本文選題：離子型稀土礦掘進(jìn)選礦一體機(jī)　切入點(diǎn)：避障　出處：《江西理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：離子型稀土礦掘進(jìn)選礦一體機(jī)具有一定的鉆進(jìn)能力,能夠較好完成對離子型稀土礦的開采工作。在掘進(jìn)過程中,掘進(jìn)工藝路線確定方法可以分為兩類:1、根據(jù)已有地質(zhì)勘探資料分析所需開采的礦床分布,預(yù)先從地質(zhì)圖上確定出掘進(jìn)工藝路線,然后用它來指導(dǎo)離子型稀土礦掘進(jìn)選礦一體機(jī)的掘進(jìn)過程。2、在沒有明確地質(zhì)勘探資料情況下,則需要在掘進(jìn)前通過不全的地質(zhì)資料來初步確定掘進(jìn)路線,然后在實(shí)際掘進(jìn)過程中,通過機(jī)載雷達(dá)獲取的數(shù)據(jù)來校正掘進(jìn)工藝路線。在離子型稀土礦掘進(jìn)選礦一體機(jī)按掘進(jìn)工藝路線進(jìn)行掘進(jìn)的過程中,經(jīng)常會遇到非常堅(jiān)硬的孤石體(普氏硬度?7),這些孤石體容易引起離子型稀土礦掘進(jìn)選礦一體機(jī)開挖機(jī)構(gòu)受力不均,刀具發(fā)生嚴(yán)重磨損,主軸承密封性破壞,進(jìn)而導(dǎo)致刀盤發(fā)生堵塞,負(fù)載加大,燒毀電機(jī)等問題,最終導(dǎo)致離子型稀土礦掘進(jìn)選礦一體機(jī)停留在礦山中,無法繼續(xù)工作,從而需要對主要部件進(jìn)行維修或替換,大量增加掘進(jìn)過程的成本和時(shí)間,降低開挖過程的效率,所以對掘進(jìn)過程進(jìn)行避障研究具有重要意義。本文首先從礦山環(huán)境檢測入手,采用地質(zhì)雷達(dá)的地球物理探測方法來對障礙物進(jìn)行檢測。通過分析礦山中障礙物性質(zhì),對地質(zhì)雷達(dá)參數(shù)如:中心頻率等,進(jìn)行了準(zhǔn)確設(shè)定,從而識別出礦山環(huán)境中障礙物的大小和具體位置。然后運(yùn)用相對定位的方法,通過離子型稀土礦掘進(jìn)選礦一體機(jī)內(nèi)部的直線位移、壓力、傾角傳感器和電子羅盤組成的位姿檢測系統(tǒng)準(zhǔn)確的定位離子型稀土礦掘進(jìn)選礦一體機(jī)的空間位置和姿態(tài),進(jìn)而實(shí)現(xiàn)對裝置的運(yùn)動控制。運(yùn)用柵格法原理對障礙物環(huán)境進(jìn)行建模,然后運(yùn)用蟻群算法對已建立的柵格矩陣進(jìn)行路徑規(guī)劃仿真,驗(yàn)證蟻群算法運(yùn)用到離子型稀土礦掘進(jìn)選礦一體機(jī)的避障路徑規(guī)劃中具有可行性。針對不同的柵格模型,蟻群算法各參數(shù)的最優(yōu)值有所區(qū)別,以三步走方式作為指導(dǎo)通過多次仿真實(shí)驗(yàn)得出了蟻群算法各參數(shù)的最優(yōu)值。蟻群算法運(yùn)用到避障路徑規(guī)劃中時(shí)存在收斂速度慢,全局尋優(yōu)能力較差等問題,本文設(shè)計(jì)了如下改進(jìn)方案:在進(jìn)行柵格初始化時(shí)對凹形障礙物進(jìn)行凸化處理;在初始信息素分配和概率轉(zhuǎn)移公式中引入了節(jié)點(diǎn)到目標(biāo)點(diǎn)的距離信息。最后對改進(jìn)蟻群算法的仿真結(jié)果和基本蟻群算法最優(yōu)結(jié)果進(jìn)行對比,驗(yàn)證了改進(jìn)蟻群算法在尋優(yōu)能力和收斂速度上具有較大提升。
[Abstract]:Ion type rare earth ore driving-dressing machine has certain drilling ability and can complete the mining work of ion rare earth ore. The method of determining the technological route of tunneling can be divided into two categories: 1.According to the existing geological exploration data, we can analyze the distribution of the mineral deposits that need to be mined, and determine in advance the technological route of the excavation from the geological map. Then it is used to guide the tunneling process of the ion type rare earth ore driving-in-one machine. In the absence of clear geological exploration data, it is necessary to preliminarily determine the tunneling route through incomplete geological data before the excavation. Then in the actual tunneling process, the tunneling process is corrected by the data obtained by airborne radar. Very hard solitoids are often encountered (Prussian hardness? 7. These solitary stone bodies are apt to cause uneven force on the excavating mechanism of the ion type rare earth ore driving-mining integrated machine, serious wear of the cutting tool, destruction of the seal of the main bearing, and then lead to blockage of the cutter head, increase of the load, burning of the motor, and so on. As a result, the ion type rare earth ore tunneling machine stays in the mine and can not continue to work, so it is necessary to repair or replace the main parts, increase the cost and time of the tunneling process, and reduce the efficiency of the excavation process. Therefore, it is of great significance to study the obstacle avoidance in the process of tunneling. Firstly, this paper starts with mine environment detection and uses geophysical detection method of geological radar to detect obstacles. The parameters of GPR, such as center frequency, are accurately set to identify the size and location of obstacles in the mine environment, and then the relative positioning method is used. Through the linear displacement, pressure, inclination angle sensor and electronic compass, the position and attitude of the ion type rare earth ore drivage machine are accurately located, and the position and attitude of the ion type rare earth ore driving machine are accurately located. Then the motion control of the device is realized. The obstacle environment is modeled by the raster method, and then the path planning simulation of the established grid matrix is carried out by using the ant colony algorithm. To verify the feasibility of applying ant colony algorithm to the obstacle avoidance path planning of ion type rare earth ore tunneling machine. For different grid models, the optimum value of each parameter of ant colony algorithm is different. Under the guidance of the three-step approach, the optimum values of the parameters of the ant colony algorithm are obtained through many simulation experiments. When the ant colony algorithm is applied to obstacle avoidance path planning, the convergence rate is slow and the global optimization ability is poor. In this paper, the following improvement schemes are designed: the concave obstacle is convoluted when the grid is initialized; The distance information from the node to the target point is introduced into the initial pheromone assignment and probability transfer formula. Finally, the simulation results of the improved ant colony algorithm are compared with the optimal results of the basic ant colony algorithm. It is verified that the improved ant colony algorithm can improve the optimization ability and convergence speed.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TD421.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 崔茂源,田彥濤,趙中祺;基于模糊邏輯的自主移動機(jī)器人實(shí)時(shí)滾動路徑規(guī)劃及控制[J];吉林工業(yè)大學(xué)自然科學(xué)學(xué)報(bào);1999年01期

2 艾海舟,張鈸;基于拓?fù)涞穆窂揭?guī)劃問題的圖形解法[J];機(jī)器人;1990年05期

3 劉祚時(shí);羅愛華;彭建云;;基于蟻群算法全自主機(jī)器人路徑規(guī)劃研究[J];制造業(yè)自動化;2010年02期

4 仲欣,呂恬生;基于遺傳算法的汽車式移動機(jī)器人路徑規(guī)劃方法[J];上海交通大學(xué)學(xué)報(bào);1999年07期

5 曹清華;曹獻(xiàn)珍;;如何提高礦產(chǎn)資源保障能力[J];中國國土資源經(jīng)濟(jì);2005年12期

6 陶建格;沈鐳;;礦產(chǎn)資源價(jià)值與定價(jià)調(diào)控機(jī)制研究[J];資源科學(xué);2013年10期

相關(guān)碩士學(xué)位論文 前5條

1 彭珍發(fā);西商高速公路建設(shè)中的地質(zhì)雷達(dá)技術(shù)應(yīng)用與研究[D];成都理工大學(xué);2011年

2 王麗;移動機(jī)器人路徑規(guī)劃方法研究[D];西北工業(yè)大學(xué);2007年

3 劉亮;基于改進(jìn)蟻群算法的移動機(jī)器人路徑規(guī)劃[D];武漢科技大學(xué);2007年

4 王磊;移動機(jī)器人路徑規(guī)劃研究[D];哈爾濱工程大學(xué);2007年

5 郭淑麗;基于GPR的復(fù)墾土壤壓實(shí)探測模型研究[D];山東農(nóng)業(yè)大學(xué);2013年

，

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