空氣環(huán)境下移動機器人羽流追蹤方法研究
發(fā)布時間：2018-10-11 15:20
【摘要】：受自然界部分生物通過追蹤散布在空氣中的化學(xué)信息來搜尋配偶、覓食、躲避敵害等行為的啟發(fā),人類開始探索給機器人搭配化學(xué)煙霧傳感器等模塊來模擬生物追蹤化學(xué)羽流的行為。希望此種機器人能夠代替人類在人為災(zāi)害、自然災(zāi)害環(huán)境中完成救援與救災(zāi)任務(wù)。此外,隨著全球化的推進,各國對社會安全和國家安全都安全事業(yè)關(guān)注度越來越高,因此研發(fā)能夠代替人力執(zhí)行搜救生命、搜尋違禁品等任務(wù)的智能機器人,具有重大的實際意義。現(xiàn)階段化學(xué)羽流追蹤(Chemical Plume Tracking,簡寫CPT)機器人成為國內(nèi)外研究學(xué)者關(guān)注的重點,相關(guān)的研究機構(gòu)已經(jīng)取得了突出的成就。目前當(dāng)CPT機器人在復(fù)雜多變的環(huán)境中執(zhí)行羽流追蹤任務(wù)時,伴隨客觀環(huán)境的復(fù)雜程度提高、羽流信息的分布更加復(fù)雜,以至于羽流追蹤難度增加。所以在滿足精確性的要求下,CPT機器人要完成復(fù)雜空氣環(huán)境中羽流追蹤任務(wù),需要具有更強的行為決策能力、自主導(dǎo)航能力。因此本論文針對機器人在羽流追蹤過程中的自主導(dǎo)航性能、速度控制精度及對機器人行為建模三個方面進行研究,其研究工作分為四部分:(1)采用模糊Petri網(wǎng)對CPT機器人在整個羽流追蹤過程中的行為進行建模,并分析其行為的可行性與正確性。(2)為提高移動機器人的自主導(dǎo)航能力,根據(jù)羽流追蹤過程的實際情況對傳統(tǒng)A*算法進行修正,然后采取修正后的A*算法與機器人傳感器結(jié)合循環(huán)導(dǎo)航的方法,用以完成整個追蹤路徑的導(dǎo)航。(3)羽流追蹤過程中驅(qū)動輪電機的速度控制,會影響著CPT機器人執(zhí)行羽流追蹤任務(wù)的精準(zhǔn)性。因此本文選擇模糊RBF神經(jīng)網(wǎng)絡(luò)PID離線學(xué)習(xí)的方式實現(xiàn)對機器人驅(qū)動輪電機進行速度控制,并達到響應(yīng)速度快、調(diào)整時間短的實際效果。(4)搭建羽流追蹤機器人,以AS-4WD移動機器人平臺為基礎(chǔ),與煙霧濃度傳感器,超聲波傳感器等模塊搭配構(gòu)成化學(xué)羽流追蹤機器人,對本文模型與方法進行驗證。
[Abstract]:Inspired by nature's behavior of tracing chemical information scattered in the air to search for spouses, forage, and avoid enemies, Humans have begun to explore how robots can be fitted with modules such as chemical smoke sensors to simulate the behavior of biological tracing chemical plumes. It is hoped that this kind of robot can replace human in man-made disasters and natural disasters to complete rescue and relief missions. In addition, with the development of globalization, countries are paying more and more attention to social security and national security, so they are developing intelligent robots that can take the place of human resources to carry out search and rescue work, search for contraband and other tasks. It is of great practical significance. At present, chemical plume tracing (Chemical Plume Tracking, (CPT) robot has become the focus of researchers at home and abroad, and relevant research institutions have made outstanding achievements. At present, when CPT robot executes plume tracking task in complex and changeable environment, with the increasing complexity of objective environment, the distribution of plume information becomes more complex, which makes plume tracking more difficult. So in order to fulfill the plume tracking task in complex air environment, CPT robot needs stronger behavior decision ability and autonomous navigation ability to meet the requirement of accuracy. In this paper, the performance of autonomous navigation, the accuracy of speed control and the modeling of robot behavior in plume tracking are studied in this paper. The research work is divided into four parts: (1) using fuzzy Petri nets to model the behavior of CPT robot in the whole plume tracking process, and analyzing the feasibility and correctness of its behavior. (2) to improve the autonomous navigation ability of mobile robot, According to the actual situation of plume tracking process, the traditional A * algorithm is modified, and then the modified A * algorithm is combined with the robot sensor in the circular navigation method. (3) the speed control of driving wheel motor in plume tracking process will affect the accuracy of plume tracking task of CPT robot. Therefore, this paper chooses the fuzzy RBF neural network PID off-line learning method to realize the speed control of robot drive wheel motor, and achieves the practical effect of fast response speed and short adjustment time. (4) build plume tracking robot. Based on AS-4WD mobile robot platform, chemical plume tracking robot is constructed with smoke concentration sensor, ultrasonic sensor and other modules. The model and method in this paper are verified.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242
本文編號：2264540
[Abstract]:Inspired by nature's behavior of tracing chemical information scattered in the air to search for spouses, forage, and avoid enemies, Humans have begun to explore how robots can be fitted with modules such as chemical smoke sensors to simulate the behavior of biological tracing chemical plumes. It is hoped that this kind of robot can replace human in man-made disasters and natural disasters to complete rescue and relief missions. In addition, with the development of globalization, countries are paying more and more attention to social security and national security, so they are developing intelligent robots that can take the place of human resources to carry out search and rescue work, search for contraband and other tasks. It is of great practical significance. At present, chemical plume tracing (Chemical Plume Tracking, (CPT) robot has become the focus of researchers at home and abroad, and relevant research institutions have made outstanding achievements. At present, when CPT robot executes plume tracking task in complex and changeable environment, with the increasing complexity of objective environment, the distribution of plume information becomes more complex, which makes plume tracking more difficult. So in order to fulfill the plume tracking task in complex air environment, CPT robot needs stronger behavior decision ability and autonomous navigation ability to meet the requirement of accuracy. In this paper, the performance of autonomous navigation, the accuracy of speed control and the modeling of robot behavior in plume tracking are studied in this paper. The research work is divided into four parts: (1) using fuzzy Petri nets to model the behavior of CPT robot in the whole plume tracking process, and analyzing the feasibility and correctness of its behavior. (2) to improve the autonomous navigation ability of mobile robot, According to the actual situation of plume tracking process, the traditional A * algorithm is modified, and then the modified A * algorithm is combined with the robot sensor in the circular navigation method. (3) the speed control of driving wheel motor in plume tracking process will affect the accuracy of plume tracking task of CPT robot. Therefore, this paper chooses the fuzzy RBF neural network PID off-line learning method to realize the speed control of robot drive wheel motor, and achieves the practical effect of fast response speed and short adjustment time. (4) build plume tracking robot. Based on AS-4WD mobile robot platform, chemical plume tracking robot is constructed with smoke concentration sensor, ultrasonic sensor and other modules. The model and method in this paper are verified.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242
【參考文獻】
相關(guān)期刊論文 前10條
1 顧辰;;改進的A*算法在機器人路徑規(guī)劃中的應(yīng)用[J];電子設(shè)計工程;2014年19期
2 朱婷婷;姜波;袁杰;;基于模糊Petri網(wǎng)的加工番茄種植管理優(yōu)化[J];湖北農(nóng)業(yè)科學(xué);2014年08期
3 林丹;;一種室內(nèi)清潔機器人返回路徑規(guī)劃算法[J];重慶科技學(xué)院學(xué)報(自然科學(xué)版);2010年01期
4 丁富玲;李承家;;模糊Petri網(wǎng)的發(fā)展[J];杭州電子科技大學(xué)學(xué)報;2008年06期
5 李俊彩;孟慶浩;梁瓊;;基于進化梯度搜索的機器人主動嗅覺仿真研究[J];機器人;2007年03期
6 蔡自興;智能控制及移動機器人研究進展[J];中南大學(xué)學(xué)報(自然科學(xué)版);2005年05期
7 孟江華,朱紀洪,孫增圻;未知環(huán)境下基于傳感器的移動機器人路徑規(guī)劃新方法[J];機器人;2005年04期
8 付宜利,顧曉宇,王樹國;基于模糊控制的自主機器人路徑規(guī)劃策略研究[J];機器人;2004年06期
9 王棟耀,馬旭東,戴先中;基于聲納的移動機器人沿墻導(dǎo)航控制[J];機器人;2004年04期
10 胡傳平;消防機器人的開發(fā)與應(yīng)用[J];機器人技術(shù)與應(yīng)用;2003年05期
相關(guān)碩士學(xué)位論文 前1條
1 徐保港;基于氣體傳感器陣列的嗅覺機器人的研究[D];哈爾濱工業(yè)大學(xué);2006年
，本文編號：2264540
本文鏈接：http://www.lk138.cn/shoufeilunwen/xixikjs/2264540.html
最近更新
教材專著
