本文選題：橋式起重機　切入點：吊裝路徑規(guī)劃　出處：《太原科技大學》2017年碩士論文　論文類型：學位論文

【摘要】：近年來,隨著各個行業(yè)的自動化、智能化水平不斷提高所帶來的便利與高效的驅動,大型工程機械也在向此方向發(fā)展。而智能路徑規(guī)劃作為一個重要的研究方向,在機器人領域已經比較成熟且得到廣泛應用,但在大型工程機械如起重機方面研究相對較少。因此,本文針對橋式起重機的特點和目前研究文獻中存在的不足,融合柵格法、蟻群算法,并將粒子群算法應用于動態(tài)環(huán)境中,提出了在靜態(tài)和動態(tài)環(huán)境下的橋式起重機吊裝路徑規(guī)劃方法,主要內容如下:首先,針對智能橋式起重機系統(tǒng),提出基于改進的蟻群算法的吊裝路徑規(guī)劃方法。研究在障礙物已知的靜態(tài)環(huán)境中,忽略橋式起重機的吊鉤升降,將其環(huán)境簡化為二維柵格模型,對蟻群算法的啟發(fā)函數(shù)、揮發(fā)系數(shù)等進行改進,且考慮到橋式起重機的運行特點,為避免大小車頻繁制動,將原有的斜線路徑改為直線,并引入代價函數(shù)來評價仿真的效果。仿真結果表明算法的可行性。其次,建立靜態(tài)且環(huán)境已知的橋式起重機三維作業(yè)環(huán)境模型,利用柵格法劃分空間,將橋式起重機所吊重物最大擺動距離與其安全通過障礙物的距離之和設為安全距離,將蟻群算法的啟發(fā)函數(shù)、揮發(fā)系數(shù)以及適應度函數(shù)等進行改進并應用于橋式起重機三維環(huán)境的吊裝路徑規(guī)劃。在MATLAB中進行仿真研究,結果證明了方案的可行性與有效性。最后,針對動態(tài)變化的吊裝環(huán)境,提出在動態(tài)環(huán)境中的橋式起重機吊裝路徑規(guī)劃。仍由柵格法建模,通過將優(yōu)先選擇的思想、敏感粒子根據適應度值改變來探測環(huán)境并根據閾值觸發(fā)響應機制、及對動態(tài)粒子群算法的適應度值的改變,來進行規(guī)劃。為避免大小車頻繁制動,仍采用直線路徑。仿真結果表明此方法不僅可用于動態(tài)環(huán)境且效果有保障,安全性高。
[Abstract]:In recent years, with the automation of various industries, intelligent level of continuous improvement of the convenience and efficiency of driving, large-scale construction machinery is also in this direction, and intelligent path planning as an important research direction, It has been mature and widely used in the field of robot, but the research on large-scale construction machinery such as cranes is relatively few. Therefore, in view of the characteristics of bridge cranes and the shortcomings of the present research literature, the fusion grid method is used in this paper. Ant colony algorithm and particle swarm optimization (PSO) algorithm are applied to dynamic environment. A method of crane hoisting path planning in static and dynamic environment is proposed. The main contents are as follows: firstly, aiming at intelligent bridge crane system, A method of hoisting path planning based on improved ant colony algorithm is proposed. In the static environment with known obstacles, the lifting hooks of bridge cranes are ignored, and their environment is simplified as a two-dimensional grid model, which is a heuristic function for ant colony algorithm. The volatilization coefficient is improved, and considering the operation characteristics of the bridge crane, in order to avoid frequent braking of the trolley, the original diagonal path is changed into a straight line. The cost function is introduced to evaluate the simulation effect. The simulation results show that the algorithm is feasible. Secondly, a static and known three-dimensional operating environment model of bridge crane is established, and the space is divided by grid method. In this paper, the sum of the maximum swing distance and the distance between the weight hoisted by the bridge crane and the distance from which the crane can safely pass through the obstacle is set as the safe distance, and the heuristic function of the ant colony algorithm is put forward. The volatilization coefficient and fitness function are improved and applied to the hoisting path planning of bridge crane in 3D environment. The simulation results in MATLAB show that the scheme is feasible and effective. In view of the dynamic changing hoisting environment, the hoisting path planning of bridge crane in dynamic environment is put forward. The sensitive particle detects the environment according to the change of fitness value, triggers the response mechanism according to the threshold, and changes the fitness value of the dynamic particle swarm optimization algorithm. The simulation results show that this method not only can be used in dynamic environment, but also has high security.
【學位授予單位】：太原科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH215;TP18

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