的同時(shí)有效改善了控制系統(tǒng)的動(dòng)態(tài)品質(zhì)，克服了常規(guī)動(dòng)態(tài)面控制（dynamic surface control，DSC）方法對(duì)于量測(cè)噪聲敏感、容易引起執(zhí)行器飽和的缺點(diǎn).理論證明了所得航跡角跟蹤控制系統(tǒng)所有狀態(tài)半全局一致最終有界，航跡角跟蹤誤差可以收斂至原點(diǎn)的指定小鄰域，且對(duì)于非時(shí)變干擾和常值指令不存在穩(wěn)態(tài)跟蹤誤差.在YF-22模型機(jī)上進(jìn)行的數(shù)值仿真驗(yàn)證了本文方法的優(yōu)越性.

航跡角控制；非線性增益；滑?？刂?；自主著陸

來(lái)源出版物：系統(tǒng)科學(xué)與電子技術(shù)，2015，37（2）: 379-384

Lightweight unmanned aerial vehicles will revolutionize spatial ecology

Anderson，Karen； Gaston，Kevin J

來(lái)源出版物：Frontiers in Ecology and the Environment，2013，11（3）: 138-146

聯(lián)系郵箱：Anderson，Karen； karen.anderson@exeter.ac.uk

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

Roberge，Vincent； Tarbouchi，Mohammed； Labonte，Gilles

來(lái)源出版物：IEEE Transactions on Industrial Informatics，2013，9（1）: 132-141聯(lián)系郵箱：Roberge，Vincent；vincent.roberge@rmc.ca

Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration

Duan，Haibin； Luo，Qinan； Ma，Guanjun； et al.

來(lái)源出版物：IEEE Computational Intelligence Magazine，2013，8（3）: 16-27

Unmanned aerial vehicles as innovative remote sensing platforms for high-resolution infrared imagery to support restoration monitoring in cut-over bogs

Knoth，Christian； Klein，Birte； Prinz，Torsten； et al.

來(lái)源出版物：Applied Vegetation Science，2013，16（3）: 509-517聯(lián)系郵箱：Kleinebecker，T； kleinebecker@uni-muenster.de

Using Unmanned Aerial Vehicles（UAV）for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments

Mancini，F(xiàn)rancesco； Dubbini，Marco； Gattelli，Mario； et al.

來(lái)源出版物：Remote Sensing，2013，5（12）: 6880-6898聯(lián)系郵箱：Mancini，F(xiàn)rancesco； f.mancini@poliba.it

Direct Georeferencing of Ultrahigh-Resolution UAV Imagery

Turner，Darren； Lucieer，Arko； Wallace，Luke

來(lái)源出版物：IEEE Transactions on Geoscience and Remote Sensing，2014，52（5）: 2738-2745

聯(lián)系郵箱：Turner，Darren； Darren.Turner@utas.edu.au

Automatic Car Counting Method for Unmanned Aerial Vehicle Images

Moranduzzo，Thomas； Melgani，F(xiàn)arid

來(lái)源出版物：IEEE Transactions on Geoscience and Remote Sensing，2014，52（3）: 1635-1647

聯(lián)系郵箱：Moranduzzo，Thomas； moranduzzo@disi.unitn.it

A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy

Burkart，Andreas； Cogliati，Sergio； Schickling，Anke； et al.

來(lái)源出版物：IEEE Sensors Journal，2014，14（1）: 62-67聯(lián)系郵箱：Burkart，Andreas； an.burkart@fz-juelich.de

High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles

Immerzeel，WW； Kraaijenbrink，PDA； Shea，JM； et al.

來(lái)源出版物：Remote Sensing of Environment，2014，150: 93-103聯(lián)系郵箱：Immerzeel，WW； w.w.immerzeel@uu.nl

A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles

Suomalainen，Juha； Anders，Niels； Iqbal，Shahzad； et al.

來(lái)源出版物：Remote Sensing，2014，6（11）: 11013-11030聯(lián)系郵箱：Suomalainen，Juha； juha.suomalainen@wur.nl

UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

Feng，Quanlong； Liu，Jiantao； Gong，Jianhua

來(lái)源出版物：Remote Sensing，2015，7（1）: 1074-1094聯(lián)系郵箱：Gong，Jianhua； gongjh@radi.ac.cn

UAV circumnavigating an unknown target under a GPS-denied environment with range-only measurements

Cao，Yongcan

來(lái)源出版物：Automatica，2015，55: 150-158聯(lián)系郵箱：CAO Yong-can； yongcan.cao@gmail.com

Survey on the novel hybrid aquatic-aerial amphibious aircraft: Aquatic unmanned aerial vehicle（AquaUAV）

Yang，Xingbang； Wang，Tianmiao； Liang，Jianhong

來(lái)源出版物：Progress in Aerospace Sciences，2015，74: 131-151聯(lián)系郵箱：Yang，Xingbang； xingbang1987@163.com

Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles（UAVs）

Pajares，Gonzalo

來(lái)源出版物：Photogrammetric Engineering and Remote Sensing，2015，81（4）: 281-329

聯(lián)系郵箱：Pajares，Gonzalo； pajares@ucm.es

來(lái)源出版物：Automatica，2015，53: 111-119聯(lián)系郵箱：Abdessameud，Abdelkader； aabdess@uwo.ca

編輯：衛(wèi)夏雯

Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles（UAVs）could fill this gap，providing low-cost approaches to meet the critical requirements of spatial，spectral，and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007，the platform was flown over agricultural fields，obtaining thermal imagery in the 7.5-13 μm region（40 cm resolution）and narrowband multispectral imagery in the 400-800 nm spectral region（20 cm resolution）. Surface reflectance and temperature imagery were obtained，after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices，namely normalized difference vegetation index，transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index，and photochemical reflectance index（PRI），coupled with SAILH and FLIGHT models. As a result，the image products of leaf area index，chlorophyll content（C（ab）），and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations，if not better，than those obtained by traditional manned airborne sensors.

Multispectral； narrowband； radiative transfer modeling； remote sensing； stress detection； thermal； unmanned aerial system（UAS）； unmanned aerial vehicles（UAVs）

This paper outlines how light Unmanned Aerial Vehicles（UAV）can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters，designed to provide multispectral images in the visible and near-infrared domains. In 2005，these instruments were fitted to powered glider and parachute，and flown at six dates staggered over the crop season. We monitored ten varieties of wheat，grown in trial micro-plots in the South-West of France. For each date，we acquired multiple views in four spectral bands corresponding to blue，green，red，and near-infrared. We then performed accurate corrections of image vignetting，geometric distortions，and radiometric bidirectional effects. Afterwards，we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally，we sought relationships between these indexes and field-measured biophysicalparameters，both generic and date-specific. Therefore，we established a robust and stable generic relationship between，in one hand，leaf area index and NDVI and，in the other hand，nitrogen uptake and GNDVI. Due to a high amount of noise in the data，it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

imagery； multispectral； precision farming； UAV

Unmanned Aerial Vehicles（UAVs）are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery，however，makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper，we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion（SfM）photogrammetric techniques. Images are processed to create three dimensional point clouds，initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point（GCP）technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model（DTM）required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65-120 cm whilst the GCP technique achieves an accuracy of approximately 10-15 cm.

UAV； Structure from Motion（SfM）； rectify； georeferencing； mosaicking； point cloud； Digital Terrain Model（DTM）

The use of unmanned aerial vehicles（UAVs）for natural resource applications has increased considerably in recent years due to their greater availability，the miniaturization of sensors，and the ability to deploy a UAV relatively quickly and repeatedly at low altitudes. We examine in this paper the potential of using a small UAV for rangeland inventory，assessment and monitoring. Imagery with a ground resolved distance of 8 cm was acquired over a 290 ha site in southwestern Idaho. We developed a semi-automated orthorectification procedure suitable for handling large numbers of small-footprint UAV images. The geometric accuracy of the orthorectified image mosaics ranged from 1.5 m to 2 m. We used object-based hierarchical image analysis to classify imagery of plots measured concurrently on the ground using standard rangeland monitoring procedures. Correlations between image-and ground-based estimates of percent cover resulted in r-squared values ranging from 0.86 to 0.98. Time estimates indicated a greater efficiency for the image-based method compared to ground measurements. The overall classification accuracies for the two image mosaics were 83 percent and 88 percent. Even under the current limitations of operating a UAV in the National Airspace，the results of this study show that UAVs can be used successfully to obtain imagery for rangeland monitoring，and that the remote sensing approach can either complement or replace some ground-based measurements. We discuss details of the UAV mission，image processing and analysis，and accuracy assessment.

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle（UAV）imaging systems. Automatic generation of high-quality，dense point clouds from digital images by image matching is a recent，cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system，an image data collection process using high image overlaps，and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems’ SOCET SET classical commercial photogrammetric software and another is built using Microsoft（R）’s Photosynth（TM）service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientationinformation or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry，but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier，which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results，we give recommendations for properties of imaging sensor，data collection and processing of UAV image data to ensure accurate point cloud generation.

unmanned aerial vehicle； photogrammetry； point cloud； small sensor digital camera； calibration

Decentralized overlapping feedback laws are designed for a formation of unmanned aerial vehicles. The dynamic model of the formation with an information structure constraint in which each vehicle，except the leader，only detects the vehicle directly in front of it，is treated as an interconnected system with overlapping subsystems. Using the mathematical framework of the inclusion principle，the interconnected system is expanded into a higher dimensional space in which the subsystems appear to be disjoint. Then，at each subsystem，a static state feedback controller is designed to robustly stabilize the perturbed nominal dynamics of the subsystem. The design procedure is based on the application of convex optimization tools involving linear matrix inequalities. As a final step，the decentralized controllers are contracted back to the original interconnected system for implementation.

decentralized control； robustness； reliability； information flows； large-scale systems

We present the design and implementation of a real-time，vision-based landing algorithm for an autonomous helicopter. The landing algorithm is integrated with algorithms for visual acquisition of the target（a helipad）and navigation to the target，from an arbitrary initial position and orientation. We use vision for precise target detection and recognition，and a combination of vision and Global Positioning System for navigation. The helicopter updates its landing target parameters based on vision and uses an onboard behavior-based controller to follow a path to the landing site. We present significant results from flight trials in the field which demonstrate that our detection，recognition，and control algorithms are accurate，robust，and repeatable.

autonomous helicopter； autonomous landing； unmanned aerial vehicle； vision-based navigation

參見(jiàn)本期“經(jīng)典文獻(xiàn)推薦”欄目.

An evolutionary algorithm based framework，a combination of modified，breeder genetic algorithms incorporating characteristics of classic genetic algorithms，is utilized to design an offline/online path planner for unmanned aerial vehicles（UAVs）autonomous navigation. The path planner calculates a curved path line with desired characteristics in a three-dimensional（3-D）rough terrain environment，represented using B-Spline curves，with the coordinates of its control points being the evolutionary algorithm artificial chromosome genes. Given a 3-D rough environment and assuming flight envelope restrictions，two problems are solved: i）UAV navigation using an offline planner in a known environment，and，ii）UAV navigation using an online planner in a completely unknown environment. The offline planner produces a single B-Spline curve that connects the starting and target points with a predefined initial direction. The online planner，based on the offline one，is given on-board radar readings which gradually produces a smooth 3-D trajectory aiming at reaching a predetermined target in an unknown environment； the produced trajectory consists of smaller B-Spline curves smoothly connected with each other. Both planners have been tested under different scenarios，and they have been proven effective in guiding an UAV to its final destination，providing near-optimal curved paths quickly and efficiently.

3-D path planning； B-splines； evolutionary algorithms； navigation； UAV

An image-based visual servo control is presented for an unmanned aerial vehicle（UAV）capable of stationary or quasi-stationary flight with the camera mounted onboard the vehicle. The target considered consists of a finite set of stationary and disjoint points lying in a plane. Control of the position and orientation dynamics is decoupled using a visual error based on spherical centroid data，along with estimations of the linear velocity and the gravitational inertial direction extracted from image features and an embedded inertial measurement unit. The visual error used compensates for poor conditioning of the image Jacobian matrix by introducing a nonhomogeneous gain term adapted to the visual sensitivity of the error measurements. A nonlinear controller，that ensures exponential convergence of the system considered，is derived for the full dynamics of the system using control Lyapunov function design techniques. Experimental results on a quadrotor UAV，developed by the French Atomic Energy Commission，demonstrate the robustness and performance of the proposed control strategy.

aerial robotic vehicle； experiments； image-based visual servo（IBVS）； underactuated systems

In September 2002，NASA’s solar-powered Pathfinder-Plus unmanned aerial vehicle（UAV）was used to conduct a proof-of-concept mission in US national airspace above the 1500 ha plantation of the Kauai Coffee Company in Hawaii. While in national airspace，the transponder-equipped UAV was supervised by regional air traffic controllers and treated like a conventionally piloted aircraft. High resolution color and multispectral imaging payloads，both drawing from the aircraft's solar power system，were housed in exterior-mounted environmental pressure pods. A local area network（LAN）using unlicensed radio frequency was used for camera control and downlink of image data at rates exceeding 5 Mbit s-1. A wide area network（WAN）allowed a project investigator stationed on the US mainland to uplink control commands during part of the mission. Images were available for enhancing，printing，and interpretation within minutes of collection. The color images were useful for mapping invasive weed outbreaks and for revealing irrigation and fertilization anomalies. Multispectral-imagery was related to mature fruit harvest from certain fields with significant fruit display on the tree canopy exterior. During 4 h “l(fā)oitering” above the plantation，ground-based pilots were able to precisely navigate the UAV along pre-planned flightlines，and also perform spontaneous maneuvers under the direction of the project scientist for image collection in cloud-free zones. Despite the presence of ground-obscuring cumulus cloud cover of ca. 70% during the image collection period，the UAV's maneuvering capability ultimately enabled collection of cloud-free imagery throughout most of the plantation. The mission demonstrated the capability of a slow-flying UAV，equipped with downsized imaging systems and line-of-sight telemetry，to monitor a localized agricultural region for an extended time period. The authors suggest that evolving long-duration（weeks to months）UAVs stand to make a valuable future contribution to regional agricultural resource monitoring.

unmanned aerial vehicle； Pathfinder-Plus UAV； multispectral imaging； local area network； ripeness monitoring； weed mapping；fertigation； coffee

In this paper，a new nonlinear controller for a quadrotor unmanned aerial vehicle（UAV）is proposed using neural networks（NNs）and output feedback. The assumption on the availability of UAV dynamics is not always practical，especially in an outdoor environment. Therefore，in this work，an NN is introduced to learn the complete dynamics of the UAV online，including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated，a novel NN virtual control input scheme is proposed which allows all six degrees of freedom（DOF）of the UAV to be controlled using only four control inputs. Furthermore，an NN observer is introduced to estimate the translational and angular velocities of the UAV，and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position，orientation，and velocity tracking errors，the virtual control and observer estimation errors，and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded（SGUUB）in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances，and simulation results are included to demonstrate the theoreticalconjecture.

Lyapunov method； neural network（NN）； observer； output feedback； quadrotor unmanned aerial vehicle（UAV）

Imagery acquired with unmanned aerial vehicles（UAVs）has great potential for incorporation into natural resource monitoring protocols due to their ability to be deployed quickly and repeatedly and to fly at low altitudes. While the imagery may have high spatial resolution，the spectral resolution is low when lightweight off-the-shelf digital cameras are used，and the inclusion or texture measures can potentially increase the classification accuracy. Texture measures have been used widely in pixel-based image analysis，but their use in an object-based environment has not been well documented. Our objectives were to determine the most suitable texture measures and the optimal image analysis scale for differentiating rangeland vegetation using UAV imagery segmented at multiple scales. A decision tree was used to determine the optimal texture features for each segmentation scale. Results indicated the following: 1）The error rate of the decision tree was lower； 2）prediction success was higher； 3）class separability was greater； and 4）overall accuracy was higher（high 90% range）at coarser segmentation scales. The inclusion of texture measures increased classification accuracies at nearly all segmentation scales，and entropy was the texture measure with the highest score in most decision trees. The results demonstrate that UAVs are viable platforms for rangeland monitoring and that the drawbacks of low-cost off-the-shelf digital cameras can be overcome by including texture measures and using object-based image analysis which is highly suitable for very high resolution imagery.

Object-based classification； rangelands； scale； texture； unmanned aircraft

A fundamental aspect of autonomous vehicle guidance is planning trajectories. Historically，two fields have contributed to trajectory or motion planning methods: robotics and dynamics and control. The former typically have a stronger focus on computational issues and real-time robot control，while the latter emphasize the dynamic behavior and more specific aspects of trajectory performance. Guidance for Unmanned Aerial Vehicles（UAVs），including fixed- and rotary-wing aircraft，involves significant differences from most traditionally defined mobile and manipulator robots. Qualities characteristic to UAVs include non-trivial dynamics，three-dimensional environments，disturbed operating conditions，and high levels of uncertainty in state knowledge. Otherwise，UAV guidance shares qualities with typical robotic motion planning problems，including partial knowledge of the environment and tasks that can range from basic goal interception，which can be precisely specified，to more general tasks like surveillance and reconnaissance，which are harder to specify. These basic planning problems involve continual interaction with the environment. The purpose of this paper is to provide an overview of existing motion planning algorithms while adding perspectives and practical examples from UAV guidance approaches.

Autonomous； UAV； Guidance； Trajectory； Motion planning； Optimization； Heuristics； Complexity； Algorithm

參見(jiàn)本期“經(jīng)典文獻(xiàn)推薦”欄目.

Ecologists require spatially explicit data to relate structure to function. To date，heavy reliance has been placed on obtaining such data from remote-sensing instruments mounted on spacecraft or manned aircraft，although the spatial and temporal resolutions of the data are often not suited to local-scale ecological investigations. Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles（UAVs）-aircraft remotely operated from the ground-and there are now many lightweight UAVs on offer at reasonable costs. Flying low and slow，UAVs offer ecologists new opportunities for scale-appropriate measurements of ecological phenomena. Equipped with capable sensors，UAVs can deliver fine spatial resolution data at temporal resolutions defined by the end user. Recent innovations in UAV platform design have been accompanied by improvements in navigation and the miniaturization of measurement technologies，allowing the study of individual organisms and their spatiotemporal dynamics at close range.

remotely-sensed data； aircraft systems； UAV； scale； temperature； photography； vegetation； imagery； biodiversity； camera

The development of autonomous unmanned aerial vehicles（UAVs）is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper，we use the genetic algorithm（GA）and the particle swarm optimization algorithm（PSO）to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment，while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments，circular arcs and vertical helices. We reduce the execution time of our solutions by using the“single-program，multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms，we conclude that by using a parallel implementation on standard multicore CPUs，real-time path planning for UAVs is possible. Moreover，our rigorous comparison of the two algorithms shows，with statistical significance，that the GA produces superior trajectories to the PSO.

Genetic algorithm（GA）； path planning； particle swarm optimization（PSO）； parallel computing； unmanned aerial vehicles（UAVs）

Given the initial state of an Unmanned Aerial Vehicle（UAV）system and the relative state of the system，the continuous inputs of each flight unit are piecewise linear by a Control Parameterization and Time Discretization（CPTD）method. The approximation piecewise linearization control inputs are used to substitute for the continuous inputs. In this way，the multi-UAV formation reconfiguration problem can be formulated as an optimal control problem with dynamical and algebraic constraints. With strict constraints and mutual interference，the multi-UAV formation reconfiguration in 3-D space is a complicated problem. The recent boom of bio-inspired algorithms has attractedmany researchers to the field of applying such intelligent approaches to complicated optimization problems in multi-UAVs. In this paper，a Hybrid Particle Swarm Optimization and Genetic Algorithm（HPSOGA）is proposed to solve the multi-UAV formation reconfiguration problem，which is modeled as a parameter optimization problem. This new approach combines the advantages of Particle Swarm Optimization（PSO）and Genetic Algorithm（GA），which can find the time-optimal solutions simultaneously. The proposed HPSOGA will also be compared with basic PSO algorithm and the series of experimental results will show that our HPSOGA outperforms PSO in solving multi-UAV formation reconfiguration problem under complicated environments.

formation flight control； evolutionary； model； spacecraft； vehicles； robots； teams

Can UAV-based NIR remote sensing support restoration monitoring of cut-over bogs by providing valid information on species distribution and surface structure？ Location Restored polders of the Uchter Moor，a bog complex in NW Germany. Methods We used autonomously flying quadrocopters，supplied with either a panchromatic or colour infrared calibrated small frame digital camera to generate high resolution images of the restored bog surface. We performed a two-step classification process of automatic image segmentation and object-based classification to distinguish between four pre-defined classes（waterlogged bare peat，Sphagnum spp.，Eriophorum vaginatum and Betula pubescens. An independent validation procedure was performed to evaluate the accuracy of the classification. Results A set-up composed of decision rules for reflectance，geometry and textural features was applied for identification of the four classes. The presented classification revealed an overall accuracy level of 91%. Most reliable attribution was obtained for waterlogged bare peat and Sphagnum-covered surfaces，revealing producer accuracies of 95% and 91%，respectively. Lower but still feasible accuracy levels were obtained for Eriophorum vaginatum and Betula pubescens individuals（89% and 84%，respectively）. Conclusions UAV-based NIR remote sensing is a promising tool for monitoring the restoration of cut-over bogs and has the potential to significantly reduce laborious field surveys. UAVs may increasingly play a significant role in future ecological monitoring studies，since they are small in size，highly flexible，easy to handle，non-emissive and available at a comparatively low cost.

Bog vegetation； Colour infrared； Eriophorum； Near-infrared； Object-based image classification； Sphagnum； UAV

The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset，this study tests the utility of the Structure from Motion（SfM）approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle（UAV）. The SfM image-based approach was selected whilst searching for a rapid，inexpensive，and highly automated method，able to produce 3D information from unstructured aerial images. In particular，it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models（DSM）of a beach dune system in Marina di Ravenna（Italy）. The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning（TLS）surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m（RMS=0.19 m）. However，although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods，the assessment of geomorphic features is usually based on multi-temporal surfaces analysis，where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System（GNSS）survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level（RMS=0.011 m）. The successive point by point direct comparison between UAV and TLS elevations show a very small average distance，0.015 m，with RMS=0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.

UAV； structure from motion； terrestrial laser scanning； digital surface model； beach dunes system

Micro-unmanned aerial vehicles often collect a large amount of images when mapping an area at an ultrahigh resolution. A direct georeferencing technique potentially eliminates the need for ground control points. In this paper，we developed a camera-global positioningsystem（GPS）module to allow the synchronization of camera exposure with the airframe's position as recorded by a GPS with 10-20 cm accuracy. Lever arm corrections were applied to the camera positions to account for the positional difference between the GPS antenna and the camera center. Image selection algorithms were implemented to eliminate blurry images and images with excessive overlap. This study compared three different software methods（Photoscan，Pix4D web service，and an in-house Bundler method）. We evaluated each based on processing time，ease of use，and the spatial accuracy of the final mosaic produced. Photoscan showed the best performance as it was the fastest and the easiest to use and had the best spatial accuracy（average error of 0.11 m with a standard deviation of 0.02 m）. This accuracy is limited by the accuracy of the differential GPS unit（10-20 cm）used to record camera position. Pix4D achieved a mean spatial error of 0.24 m with a standard deviation of 0.03 m，while the Bundler method had the worst mean spatial accuracy of 0.76 m with a standard deviation of 0.15 m. The lower performance of the Bundler method was due to its poor performance in estimating camera focal length，which，in turn，introduced large errors in the Z-axis for the translation equations.

Remote sensing； unmanned aerial vehicles（UAVs）

This paper presents a solution to solve the car detection and counting problem in images acquired by means of unmanned aerial vehicles（UAVs）. UAV images are characterized by a very high spatial resolution（order of few centimeters），and consequently by an extremely high level of details which calls for appropriate automatic analysis methods. The proposed method starts with a screening step of asphalted zones in order to restrict the areas where to detect cars and thus to reduce false alarms. Then，it performs a feature extraction process based on scalar invariant feature transform thanks to which a set of keypoints is identified in the considered image and opportunely described. Successively，it discriminates between keypoints assigned to cars and all the others，by means of a support vector machine classifier. The last step of our method is focused on the grouping of the keypoints belonging to the same car in order to get a “one keypoint-one car”relationship. Finally，the number of cars present in the scene is given by the number of final keypoints identified. The experimental results obtained on a real UAV scene characterized by a spatial resolution of 2 cm show that the proposed method exhibits a promising car counting accuracy.

Car detection； feature extraction； scale invariant feature transform（SIFT）； support vector machine（SVM）； unmanned aerial vehicle（UAV）

A novel hyperspectral measurement system for unmanned aerial vehicles（UAVs）in the visible to near infrared（VIS/NIR）range（350-800 nm）was developed based on the Ocean Optics STS microspectrometer. The ultralight device relies on small open source electronics and weighs a ready-to-fly 216 g. The airborne spectrometer is wirelessly synchronized to a second spectrometer on the ground for simultaneous white reference collection. In this paper，the performance of the system is investigated and specific issues such as dark current correction or second order effects are addressed. Full width at half maximum was between 2.4 and 3.0 nm depending on the spectral band. The functional system was tested in flight at a 10-m altitude against a current field spectroscopy gold standard device Analytical Spectral Devices Field Spec 4 over an agricultural site. A highly significant correlation（r（2）＞0.99）was found in reflection comparing both measurement approaches. Furthermore，the aerial measurements have a six times smaller standard deviation than the hand held measurements. Thus，the present spectrometer opens a possibility for low-cost but high-precision field spectroscopy from UAVs.

Hyperspectral sensors； remote sensing； unmanned aerial vehicles； vegetation； calibration

Himalayan glacier tongues are commonly debris covered and they are an important source of melt water. However，they remain relatively unstudied because of the inaccessibility of the terrain and the difficulties in field work caused by the thick debris mantles. Observations of debris-covered glaciers are therefore scarce and airborne remote sensing may bridge the gap between scarce field observations and coarse resolution space-borne remote sensing. In this study we deploy an Unmanned Aerial Vehicle（UAV）before and after the melt and monsoon season（May and October 2013）over the debris-covered tongue of the Lirung Glacier in Nepal. Based on stereo-imaging and the structure for motion algorithm we derive highly detailed ortho-mosaics and digital elevation models（DEMs），which we geometrically correct using differential GPS observations collected in the field. Based on DEM differencing and manual featuretracking we derive the mass loss and the surface velocity of the glacier at a high spatial accuracy. On average，mass loss is limited and the surface velocity is very small. However，the spatial variability of melt rates is very high，and ice cliffs and supra-glacial ponds show mass losses that can be an order of magnitude higher than the average. We suggest that future research should focus on the interaction between supraglacial ponds，ice cliffs and englacial hydrology to further understand the dynamics of debris-covered glaciers. Finally，we conclude that UAV deployment has large potential in glaciology and it may revolutionize methods currently applied in studying glacier surface features.

UAV； Photogrammetry； DEM differencing； Himalaya； Glacier dynamics； Climate change； Ice cliffs； Supra-glacial ponds

During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles（UAV）. However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg，forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system（HYMSY）for rotor-based UAVs，the novel processing chain for the system，and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer（400-950 nm，9 nm FWHM，25 lines/s，328 px/line），a photogrammetric camera，and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model（DSM）and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally，no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight，an area of 2-10 ha was mapped，producing a RGB orthomosaic at 1-5 cm resolution，a DSM at 5-10 cm resolution，and a hyperspectral datacube at 10-50 cm resolution.

Unmanned Aerial Vehicle（UAV）； hyperspectral mapping system； agriculture； remote sensing； photogrammetry

Unmanned aerial vehicle（UAV）remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions，off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications，but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas，and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue（RGB）images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following:（1）Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification；（2）The inclusion of texture features improved classification accuracy significantly；（3）classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.

UAV； vegetation mapping； urban landscape； random forest； texture analysis

One typical application of unmanned aerial vehicles（UAVs）is the intelligence，surveillance，and reconnaissance mission，where the objective is to improve situation awareness through information acquisition. For example，an efficient way to gather information regarding a target is to deploy UAV in such a way that it orbits around this target at a desired distance. Such a UAV motion is called circumnavigation. The objective of this paper is to design a control algorithm such that this circumnavigation mission is achieved under a GPS-denied environment when only range measurement is used. The control algorithm is constructed in two steps. The first step is to design a control algorithm by assuming the availability of both range and range rate measurements，where the associated control input isalways bounded. The second stevis to further eliminate the use of range rate measurement by using an estimated range rate，obtained via a sliding-mode estimator based on range measurement，to replace actual range rate measurement. Such a controller design technique is applicable in other UAV navigation and control missions when a GPS-denied environment is considered.

UAV； Autonomy； Joint estimation and control； Sliding-mode estimator； GPS-denied environment

The aquatic unmanned aerial vehicle（AquaUAV），a kind of vehicle that can operate both in the air and the water，has been regarded as a new breakthrough to broaden the application scenario of UAV. Wide application prospects in military and civil field are more than bright，therefore many institutions have focused on the development of such a vehicle. However，due to the significant difference of the physical properties between the air and the water，it is rather difficult to design a fully-featured AquaUAV. Until now，majority of partially-featured AquaUAVs have been developed and used to verify the feasibility of an aquatic-aerial vehicle. In the present work，we classify the current partially-featured AquaUAV into three categories from the scope of the whole UAV field，i.e.，the seaplane UAV，the submarine-launched UAV，and the submersible UAV. Then the recent advancements and common characteristics of the three kinds of AquaUAVs are reviewed in detail respectively. Then the applications of bionics in the design of AquaUAV，the transition mode between the air and the water，the morphing wing structure for air-water adaptation，and the power source and the propulsion type are summarized and discussed. The tradeoff analyses for different transition methods between the air and the water are presented. Furthermore，it indicates that applying the bionics into the design and development of the AquaUAV will be essential and significant. Finally，the significant technical challenges for the AquaUAV to change from a conception to a practical prototype are indicated.

Bionics； Aquatic-aerial operation； Aquatic unmanned aerial vehicle（AquaUAV）； Air-water transition； Water-air transition；Morphing structure

Remotely Piloted Aircraft（RPA）is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles（UAVs）or more extensively Unmanned Aerial Systems（UAS）are platforms considered under the RPAs paradigm. Simultaneously，the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus，the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First，some examples of typical platforms used in remote sensing are provided. Second，a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third，multi-UAVs in collaboration，coordination，and cooperation in remote sensing are considered. Finally，a collection of applications in several areas are proposed，where the combination of unmanned platforms and sensors，together with methods，algorithms，and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas，each independent from the others，so that the reader does not need to read the full paper when a specific application is of interest.

structure-from-motion； sagebrush steppe ecosystems； synthetic-aperture radar； wireless sensor network； narrow-band indexes；antarctic moss beds； laser-scanning data； water-stress index； leaf-area index； high-resolution

Image-based tracking control of VTOL unmanned aerial vehicles

Abdessameud，Abdelkader； Janabi-Sharifi，F(xiàn)arrokh

This paper addresses the image-based control problem of vertical take-off and landing（VTOL）unmanned aerial vehicles（UAVs）. Specifically，we propose a control scheme allowing the aircraft to track a moving target captured by an onboard camera where the orientation and angular velocity of the vehicle are assumed available for feedback. The proposed approach relies on appropriate image features，defined based on perspective image moments along with a useful projection，and the design of a bounded adaptive translational controller without linear velocity measurements in the presence of external disturbances. Estimates of the target’s acceleration and the disturbances as well as some auxiliary variables are used to simplify the control design and guarantee the stability of the overall closed loop system. Simulation examples are provided to show the effectiveness of the proposed theoretical results.

Image-based control； Unmanned aerial vehicles； VTOL； Target tracking

本領(lǐng)域經(jīng)典文章題目第一作者來(lái)源出版物1Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial VehicleBerni，Jose A. JIEEE Transactions on Geoscience and Remote Sensing，2009，47（3）: 722-738 2 Assessment of unmanned aerial vehicles imagery for Lelong，Camille quantitative monitoring of wheat crop in small plots C. D SENSORS，2008，8（5）: 3557-3585 3 An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle（UAV）Imagery，Based on Structure from Motion（SfM）Point Clouds Turner，DarrenRemote Sensing，2012，4（5）: 1392-1410 Acquisition，Orthorectification，and Object-based 4 Classification of Unmanned Aerial Vehicle（UAV）Laliberte，Photogrammetric Engineering and Remote Imagery for Rangeland Monitoring Andrea S Sensing，2010，76（6）: 661-672 5 Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera Rosnell，TomiSENSORS，2012，12（1）: 453-480

*摘編自《中國(guó)科學(xué)：技術(shù)科學(xué)》2010年40卷8期：853～860頁(yè)，圖、表及參考文獻(xiàn)已省略。