基于ANSYS的汽車(chē)驅(qū)動(dòng)橋殼的有限元分析
基于ANSYS的汽車(chē)驅(qū)動(dòng)橋殼的有限元分析,基于,ansys,汽車(chē),驅(qū)動(dòng),有限元分析
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設(shè)計(jì)(論文)題目:基于ANSYS的汽車(chē)驅(qū)動(dòng)橋殼的有限元分析
學(xué)生姓名:
任務(wù)書(shū)填寫(xiě)要求
1.畢業(yè)設(shè)計(jì)(論文)任務(wù)書(shū)由指導(dǎo)教師根據(jù)各課題的具體情況填寫(xiě),經(jīng)學(xué)生所在專(zhuān)業(yè)的負(fù)責(zé)人審查、系
(院)領(lǐng)導(dǎo)簽字后生效。此任務(wù)書(shū)應(yīng)在畢業(yè)設(shè)計(jì)(論文)開(kāi)始前一周內(nèi)填好并發(fā)給學(xué)生。
2.任務(wù)書(shū)內(nèi)容必須用黑墨水筆工整書(shū)寫(xiě),不得涂改或潦草書(shū)寫(xiě);或者按教務(wù)處統(tǒng)一設(shè)計(jì)的電子文檔標(biāo)準(zhǔn)格式
(可從教務(wù)處網(wǎng)頁(yè)上下載)打印,要求正文小4號(hào)宋體,1.5倍行距, 打印在 上 。
3.任務(wù)書(shū)內(nèi)填寫(xiě)的內(nèi)容,必須 學(xué)生畢業(yè)設(shè)計(jì)(論文) 的情況 一 , 有 ,應(yīng) 經(jīng) 所在專(zhuān)業(yè)
系(院) 領(lǐng)導(dǎo)審 后 可 填寫(xiě)。
4.任務(wù)書(shū)內(nèi)有 學(xué)院 、 專(zhuān)業(yè) 名 的填寫(xiě),應(yīng)寫(xiě) 文 ,不 寫(xiě) 字?¢。學(xué)生的 學(xué)號(hào) 要寫(xiě)
號(hào),不 £寫(xiě)?后2¥或1¥ 字。
5.任務(wù)書(shū)內(nèi) 要?§文currency1 的填寫(xiě),應(yīng)按'“???fi學(xué)院fl?畢業(yè)設(shè)計(jì)(論文) 寫(xiě)–??的要求書(shū)寫(xiě)。
6.有 · ? ??的填寫(xiě),應(yīng) 按'?標(biāo)GB/T 7408—94“ 據(jù)元 ?”格式、?…?”、?? ‰
? `?–′的要求,一?用?ˉ? 字書(shū)寫(xiě)。˙ 2002·4 2? 或 2002-04-02”。
畢 業(yè) 設(shè) 計(jì)(論 文)任 務(wù) 書(shū)
1.fl畢業(yè)設(shè)計(jì)(論文)課題應(yīng)¨ 的目的:
汽車(chē)驅(qū)動(dòng)橋殼?汽車(chē)的 要?載 ?? ,橋殼的ˇ — 汽車(chē)的有效 用 。由于 況
, 驅(qū)動(dòng)橋殼的 載 ,? 橋殼 a 的情況‰有發(fā)生,fl課題 求??˙?o 橋殼
用 的有效 。
fl課題 ?驅(qū)動(dòng)橋殼的 ?, ANSYS 驅(qū)動(dòng)橋殼?行? ?? 分析,? ?? 分析,
— 分析 ;?后 出改? `, ?行有限元分析。
2.fl畢業(yè)設(shè)計(jì)(論文)課題任務(wù)的內(nèi)容 要求(包括原始 據(jù)、fi術(shù)要求、工作要求 ):
fl課題 ?驅(qū)動(dòng)橋殼的 ?, ANSYS 驅(qū)動(dòng)橋殼?行? ?? 分析,? ?? 分
析,— 分析 ;?后 出改? `, ?行有限元分析。
畢 業(yè) 設(shè) 計(jì)(論 文)任 務(wù) 書(shū)
3. fl畢業(yè)設(shè)計(jì)(論文)課題 果的要求〔包括 ?、實(shí)物 硬 要求〕:
1、 Creo 汽車(chē)驅(qū)動(dòng)橋殼?行實(shí)體
2、在ANSYS軟 ?行有限元 ?的分析
3、得出驅(qū)動(dòng)橋殼不同工作狀態(tài)下各種應(yīng)?云 并?行優(yōu)化設(shè)計(jì)
4、畢業(yè)論文1萬(wàn)字左右(并附 的分析 據(jù))
5、外文?§資料譯文(附原文)3000字
4. 要?§文currency1:
[1]楊波,羅?橋 基于ANSYS汽車(chē)驅(qū)動(dòng)橋殼的有限元分析 [J].機(jī)械研究與應(yīng)用,2005,18(6).
[2]浦廣益.ANSYS Workbench 12 基礎(chǔ)教程與實(shí)例詳? [M].北京: ?水利水電出版社,2010.
[3]宋志安,于濤,李紅艷, .機(jī)械結(jié)構(gòu)有限元分析— ANSYS 與 ANSYS Workbench 工程應(yīng)用 [M].北京:
?防工業(yè)出版社, 2010.
[4]江見(jiàn)鯨,?放龍,?益斌,陸 征.有限元` 應(yīng)用[M].北京:機(jī)械工業(yè)出版社,2006.
[5]譚繼錦.汽車(chē)有限元`[M]北京:人民? 出版社,2005.
[6]林正祥.汽車(chē)驅(qū)動(dòng)橋橋殼動(dòng)?特ˇ分析與— 預(yù)測(cè) [D].[碩士學(xué)¥論文].安徽:合肥工 學(xué),2009.
[7]劉惟?.汽車(chē)車(chē)橋設(shè)計(jì)[M].北京:清華 學(xué)出版社,2004.
[8]盧耀祖,張 慧.汽車(chē)后橋— 驗(yàn)‰ 紋擴(kuò)展與應(yīng) 化的 驗(yàn)研究[J].機(jī)械 ,2001.
[9]褚志 ,鄧兆祥,李偉 .汽車(chē)驅(qū)動(dòng)橋殼結(jié)構(gòu)破壞機(jī)理分析研究[J].汽車(chē)研究與開(kāi)發(fā),2001,(6).
[10]余 龍,董益亮.雜?系統(tǒng)動(dòng)?學(xué)分析的有限元`-汽車(chē)后橋有限元 與分析[J]. ?機(jī)械工
程,2003,14(20).
[11]衛(wèi)?橋.壓縮天然氣城市公?客車(chē)車(chē)身有限元分析與優(yōu)化[D]上海? 學(xué),2003.
[12]朱崢濤,丁 輝.不同厚 驅(qū)動(dòng)橋橋殼有限元分析[J]現(xiàn)?制?工程,2006,11.
[13]李麗.汽車(chē)后橋有限元分析 預(yù)測(cè)[D].吉林:吉林 學(xué),2008.
[14]左曙光, 偉.基于有限元分析的 車(chē)后橋— 分析[J].汽車(chē)工程,2004,26(4).
[15] , , .驅(qū)動(dòng)橋橋殼 ?工況的有限元計(jì) [J].林業(yè)機(jī)械與 工設(shè) ,2004,32(12).
[16]劉斌.橋殼的有限元分析 結(jié)構(gòu)優(yōu)化[J].上海汽車(chē),2004(8).
畢 業(yè) 設(shè) 計(jì)(論 文)任 務(wù) 書(shū)
5.fl畢業(yè)設(shè)計(jì)(論文)課題工作? 計(jì) :
2015.12.05-2016.01.15 ′ 題,填寫(xiě)審題?;指導(dǎo)教師下發(fā)任務(wù)書(shū),學(xué)生查 課題 ?§文currency1、資
料, 寫(xiě)開(kāi)題 。
2016.01.16-2016.02.25 ?開(kāi)題 、外文?§資料 譯文、畢業(yè)設(shè)計(jì)(論文) ;開(kāi)始畢業(yè)設(shè)計(jì)
(論文)。
2016.02.26-2016.04.15具體設(shè)計(jì)或研究 實(shí) , ?畢業(yè)設(shè)計(jì)(論文)草 ,填寫(xiě) ? 查?。
2016.04.16-2016.05.05 論文或設(shè)計(jì) 書(shū)、 料, ?畢業(yè)設(shè)計(jì)(論文)′ ,指導(dǎo) 師審
。
2016.05.06-2016.05.13 ?畢業(yè)設(shè)計(jì) 文檔,學(xué)生準(zhǔn) ;? 教師? 學(xué)生畢業(yè)設(shè)計(jì)(論文)。
2016.05.13-2016.05.26根據(jù)學(xué)院統(tǒng)一安¢,?行畢業(yè)設(shè)計(jì)(論文) 。
所在專(zhuān)業(yè)審查£見(jiàn):
負(fù)責(zé)人: 2016 · 1 22 ?
畢 業(yè) 設(shè) 計(jì)(論 文)開(kāi) 題 報(bào) 告
設(shè)計(jì)(論文)題目:基于ANSYS的汽車(chē)驅(qū)動(dòng)橋殼的有限元分析
學(xué)生姓名:
開(kāi)題報(bào)告填寫(xiě)要求
1.開(kāi)題報(bào)告(含“文獻(xiàn)綜述”)作為畢業(yè)設(shè)計(jì)(論文)答辯委員會(huì)對(duì)學(xué)生答辯資格審查的依據(jù)材料之一。此
報(bào)告應(yīng)在指導(dǎo)教師指導(dǎo)下,由學(xué)生在畢業(yè)設(shè)計(jì)(論文)工作前期內(nèi)完成,經(jīng)指導(dǎo)教師簽署意見(jiàn)及所在專(zhuān)業(yè)審查
后生效;
2.開(kāi)題報(bào)告內(nèi)容必須用黑墨水筆工整書(shū)寫(xiě)或按教務(wù)處統(tǒng)一設(shè)計(jì)的電子文檔標(biāo)準(zhǔn)格式打印,禁止打印在其它紙
上后剪貼,完成后應(yīng)及時(shí)交給指導(dǎo)教師簽署意見(jiàn);
3.“文獻(xiàn)綜述”應(yīng)按論文的框架成文,并 書(shū)寫(xiě)(或打印)在 開(kāi)題報(bào)告 一 目?jī)?nèi),學(xué)生寫(xiě)文獻(xiàn)綜述的
文獻(xiàn)應(yīng) 于15 ( );
4.有 期的填寫(xiě),應(yīng) 按 標(biāo)GB/T 7408—94 據(jù)元 交 格式 交 期 時(shí)
?¢£?¥的要求,一?用§currency1' “書(shū)寫(xiě)。?“2004 4 26 ”或“2004-04-26”。
5 開(kāi)題報(bào)告(文獻(xiàn)綜述)“??按fi? fl –書(shū)寫(xiě),? ?1.5·。
畢 業(yè) 設(shè) 計(jì)(論文) 開(kāi) 題 報(bào) 告
1. ?畢業(yè)設(shè)計(jì)(論文)?題??,”據(jù)所查?的文獻(xiàn)資料,…‰ 寫(xiě) 于1000“? 的文獻(xiàn)綜述:
(1)?題`′的??ˉ?汽車(chē)工業(yè)的˙¨ ?,對(duì)汽車(chē)的? 要求???˙,ˇ— 統(tǒng)的驅(qū)動(dòng)橋殼的設(shè)計(jì)
計(jì) ¢ 經(jīng) ¢ 汽車(chē)設(shè)計(jì)的要求。電子計(jì) 的 及有限元¢的 ¨ ?為驅(qū)動(dòng)橋殼的 ?
的計(jì) 分析 ?? 的a 。由于汽車(chē)驅(qū)動(dòng)橋殼 汽車(chē) 要的 ?? ??,驅(qū)動(dòng)橋殼的? o
汽車(chē)的有效—用 , 此,驅(qū)動(dòng)橋殼應(yīng)? 有 的 ? ? ?的o ????。驅(qū)動(dòng)橋殼
分為整?式橋殼,分 式橋殼 ?式橋殼。整?式橋殼 有 的 ? ?,便于主減¨器的裝配 調(diào)整
維修, 此普遍用于各類(lèi)汽車(chē)上。由于其形狀復(fù)雜, 此應(yīng)?計(jì) 比 困難?!睋?jù)汽車(chē)設(shè)計(jì)理論,驅(qū)動(dòng)橋殼
的常?設(shè)計(jì) ¢將橋殼看成一簡(jiǎn)支梁并校核幾種 型計(jì) 工?下某些?¥斷面的最 應(yīng)?值,再 慮一個(gè)安全
系 ?確¥許用的工作應(yīng)??!ΨN設(shè)計(jì) ¢有很多局限?, 此許多`′‰員利用有限元 ¢對(duì)驅(qū)動(dòng)橋殼進(jìn)?
?計(jì) 分析。(2)?題`′的意義通過(guò)有限元模擬 ¢,分析汽車(chē)驅(qū)動(dòng)橋殼在 同工?下對(duì)應(yīng)的應(yīng)? 變
形,為汽車(chē)驅(qū)動(dòng)橋的 ?評(píng)價(jià)及o 估 提供?所需的 據(jù),也為汽車(chē)安全運(yùn)?提供?必須的依據(jù)。同時(shí),
有限元 ¢的利用,可 降低開(kāi) 成 縮短設(shè)計(jì)開(kāi) 周期 提˙產(chǎn)品質(zhì)量。(3)?題`′的內(nèi)容 ①?題`
′知識(shí)?? 汽車(chē)驅(qū)動(dòng)橋殼 汽車(chē)的主要 ??之一,其形狀復(fù)雜,而汽車(chē)的?駛條?又 千變?nèi)f化的, 此
要精確計(jì) 汽車(chē)?駛時(shí)作用于橋殼各處應(yīng)?的 fl 很困難的。將橋殼復(fù)雜的受?狀?簡(jiǎn)化成三種 型的計(jì)
工?,即 車(chē)輪 受最 的鉛垂?時(shí); 車(chē)輪 受最 切向?時(shí); 及 車(chē)輪 受最 側(cè)向?時(shí)。只要在ˇ三
種?荷計(jì) 工?下橋殼的 ? 到保證,就認(rèn)為?橋殼在汽車(chē)各種?駛條?下 可靠的。有限元¢ 一種
化的 計(jì) ¢,在一¥前提條?下,它可 計(jì) 各種 械零?的幾乎所有幾何部位的應(yīng)? 應(yīng)變。 內(nèi)外
都曾用它分析過(guò)汽車(chē)驅(qū)動(dòng)橋殼的靜 動(dòng)態(tài) ?問(wèn)題,事實(shí)上 型工?的有限元分析計(jì) 更 有 實(shí)的工程意義。
② ?`′內(nèi)容 `′ ¢通過(guò)CATIA建立汽車(chē)驅(qū)動(dòng)橋殼的三維模型,將三維模型導(dǎo)入到ANSYS中?;?
ANSYS的汽車(chē)驅(qū)動(dòng)橋殼的 化有限元模型,在最 垂向?工?下對(duì)橋殼進(jìn)?靜?分析, 到橋殼的應(yīng)?
位移分布??。對(duì)橋殼進(jìn)?模態(tài)分析, 到橋殼1-5 有 動(dòng) 。通過(guò)o 分析, 橋殼各部分
的o 安全系 。分析所 據(jù), 斷驅(qū)動(dòng)橋殼的 ?水 , 而提 化 。 文獻(xiàn) [1] ,
橋 基于ANSYS汽車(chē)驅(qū)動(dòng)橋殼的有限元分析 [J]. 械`′ 應(yīng)用,2005,18(6). [2] .ANSYS
Workbench 12 基 教程 實(shí) [M]. :中 水利水電 ,2010. [3]fi 安,于 , , .
械 有限元分析 ANSYS ANSYS Workbench 工程應(yīng)用 [M]. : ?工業(yè) , 2010.
[4]¢見(jiàn)£,何?¥,何 ?,§ currency1.有限元¢及其應(yīng)用[M]. : 械工業(yè) ,2006. [5]'“?.汽車(chē)
有限元¢[M] :‰?交通 ,2005. [6]?fifl.汽車(chē)驅(qū)動(dòng)橋橋殼動(dòng)???分析 o – [D].
[??學(xué)位論文].安·:? 工 學(xué),2009. [7]?? .汽車(chē)車(chē)橋設(shè)計(jì)[M]. :?? 學(xué) ,2004.
[8]”?…,‰全 .汽車(chē)后橋o ? 時(shí)`′?? 應(yīng)變變化的? `′[J]. 械 ?,2001. [9]? ,
ˉ?fl, ˙ .汽車(chē)驅(qū)動(dòng)橋殼 ¨ 理分析`′[J].汽車(chē)`′ 開(kāi) ,2001,(6). [10]?成¥,?
.雜交系統(tǒng)動(dòng)?學(xué)分析的有限元¢-汽車(chē)后橋有限元建模 分析[J].中 械工程,2003,14(20). [11]
? 橋.?縮ˇ— 交 車(chē)車(chē) 有限元分析 化[D]上 交通 學(xué),2003. [12] , 成 .
同 ?驅(qū)動(dòng)橋橋殼有限元分析[J] 工程,2006,11. [13] .汽車(chē)后橋有限元分析及 –
[D]. ?: ? 學(xué),2008. [14]?? ,a˙.基于有限元分析的 車(chē)后橋o 分析[J].汽車(chē)工
程,2004,26(4). [15] ,??,?o .驅(qū)動(dòng)橋橋殼 型工?的有限元計(jì) [J].?業(yè) 械 工設(shè)
,2004,32(12). [16]??.橋殼的有限元分析及 化[J].上 汽車(chē),2004(8).
畢 業(yè) 設(shè) 計(jì)(論文) 開(kāi) 題 報(bào) 告
2. ?題要`′或 的問(wèn)題 擬 用的`′ (? ):
一 ?題`′及 的問(wèn)題 1 ?題`′的問(wèn)題:通過(guò)對(duì)汽車(chē)驅(qū)動(dòng)橋殼進(jìn)?有限元分析,`′汽車(chē)驅(qū)動(dòng)橋殼的
靜動(dòng)態(tài)??,為驅(qū)動(dòng)橋殼的 化設(shè)計(jì)提供 2 ?題 的問(wèn)題: `′需要”據(jù)分析所 據(jù), 斷橋殼
的 ?水 , 而提 化 。 `′ (? ) 1 查?文獻(xiàn)資料,? 汽車(chē)驅(qū)動(dòng)橋殼 ? 模
態(tài)分析ˇ一 ?的 內(nèi)外`′ 狀及 ? ,? 汽車(chē)驅(qū)動(dòng)橋殼的主要 ?; 2 學(xué)?CATIA建立汽
車(chē)驅(qū)動(dòng)橋殼的三維模型; 3 學(xué)?有限元的基 理論 ??,為后 工作 ; 4 對(duì)汽車(chē)驅(qū)動(dòng)橋殼進(jìn)?
靜態(tài)分析(三種 型工?的分析:最 鉛垂?工? 最 動(dòng)?工? 最 側(cè)向?工?) 模態(tài)分析(計(jì) 驅(qū)
動(dòng)橋殼的 動(dòng)模態(tài) 有 ); 5 分析所 果, 斷橋殼的 ?水 。
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1.對(duì)“文獻(xiàn)綜述”的評(píng)語(yǔ):
針對(duì)?題所涉及的問(wèn)題 泛?讀文獻(xiàn),并 對(duì)?題`′ ?的 狀 動(dòng)態(tài) ?前? 進(jìn)?綜?分析 評(píng)述,
符?文獻(xiàn)綜述要求。
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對(duì)?題所涉及的`′內(nèi)容在 有相 專(zhuān)業(yè)知識(shí)的基 上,進(jìn)一步深入學(xué)?相 軟?,應(yīng) ?期完成 次畢
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畢 業(yè) 設(shè) 計(jì)(論 文)外 文 參 考 資 料 及 譯 文
譯文題目: Autonomous Intelligent Vehicles
自動(dòng)駕駛智能汽車(chē)
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Autonomous Intelligent Vehicles
1.1 Research Motivation and Purpose
Autonomous intelligent vehicles are generic technology sets to augment vehicle autonomous driving entirely or in part for autonomous and safety purposes. Fundamentally, autonomous intelligent vehicles refer to many mobile robot technologies. In principle, we consider autonomous intelligent vehicles as mobile robot platforms in this book. Hence, an intelligent vehicle consists of four fundamental technologies: environment perception and modeling, localization and map building, path planning and decision-making, and motion control [26], shown in Fig. 1.1.
The dreams of a human being are the power and source of pushing the world forward. The National Research Council once predicted that the core weapon in the twentieth century would be the tank, while that in the twenty-first century—an unmanned battle system [1]. Moreover, a third of the U.S. military ground vehicles must be unmanned by 2015. Therefore, since 1980s, the Defense Advanced Research Projects Agency (DARPA) initiated a new project, namely the unmanned battle project. Its goal is to design a car which can autonomously implement navigation, obstacle avoidance, and path planning. Afterwards, it opened an intelligent vehicle era. Moreover, the U.S. Department of Energy launched a ten year robot and intelligent system plan (1986–1995), and also the space robot plan. In terms of space exploration, the National Aeronautics and Space Administration (NASA) has developed several wheeled rovers, such as Spirit and Opportunity, for science explorations.1
A major concern associated with the rapid growth in automotive production is an increase in traffic congestion and accidents [36]. To solve the problem, the governments all over the world have been increasing funds to improve the traffic infrastructure, enforce traffic laws, and educate drivers about traffic regulations. In addition, research institutes have launched R&D projects in driver assistance and safety warning systems. Therefore, in the last decade, many research works in the area of intelligent vehicles all over the world led to Intelligent Transportation Systems (ITS) for improving road safety and reducing traffic accidents [7]. Autonomous intelligent vehicles are now widely applied to Driver Assistance and Safety Warning Systems (DASWS) [36], such as Forward Collision Warning [9, 27], Adaptive Cruise Control [32], Lane Departure Warning [16]. In recent years, with the development of economy and society, the issues of traffic safety, energy shortage, and environment pollution became more serious. Those problems then led to higher volumes of research and applications. Toward this end, combining vehicles, drivers and lanes together, we can implement better traffic capacity and traffic safety using computer control, artificial intelligence and communication technologies [3].
The most important reasons for the large numbers of traffic accidents are burdensome driving and fatigue driving. When driving on the traffic congestion lanes, drivers have to do a lot of operations, such as shifting and pulling clutches, and they have to complete 20 to 30 coordination operations of hand and foot movements each minute. With the economic development and the increase of vehicle ownership, the number of non-professional drivers are rising, leading to frequent traffic accidents. As a result, traffic accidents have become the first public nuisance in modern society. Traffic problems have troubled the whole world, and then, the question of how to improve traffic safety has become an urgent social issue. Lane departure systems, fatigue detection systems, and automatic cruise control can greatly reduce driver’s workload and improve transportation system safety.
The widely application prospects of intelligent vehicles promote the development of transportation systems which attracts a growing number of research institutions and auto manufacturers. The DARPA had held the Grand Challenges and the Ur- ban Challenge since 2004. Their goal is to develop autonomous intelligent vehicles capable of both perceiving various environments, such as desert trails, roads, and urban areas, and navigating at high speeds2 [5, 30, 31]. In the first Grand Challenge, CMU’s Sandstorm went for 7.4 miles from the start, opening the possibilities of autonomous capability [30]. In 2005, five vehicles, namely Stanley, Sand- storm, Highlander, Kat-5, and Terra-Max, were able to complete that challenge, and Stanley took the first place ahead of Sandstorm [31]. After the success of the two Grand Challenges, the DARPA organized the Urban Challenge [5]. In the Urban Challenge, based on the technical reports of implementing safe and capable autonomous vehicles, the DARPA allowed 53 teams to demonstrate how they navigate simple urban driving scenes. After these demonstrations, only 36 teams were invited to attend the National Qualification Event (NQE). Finally, only 11 teams were qualified for the Urban Challenge Final Event (UCFE). In China, the 2008 Beijing Olympic Games whose slogans were Hi-tech Olympics and Green Olympics adopted many advanced traffic management systems, intelligent vehicles, electric vehicles for improving vehicle safety performance, reducing pollution, easing traffic congestion. Consequently, those innovations drew attention of many researchers. In 2011, China released ten leading edge technologies and modern transportation technologies among which were technologies aiming at developing intelligent vehicles. Moreover, the National Natural Science Foundation of China launched the state key development plan in 2008, so that audio-visual information based cognizing computation3 could integrate human–computer interfaces, computer vision, language understanding, and cooperative computing. Finally, upon those achievements, the goal of this plan is to develop autonomous intelligent vehicles which are capable of both perceiving natural environment and making intelligent decisions. Meanwhile, similar to the Grand Challenge supported by DARPA, the plan holds the Future Challenge each year.
The research on intelligent vehicles can greatly facilitate the rapid development of other disciplines, such as exploring planets. The U.S. Mars vehicles Spirit and Opportunity play an irreplaceable role in exploring Mars and the vast universe be- yond Mars [13, 23]. In China, the government released the White Paper “China Aerospace” in November 2000, which targets exploring the moon and other planets in the near future. Furthermore, space mobile robots are the key part for exploring planets which could benefit the utilizing solar energy.
1.2 The Key Technologies of Intelligent Vehicles
As we mentioned before, intelligent vehicles are a set of intelligent agents which integrate multi-sensor fusion based environment perception and modeling, localization and map building, path planning and decision-making, and motion control, shown in Fig. 1.1. The environment perception and modeling module is responsible for sensing environment structures in a multi-sensor way and providing a model of the surrounding environment. Here, the environment model includes a list of moving objects,that of static obstacles, vehicle position relative to the current road, the road shape, etc. Finally, this module provides the environment model and the local map to the localization and map building module by processing the original data, vision, lidar, and radar. The second module, vehicle localization and map building, is to use geometric feature location estimate in the map to determine the vehicle’s position, and to interpret sensor information to estimate the locations of geometric features in a global map. As a result, the second module yields a global map based on the environment model and a local map. The path planning and decision-making module is to assist in ensuring that the vehicle is operated in accordance with the rules of the ground, safety, comfortability, vehicle dynamics, and environment con- texts. Hence, this module can potentially improve mission efficiency and generate the desired path. The final module, motion control, is to execute the commands necessary to achieve the planned paths, thus yielding interaction between the vehicle and its surrounding environment. A brief introduction of these modules is presented below.
1.2.1 Multi-sensor Fusion Based Environment Perception and Modeling
Figure 1.2 illustrates a general environment perception and modeling framework. From this framework, we can see that: (i) The original data are collected by various sensors; (ii) Various features are extracted from the original data, such as road (object) colors, lane edges, building contours; (iii) Semantic objects are recognized using classifiers, and consist of lanes, signs, vehicles, pedestrians; (iv) We can de- duce driving contexts, and vehicle positions.
Multi-sensor fusion
Multi-sensor fusion is the basic framework of intelligent vehicles for better sensing surrounding environment structures, and detecting objects/obstacles. Roughly, the sensors used for surrounding environment perception are divided into two categories: active and passive ones. Active sensors include lidar, radar, ultrasonic and radio, while the commonly-used passive sensors are infrared and visual cameras. Different sensors are capable of providing different detection precision and range, and yielding different effects on environment. That is, combining various sensors could cover not only short-range but also long-range objects/obstacles, and also work in various weather conditions. Furthermore, the original data of different sensors can be fused in low-level fusion, high-level fusion, and hybrid fusion [4, 14, 20, 35].
Dynamic Environment Modeling
Dynamic environment modeling based on moving on-vehicle cameras plays an important role in intelligent vehicles [17]. However, this is extremely challenging due to the combined effects of ego-motion, blur, light changing. Therefore,traditional methods for gradual illumination change, small motion objects [28] such as background subtraction, do not work well any more, even those that have been widely used in surveillance applications. Consequently, more and more approaches try to handle these issues [2, 17]. Unfortunately, it is still an open problem to reliably model and update background.
To select different driving strategies, several broad scenarios are usually considered in path planning and decision-making, when navigating roads, intersections, parking lots, jammed intersections. Hence, scenario estimators are helpful for further decision-making, which is commonly used in the Urban Challenge.
Object Detection and Tracking
In general, in a driving environment, we are interested in static/dynamic obstacles, lane markings, traffic signs, vehicles, and pedestrians. Correspondingly, object detection and tracking are the key parts of environment perception and modeling.
1.2.2 Vehicle Localization and Map Building
The goal of vehicle localization and map building is to generate a global map by combining the environment model, a local map and global information. In autonomous driving, vehicle localization is either to estimate road geometry or to localize the vehicle relative to roads under the conditions of known maps or un- known maps. Hence, vehicle localization refers to road shape estimation, position filtering, transforming the vehicle pose into a coordinate frame. For vehicle localization, we face several challenges as follows: (i) Usually, the absolute positions from GPS/DGPS and its variants are insufficient due to signal transmission; (ii) The path planning and decision-making module needs more than just the vehicle absolute position as input; (iii) Sensor noises greatly affect the accuracy of vehicle localization. Regarding the first issue, though the GPS and its variants have been widely used in vehicle localization, its performance could degrade due to signal blockages and reflections of buildings and trees. In the worst case, Inertia Navigation Sys- tem (INS) can maintain a position solution. As for the second issue, local maps fusing laser, radar, and vision data with vehicle states are used to locate and track both static/dynamic obstacles and lanes. Furthermore, global maps could contain lane geometric information, lane makings, step signs, parking lots, check points and provide global environment information. Referring to the third issue, various noise modules are considered to reduce localization error [26].
自動(dòng)駕駛智能汽車(chē)
1.1研究的初衷和目的
自主智能車(chē)是通用的技術(shù)設(shè)置,以增加車(chē)輛自主駕駛或部分自主駕駛的安全性為目的。從根本上說(shuō),自主的智能車(chē)輛是指多移動(dòng)機(jī)器人技術(shù)。原則上,我們認(rèn)為自主智能車(chē)應(yīng)該放在移動(dòng)機(jī)器人平臺(tái)這本書(shū)中。因此,智能車(chē)輛包括四個(gè)基本技術(shù):環(huán)境感知和建模,地圖創(chuàng)建與定位,路徑規(guī)劃和決策,以及運(yùn)動(dòng)控制。[26] 如圖1.1所示。
一個(gè)人的夢(mèng)想是推動(dòng)世界前進(jìn)的動(dòng)力和源泉。美國(guó)國(guó)家研究委員會(huì)曾預(yù)測(cè),二十世紀(jì)的核心武器是坦克,而在二十一世紀(jì)是無(wú)人戰(zhàn)斗系統(tǒng)[ 1 ] 。此外到2015年,美國(guó)軍隊(duì)的地面無(wú)人車(chē)輛要達(dá)到總車(chē)輛的三分之一。因此,20世紀(jì)80年代以來(lái),美國(guó)國(guó)防高級(jí)研究計(jì)劃局(DARPA )啟動(dòng)了一個(gè)新項(xiàng)目,即無(wú)人駕駛戰(zhàn)斗項(xiàng)目。它的目標(biāo)是設(shè)計(jì)一款能實(shí)現(xiàn)自主導(dǎo)航,避障,路徑規(guī)劃的車(chē)。此后,它開(kāi)啟了一個(gè)智能汽車(chē)時(shí)代。此外,美國(guó)能源部推出一個(gè)十年機(jī)器人和智能系統(tǒng)規(guī)劃( 1986-1995 ) ,也是太空機(jī)器人計(jì)劃。在太空探索方面,美國(guó)國(guó)家航空和航天局(NASA )已經(jīng)開(kāi)發(fā)了數(shù)個(gè)輪式探測(cè)器,如勇氣號(hào)和機(jī)遇號(hào)等。
隨著汽車(chē)產(chǎn)量的快速增長(zhǎng),出現(xiàn)了交通擁堵和交通事故等主要問(wèn)題。[36]。為了解決這個(gè)問(wèn)題,世界各地的政府一直在加大資金改善交通基礎(chǔ)設(shè)施,加強(qiáng)交通法規(guī),和教給司機(jī)交通法規(guī)。此外,研究機(jī)構(gòu)開(kāi)展了駕駛員輔助和安全預(yù)警系統(tǒng)研發(fā)項(xiàng)目。因此,在過(guò)去的十年中,世界各地在智能車(chē)系統(tǒng)(ITS)上開(kāi)展研究工作,以改善道路安全,減少區(qū)域交通事故來(lái)實(shí)現(xiàn)智能交通。[7]。巡航智能車(chē)現(xiàn)在被廣泛應(yīng)用到駕駛輔助和安全預(yù)警系統(tǒng)(DASWS)[36],如前部碰撞警告[9,27],自適應(yīng)巡航控制系統(tǒng)[32],車(chē)道偏離警告[16]。近年來(lái),隨著社會(huì)經(jīng)濟(jì)的發(fā)展,交通安全,能源短缺和環(huán)境污染問(wèn)題變得更加嚴(yán)重。這些問(wèn)題則需要更高層次的研究。為實(shí)現(xiàn)這一目標(biāo),把車(chē)輛,駕駛員和通道結(jié)合起來(lái),我們就可以用電腦控制,人工智能和通訊技術(shù)才能實(shí)現(xiàn)更好的通行能力和交通安全[3]。
發(fā)生大量交通事故的最主要的原因是繁重的駕駛和疲勞駕駛。當(dāng)交通擁堵時(shí),司機(jī)必須做很多操作,比如踩離合器和拉手剎,他們必須每分鐘完成20至30次的協(xié)調(diào)動(dòng)作。隨著經(jīng)濟(jì)的發(fā)展和汽車(chē)擁有量的增加,非職業(yè)司機(jī)的人數(shù)正在上升,導(dǎo)致交通事故頻發(fā)。這樣一來(lái),交通事故已經(jīng)成為現(xiàn)代社會(huì)第一公害。交通問(wèn)題已經(jīng)困擾了整個(gè)世界,那么,如何提高行車(chē)安全已成為一個(gè)緊迫的社會(huì)問(wèn)題。車(chē)道偏離系統(tǒng),疲勞檢測(cè)系統(tǒng),自動(dòng)巡航控制系統(tǒng)可大大降低駕駛員的勞動(dòng)強(qiáng)度,提高交通系統(tǒng)的安全性。
智能汽車(chē)應(yīng)用前景廣泛,促進(jìn)了交通運(yùn)輸系統(tǒng)的發(fā)展,吸引了越來(lái)越多的研究機(jī)構(gòu)和汽車(chē)制造商。從2004年開(kāi)始,美國(guó)國(guó)防高級(jí)研究計(jì)劃局開(kāi)始舉辦智能汽車(chē)大挑戰(zhàn)和城市挑戰(zhàn)賽。他們的目標(biāo)是開(kāi)發(fā)自主智能車(chē)輛在高速行駛的情況下能夠感知各種環(huán)境的功能,如沙漠小徑,道路和城市地區(qū)。[5,30,31].在第一次大挑戰(zhàn)中CMU的沙塵暴從一開(kāi)始行駛到7.4英里處開(kāi)始實(shí)現(xiàn)自主智能。[30].2005年有5輛汽車(chē)能夠完成這一挑戰(zhàn),分別是斯坦利、沙塵暴、漢蘭達(dá)、
kat-5和TerraMax。斯坦利取代了沙塵暴第一的位置。[31]. 這兩個(gè)大挑戰(zhàn)成功后,DARPA舉辦了城市挑戰(zhàn)賽。在城市挑戰(zhàn)賽中,基于車(chē)輛自主安全的技術(shù)檢測(cè),DARPA允許53支車(chē)隊(duì)展示他們是如何在城市中簡(jiǎn)單駕駛的場(chǎng)景。在這些展示之后,只有36支車(chē)隊(duì)有資格被邀請(qǐng)參加全國(guó)性的賽事。最后,只有11支車(chē)隊(duì)有資格進(jìn)入城市挑戰(zhàn)賽的決賽。在中國(guó),2008年北京奧運(yùn)會(huì)的宗旨是科技奧運(yùn)、綠色奧運(yùn),采用了許多先進(jìn)的交通管理系統(tǒng)和電動(dòng)汽車(chē),來(lái)提高車(chē)輛的安全性能,減少污染,緩解交通堵塞。因此,這些創(chuàng)新吸引了眾多研究者的關(guān)注。2011,中國(guó)發(fā)布了十大前沿技術(shù)和現(xiàn)代交通技術(shù),其中以開(kāi)發(fā)智能車(chē)技術(shù)為目標(biāo)。此外,2008年,中國(guó)國(guó)家自然科學(xué)基金推出了國(guó)家重點(diǎn)發(fā)展規(guī)劃,以視聽(tīng)信息為基礎(chǔ)
使認(rèn)知計(jì)算來(lái)整合人機(jī)界面、計(jì)算機(jī)視覺(jué)、語(yǔ)言理解、協(xié)同計(jì)算。最后,該計(jì)劃的目標(biāo)是開(kāi)發(fā)能夠自主認(rèn)知自然環(huán)境和智能決策的智能車(chē)輛,直到這些計(jì)劃能夠達(dá)到要求。同時(shí),類(lèi)似于DARPA支持的大挑戰(zhàn),這種賽事在未來(lái)的每年都會(huì)舉行。
對(duì)智能車(chē)輛的研究可以大大促進(jìn)其他學(xué)科的快速發(fā)展,如探索行星。美國(guó)火星車(chē)勇氣號(hào)和機(jī)遇號(hào)在探索火星和浩瀚的宇宙方面起到了不可替代的作用。[13,23].在中國(guó),2000年11月中國(guó)政府發(fā)布了有關(guān)“中國(guó)航天”的白皮書(shū),它的目標(biāo)是在不久的將來(lái)探索月球和其他行星。此外,空間移動(dòng)機(jī)器人是探索行星的關(guān)鍵,這可能會(huì)受益于利用太陽(yáng)能。
1.2智能汽車(chē)的關(guān)鍵技術(shù)
正如我們之前提到的,智能車(chē)是一套集環(huán)境感知和建模,定位和地圖構(gòu)建,路徑規(guī)劃和決策,以及運(yùn)動(dòng)控制于一身的多傳感器系統(tǒng),如圖1.1所示。環(huán)境感知和建模模塊負(fù)責(zé)用于感測(cè)環(huán)境結(jié)構(gòu)的多感官方式,并提供周?chē)h(huán)境的模型。在這里,環(huán)境模型,包括一系列的運(yùn)動(dòng)對(duì)象,即靜態(tài)障礙物,車(chē)輛相對(duì)于當(dāng)前道路的位置,路的形狀,等等。最后,該模塊提供了環(huán)境模型和本地化地圖構(gòu)建模塊,這些模塊通過(guò)視覺(jué),激光雷達(dá)和雷達(dá)處理原始數(shù)據(jù)。第二模塊,車(chē)輛定位和地圖創(chuàng)建模塊,是使用幾何特征來(lái)估計(jì)其在地圖中的位置以確定車(chē)輛的位置,并解釋傳感器信息來(lái)估算其在世界地圖中的位置。其結(jié)果是,第二模塊根據(jù)環(huán)境模型和局部映射得到全局地圖。路徑規(guī)劃和決策模塊的作用是確保車(chē)輛按照地面,安全性,舒適性,車(chē)輛動(dòng)力學(xué),和環(huán)境背景的規(guī)則操作。因此,該模塊可以提高任務(wù)效率和生成所需的路徑。最后的模塊,運(yùn)動(dòng)控制模塊是執(zhí)行車(chē)輛及其周?chē)h(huán)境之間相互作用的命令,以實(shí)現(xiàn)計(jì)劃的路徑。這些模塊的簡(jiǎn)單介紹如下。
1.2.1基于多傳感器融合的環(huán)境感知與建模
圖1.2示出了一般的環(huán)境感知和建??蚣堋倪@個(gè)框架,我們可以看出:(i)原始數(shù)據(jù)是由不同的傳感器收集;(ii)從原始數(shù)據(jù)中可以提取不同的特征,如道路(對(duì)象)的顏色,車(chē)道邊,建筑輪廓;(iii)通過(guò)分類(lèi)器認(rèn)知語(yǔ)義對(duì)象包括車(chē)道標(biāo)志,車(chē)輛,行人;(iv)我們可以在駕駛環(huán)境下推斷出車(chē)輛的位置。
1.多傳感器融合
多傳感器融合是智能車(chē)輛的基本框架,以便更好地感知周?chē)沫h(huán)境結(jié)構(gòu),以及探測(cè)物體/障礙的基本框架。粗略地說(shuō),用于周?chē)h(huán)境感知的傳感器分為2類(lèi):主動(dòng)和被動(dòng)的。有源傳感器包括激光雷達(dá),雷達(dá),超聲波和無(wú)線(xiàn)電,而常用的無(wú)源傳感器是紅外線(xiàn)和視覺(jué)照相機(jī)。不同的傳感器能夠提供不同的檢測(cè)精度和范圍,并對(duì)環(huán)境產(chǎn)生不同的影響。也就是說(shuō),結(jié)合各種傳感器不僅可以覆蓋短距離,而且長(zhǎng)距離的對(duì)象/障礙也可以覆蓋,并在各種天氣條件下工作。此外,不同傳感器的原始數(shù)據(jù)可以被低級(jí)別,高級(jí)別和混合級(jí)別的傳感器融合。[4,14,20,35].
2.動(dòng)態(tài)環(huán)境建模
基于動(dòng)態(tài)環(huán)境建模的車(chē)載攝像頭在智能車(chē)輛中起著重要的作用。[17]. 然而,由于自運(yùn)動(dòng),模糊,光變化的綜合影響這是極具挑戰(zhàn)性的。因此,對(duì)于傳統(tǒng)的漸進(jìn)光照變化的方法,小型運(yùn)動(dòng)物體,如背景減除,工作的不是很好,即使是那些已被廣泛用于在監(jiān)視中的應(yīng)用程序。因此,越來(lái)越多的方法用來(lái)處理這些問(wèn)題[2,17]. 不幸的是,以可靠的模型和更新背景,它仍然是一個(gè)懸而未決的問(wèn)題。
當(dāng)在導(dǎo)航道路,十字路口,停車(chē)場(chǎng),擁擠的十字路口要選擇不同的駕駛策略時(shí),一些廣泛的場(chǎng)景通常被認(rèn)為在路徑規(guī)劃和決策。因此,場(chǎng)景估計(jì)有助于進(jìn)一步的決策,這經(jīng)常用于城市挑戰(zhàn)中。
3、目標(biāo)檢測(cè)與跟蹤
在一般情況下,在駕駛環(huán)境中,我們要注意的的是靜態(tài)/動(dòng)態(tài)障礙,車(chē)道標(biāo)記,交通標(biāo)志,車(chē)輛和行人。相應(yīng)地,目標(biāo)檢測(cè)和跟蹤是環(huán)境感知與建模的關(guān)鍵部分。
1.2.2車(chē)輛定位與地圖創(chuàng)建
車(chē)輛定位與地圖構(gòu)建的目標(biāo)是通過(guò)結(jié)合環(huán)境模型、局部地圖和全局信息來(lái)生成全局地圖。在自動(dòng)駕駛過(guò)程中,車(chē)輛定位是可以估計(jì)道路幾何形狀或相對(duì)于道路的車(chē)輛的已知地圖或未知的地圖。因此,車(chē)輛定位是指道路形狀估計(jì),過(guò)濾,改變車(chē)輛構(gòu)成坐標(biāo)系。對(duì)于車(chē)輛定位,我們面臨的挑戰(zhàn)如下:(i)通常,來(lái)自GPS / DGPS的絕對(duì)位置會(huì)由于信號(hào)傳輸導(dǎo)致位置發(fā)生變化;(ii)路徑規(guī)劃和決策模塊需要的不僅僅是輸入車(chē)輛的絕對(duì)位置;(iii)傳感器噪聲大大影響車(chē)輛定位的準(zhǔn)確性。關(guān)于第一個(gè)問(wèn)題,雖然在GPS和其變體已在車(chē)輛的定位得到廣泛應(yīng)用,它的性能可能會(huì)由于建筑物和樹(shù)木對(duì)信號(hào)的堵塞和反射而降低。在最壞的情況下,慣性導(dǎo)航系統(tǒng)(INS)可以保持一個(gè)位置的解決方案。至于第二個(gè)問(wèn)題,本地地圖會(huì)融合激光,雷達(dá)和視覺(jué)數(shù)據(jù)與車(chē)輛狀態(tài)來(lái)用于定位和追蹤靜態(tài)/動(dòng)態(tài)障礙物和車(chē)道。此外,全球地圖可能包含車(chē)道的幾何信息,車(chē)道標(biāo)線(xiàn),標(biāo)牌步,停車(chē)場(chǎng),檢查點(diǎn)和提供全球環(huán)境信息。談到第三個(gè)問(wèn)題,各種噪聲模塊被視為降低定位誤差。[26].
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