本文選題：移動機(jī)器人 + 導(dǎo)航�。� 參考：《廣西大學(xué)》2017年碩士論文

【摘要】：隨著科學(xué)技術(shù)的不斷發(fā)展,人類社會的智能化程度與自動化程度在不斷提高,智能化的移動機(jī)器人變得越來越貼近人類的生活,像掃地機(jī)器人,服務(wù)機(jī)器人,救災(zāi)機(jī)器人等與人們的生活息息相關(guān)。對移動機(jī)器人的導(dǎo)航和環(huán)境建模的研究一直以來都是國內(nèi)外學(xué)術(shù)研究工作者們研究的重點(diǎn)。如何才能使得移動機(jī)器人在未知環(huán)境中導(dǎo)航時(shí),能像人類一樣做出合理的決定,一直是所有機(jī)器人技術(shù)研究者們期望實(shí)現(xiàn)的目標(biāo)。而如何使得移動機(jī)器人能在移動過程中完成對未知環(huán)境的精確建模也是移動機(jī)器人研究的一個(gè)重要方向。本論文針對采用傳統(tǒng)人工勢場方法實(shí)現(xiàn)移動機(jī)器人導(dǎo)航存在的缺點(diǎn)和不足,提出了一種改進(jìn)的人工勢場方法。該方法通過改進(jìn)斥力函數(shù)和添加旋轉(zhuǎn)力對人工勢場法進(jìn)行改進(jìn),最小化斥力勢場的扭曲程度,實(shí)現(xiàn)在目標(biāo)點(diǎn)取到勢場的全局最小值。仿真表明采用這種方法進(jìn)行的人工勢場法的改進(jìn),可以提升移動機(jī)器人運(yùn)動軌跡的平滑度,減少障礙物周圍的不規(guī)則抖動,實(shí)現(xiàn)目標(biāo)點(diǎn)的可達(dá)性。針對移動機(jī)器人的勢場函數(shù)參數(shù),步長等參數(shù),提出了基于粒子群優(yōu)化細(xì)菌覓食算法(BF-PSO)算法的參數(shù)優(yōu)化。為研究BF-PSO算法優(yōu)化所獲得參數(shù)對移動機(jī)器人導(dǎo)航的優(yōu)化效果,分別設(shè)計(jì)了基于優(yōu)化參數(shù)設(shè)置和基于經(jīng)驗(yàn)參數(shù)設(shè)置的改進(jìn)人工勢場法移動機(jī)器人導(dǎo)航實(shí)驗(yàn)。仿真實(shí)驗(yàn)驗(yàn)證了BF-PSO優(yōu)化參數(shù)對移動機(jī)器人導(dǎo)航優(yōu)化效果提升的可行性,和獲得更短路徑的有效性。分別研究了基于擴(kuò)展卡爾曼濾波算法(EKF)算法與無跡卡爾曼濾波器算法(UKF)算法在未知環(huán)境下的移動機(jī)器人環(huán)境建模方法。兩種環(huán)境建模算法采用同樣的仿真環(huán)境進(jìn)行了仿真實(shí)驗(yàn)。兩種方法的實(shí)驗(yàn)均獲得了一條估計(jì)到的機(jī)器人運(yùn)動路徑和觀測到的路標(biāo),輸出了估計(jì)路徑與真實(shí)路徑的誤差,觀測路標(biāo)位置與真實(shí)路標(biāo)位置的誤差。實(shí)驗(yàn)證明了,基于UKF算法的移動機(jī)器人環(huán)境建模方法獲得機(jī)器人的估計(jì)路徑與觀測路標(biāo)具有更好的精確度。
[Abstract]:With the continuous development of science and technology, the degree of intelligence and automation of human society is constantly improved, intelligent mobile robots become more and more close to human life, such as floor sweeping robots, service robots, Disaster relief robots and so on are closely related to people's lives. The research on navigation and environmental modeling of mobile robots has been the focus of academic researchers at home and abroad. How to make the mobile robot navigate in the unknown environment and make reasonable decisions like human beings is always the goal that all the robotics researchers expect to achieve. How to make the mobile robot complete the accurate modeling of the unknown environment in the mobile process is also an important research direction of the mobile robot. In this paper, an improved artificial potential field method is proposed to solve the shortcomings and shortcomings of the traditional artificial potential field method for mobile robot navigation. This method improves the artificial potential field by improving the repulsion function and adding the rotation force to minimize the distortion of the repulsion potential field and achieve the global minimum value of the potential field at the target point. Simulation results show that the improved artificial potential field method can improve the smoothness of mobile robot trajectory reduce the irregular jitter around obstacles and achieve the reachability of target points. Aiming at the parameters of potential field function and step size of mobile robot, the parameter optimization of BF-PSO-based bacterial foraging algorithm based on particle swarm optimization (PSO) is proposed. In order to study the optimization effect of the parameters obtained by BF-PSO algorithm on mobile robot navigation, an improved artificial potential field method for mobile robot navigation experiments based on optimized parameters setting and empirical parameter setting was designed respectively. The simulation results show that the optimization parameters of BF-PSO are feasible to improve the navigation efficiency of mobile robot, and the effectiveness of obtaining shorter path is verified. The modeling methods of mobile robot environment based on extended Kalman filter (EKF) algorithm and unscented Kalman filter algorithm (UKF) in unknown environment are studied respectively. Two environmental modeling algorithms are simulated in the same simulation environment. In the experiments of both methods, an estimated robot motion path and observed road sign are obtained, and the errors between the estimated path and the real path are outputted, and the errors between the position of the road sign and the position of the real road sign are obtained. The experimental results show that the environment modeling method of mobile robot based on UKF algorithm has better accuracy in obtaining the estimated path and observation signpost of the robot.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242

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本文編號：1879023