本文選題：移動機器人 + 路徑規(guī)劃　； 參考：《長春大學》2017年碩士論文

【摘要】：在機器人研究領(lǐng)域中,路徑規(guī)劃問題一直都是研究的重點。近年來,隨著機器人技術(shù)和產(chǎn)業(yè)的飛速發(fā)展,對路徑規(guī)劃的研究受到越來越多的學者、專家的關(guān)注和重視�，F(xiàn)今,對路徑規(guī)劃方法的研究已取得豐碩的成果,但是也存在著不足。許多算法的提出只是在實驗仿真的基礎(chǔ)上進行的,并不能用于實際情況中。這些不足需要研究者們?nèi)ミM一步完善。所以本文結(jié)合吉林省教育廳項目“大型養(yǎng)殖場監(jiān)控機器人控制系統(tǒng)設(shè)計”,對養(yǎng)殖場中機器人路徑規(guī)劃進行研究。首先,針對靜態(tài)環(huán)境下的機器人路徑進行研究,提出了一種改進的遺傳算法。在該方法中,一是改進了地圖環(huán)境的建立,將預先設(shè)定的靜態(tài)障礙物直接引入到算法的初始種群中,避開了環(huán)境建模問題;二是在算法中設(shè)置了檢查裝置,保證了生成的新個體不在障礙物內(nèi);三是改進了適應(yīng)度函數(shù)的設(shè)計,考慮到機器人路徑的最短距離、路徑平滑度、安全性能(避開障礙物),將這三個因素加入到適應(yīng)度函數(shù)的設(shè)計中;四是對適應(yīng)度函數(shù)中的三個因素人為加入權(quán)值系數(shù),進一步確保得出最優(yōu)路徑。通過仿真實驗,表明該方法是可以實現(xiàn)的。其次,對基于改進遺傳算法的機器人路徑規(guī)劃方法進行優(yōu)化研究,針對遺傳算法中適應(yīng)度函數(shù)的權(quán)值,提出利用粒子群優(yōu)化算法進行自主尋優(yōu),避開了人為設(shè)置的不足。根據(jù)遺傳算法適應(yīng)度函數(shù)中三個權(quán)值的關(guān)系,設(shè)計出粒子群優(yōu)化算法的適應(yīng)度函數(shù)。該方法能夠自行確定各個因子的權(quán)值,實現(xiàn)權(quán)值因子的自主協(xié)調(diào),從而得到最優(yōu)路徑。經(jīng)過仿真實驗,表明該方法可行。再次,運用蟻群算法對機器人進行路徑規(guī)劃仿真研究。仔細分析了蟻群算法的原理、參數(shù)和基本公式模型。通過仿真實驗得出該算法的可行性。最后,針對養(yǎng)殖場的實際環(huán)境,對機器人路徑規(guī)劃進行模擬仿真研究。以養(yǎng)雞場環(huán)境為模型,根據(jù)機器人在養(yǎng)雞場中執(zhí)行任務(wù)的不同,分別研究了監(jiān)控模式(巡航模式)和路徑規(guī)劃模式。機器人在不同的工作模式下,路徑規(guī)劃方法不同。監(jiān)控模式下采用了步長法,路徑規(guī)劃模式下采用了遺傳算法和蟻群算法。根據(jù)得到的遺傳算法和蟻群算法路徑規(guī)劃仿真結(jié)果進行對比,得出遺傳算法更適用于養(yǎng)雞場環(huán)境下的機器人路徑規(guī)劃,相較于蟻群算法,遺傳算法更具有優(yōu)越性。
[Abstract]:In the field of robot research, path planning has always been the focus of research. In recent years, with the rapid development of robot technology and industry, more and more scholars and experts pay attention to the research of path planning. Nowadays, the research on the path planning method has made a lot of achievements, but there are still some shortcomings. Many algorithms are proposed only on the basis of experimental simulation, and can not be used in the actual situation. These deficiencies need to be further improved by researchers. So this paper studies the path planning of the robot in the breeding farm with the project of Jilin Provincial Education Department "Design of the Control system of the Monitoring Robot in the Large-scale breeding Farm". Firstly, an improved genetic algorithm is proposed to study the robot path in static environment. In this method, one is to improve the establishment of the map environment, the other is to introduce the pre-set static obstacles directly into the initial population of the algorithm, to avoid the environmental modeling problem, and to set up a checking device in the algorithm. Thirdly, the design of fitness function is improved, considering the shortest distance of robot path, path smoothness, Safety performance (avoiding obstacles and adding these three factors to the design of fitness function) and adding weights to three factors in fitness function to ensure that the optimal path can be obtained. The simulation results show that the method is feasible. Secondly, the robot path planning method based on improved genetic algorithm is optimized. Aiming at the weight of fitness function in genetic algorithm, the particle swarm optimization algorithm is proposed for autonomous optimization, which avoids the deficiency of artificial setting. According to the relation of three weights in the fitness function of genetic algorithm, the fitness function of particle swarm optimization algorithm is designed. This method can determine the weight value of each factor and realize the independent coordination of the weight factor, thus the optimal path can be obtained. The simulation results show that this method is feasible. Thirdly, the ant colony algorithm is used to simulate the path planning of the robot. The principle, parameters and basic formula model of ant colony algorithm are analyzed in detail. Simulation results show the feasibility of the algorithm. Finally, the robot path planning is simulated and simulated according to the actual environment of the farm. Based on the model of chicken farm environment, the monitoring mode (cruise mode) and the path planning mode were studied according to the different tasks performed by the robot in the chicken farm. Under different working modes, the path planning method of robot is different. Step size method is used in monitoring mode, genetic algorithm and ant colony algorithm are used in path planning mode. According to the simulation results of genetic algorithm and ant colony algorithm, it is concluded that genetic algorithm is more suitable for robot path planning in chicken farm, and genetic algorithm is more superior than ant colony algorithm.
【學位授予單位】：長春大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242

【參考文獻】

相關(guān)期刊論文 前9條

1 羅竹青;;基于柵格法的機器人路徑規(guī)劃調(diào)節(jié)[J];信息與電腦(理論版);2009年11期

2 鮑慶勇;李舜酩;沈\，

本文編號：1846659