本文選題：無人機(jī)航跡規(guī)劃 + 人工魚群算法　； 參考：《南昌航空大學(xué)》2015年碩士論文

【摘要】：當(dāng)今世界,無人機(jī)得到了越來越廣泛的應(yīng)用。在軍事方面,無人機(jī)的使用能夠降低戰(zhàn)斗中的傷亡,提升人力的效率和飛行器的性能。在民用方面,無人機(jī)的使用能夠克服地理?xiàng)l件的限制,降低工作的成本以完成貨物的運(yùn)輸、空中拍攝等任務(wù)。我國是世界上軍事和經(jīng)濟(jì)的大國,所以無論是軍用還是民用領(lǐng)域都對無人機(jī)有較大的需求。無人機(jī)任務(wù)規(guī)劃中的重要部分就是無人機(jī)的航跡規(guī)劃,應(yīng)用于無人機(jī)航跡規(guī)劃的算法有很多種,然而每種算法都有自己的優(yōu)點(diǎn)和缺點(diǎn),優(yōu)秀的航跡規(guī)劃算法能夠幫助無人機(jī)快速地自動(dòng)找到符合要求的最短航跡,因此研究并提出一種性能優(yōu)秀的航跡規(guī)劃算法具有廣闊的應(yīng)用價(jià)值和實(shí)際意義。本文主要對無人機(jī)航跡規(guī)劃的算法進(jìn)行了研究,其中針對使用基于網(wǎng)格劃分策略的改進(jìn)人工魚群算法計(jì)算無人機(jī)路徑規(guī)劃問題中尋優(yōu)精度與算法計(jì)算量的矛盾,提出一種改進(jìn)人工魚群算法,并將該算法應(yīng)用于無人機(jī)航跡規(guī)劃問題中,對其進(jìn)行了軟件仿真。主要從以下方面對課題進(jìn)行了研究。首先明確了航跡規(guī)劃的工作目的和描述航跡規(guī)劃問題的建模方法,回顧了前人已經(jīng)提出的各種應(yīng)用于航跡規(guī)劃問題的算法。其次介紹了基本人工魚群算法中的主要概念,對人工魚的行為進(jìn)行了描述,并闡述了算法的執(zhí)行步驟和尋優(yōu)原理。分析了算法中各個(gè)主要參數(shù)對人工魚群算法的性能的影響,為算法的改進(jìn)提供了理論依據(jù)。隨后總結(jié)了當(dāng)前主要的人工魚群算法改進(jìn)策略,詳細(xì)介紹了基于網(wǎng)格劃分策略的改進(jìn)人工魚群算法,根據(jù)該算法的特點(diǎn),提出一種改進(jìn)人工魚群算法,該算法引入自適應(yīng)步長和執(zhí)行概率自適應(yīng)分段網(wǎng)格遍歷策略。算法前期用較大步長全局搜索較優(yōu)路徑,后期用較小步長及網(wǎng)格分段遍歷策略在較優(yōu)解附近進(jìn)行局部遍歷得到更精確最優(yōu)解。最后將提出的改進(jìn)魚群算法應(yīng)用于無人機(jī)航跡規(guī)劃問題中,在MATLAB仿真環(huán)境下建立了航跡規(guī)劃任務(wù)的模型,分別使用包括所提改進(jìn)人工魚群算法在內(nèi)的三種算法進(jìn)行尋優(yōu),通過軟件仿真結(jié)果數(shù)據(jù)的對比分析,表明所提改進(jìn)人工魚群算法比原始魚群算法和自適應(yīng)步長人工魚群算法結(jié)果更精確、穩(wěn)定,較基于簡單網(wǎng)格劃分策略的人工魚群算法計(jì)算量更小。
[Abstract]:In today's world, unmanned aerial vehicles (UAVs) have been used more and more widely. On the military side, the use of UAVs can reduce combat casualties, improve human efficiency and aircraft performance. In civilian use, UAVs can overcome geographical constraints and reduce the cost of work to complete cargo transport, aerial photography and other tasks. China is a large military and economic country in the world, so there is a great demand for UAVs in both military and civilian fields. The important part of UAV mission planning is UAV track planning. There are many algorithms used in UAV track planning. However, each algorithm has its own advantages and disadvantages. The excellent track planning algorithm can help UAV find the shortest track quickly and automatically, so it has broad application value and practical significance to study and propose an excellent track planning algorithm. In this paper, the algorithms of UAV path planning are studied, and the contradiction between the optimization accuracy and the computational complexity of the UAV path planning problem based on the improved artificial fish swarm algorithm based on mesh division strategy is discussed. An improved artificial fish swarm algorithm is proposed and applied to UAV trajectory planning problem. Mainly from the following aspects of the study of the subject. Firstly, the purpose of track planning and the modeling method to describe the problem of track planning are defined, and the algorithms used in track planning are reviewed. Secondly, this paper introduces the main concepts of the basic artificial fish swarm algorithm, describes the behavior of the artificial fish, and expounds the execution steps and optimization principle of the algorithm. The influence of main parameters on the performance of artificial fish swarm algorithm is analyzed, which provides a theoretical basis for the improvement of the algorithm. Then it summarizes the main improvement strategies of artificial fish swarm algorithm, and introduces the improved artificial fish swarm algorithm based on mesh generation strategy in detail. According to the characteristics of this algorithm, an improved artificial fish swarm algorithm is proposed. The algorithm introduces adaptive step size and execution probability adaptive piecewise traversal strategy. The algorithm uses a large step size to search for the optimal path globally, and a smaller step size and a mesh segment traversal strategy to obtain a more accurate optimal solution by local traversal near the optimal solution. Finally, the proposed improved fish swarm algorithm is applied to UAV trajectory planning problem. In the MATLAB simulation environment, the model of track planning task is established, and three algorithms, including the proposed improved artificial fish swarm algorithm, are used to optimize the flight path planning. Compared with the original fish swarm algorithm and the adaptive step size artificial fish swarm algorithm, the improved artificial fish swarm algorithm is more accurate and stable than the original fish swarm algorithm and the adaptive step size artificial fish swarm algorithm. Compared with the artificial fish swarm algorithm based on simple mesh generation strategy, the computational complexity of artificial fish swarm algorithm is much smaller.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：V279

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本文編號：1927255