本文選題：人群疏散仿真 + 蟻群算法�。� 參考：《山東師范大學(xué)》2017年碩士論文

【摘要】：近幾年隨著我國軟實力的增強,經(jīng)濟、文化、娛樂業(yè)大力發(fā)展,人口規(guī)模不斷擴大,各種高樓大廈、大型體育場等比比皆是。這些人流密集的場所其實存在著安全隱患,在緊急情況發(fā)生之前能科學(xué)地預(yù)防,在緊急情況發(fā)生時能正確地疏導(dǎo)人群,是現(xiàn)在相關(guān)部門問題解決的根本。因此這類場所必須隨時做好充分的應(yīng)急疏散準備,防止當(dāng)遇到這種危險情況時因為逃離現(xiàn)場方法不當(dāng)或由于時間的延誤等原因?qū)е聯(lián)頂D、踩踏等現(xiàn)象造成人員的傷亡。研究人群的逃生規(guī)律,可高效地提高人們在遇到緊急危險情況時的自救能力。針對現(xiàn)在的疏散仿真技術(shù)中存在的人群行為單一,模擬精度不高等問題,社會力蟻群模型在本文中被提出,該方法一方面控制了成本的輸出,還提高了模型精度,對大型建筑內(nèi)法的人群疏散有很好的指導(dǎo)作用。本文采用基于粒子群算法和蟻群算法作為微觀模型指導(dǎo)微觀路徑規(guī)劃,用社會力和蟻群算法結(jié)合的模型指導(dǎo)人群疏散過程中的個體行為,通過大量仿真實驗的提出與實現(xiàn),證明了其有效性。三維動畫基于圖形學(xué)蓬勃發(fā)展的大背景下在各個領(lǐng)域都占據(jù)著不可替代的位置,比如電視廣告,游戲研發(fā),建筑物模型的構(gòu)建等等。本文基于擁有強大功能的Maya軟件對建筑物和人物模型進行創(chuàng)建,利用Mel語言的優(yōu)勢,效果非常清晰逼真,同時縮短了研發(fā)的時間,提高了工作效率。本文主要工作及創(chuàng)新點概括如下:1.提出一種新的人群疏散模型,該模型將社會力模型與蟻群算法相結(jié)合,用社會力作為蟻群算法中信息素更新策略的度量值。建立社會力蟻群模型,該模型充分考慮了人群疏散中個體間的作用力關(guān)系,解決了算法中易于出現(xiàn)的停滯和早熟現(xiàn)象。該模型可以有效發(fā)揮蟻群算法在人群模擬中的優(yōu)勢,較傳統(tǒng)算法具有更高的效率和運算速度,對大規(guī)模建筑內(nèi)人群疏散有重要參考價值。2.提出一種路徑規(guī)劃方法,它是基于拓撲圖的,將粒子群算法和蟻群算法相結(jié)合的粒子群蟻群算法(PSACO)。這種算法先用PSO算法生成一條初始路徑,然后把這條初始路徑轉(zhuǎn)化為蟻群算法(ACO)的初始信息素分布,這樣做很大程度上克服蟻群盲目搜素的缺點,在時間效率上起到了很好的效果。最后仿真實驗中驗證,基于拓撲圖的這種結(jié)合算法在實際場景系統(tǒng)中的確比其他算法在路徑規(guī)劃上取得了小小進步,路徑規(guī)劃的性能被大大提高,為今后的工作奠定了重要的基礎(chǔ)。3.基于Maya這一強大場景制作軟件,結(jié)合Mel語言創(chuàng)建真實性的建筑物模型和人物模型。然后應(yīng)用于實驗室項目之中,以VS2012+OSG2.3.1為平臺,生成仿真動畫,渲染精美效果。結(jié)果顯示本文所述方法實現(xiàn)了高質(zhì)量的疏散仿真現(xiàn)象,效果非常清晰逼真,同時縮短了研發(fā)的時間,提高了工作效率,也為以后的疏散研究提供了思路,具有一定的使用價值。
[Abstract]:In recent years, with the strengthening of soft power, the development of economy, culture, entertainment industry, the scale of population is expanding, various tall buildings, large stadiums and so on are everywhere. In fact, there are hidden safety problems in these crowded places, which can be prevented scientifically before an emergency occurs, and can correctly divert the crowd when an emergency occurs, which is the fundamental to solve the problems of relevant departments now. Therefore, such places must be prepared for emergency evacuation at any time, so as to prevent people from being injured or injured due to improper methods of escaping from the scene or delays in time, such as stampede and so on. The study of crowd escape law can effectively improve the ability of self-rescue in emergency situations. In order to solve the problems of single crowd behavior and low simulation precision in the current evacuation simulation technology, the social force ant colony model is proposed in this paper. On the one hand, the method not only controls the output of cost, but also improves the precision of the model. It is a good guide to the evacuation of large buildings. In this paper, particle swarm optimization (PSO) and ant colony algorithm (ACA) are used as microscopic models to guide micro-path planning, and social forces and ant colony algorithm (ACA) are used to guide individual behavior in evacuation process. Its validity is proved. 3D animation occupies an irreplaceable position in all fields under the background of the vigorous development of graphics, such as TV advertising, game development, building models and so on. Based on Maya software which has powerful functions, this paper builds buildings and human models, makes use of the advantages of Mel language, the effect is very clear and lifelike, at the same time, it shortens the time of research and development, and improves the working efficiency. The main work and innovation of this paper are summarized as follows: 1. A new crowd evacuation model is proposed, which combines social force model with ant colony algorithm and uses social force as a measure of pheromone updating strategy in ant colony algorithm. A social force ant colony model is established, which fully takes into account the interaction between individuals in crowd evacuation, and solves the stagnation and precocity phenomena that are easy to occur in the algorithm. This model can give full play to the advantage of ant colony algorithm in crowd simulation, and has higher efficiency and speed than traditional algorithm. It has important reference value for evacuation of large scale buildings. This paper presents a path planning method, which is based on topological graph and combines particle swarm optimization algorithm with ant colony algorithm, particle swarm ant colony algorithm (PSACO). This algorithm uses PSO algorithm to generate an initial path, and then converts the initial path into the initial pheromone distribution of ant colony algorithm (ACO). In time efficiency has played a very good effect. Finally, the simulation results show that the algorithm based on topology graph has made a little progress in path planning, and the performance of path planning has been greatly improved. For the future work laid an important foundation. Based on Maya, a powerful scene making software, a real building model and a character model are created with Mel language. Then applied to the lab project, VS2012 OSG 2.3.1 as the platform, generate simulation animation, render beautiful effects. The results show that the method presented in this paper realizes the high quality evacuation simulation, and the effect is very clear and lifelike. At the same time, the research and development time is shortened, the work efficiency is improved, and the thought is provided for the later evacuation research, which has certain practical value.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：C912.6;TP18

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