【摘要】： 移動機器人路徑規(guī)劃是機器人研究領(lǐng)域的核心內(nèi)容之一,具有復雜性、約束性和非線性等特點。蟻群算法(ACA)是最近十幾年發(fā)展起來的仿生優(yōu)化算法,該算法在解決許多復雜問題方面已經(jīng)展現(xiàn)出其優(yōu)異的性能和巨大的發(fā)展?jié)摿Α?本文主要研究靜態(tài)環(huán)境下基于蟻群算法的移動機器人全局路徑規(guī)劃。 首先,采用柵格法建立環(huán)境模型,并利用做過改進的基本蟻群算法在柵格環(huán)境模型中進行路徑規(guī)劃,這些改進有:利用偽隨機比例規(guī)則代替隨機比例規(guī)則進行路徑轉(zhuǎn)移;限制了螞蟻行至當前柵格時下一步允許選擇的柵格范圍;對啟發(fā)函數(shù)進行了重新定義;讓螞蟻根據(jù)轉(zhuǎn)移概率利用“輪盤賭”方法選擇下一個柵格。 其次,針對基本蟻群算法在某些方面的不足和缺陷提出了三種改進算法:針對螞蟻在搜索路徑過程中落入障礙物陷阱而導致的算法停滯現(xiàn)象,提出了帶夭折策略的蟻群算法;針對蟻群在路徑搜索初始階段建立的非最優(yōu)路徑上的信息素對以后蟻群的信息誤導作用,提出了帶獎罰機制的蟻群算法;針對機器人在實際工作中的安全避碰問題,提出了基于保守螞蟻的蟻群算法。 最后,在蟻群算法的基礎(chǔ)上結(jié)合遺傳算法(GA)提出了兩種改進算法:GA-ACA算法和ACA-GA算法,并將其應(yīng)用于機器人路徑規(guī)劃。 為了驗證本文所提各種算法的有效性,基于MATLAB 7.5軟件開發(fā)環(huán)境設(shè)計了基于蟻群算法的移動機器人路徑規(guī)劃仿真系統(tǒng)。仿真結(jié)果驗證了所提算法的有效性。
[Abstract]:The path planning of mobile robot is one of the core contents in the field of robot research, which has the characteristics of complexity, constraint and nonlinearity. Ant colony algorithm (ACA) is a bionic optimization algorithm developed in recent years. It has shown excellent performance and great potential in solving many complex problems. This paper mainly studies the global path planning of mobile robot based on ant colony algorithm in static environment. Firstly, the environmental model is built by grid method, and the improved basic ant colony algorithm is used to plan the path in the grid environment model. These improvements are as follows: the pseudo-random proportional rule is used to replace the random proportional rule for path transfer; It limits the range of grid that ants can select next when they go to the current grid; redefines the heuristic function; and allows ants to select the next grid according to the transfer probability using the method of "roulette". Secondly, three improved algorithms are put forward in view of the shortcomings and defects of basic ant colony algorithm in some aspects: aiming at the stagnation of the algorithm caused by ants falling into obstacle trap in the course of searching path, the ant colony algorithm with abortive strategy is put forward; Aiming at the misguided effect of pheromone on the non-optimal path set up by ant colony in the initial stage of path search, an ant colony algorithm with reward and penalty mechanism is proposed to solve the problem of safe collision avoidance in actual work. Ant colony algorithm based on conserved ants is proposed. Finally, based on ant colony algorithm (ACA) and genetic algorithm (GA), two improved algorithms, namely: GA-ACA algorithm and ACA-GA algorithm, are proposed and applied to robot path planning. In order to verify the validity of the algorithms proposed in this paper, a path planning simulation system for mobile robots based on ant colony algorithm is designed based on MATLAB 7.5 software development environment. Simulation results verify the effectiveness of the proposed algorithm.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2009
【分類號】：TP301.6

【引證文獻】

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本文編號：2152195