本文選題：無人機路徑規(guī)劃　切入點：粒子群算法　出處：《東北石油大學》2017年碩士論文　論文類型：學位論文

【摘要】：無人機巡檢技術(shù)作為一門新興技術(shù),近年來在環(huán)保、通信、電力、氣象等多個領(lǐng)域得到了廣泛的推廣和應(yīng)用。無人機路徑規(guī)劃是無人機巡檢的關(guān)鍵問題。隨著油田產(chǎn)量和規(guī)模的不斷提升,傳統(tǒng)的人工巡井方式已經(jīng)不足以滿足油田的管理需要,因此結(jié)合油田行業(yè)的特點,研究油田巡井無人機路徑規(guī)劃方法具有重要意義。無人機路徑規(guī)劃是根據(jù)任務(wù)需要規(guī)劃從出發(fā)點到目標點滿足約束條件的飛行路徑,是無人機研究的重要內(nèi)容之一。本文在油田巡井的實際背景下,圍繞無人機路徑規(guī)劃方法進行了研究,主要內(nèi)容如下:首先,研究無人機路徑規(guī)劃問題及環(huán)境建模。結(jié)合無人機油井巡檢過程中的飛行環(huán)境和任務(wù)要求,確定無人機路徑表達形式和約束條件。根據(jù)無人機定高飛行,將三維飛行環(huán)境抽象為虛擬平面截成的二維空間,采用幾何描述法將油井表達為平面坐標特征點,將障礙物表達為平面多邊形,利用高斯-克呂格投影法對油井經(jīng)緯坐標進行無角變處理,建立環(huán)境模型。其次,確定無人機路徑規(guī)劃算法,并對其進行改進。通過對遺傳算法、蟻群算法和粒子群算法的比較分析,選擇粒子群算法作為無人機路徑規(guī)劃算法。針對粒子群算法的不足,提出一種混合蛙跳粒子群算法。在算法搜索前期,利用混合蛙跳算法的分組策略分割初始種群,以局部深搜索思想優(yōu)化次優(yōu)個體,提取各層次個體作為新種群,提高算法搜索效率;在算法搜索后期,對最優(yōu)個體進行三重交叉操作,同時引入基于疏密性的引導變異操作,對稀疏點以較大概率變異,提高粒子多樣性。通過TSP標準測試庫數(shù)據(jù),將改進算法與其他算法進行規(guī)劃性能比較。然后,研究無人機避障問題。針對已知障礙,在障礙物模型的基礎(chǔ)上設(shè)計基于計算幾何的障礙檢測方法,并根據(jù)障礙檢測結(jié)果將障礙威脅系數(shù)引入到路徑規(guī)劃算法的評價函數(shù)中,使生成的路徑滿足避障要求;針對突發(fā)障礙,利用人工勢場法規(guī)劃生成局部避障路徑,躲避突發(fā)障礙物。最后,以大慶某油田巡井問題為實例,規(guī)劃無人機巡井路徑。針對巡井環(huán)境模型,采用混合蛙跳粒子群算法和基于計算幾何的避障方法規(guī)劃初始最優(yōu)參考路徑,并進行路徑平滑,使生成的路徑同時滿足巡井遍歷、路徑最短和避障要求;采用人工勢場法生成局部動態(tài)路徑,保證局部路徑能夠規(guī)避突發(fā)障礙威脅。給出無人機巡井路徑規(guī)劃的基本步驟,并將本文方法的路徑規(guī)劃結(jié)果與其他算法的路徑規(guī)劃結(jié)果進行比較。
[Abstract]:As a new technology, UAV patrol and inspection technology has been used in environmental protection, communication, electric power in recent years. Many fields such as meteorology have been widely popularized and applied. UAV path planning is the key problem of UAV inspection. With the increasing production and scale of oil field, the traditional manual well inspection method is not enough to meet the needs of oil field management. Therefore, considering the characteristics of oilfield industry, it is of great significance to study the path planning method of oil well patrol UAV. UAV path planning is to plan the flight path from starting point to target point to meet the constraint conditions according to mission needs. It is one of the important contents of UAV research. In this paper, the path planning method of UAV is studied under the actual background of oil field survey. The main contents are as follows: first of all, The problem of UAV path planning and environment modeling are studied. According to the flight environment and mission requirements of UAV oil well inspection, the UAV path expression form and constraint conditions are determined, and the UAV altitude flight is determined according to the UAV. The 3D flight environment is abstracted into a two-dimensional space cut by a virtual plane, and the oil well is expressed as a plane coordinate feature point by the geometric description method, and an obstacle is expressed as a plane polygon. Gauss-Kruger projection method is used to deal with the warp and weft coordinates of oil wells, and an environment model is established. Secondly, the path planning algorithm of UAV is determined and improved. Comparing and analyzing the ant colony algorithm and particle swarm optimization algorithm, the particle swarm optimization algorithm is selected as the path planning algorithm of UAV. In view of the deficiency of particle swarm optimization algorithm, a hybrid leapfrog particle swarm optimization algorithm is proposed. The initial population is segmented by the grouping strategy of hybrid leapfrog algorithm, the sub-optimal individuals are optimized by the idea of local deep search, and the individuals at all levels are extracted as new populations to improve the search efficiency of the algorithm. Triple crossover operation is carried out on the optimal individual, at the same time, the guided mutation operation based on density is introduced, and the sparsity point is mutated with a large probability to improve the particle diversity. Through the TSP standard test library data, the particle diversity is improved. The improved algorithm is compared with other algorithms in planning performance. Then, the obstacle avoidance problem of UAV is studied. Aiming at the known obstacles, a new obstacle detection method based on computational geometry is designed on the basis of obstacle model. According to the result of obstacle detection, the obstacle threat coefficient is introduced into the evaluation function of the path planning algorithm to make the generated path meet the requirements of obstacle avoidance, and for the sudden obstacle, the artificial potential field method is used to plan and generate the local obstacle avoidance path. Finally, taking a well inspection problem in Daqing oilfield as an example, planning the patrol path of UAV, aiming at the survey environment model, The hybrid leapfrog particle swarm optimization algorithm and the obstacle avoidance method based on computational geometry are used to plan the initial optimal reference path, and the path is smoothed so that the generated path can meet the requirements of well patrol, shortest path and obstacle avoidance simultaneously. The artificial potential field method is used to generate the local dynamic path to ensure that the local path can avoid the threat of sudden obstacles. The results of path planning of this method are compared with those of other algorithms.
【學位授予單位】：東北石油大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：V249;TE938.2
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本文編號：1601815