本文關(guān)鍵詞：基于群體智能優(yōu)化的AGV路徑規(guī)劃算法研究　出處：《吉林大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： AGV路徑規(guī)劃 群體智能優(yōu)化算法 貓群算法 蟻群算法 差分進(jìn)化算法

【摘要】：自動(dòng)導(dǎo)引車(chē)隸屬于移動(dòng)機(jī)器人范疇,隨著社會(huì)的進(jìn)步和工業(yè)技術(shù)的發(fā)展,AGV已經(jīng)成為當(dāng)今工業(yè)生產(chǎn)、自動(dòng)化倉(cāng)儲(chǔ)系統(tǒng)中的重要工具之一,通過(guò)對(duì)AGV及其關(guān)鍵技術(shù)的研究可以降低生產(chǎn)成本,提高生產(chǎn)效率,具有重要現(xiàn)實(shí)意義。自動(dòng)導(dǎo)引車(chē)的路徑規(guī)劃是整個(gè)系統(tǒng)的關(guān)鍵技術(shù)之一,其特點(diǎn)有非線性、復(fù)雜性、約束性等,多年來(lái)作為特殊機(jī)器人的自動(dòng)導(dǎo)引車(chē)路徑規(guī)劃算法的研究方興未艾,基于數(shù)學(xué)模型的傳統(tǒng)算法如禁忌法、柵格法、人工勢(shì)場(chǎng)法等難以取得理想的效果,均有魯棒性差、精度差、效率差等問(wèn)題;而基于對(duì)社會(huì)性昆蟲(chóng)行為的模擬產(chǎn)生的一系列全局尋優(yōu)的群體智能優(yōu)化算法如蟻群算法、貓群算法等,則具有魯棒性強(qiáng)、全局尋優(yōu)、并行性等特點(diǎn)。因此本論文主要工作是基于群體智能優(yōu)化算法研究自動(dòng)導(dǎo)引車(chē)的路徑規(guī)劃。本文根據(jù)AGV的應(yīng)用需求,將AGV的路徑規(guī)劃分為單任務(wù)目標(biāo)路徑規(guī)劃和多任務(wù)目標(biāo)路徑規(guī)劃。通過(guò)對(duì)比諸多文獻(xiàn)中將機(jī)器人路徑規(guī)劃模型轉(zhuǎn)為旅行商問(wèn)題的思路,分析多任務(wù)目標(biāo)AGV小車(chē)的路徑規(guī)劃特點(diǎn),將其轉(zhuǎn)化為旅行商問(wèn)題,并且提出了三種算法來(lái)解決AGV系統(tǒng)的路徑規(guī)劃。(1)首先通過(guò)分析差分進(jìn)化算法求解旅行商問(wèn)題的思路,提出了離散貓群算法來(lái)求解AGV路徑規(guī)劃,通過(guò)引入位置-次序編碼,將貓群算法擴(kuò)展到離散域,使之可以用于求解該問(wèn)題,并進(jìn)一步分析了貓群算法中的重要參數(shù)對(duì)算法性能的影響。(2)其次針對(duì)蟻群算法在求解旅行商問(wèn)題存在局部尋優(yōu)與收斂性的矛盾,提出兩種改進(jìn)蟻群算法求解AGV路徑規(guī)劃:其一基于差分進(jìn)化算法的全局尋優(yōu)性,引入多種群差分蟻群算法,通過(guò)對(duì)分組后的蟻群進(jìn)行不同的差分進(jìn)化方式,有效提高最優(yōu)解的搜索概率;其二根據(jù)貓群算法搜索模式的特點(diǎn),引入基于貓群搜索的蟻群算法,實(shí)現(xiàn)蟻群個(gè)體在當(dāng)前解集周?chē)木植克阉?有效改善蟻群算法尋優(yōu)性。最后,分析了多AGV系統(tǒng)路徑中出現(xiàn)的沖突問(wèn)題,針對(duì)在交叉沖突中,現(xiàn)有的解決方案存在算法復(fù)雜,參數(shù)過(guò)多等問(wèn)題,提出一種簡(jiǎn)單可控的解決方案,即采用提前交叉和蟻群算法局部二次規(guī)劃�；谙伻核惴▽�(duì)每輛AGV小車(chē)進(jìn)行預(yù)路徑規(guī)劃,通過(guò)檢測(cè)交叉柵格,判斷是否出現(xiàn)沖突,如果出現(xiàn)沖突,對(duì)于優(yōu)先級(jí)較低的小車(chē)采用人工交叉法,使得小車(chē)經(jīng)過(guò)交叉路徑時(shí)存在時(shí)間差,并進(jìn)行蟻群局部二次路徑規(guī)劃。通過(guò)Matlab仿真實(shí)驗(yàn)表明,該方案可以用于解決單任務(wù)目標(biāo)多AGV系統(tǒng)中的交叉沖突現(xiàn)象。
[Abstract]:Automatic guided vehicle belongs to the category of mobile robot. With the progress of society and the development of industrial technology, AGV has become one of the most important tools in industrial production and automatic warehousing system. The research of AGV and its key technology can reduce the production cost and improve the production efficiency. The path planning of the automatic guided vehicle is one of the key technologies of the whole system. Its characteristics are nonlinear, complexity, constraint and so on. As a special robot, the path planning algorithm of automatic guided vehicle is in the ascendant for many years. The traditional algorithms based on mathematical model, such as Tabu method and grid method, are in the ascendant. The artificial potential field method is difficult to achieve the ideal effect, which has the problems of poor robustness, poor precision and low efficiency. Based on the simulation of social insect behavior, a series of global optimization algorithms, such as ant colony algorithm, cat swarm optimization algorithm, have strong robustness and global optimization. Therefore, the main work of this paper is to study the path planning of automatic guided vehicle based on swarm intelligence optimization algorithm. This paper is based on the application requirements of AGV. The path planning of AGV is divided into single-task target path planning and multi-task objective path planning. By comparing the robot path planning model into the traveling salesman problem in many literatures. This paper analyzes the path planning characteristics of multi-task target AGV vehicle and transforms it into a traveling salesman problem. And three algorithms are proposed to solve the path planning of AGV system. Firstly, through the analysis of differential evolution algorithm to solve the traveling salesman problem, a discrete cat swarm algorithm is proposed to solve the AGV path planning. By introducing position-order coding, the cat swarm algorithm is extended to discrete domain, which can be used to solve the problem. Furthermore, the influence of important parameters on the performance of the algorithm is analyzed. Secondly, the ant colony algorithm has the contradiction of local optimization and convergence in solving the traveling salesman problem. Two improved ant colony algorithms are proposed to solve the AGV path planning. One is based on the global optimization of differential evolutionary algorithm, the introduction of multi-colony differential ant colony algorithm, through the grouping ant colony differential evolution of different ways. Improve the search probability of the optimal solution effectively; Secondly, according to the characteristics of the cat colony algorithm search pattern, the ant colony algorithm based on the cat colony search is introduced to realize the local search of the ant colony individual around the current solution set, so as to effectively improve the optimization of the ant colony algorithm. This paper analyzes the conflict problem in the path of multi-#en0# system, and proposes a simple and controllable solution to the problem of complex algorithm and excessive parameters in the existing solutions. Based on the ant colony algorithm, the pre-path planning of each AGV vehicle is carried out, and the crossover grid is detected to determine whether or not there is a conflict, if there is a conflict. Artificial crossover method is used for the vehicles with lower priority, which makes the cars have time difference when they cross the path, and carries out the local secondary path planning of ant colony. The simulation results of Matlab show that. This scheme can be used to solve the cross-conflict phenomenon in single-task multi-AGV system.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18;TP23

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