發(fā)布時(shí)間：2018-01-26 05:41

  本文關(guān)鍵詞： 生物啟發(fā) 廣義tau理論 多無(wú)人機(jī) 四維航跡規(guī)劃 多智能體Q學(xué)習(xí) 矢量場(chǎng)制導(dǎo) 協(xié)同目標(biāo)跟蹤　出處：《浙江大學(xué)》2016年博士論文　論文類(lèi)型：學(xué)位論文

【摘要】：多無(wú)人機(jī)在協(xié)同執(zhí)行同時(shí)攻擊、編隊(duì)形成等飛行任務(wù)時(shí),通常需要嚴(yán)格按照期望時(shí)間飛抵指定的目標(biāo)點(diǎn),因而多機(jī)協(xié)同四維航跡(三維位置與時(shí)間)規(guī)劃技術(shù)日益受到青睞。該技術(shù)能夠有效降低航跡的不確定性,提高任務(wù)執(zhí)行的安全性和可靠性；并可對(duì)任務(wù)空域進(jìn)行有效利用,獲得更優(yōu)化的協(xié)同航跡。針對(duì)多機(jī)協(xié)同四維航跡規(guī)劃問(wèn)題進(jìn)行研究具有良好的理論意義和廣闊的應(yīng)用前景。廣義tau理論是一種生物啟發(fā)的四維運(yùn)動(dòng)規(guī)劃理論,采用當(dāng)前與目標(biāo)運(yùn)動(dòng)狀態(tài)之間的間距閉合的時(shí)間信息進(jìn)行制導(dǎo)�；趶V義tau理論的tau制導(dǎo)策略能夠真正進(jìn)行時(shí)空的同步規(guī)劃,且形式簡(jiǎn)潔,適合應(yīng)用于多機(jī)協(xié)同四維航跡規(guī)劃中。本文以生物啟發(fā)的多無(wú)人機(jī)協(xié)同四維航跡規(guī)劃方法為研究?jī)?nèi)容,將tau制導(dǎo)策略應(yīng)用于多機(jī)協(xié)同四維航跡規(guī)劃中,重點(diǎn)研究了符合無(wú)人機(jī)需求的tau制導(dǎo)策略,并在此基礎(chǔ)上對(duì)集中式、分布式和基于多智能體Q學(xué)習(xí)算法的協(xié)同四維航跡規(guī)劃方法進(jìn)行研究；進(jìn)而,利用tau制導(dǎo)策略的時(shí)空協(xié)同性質(zhì),構(gòu)建了四維tau制導(dǎo)矢量場(chǎng),并用其解決多機(jī)協(xié)同standoff跟蹤問(wèn)題。本文的主要研究工作和貢獻(xiàn)如下：1、系統(tǒng)地綜述了多無(wú)人機(jī)協(xié)同四維航跡規(guī)劃的研究現(xiàn)狀,回顧了生物啟發(fā)的廣義tau理論及其制導(dǎo)策略的研究歷程和進(jìn)展,并總結(jié)了已有tau制導(dǎo)策略的優(yōu)缺點(diǎn)和應(yīng)用范圍。2、針對(duì)已有tau制導(dǎo)策略存在的缺陷和四維航跡規(guī)劃的應(yīng)用需求,對(duì)制導(dǎo)策略進(jìn)行創(chuàng)新。首先,針對(duì)已有策略航跡空間調(diào)整范圍小、初始加速度非零的缺陷,提出了基于簡(jiǎn)諧運(yùn)動(dòng)的本征tau-harmonic制導(dǎo)策略,提升了航跡的可飛性和空間形狀調(diào)整能力。其次,針對(duì)始末速度非零的四維運(yùn)動(dòng)規(guī)劃問(wèn)題,通過(guò)在本征tau-gravity (tau-G)制導(dǎo)策略的虛擬制導(dǎo)運(yùn)動(dòng)中加入初速度,提出了改進(jìn)tau-G制導(dǎo)策略,能夠滿(mǎn)足編隊(duì)形成、目標(biāo)的持續(xù)跟蹤等任務(wù)的應(yīng)用需求。3、基于tau-harmonic制導(dǎo)策略,提出了一種集中式多機(jī)協(xié)同四維航跡規(guī)劃方法。該方法采用tau-harmonic策略描述四維航跡,設(shè)計(jì)了集中式四維航跡規(guī)劃問(wèn)題,并用粒子群優(yōu)化獲取最佳航跡參數(shù)。航跡之間的無(wú)碰需求通過(guò)沖突檢測(cè)和消解方法加以保證。對(duì)多機(jī)同時(shí)到達(dá)任務(wù)的大量仿真結(jié)果表明,該集中式四維航跡規(guī)劃方法具有較好的收斂性和更強(qiáng)的沖突消解能力,能夠提供更可飛和更安全的四維航跡。4、利用改進(jìn)tau-G制導(dǎo)策略,提出了一種分布式多機(jī)協(xié)同四維航跡規(guī)劃方法。該方法將集中式航跡優(yōu)化問(wèn)題分解為局部?jī)?yōu)化問(wèn)題,每架飛機(jī)根據(jù)本地和鄰居信息對(duì)局部?jī)?yōu)化問(wèn)題進(jìn)行求解。為了應(yīng)對(duì)環(huán)境和通信拓?fù)涞耐蛔?采用時(shí)間采樣和沖突事件雙重驅(qū)動(dòng)的分布式滾動(dòng)優(yōu)化方法不斷對(duì)航跡進(jìn)行優(yōu)化和改進(jìn)。與分布式預(yù)測(cè)控制對(duì)照的綜合飛行任務(wù)仿真結(jié)果表明,該分布式協(xié)同航跡規(guī)劃方法能夠嚴(yán)格按照期望時(shí)間導(dǎo)引無(wú)人機(jī)以期望速度通過(guò)目標(biāo)點(diǎn),且能夠更高效地提供費(fèi)用小、安全性高韻四維航跡。5、為了提升多無(wú)人機(jī)航跡規(guī)劃的自主性和靈活性,提出了一種基于多智能體Q學(xué)習(xí)算法的協(xié)同四維航跡規(guī)劃方法。各無(wú)人機(jī)采用連續(xù)狀態(tài)-連續(xù)動(dòng)作的Wire Fitting Neural Network Q學(xué)習(xí)算法對(duì)基于改進(jìn)tau-G策略的航跡規(guī)劃任務(wù)進(jìn)行學(xué)習(xí)訓(xùn)練,多無(wú)人機(jī)系統(tǒng)的學(xué)習(xí)用Win or Learn Fast-Policy Hill Climbing算法進(jìn)行組織；為提升算法對(duì)不同任務(wù)環(huán)境的適應(yīng)性,在有效通信的機(jī)群中引入學(xué)習(xí)經(jīng)驗(yàn)交流機(jī)制。對(duì)綜合飛行任務(wù)的動(dòng)態(tài)仿真結(jié)果表明,該方法能夠在分布式航跡規(guī)劃任務(wù)中更加高效地提供滿(mǎn)足可飛性、安全性和任務(wù)需求的四維航跡。6、利用tau-G制導(dǎo)策略將時(shí)間信息引入矢量場(chǎng)中,構(gòu)建了四維tau制導(dǎo)矢量場(chǎng),可嚴(yán)格按照期望時(shí)間將無(wú)人機(jī)的位置導(dǎo)引至目標(biāo)圓上；進(jìn)而利用tau矢量場(chǎng)解決有時(shí)間約束的多機(jī)協(xié)同standoff跟蹤問(wèn)題。對(duì)典型協(xié)同standoff跟蹤任務(wù)的仿真結(jié)果表明,tau矢量場(chǎng)提供的制導(dǎo)策略具有計(jì)算負(fù)荷低、跟蹤偏差小、可飛性好、安全性高等優(yōu)勢(shì),能夠更好地滿(mǎn)足多機(jī)協(xié)同任務(wù)的四維制導(dǎo)需求。
[Abstract]:Multi UAV attack in collaborative execution at the same time, the formation of the formation of the mission, usually need to specified targets in strict accordance with the expected time, and multi Aircraft Cooperative four-dimensional trajectory (3D position and time) planning technology has been increasingly popular. This technology can effectively reduce the uncertainty of the track, improve the safety and reliability of task execution and on the task; the effective use of airspace, get more cooperative route optimization. For multi Aircraft Cooperative four-dimensional path planning problem research has good theoretical significance and wide application prospect. The generalized tau theory is four-dimensional motion planning theory of a bio inspired, using the time information between the current and target state space closure the guidance planning guidance strategy tau. Synchronization of the generalized tau theory truly based on space time, and concise form, suitable for multi machine Four cooperative path planning. Multi UAV cooperative four-dimensional path planning method as the research content in this paper, bio inspired, the application of tau guidance strategy for multi Aircraft Cooperative four-dimensional path planning, focusing on the UAV guidance strategy in accordance with tau requirements, and on the basis of centralized, distributed and collaborative D route planning method agent based on Q learning algorithm is studied; then, the nature of collaborative use of space-time tau guidance strategy, establishes four-dimensional tau guidance vector field, and used to solve the multi machine collaborative standoff tracking problem. In this paper, the main research work and contributions are as follows: 1, summarized the research status of collaborative multiple D route planning the aircraft, reviews the history and research progress of bio inspired generalized tau theory and its guidance strategy, and summarizes the advantages and disadvantages of the existing tau guidance strategy and application scope of.2, according to The existing tau guidance strategy defects and D route planning application needs, to innovate the guidance strategy. Firstly, aiming at the existing strategy adjustment range of track space is small, the initial acceleration of non zero defects, put forward the intrinsic tau-harmonic guidance strategy based on simple harmonic motion, improve the trajectory of flying and spatial shape adjustment ability. Secondly, according to the motion planning problem by four-dimensional non zero speed, the intrinsic tau-gravity (tau-G) virtual guidance guidance strategy is added in the initial movement speed, this paper proposes an improved tau-G guidance strategy can satisfy the formation, continuous tracking target application requirements.3 tau-harmonic guidance strategy based on a multi machine collaborative four-dimensional path planning method. This method adopts tau-harmonic strategy description D route design, route planning and centralized four, with grain Swarm optimization to obtain the optimal trajectory parameters. The collision free needs to be ensured by the conflict detection and resolution method between tracks. A large number of simulation results of multi machine task that arrived at the same time, the conflict of the centralized D route planning method has better convergence and stronger ability, can provide more and more secure 4D flight path.4 and using the improved tau-G guidance strategy, this paper proposes a distributed multi Aircraft Cooperative four-dimensional path planning method. This method will be centralized track optimization problem is decomposed into local optimization problems, according to each plane to solve the local optimization problem of local and neighbor information. In order to cope with environmental mutation and communication topologies, using time sampling and dual conflict drive the rolling optimization method of continuous distributed route was optimized and improved. And the distributed predictive control integrated flight control simulation The results show that the distributed cooperative path planning method can be in strict accordance with the expected time of the UAV to the desired speed through the target point, and can more efficiently provide low cost, high safety rhyme four-dimensional track.5, in order to enhance the autonomy and flexibility of multi UAV path planning, the paper presents a multi-agent Q learning algorithm of cooperative path planning method based on four. Each UAV using continuous state continuous action of the Wire Fitting Neural Network Q learning algorithm to learn the training task improved path planning based on tau-G strategy for multi UAV system using Win or Learn Fast-Policy Hill learning Climbing algorithm to enhance the adaptability of the algorithm; different task environment the introduction of learning experience exchange mechanism in the effective communication in the cluster. The dynamic simulation of the integrated flight task. The results show that this method can In the distributed path planning tasks more efficiently provide flying.6, four-dimensional track safety and mission requirements, the use of tau-G guidance strategy and time information is introduced in the vector field, establishes four-dimensional tau guidance vector field, can be in strict accordance with the expected time of the UAV's location is guided to a target circle; then using tau the vector field problem with time constraints for cooperative standoff tracking problem. Simulation of a typical collaborative standoff tracking task. The results show that the tau vector field provides a guidance strategy with a low computational load, tracking deviation is small, flying, high security advantages, can better meet the needs of four-dimensional guidance for cooperative tasks.

【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：V279

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2 稅薇;基于貝葉斯推理與蟻群算法的仿真飛行器航跡規(guī)劃研究[D];青島科技大學(xué);2009年

3 毛云云;多飛行器協(xié)同航跡規(guī)劃算法研究[D];哈爾濱工業(yè)大學(xué);2010年

4 趙玲玲;飛行器航跡規(guī)劃與航跡控制算法研究與實(shí)現(xiàn)[D];哈爾濱工業(yè)大學(xué);2006年

5 盧江松;基于改進(jìn)蟻群算法的多機(jī)協(xié)同突防航跡規(guī)劃方法研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2011年

6 張建峰;一種飛行器航跡規(guī)劃算法[D];哈爾濱工程大學(xué);2012年

7 楊杰;具有端點(diǎn)方向約束的快速航跡規(guī)劃方法研究[D];華中科技大學(xué);2013年

8 陶鴻濤;航跡規(guī)劃算法研究及視景顯示[D];上海交通大學(xué);2015年

9 馬艷;多旋翼無(wú)人機(jī)航跡規(guī)劃與監(jiān)控研究[D];東南大學(xué);2015年

10 朱志聰;最后進(jìn)近航段航跡優(yōu)化算法的研究[D];中國(guó)民用航空飛行學(xué)院;2016年

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