發(fā)布時(shí)間：2018-03-26 00:08

  本文選題：移動(dòng)機(jī)器人　切入點(diǎn)：路徑規(guī)劃　出處：《安徽大學(xué)》2017年碩士論文

【摘要】：隨著人工智能技術(shù)在當(dāng)今社會(huì)的逐步發(fā)展與進(jìn)步,其在生活與生產(chǎn)中的運(yùn)用愈加廣泛,也吸引了更多的研究者投入其中,人工智能已然成為可當(dāng)今社會(huì)炙手可熱的研究熱點(diǎn)。人工智能的研究分支眾多,其中智能機(jī)器人的研究隨著技術(shù)的不斷進(jìn)步受到了越來越多的關(guān)注。為了提高機(jī)器人完成任務(wù)的效率,我們希望機(jī)器人能夠擁有自主安全尋路的功能。通常,路徑規(guī)劃的目標(biāo)不僅限于尋找起點(diǎn)與終點(diǎn)之間的可行路徑,還要在眾多可行的道路中,規(guī)劃出一條路程短,耗時(shí)短且安全性高的路徑,以此來提高工作的效率。近年來,在路徑規(guī)劃的問題上,國(guó)內(nèi)外專家學(xué)者給出了各自的問題解決方案,并在各自的問題模型中得出了有效的結(jié)果,其中包括遺傳算法、粒子群算法、人工免疫算法、神經(jīng)網(wǎng)絡(luò)法、人工勢(shì)場(chǎng)法等。在眾多的應(yīng)用算法中,蟻群算法自提出以來,就受到了廣泛的關(guān)注。本文主要描述了在移動(dòng)機(jī)器人路徑規(guī)劃這一研究課題下,基于蟻群算法展開的分析研究以及改進(jìn)優(yōu)化。論文的主要工作如下:1.文中系統(tǒng)地探討了蟻群算法的思想和實(shí)現(xiàn)步驟,從經(jīng)典蟻群算法的理念談起,分析了在路徑規(guī)劃問題中蟻群算法表現(xiàn)出的優(yōu)缺點(diǎn)。蟻群算法應(yīng)用仿生的手段,根據(jù)螞蟻在覓食過程中的尋路行為,通過給以后代正反饋信息,逐步收斂得出全局最優(yōu)路徑,有著魯棒性強(qiáng)等優(yōu)點(diǎn)。但同時(shí)也有搜索時(shí)間較長(zhǎng),容易陷入局部收斂的問題。2.文中列舉了眾多學(xué)者對(duì)蟻群算法做出的改進(jìn)與優(yōu)化,其中有些是在經(jīng)典蟻群算法的算法基礎(chǔ)上加以改進(jìn),有些則讓蟻群算法與其他算法相結(jié)合,取長(zhǎng)補(bǔ)短,使得蟻群算法日益優(yōu)化。不同的改進(jìn)策略在相對(duì)應(yīng)的應(yīng)用場(chǎng)景中都得到了較好的效果,文中緒論部分對(duì)這些改進(jìn)做出分析與論述。文中提出的主要?jiǎng)?chuàng)新點(diǎn)如下:1.針對(duì)經(jīng)典蟻群算法在復(fù)雜環(huán)境下的機(jī)器人路徑規(guī)劃問題中表現(xiàn)出的收斂速度慢,容易陷入局部最優(yōu)等問題,本章提出一種改進(jìn)算法。依據(jù)方向指導(dǎo)信息來優(yōu)化初始信息素的分布,加快搜索速度,縮減搜索初期的時(shí)間消耗;通過優(yōu)化信息素的揮發(fā)與更新規(guī)則,保留局部與全局優(yōu)秀路徑的優(yōu)勢(shì)信息,改善收斂速度慢的問題;基于區(qū)域安全因素對(duì)轉(zhuǎn)移概率進(jìn)行改進(jìn),從而避免陷入局部最優(yōu)和死鎖等問題。為了驗(yàn)證改進(jìn)的有效性,通過柵格法對(duì)仿真環(huán)境二維建模,對(duì)不同復(fù)雜度和規(guī)模的地圖進(jìn)行仿真實(shí)驗(yàn)。2.在帶有路徑代價(jià)的多目標(biāo)規(guī)劃問題上提出一種改進(jìn)蟻群算法。在前文中提到的初始信息素分布規(guī)則的基礎(chǔ)上,添加路徑代價(jià)因子,為初始螞蟻提供尋路方向。依據(jù)多目標(biāo)規(guī)劃的特性,提出一種螞蟻群體劃分的策略,賦予不同群體的螞蟻不同的規(guī)劃任務(wù),從分到總地適應(yīng)多目標(biāo)規(guī)劃需求。另外,在信息素的分布上,根據(jù)螞蟻群體任務(wù)的不同設(shè)置不同的規(guī)則,再經(jīng)過轉(zhuǎn)移概率的優(yōu)化選擇,在仿真實(shí)驗(yàn)中得出了不錯(cuò)的結(jié)果。
[Abstract]:With the gradual development and progress of artificial intelligence technology in today's society, its application in life and production has become more widespread, and attracted more and more researchers into it. Artificial intelligence has become a hot research hotspot in today's society. The research of intelligent robot has attracted more and more attention with the development of technology. In order to improve the efficiency of robot to complete the task, we hope that the robot can have the function of finding its own path safely. The goal of path planning is not only to find the feasible path between the starting point and the end point, but also to plan a path that is short, time consuming and high safety among the many feasible paths, in order to improve the efficiency of the work in recent years. On the problem of path planning, experts and scholars at home and abroad give their own solutions, and get effective results in their respective problem models, including genetic algorithm, particle swarm optimization algorithm, artificial immune algorithm, neural network method. The artificial potential field method and so on. In many application algorithms, the ant colony algorithm has received extensive attention since it was put forward. This paper mainly describes the research subject of path planning of mobile robot. The main work of this paper is as follows: 1. This paper systematically discusses the idea and implementation steps of ant colony algorithm, starting with the idea of classical ant colony algorithm. The advantages and disadvantages of ant colony algorithm in path planning are analyzed. Ant colony algorithm uses bionic means, according to the path finding behavior of ants in the course of foraging, by giving positive feedback information to offspring, the global optimal path is gradually converged. It has the advantages of strong robustness, but also has the problem of long search time and easy to fall into local convergence. In this paper, the improvement and optimization of ant colony algorithm made by many scholars are listed. Some of them are improved on the basis of the classical ant colony algorithm, and some of them combine the ant colony algorithm with other algorithms to complement each other. The ant colony algorithm is becoming more and more optimized. Different improved strategies have better results in the corresponding application scenarios. In the introduction part of the paper, we analyze and discuss these improvements. The main innovation points in this paper are as follows: 1. The convergence speed of classical ant colony algorithm in the robot path planning problem in complex environment is slow. In this chapter, an improved algorithm is proposed to optimize the distribution of initial pheromone according to the direction guidance information, accelerate the search speed and reduce the initial time consumption. By optimizing the rules of volatilization and renewal of pheromone, the advantage information of local and global excellent paths is preserved, and the problem of slow convergence is improved, and the transfer probability is improved based on regional security factors. In order to verify the effectiveness of the improved method, the simulation environment is modeled by grid method. Simulation experiments on maps with different complexity and scale. 2. An improved ant colony algorithm for multi-objective programming with path cost is proposed. Based on the initial pheromone distribution rules mentioned above, the path cost factor is added. According to the characteristics of multi-objective programming, a strategy of ant population division is proposed, which gives ants of different populations different planning tasks, and adapts to the needs of multi-objective planning from division to total. In the distribution of pheromone, according to the different rules of ant colony task, and through the optimal selection of transfer probability, a good result is obtained in the simulation experiment.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TP242

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本文編號(hào)：1665467