【摘要】：隨著信息技術(shù)的日益革新,航空技術(shù)得到了迅猛的發(fā)展,復(fù)雜多變的飛行環(huán)境對無人機(jī)的機(jī)動(dòng)性提出了巨大挑戰(zhàn)。航跡規(guī)劃是實(shí)現(xiàn)無人機(jī)自主飛行的關(guān)鍵技術(shù),其所采用的規(guī)劃算法是必要的技術(shù)保障,所以對航跡規(guī)劃算法的研究在理論和實(shí)際應(yīng)用方面都有很重要的意義。本文圍繞航跡規(guī)劃算法展開研究,給出了相應(yīng)的航跡規(guī)劃策略,主要研究內(nèi)容如下:(1)針對航跡規(guī)劃中所需要滿足的約束條件,建立了無人機(jī)機(jī)動(dòng)性約束模型。根據(jù)任務(wù)需求,對飛行環(huán)境中的靜態(tài)威脅源(如靜態(tài)雷達(dá)等)進(jìn)行了深入分析并建立相應(yīng)模型。同時(shí)也構(gòu)建了綜合代價(jià)函數(shù)模型,這些模型的建立為研究航跡規(guī)劃奠定了基礎(chǔ)。(2)對靜態(tài)環(huán)境下的航跡規(guī)劃問題,采用了改進(jìn)后的遺傳算法。設(shè)計(jì)了一種基于航向變化量的實(shí)數(shù)編碼方式,對航向變化量進(jìn)行編碼構(gòu)造個(gè)體,再根據(jù)個(gè)體編碼向量構(gòu)造臨時(shí)路徑。通過設(shè)計(jì)的編碼方式和相應(yīng)的遺傳操作來進(jìn)行靜態(tài)規(guī)劃,并對算法改進(jìn)前后的仿真結(jié)果進(jìn)行了對比分析。(3)對動(dòng)態(tài)環(huán)境下的航跡規(guī)劃問題,提出了基于交互式多模型算法和模型預(yù)測控制算法的融合算法。根據(jù)運(yùn)動(dòng)目標(biāo)的當(dāng)前位置信息,采用交互式多模型算法預(yù)測其軌跡,實(shí)現(xiàn)對移動(dòng)障礙物的提前避規(guī)。同時(shí),利用轉(zhuǎn)換測量卡爾曼濾波將非線性問題轉(zhuǎn)化為線性問題來解決。綜合考慮靜態(tài)、動(dòng)態(tài)等多種威脅約束同時(shí)存在的飛行環(huán)境,采用融合算法進(jìn)行仿真,對算法的實(shí)時(shí)性和有效性進(jìn)行了驗(yàn)證。(4)針對三維空間航跡規(guī)劃問題,提出了一種基于改進(jìn)蟻群算法的航跡規(guī)劃方法。利用數(shù)字地圖技術(shù)建立了三維地形模擬圖,在考慮威脅代價(jià)、航程代價(jià)和高度代價(jià)約束條件下,構(gòu)建了綜合代價(jià)目標(biāo)函數(shù)。在綜合考慮可選節(jié)點(diǎn)與目標(biāo)點(diǎn)之間距離的基礎(chǔ)上,重新設(shè)定了狀態(tài)轉(zhuǎn)移概率。此外,結(jié)合分類思想在信息素濃度更新時(shí),在一定范圍內(nèi)對信息素殘留因子進(jìn)行設(shè)定,提高了搜索時(shí)路徑的選擇性,避免產(chǎn)生“早熟”現(xiàn)象,同時(shí)改善了搜索時(shí)間長、容易陷入局部最優(yōu)解等問題。結(jié)合算法的理論分析進(jìn)行仿真,結(jié)果表明三維空間中利用改進(jìn)后的蟻群算法進(jìn)行航跡規(guī)劃可以得到期望航線。
[Abstract]:With the development of information technology, aeronautical technology has been developed rapidly, and the complex and changeable flight environment has posed a great challenge to the maneuverability of UAVs. Track planning is the key technology to realize autonomous flight of UAV, and the planning algorithm is the necessary technical guarantee. Therefore, the research of track planning algorithm is of great significance in both theory and practical application. In this paper, the algorithm of track planning is studied, and the corresponding route planning strategies are given. The main contents are as follows: (1) aiming at the constraints that need to be met in track planning, the maneuverability constraint model of UAV is established. According to the mission requirements, the static threat sources (such as static radar) in flight environment are analyzed and the corresponding models are established. At the same time, the synthetic cost function model is constructed, which lays a foundation for the research of track planning. (2) the improved genetic algorithm is used to solve the problem of track planning in static environment. A real number coding method based on course variable is designed to construct the individual and construct the temporary path according to the individual coding vector. The static programming is carried out by the coding method designed and the corresponding genetic operation, and the simulation results before and after the improvement of the algorithm are compared and analyzed. (3) the trajectory planning problem in dynamic environment is discussed. A fusion algorithm based on interactive multi-model algorithm and model predictive control algorithm is proposed. Based on the current position information of moving objects, an interactive multi-model algorithm is used to predict the trajectory of moving obstacles. At the same time, the nonlinear problem is transformed into a linear problem by using the transform measurement Kalman filter. Considering the flight environment with static and dynamic threat constraints, the fusion algorithm is used to simulate the real-time and validity of the algorithm. (4) aiming at the problem of 3D flight path planning, A route planning method based on improved ant colony algorithm is proposed. The 3D terrain simulation map is established by using digital map technology. The objective function of synthetic cost is constructed under the constraints of threat cost, voyage cost and height cost. On the basis of considering the distance between the optional node and the target point, the state transition probability is reset. In addition, when the pheromone concentration is updated according to the classification idea, the pheromone residue factor is set within a certain range, which improves the selectivity of the search path, avoids the phenomenon of "precocity" and improves the long search time. It is easy to fall into local optimal solutions and other problems. Based on the theoretical analysis of the algorithm, the simulation results show that the improved ant colony algorithm can get the desired route in 3D space.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V279;V249;TP18

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本文編號(hào)：2378875