發(fā)布時(shí)間：2018-06-16 10:19

  本文選題：自主泊車 + 時(shí)空分割�。� 參考：《浙江大學(xué)》2016年碩士論文

【摘要】：和人類駕駛員相比,無人車能夠更加全面的掌握即時(shí)路況并及時(shí)對(duì)汽車巡航狀態(tài)進(jìn)行調(diào)整,從而改善交通擁堵狀況、避免了人為失誤造成的交通事故和傷亡,因此近些年來無人駕駛技術(shù)發(fā)展迅速。許多半自動(dòng)駕駛技術(shù)在汽車上已經(jīng)得到了大規(guī)模的普及,比如緊急制動(dòng),定速巡航和車道保持等。然而,在行車環(huán)境建模、避障軌跡優(yōu)化等方面還需要深入的研究。如車載傳感器精度有限的情況下如何對(duì)障礙環(huán)境建模,如何處理環(huán)境中意外出現(xiàn)的動(dòng)態(tài)障礙物,如何針對(duì)不同的泊車位實(shí)現(xiàn)標(biāo)準(zhǔn)化的軌跡優(yōu)化算法設(shè)計(jì),如何優(yōu)化智能無信號(hào)燈路口下的多車避障軌跡等都是值得研究的問題。本文用動(dòng)態(tài)優(yōu)化全聯(lián)立算法對(duì)上述問題做了一些研究。主要內(nèi)容和成果如下：1.對(duì)城市環(huán)境下的自主泊車問題,采用MPCC和R函數(shù)方法對(duì)車位環(huán)境建模,與車輛運(yùn)動(dòng)學(xué)模型、物理約束共同構(gòu)成了行車系統(tǒng)模型,構(gòu)造了聯(lián)立框架下的自主泊車動(dòng)態(tài)優(yōu)化命題。采用有限元正交配置法將原命題離散化為非線性數(shù)學(xué)規(guī)劃問題,由非線性求解器高效求解得到具有時(shí)間信息的可直接用于指導(dǎo)車輛跟蹤的泊車軌跡。2.針對(duì)自主泊車軌跡動(dòng)態(tài)優(yōu)化命題含有較多復(fù)雜約束可能引起的求解困難,提出了時(shí)空分割策略來增強(qiáng)優(yōu)化算法的收斂性。通過在軌跡優(yōu)化命題中引入吸引區(qū)、塌縮區(qū)來分割泊車空間,將非線性的復(fù)雜環(huán)境約束在割裂空間下進(jìn)行簡(jiǎn)化,重構(gòu)泊車軌跡優(yōu)化命題。仿真實(shí)驗(yàn)證明了時(shí)空分割策略的有效性。3.在城市環(huán)境下基于信息完整假設(shè)進(jìn)行多車軌跡優(yōu)化的全局規(guī)劃算法研究。在多車模型、環(huán)境模型下融合了車-車、車與動(dòng)態(tài)可預(yù)測(cè)障礙物的復(fù)雜避障約束,構(gòu)造多車協(xié)作避讓軌跡優(yōu)化命題。數(shù)值實(shí)驗(yàn)表明了基于全聯(lián)立的全局規(guī)劃算法的有效性。4.對(duì)于環(huán)境感知不完整的車輛軌跡規(guī)劃問題,基于障礙環(huán)境預(yù)測(cè)模型進(jìn)行局部滾動(dòng)優(yōu)化。運(yùn)用假設(shè)靜態(tài)法、速度切線預(yù)測(cè)法、完整預(yù)測(cè)法對(duì)障礙環(huán)境建模,根據(jù)障礙車輛進(jìn)出我車的沖突檢測(cè)域來切換重構(gòu)行車系統(tǒng)軌跡優(yōu)化命題,并比較了預(yù)測(cè)模型對(duì)車輛避障性能的影響。
[Abstract]:Compared with human drivers, the UAV can master the real-time traffic conditions more comprehensively and adjust the vehicle cruising state in time, thus improving the traffic congestion and avoiding the traffic accidents and casualties caused by human error. As a result, driverless technology has developed rapidly in recent years. Many semi-autonomous driving techniques have been widely used in automobiles, such as emergency braking, constant speed cruising and lane maintenance. However, further research is needed in traffic environment modeling and obstacle avoidance trajectory optimization. For example, how to model the obstacle environment, how to deal with the unexpected dynamic obstacles, how to design the standardized trajectory optimization algorithm for different parking spaces, how to model the obstacle environment under the condition of limited precision of the vehicle sensor, how to deal with the unexpected dynamic obstacles in the environment, It is worth studying how to optimize the trajectory of multi-vehicle obstacle avoidance at the intersection of intelligent signal-free. In this paper, the dynamic optimization algorithm is used to study the above problems. The main contents and results are as follows: 1. For the problem of autonomous parking in urban environment, the vehicle parking environment is modeled by MPCC and R function method, and the vehicle kinematics model and physical constraints are combined to form the vehicle system model, and the dynamic optimization proposition of autonomous parking under the simultaneous frame is constructed. The finite element orthogonal collocation method is used to discretize the original proposition into a nonlinear mathematical programming problem. The nonlinear solver is used to efficiently solve the parking trajectory with time information which can be directly used to guide the vehicle tracking. In view of the difficulty of solving the dynamic optimization proposition of autonomous parking trajectory with more complex constraints, a spatio-temporal segmentation strategy is proposed to enhance the convergence of the optimization algorithm. By introducing attraction region and collapsing area into the trajectory optimization proposition, the parking space is separated, and the nonlinear complex environment constraint is simplified in the split space, and the parking trajectory optimization proposition is reconstructed. The simulation results show that the spatio-temporal segmentation strategy is effective. The global planning algorithm for multi-vehicle trajectory optimization based on the assumption of information integrity in urban environment is studied. Under the multi-vehicle model and environment model, the complex obstacle avoidance constraints of vehicle-vehicle, vehicle-vehicle and dynamic predictable obstacles are combined, and the proposition of multi-vehicle cooperative avoidance trajectory optimization is constructed. Numerical experiments show the effectiveness of the global programming algorithm based on full synchronization. 4. 4. For the vehicle trajectory planning problem with incomplete environmental perception, the local rolling optimization based on the obstacle environment prediction model is carried out. Using the hypothesis static method, the velocity tangent prediction method, the complete forecast method to model the obstacle environment, according to the obstacle vehicle entering and leaving our vehicle conflict detection domain to switch the reconstruction train system trajectory optimization proposition, The effect of prediction model on vehicle obstacle avoidance performance is compared.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U463.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 韓鵬;單家元;;基于Radau偽譜法的重復(fù)使用運(yùn)載器再入軌跡優(yōu)化(英文)[J];控制理論與應(yīng)用;2013年08期

2 林蓁蓁;李慶;梁艷菊;陳大鵬;;一種基于路徑規(guī)劃的自動(dòng)平行泊車算法[J];計(jì)算機(jī)應(yīng)用研究;2012年05期

3 郭偉;楊明;王冰;王春香;;基于博弈論的路口多車協(xié)作算法[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年S2期

4 肖永劍;楊建國(guó);王兆安;;基于占先度的駕駛員沖突避碰決策模型[J];交通運(yùn)輸工程學(xué)報(bào);2009年05期

5 郭孔輝;姜輝;張建偉;丁海濤;;基于模糊邏輯的自動(dòng)平行泊車轉(zhuǎn)向控制器[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2009年S2期

6 吳堅(jiān),王小椿,姜虹,鄭康平,李潤(rùn)方;多邊形的隱函數(shù)表示法[J];計(jì)算機(jī)工程與應(yīng)用;2003年32期

7 劉金義,張紅玲;R-函數(shù)理論介紹及其應(yīng)用評(píng)述[J];工程圖學(xué)學(xué)報(bào);2001年02期

相關(guān)博士學(xué)位論文 前7條

1 朱株;基于三維數(shù)據(jù)面向無人車導(dǎo)航的非結(jié)構(gòu)化場(chǎng)景理解[D];浙江大學(xué);2014年

2 孫勇;基于改進(jìn)Gauss偽譜法的高超聲速飛行器軌跡優(yōu)化與制導(dǎo)[D];哈爾濱工業(yè)大學(xué);2012年

3 陳楊;基于微分—代數(shù)混合方程機(jī)理模型的非線性預(yù)測(cè)控制[D];浙江大學(xué);2011年

4 王志強(qiáng);微分代數(shù)方程動(dòng)態(tài)優(yōu)化問題的快速求解策略研究[D];浙江大學(xué);2012年

5 陳偉鋒;大規(guī)模復(fù)雜過程系統(tǒng)的高性能優(yōu)化理論與方法研究[D];浙江大學(xué);2011年

6 姜輝;自動(dòng)平行泊車系統(tǒng)轉(zhuǎn)向控制策略的研究[D];吉林大學(xué);2010年

7 王可心;大規(guī)模過程系統(tǒng)非線性優(yōu)化的簡(jiǎn)約空間理論與算法研究[D];浙江大學(xué);2008年

相關(guān)碩士學(xué)位論文 前6條

1 顏豐琳;自由飛行模式的多飛行器軌跡優(yōu)化[D];浙江大學(xué);2015年

2 李勇;無信號(hào)燈十字交叉口協(xié)作車輛控制研究[D];北京理工大學(xué);2015年

3 黃森;動(dòng)態(tài)優(yōu)化問題的移動(dòng)有限元算法研究[D];浙江大學(xué);2012年

4 高航;自動(dòng)垂直泊車方法研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2011年

5 孟繁微;車輛垂直泊車轉(zhuǎn)向控制算法研究[D];吉林大學(xué);2011年

6 王芳成;自動(dòng)平行泊車系統(tǒng)的研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2010年

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