本文選題：AGV + 路徑規(guī)劃　； 參考：《河南工業(yè)大學(xué)》2017年碩士論文

【摘要】：AGV(自動(dòng)導(dǎo)引運(yùn)輸車)路徑規(guī)劃在AGV的運(yùn)行過程中起著關(guān)鍵作用,雖然有關(guān)AGV路徑規(guī)劃的研究已經(jīng)有幾十年,但由于AGV所處的工作環(huán)境具有復(fù)雜程度高、避障困難、優(yōu)化路徑難度大的特點(diǎn),所以仍然有許多值得研究探索的地方。路徑規(guī)劃方法是對(duì)路徑規(guī)劃的關(guān)鍵,它在AGV的運(yùn)行過程中起著重要作用。在長期的路徑規(guī)劃研究歷程中,逐漸涌出了眾多的路徑規(guī)劃算法,主要分為兩大類,一是傳統(tǒng)的算法,二是最近逐漸興起的智能算法。但不管是傳統(tǒng)的算法還是智能算法,它們都具有各自的優(yōu)缺點(diǎn),運(yùn)用這些算法所得到的結(jié)果并不一定是最理想的,因此,這就需要研究者對(duì)路徑規(guī)劃方法做進(jìn)一步深入的研究。本文在了解有關(guān)AGV路徑規(guī)劃問題的現(xiàn)狀、方法及目前存在的問題之上,并考慮到AGV在現(xiàn)實(shí)工作環(huán)境中的運(yùn)行情況,針對(duì)單激光導(dǎo)航AGV路徑規(guī)劃問題進(jìn)行研究。運(yùn)用目前AGV路徑規(guī)劃方法規(guī)劃出的路徑普遍存在長度不夠理想、圓滑度欠缺的缺點(diǎn),因此有必要對(duì)現(xiàn)存的路徑規(guī)劃方法進(jìn)行改進(jìn)。本文主要采用兩種路徑規(guī)劃方法,一種為改進(jìn)的遺傳算法,其改進(jìn)主要有三處,第一處為對(duì)初始種群生成方法的改進(jìn),第二處為對(duì)變異算子的改進(jìn),第三處為平滑算子的引入。采用改進(jìn)遺傳算法的原因主要是其具有較好的并行性的特點(diǎn),可以同時(shí)對(duì)多個(gè)解空間進(jìn)行搜索。在本文中,在不同障礙物數(shù)量及不同障礙物擺放位置的環(huán)境下運(yùn)用改進(jìn)遺傳算法對(duì)單激光導(dǎo)航AGV的運(yùn)行路線進(jìn)行規(guī)劃,通過與基本遺傳算法得到的結(jié)果對(duì)比來分析改進(jìn)算法的性能,從而探究在不同環(huán)境下改進(jìn)遺傳算法相對(duì)于基本遺傳算法對(duì)單激光導(dǎo)航AGV進(jìn)行路徑規(guī)劃的優(yōu)劣。針對(duì)遺傳算法中隨機(jī)生成的初始種群個(gè)體適應(yīng)度不高的缺點(diǎn),本文采用另一種AGV路徑規(guī)劃方法,即遺傳蟻群算法。在此方法中,初始種群個(gè)體不是隨機(jī)產(chǎn)生,而是來自于蟻群算法每一代的迭代結(jié)果,然后經(jīng)過選擇、交叉、變異、平滑得出最終的結(jié)果,并經(jīng)過與其它算法對(duì)比,證明了此算法具有一定的改進(jìn)性。本文針對(duì)以上兩種不同的路徑規(guī)劃算法,編制相應(yīng)的MATLAB代碼進(jìn)行仿真驗(yàn)證,并基于C#和MATLAB軟件編制成相應(yīng)的路徑規(guī)劃原型系統(tǒng),并在兩個(gè)激光導(dǎo)航AGV經(jīng)常工作的環(huán)境中運(yùn)用此原型系統(tǒng)進(jìn)行規(guī)劃,證明了此原型系統(tǒng)具有一定的實(shí)用價(jià)值。
[Abstract]:AGV (automatic guided Transport vehicle) path planning plays a key role in the operation of AGV. Although the research on AGV path planning has been for decades, it is difficult to avoid obstacles because of the complexity of the working environment of AGV. It is difficult to optimize the path, so there are still many places worth exploring. Path planning is the key to path planning, and it plays an important role in the operation of AGV. In the long course of path planning research, there are many path planning algorithms, mainly divided into two categories, one is the traditional algorithm, the other is the intelligent algorithm that has been emerging recently. However, both traditional algorithms and intelligent algorithms have their own advantages and disadvantages, and the results obtained by using these algorithms are not necessarily the best. Therefore, it is necessary for researchers to further study the path planning methods. On the basis of understanding the current situation, methods and existing problems of AGV path planning, and considering the running situation of AGV in real working environment, this paper studies the path planning problem of AGV for single laser navigation. It is necessary to improve the existing path planning method because the path planning method of AGV is not ideal in length and lack in roundness. This paper mainly adopts two kinds of path planning methods, one is the improved genetic algorithm, there are three main improvements, the first is the improvement of the initial population generation method, the second is the improvement of the mutation operator, the third is the introduction of the smoothing operator. The main reason for adopting the improved genetic algorithm is that it has good parallelism and can be searched for multiple solution spaces at the same time. In this paper, an improved genetic algorithm is used to plan the running route of a single laser navigation AGV in the environment of different number of obstacles and different location of obstacles. The performance of the improved genetic algorithm is analyzed by comparing with the results obtained by the basic genetic algorithm, and the advantages and disadvantages of the improved genetic algorithm compared with the basic genetic algorithm in the path planning of the AGV for single laser navigation are explored in different environments. In this paper, another AGV path planning method, genetic ant colony algorithm, is used to solve the problem that the individual fitness of the initial population generated by random genetic algorithm is not high. In this method, the initial population individuals are not randomly generated, but the iterative results from each generation of ant colony algorithm are obtained, then the final results are obtained by selection, crossover, mutation, smoothing, and compared with other algorithms. It is proved that the algorithm is improved to some extent. According to the above two different path planning algorithms, the corresponding MATLAB codes are compiled for simulation verification, and the corresponding path planning prototype system is developed based on C # and MATLAB software. The prototype system is used to plan in the environment of two laser navigation AGVs, and it is proved that the prototype system has certain practical value.
【學(xué)位授予單位】：河南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH221;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 鄭佳春;吳建華;馬勇;龍延;;混合模擬退火與粒子群優(yōu)化算法的無人艇路徑規(guī)劃[J];中國海洋大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年09期

2 郭二東;劉楠]，

本文編號(hào)：2090595