發(fā)布時(shí)間：2018-06-21 15:23

  本文選題：無(wú)人飛行器 + 航跡規(guī)劃��； 參考：《中北大學(xué)》2015年碩士論文

【摘要】：無(wú)人飛行器航跡規(guī)劃是指在綜合考慮無(wú)人飛行器的機(jī)動(dòng)性能、燃料消耗，戰(zhàn)場(chǎng)威脅等各種制約因素的基礎(chǔ)上，為保證飛行器順利完成任務(wù)而規(guī)劃出一條滿足要求的從起始點(diǎn)到目標(biāo)點(diǎn)的最優(yōu)航跡。航跡規(guī)劃系統(tǒng)作為無(wú)人飛行器研究領(lǐng)域的重要組成部分，，是提高無(wú)人飛行器飛行能力和作戰(zhàn)能力的重要保障。面對(duì)復(fù)雜多變的飛行環(huán)境和靈活多樣的飛行任務(wù)，尋找更加有效的航跡規(guī)劃方法成為航跡規(guī)劃研究的重要內(nèi)容。 本課題主要圍繞無(wú)人飛行器航跡規(guī)劃的算法問(wèn)題展開研究，首先介紹了航跡規(guī)劃的研究背景以及國(guó)內(nèi)外航跡規(guī)劃技術(shù)的發(fā)展動(dòng)態(tài)，分析并建立了合理的航跡規(guī)劃問(wèn)題模型，然后描述了無(wú)人飛行器自身機(jī)動(dòng)性能約束，給出了在航跡規(guī)劃時(shí)對(duì)戰(zhàn)場(chǎng)威脅的處理方法并建立了相應(yīng)的數(shù)學(xué)模型；其次，研究了目前在航跡規(guī)劃中所應(yīng)用的主流算法，然后根據(jù)戰(zhàn)場(chǎng)已知威脅源構(gòu)造Voronoi加權(quán)圖，接著在Voronoi圖的基礎(chǔ)上，研究了全局航跡的優(yōu)化方法。為了提高航跡規(guī)劃問(wèn)題最優(yōu)解的質(zhì)量及全局求解能力，針對(duì)蟻群算法存在的不足，提出了一種改進(jìn)蟻群算法，采用全新的目標(biāo)吸引策略、引入信息素增量調(diào)節(jié)因子并自適應(yīng)調(diào)整信息素?fù)]發(fā)系數(shù)來(lái)對(duì)基本蟻群算法進(jìn)行了改進(jìn)設(shè)計(jì)，通過(guò)對(duì)實(shí)驗(yàn)數(shù)據(jù)進(jìn)行分析后發(fā)現(xiàn)，對(duì)于相同的規(guī)劃問(wèn)題，改進(jìn)算法相比基本蟻群算法在規(guī)劃時(shí)間上明顯縮短，航跡長(zhǎng)度值也顯著減��；再次，由于戰(zhàn)場(chǎng)環(huán)境是動(dòng)態(tài)不確定的，無(wú)法準(zhǔn)確地預(yù)知全局的威脅障礙信息，因此在參考航跡規(guī)劃的基礎(chǔ)上，研究無(wú)人飛行器實(shí)時(shí)航跡規(guī)劃。針對(duì)出現(xiàn)突發(fā)威脅的情況闡述了實(shí)時(shí)重規(guī)劃的原理，根據(jù)Voronoi圖的局域動(dòng)態(tài)特性提出了一種基于改進(jìn)蟻群算法的實(shí)時(shí)重規(guī)劃方法，并且對(duì)各種不同的實(shí)驗(yàn)設(shè)定情況分別進(jìn)行了實(shí)例仿真，結(jié)果表明這種方法可以較好地解決突發(fā)威脅下的航跡規(guī)劃問(wèn)題，保證無(wú)人飛行器能夠成功回避戰(zhàn)場(chǎng)威脅，順利完成任務(wù)。 最后總結(jié)本文所做的研究工作及成果，并對(duì)需要展開深入的研究以及完善的內(nèi)容進(jìn)行了進(jìn)一步探討。
[Abstract]:Trajectory planning of unmanned aerial vehicles (UAV) is based on the comprehensive consideration of the maneuverability, fuel consumption, battlefield threat and other constraints of UAV. In order to ensure the successful completion of the mission, an optimal track from the starting point to the target point was designed. As an important part of unmanned aerial vehicle (UAV) research field, track planning system is an important guarantee to improve UAV's flight capability and combat capability. In the face of complex and changeable flight environment and flexible and diverse flight missions, finding more effective route planning methods has become an important content in the research of flight path planning. This topic mainly focuses on the algorithm of unmanned aerial vehicle (UAV) track planning. Firstly, the research background of track planning and the development trend of domestic and foreign track planning technology are introduced, and a reasonable trajectory planning model is established. Then, the maneuvering performance constraints of unmanned aerial vehicles are described, and the methods to deal with battlefield threats in track planning are given, and the corresponding mathematical models are established. Secondly, the mainstream algorithms used in track planning are studied. Then the Voronoi weighted diagram is constructed according to the known threat sources in the battlefield, and then the optimization method of the global track is studied based on the Voronoi diagram. In order to improve the quality of the optimal solution and the ability to solve the global problem, an improved ant colony algorithm (ACA) is proposed to improve the quality of the optimal solution and the ability to solve the problem globally, and an improved ant colony algorithm (ACA) is proposed, which adopts a new target attraction strategy. The pheromone increment regulation factor is introduced and the pheromone volatilization coefficient is adaptively adjusted to improve the design of the basic ant colony algorithm. After analyzing the experimental data, it is found that, for the same programming problem, Compared with the basic ant colony algorithm, the improved algorithm can significantly reduce the planning time and track length. Thirdly, because the battlefield environment is dynamic and uncertain, it is impossible to accurately predict the global threat obstacle information. Therefore, on the basis of reference track planning, the real-time track planning of unmanned aerial vehicles is studied. According to the local dynamic characteristics of Voronoi diagram, a real-time replanning method based on improved ant colony algorithm is proposed. The simulation results show that this method can solve the problem of trajectory planning under sudden threat and ensure that UAV can successfully avoid the threat of battlefield and complete the mission successfully. Finally, this paper summarizes the research work and results, and the need to carry out in-depth research and improve the content of further discussion.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：V279

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