本文關(guān)鍵詞： 超圖 自動導(dǎo)引小車 動態(tài)調(diào)度 路徑規(guī)劃 時間窗　出處：《沈陽大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)今,自動化物流系統(tǒng)、柔性制造系統(tǒng)(Flexible Manufacturing System,FMS),自動化立體倉庫等的發(fā)展十分迅速,自動導(dǎo)引小車(Automated Guided Vehicle system,AGVs)在生產(chǎn)系統(tǒng)中占重要的地位,作為柔性生產(chǎn)的重要手段,得到越來越多的應(yīng)用。AGVs替代了傳統(tǒng)的人力、拖車、傳送帶的輸送,不存在傳統(tǒng)車間物流運輸?shù)木窒扌?降低了人力成本,方便了自動化管理,提高了系統(tǒng)的柔性和靈活性,提高了企業(yè)效益。AGV的應(yīng)用面對的是一個完整的系統(tǒng)。就系統(tǒng)而言,AGV路徑規(guī)劃及多AGV避碰就顯得尤為重要。路徑規(guī)劃是AGV研究中一個重要的課題。本文結(jié)合遼寧省科技計劃項目“裝備制造綜合自動化省級重點實驗室建設(shè)”以及市場對AGV的實際需要,研究了柔性制造系統(tǒng)中AGV路徑規(guī)劃算法。結(jié)合知識和實際生產(chǎn)對自動導(dǎo)引小車動態(tài)作業(yè)調(diào)度進行研究,主要從這幾個方面:(1)將AGV運行環(huán)境抽象成賦權(quán)有向圖,對圖論進行理論儲備,且對無環(huán)賦權(quán)有向圖建立路徑規(guī)劃模型。基于超圖的路徑規(guī)劃模型采用遺傳算法求解,并對其進行了仿真分析。(2)研究對單AGV路徑規(guī)劃選擇了基于有向圖的算法,通過分析Dijkstra算法、A*算法、極小代數(shù)的路徑規(guī)劃方法的特點,選擇極小代數(shù)求解單AGV路徑規(guī)劃問題,考慮實際系統(tǒng)的運行狀況該算法更加適合實際應(yīng)用。(3)針對多AGV系統(tǒng),其中有沖突和死鎖問題,提出時間窗的動態(tài)路徑規(guī)劃算法,能有效地避免車—車沖突、碰撞等問題,并且得到了時間最優(yōu)的路徑。通過對時間窗的更新、時間計算,能達到多輛車一起處在工作狀態(tài),車與車之間不會碰撞。仿真表明算法有效,能處理很多情況下的多車運行問題,其具有可行性、正確性。
[Abstract]:Nowadays, the development of automatic logistics system, flexible Manufacturing system, automatic three-dimensional warehouse and so on is very rapid. Automated Guided Vehicle system (AGVs) plays an important role in the production system, as an important means of flexible production. More and more applications. AGVs replace the traditional labor, trailers, conveyor belt transportation, there is no limitation of traditional workshop logistics transportation, reduce labor costs, facilitate automatic management, improve the flexibility and flexibility of the system. The application of improved enterprise benefit. AGV is faced with a complete system. In terms of system, it is particularly important to plan the path of AGV and avoid collision with multiple AGV. Path planning is an important subject in the study of AGV. This paper combines with Liaoning Province. "Construction of Provincial key Laboratory of Integrated equipment Manufacturing Automation" and the actual needs of the market for AGV, a scientific and technological planning project, In this paper, the AGV path planning algorithm in flexible manufacturing system is studied. Combining knowledge and actual production, the dynamic job scheduling of automatic guided vehicle is studied. The AGV running environment is abstracted into weighted directed graph from these aspects. The graph theory is theoretically reserved, and the path planning model is established for the unannular weighted directed graph. The path planning model based on hypergraph is solved by genetic algorithm. The algorithm based on directed graph is selected for single AGV path planning. By analyzing the characteristics of Dijkstra algorithm and path planning method of minimal algebra, we select minimal algebra to solve the single AGV path planning problem. Considering the running condition of real system, the algorithm is more suitable for practical application. (3) for multi-#en0# systems, where there are conflicts and deadlocks, a time-window dynamic path planning algorithm is proposed, which can effectively avoid vehicle-vehicle conflicts and collisions. By updating the time window and calculating the time, we can get many cars working together, and there will be no collision between the vehicles. The simulation shows that the algorithm is effective. Can deal with many cases of multi-vehicle operation problems, its feasibility, correctness.
【學(xué)位授予單位】：沈陽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP23

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