本文選題：輪式足球機(jī)器人 + 仿真�。� 參考：《成都理工大學(xué)》2009年碩士論文

【摘要】： 足球機(jī)器人是人工智能與機(jī)器人領(lǐng)域極富挑戰(zhàn)性的高技術(shù)密集項(xiàng)目,同時(shí)又是人工智能技術(shù)的一個(gè)理想突破點(diǎn)。它涵蓋了機(jī)器人學(xué)、人工智能和智能控制等多個(gè)領(lǐng)域,己成為研究多智能體系統(tǒng)和人工智能應(yīng)用技術(shù)研究的重要實(shí)驗(yàn)平臺。機(jī)器人踢足球,看似游戲,其實(shí)展示了一個(gè)國家信息和自動(dòng)化技術(shù)的綜合實(shí)力。 作為開發(fā)足球機(jī)器人真實(shí)系統(tǒng)的輔助部分,仿真系統(tǒng)以其經(jīng)濟(jì)、靈活的特性一直受到人們的重視。 實(shí)物的足球機(jī)器人因?yàn)榭刂齐y度大,實(shí)時(shí)性要求高和硬件的一系列問題,現(xiàn)在還很難做出很有技術(shù)性的動(dòng)作。 對于仿真比賽,由于平臺近于理想,不像實(shí)物比賽易受周圍環(huán)境的影響,并且平臺為官方提供的統(tǒng)一平臺,因而具有更好的客觀性,更便于比賽的開展。先進(jìn)的控制方法在仿真足球機(jī)器人比賽比在實(shí)物機(jī)器人比賽中更易得到應(yīng)用和檢驗(yàn)。 本文正是以機(jī)器人足球比賽為背景,以The Robot Soccer Simulator為仿真平臺,針對足球機(jī)器人運(yùn)動(dòng)控制系統(tǒng)進(jìn)行深入研究,并進(jìn)行了算法、性能和應(yīng)用上的一系列改進(jìn),并可以作為其他仿真及決策系統(tǒng)開發(fā)的基礎(chǔ)。 本文首先對足球機(jī)器人比賽進(jìn)行了回顧,分析了足球機(jī)器人關(guān)鍵技術(shù)、國內(nèi)外研究現(xiàn)狀、科研意義及應(yīng)用。然后,介紹了仿真平臺及仿真環(huán)境,并推導(dǎo)出足球機(jī)器人的運(yùn)動(dòng)學(xué)模型,為本文的后續(xù)研究提供了模型基礎(chǔ)及平臺環(huán)境。 要讓足球機(jī)器人實(shí)現(xiàn)戰(zhàn)術(shù)策略,首先要有好的運(yùn)動(dòng)控制。常規(guī)PID算法在足球機(jī)器人控制中有廣泛的應(yīng)用,然而足球機(jī)器人控制過程機(jī)理復(fù)雜,難以確定精確的數(shù)學(xué)模型,并存在著不同程度的非線性、時(shí)變等不確定性,同時(shí)隨著對機(jī)器人控制的要求進(jìn)一步提高,利用常規(guī)的PID控制很難滿足系統(tǒng)的要求。 神經(jīng)網(wǎng)絡(luò)作為一門非常熱門的交叉學(xué)科,以其強(qiáng)大的非線性映射能力、并行處理能力、自學(xué)習(xí)能力,在控制領(lǐng)域得到廣泛的應(yīng)用。 文中研究分析了BP神經(jīng)網(wǎng)絡(luò),BP神經(jīng)網(wǎng)絡(luò)是目前應(yīng)用較多的一種神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),是一種性能優(yōu)良的神經(jīng)網(wǎng)絡(luò)。主要研究了BP神經(jīng)網(wǎng)絡(luò)的數(shù)學(xué)理論,詳細(xì)分析了幾種流行的BP神經(jīng)網(wǎng)絡(luò)學(xué)算法的優(yōu)缺點(diǎn)及其改進(jìn)的BP算法。這些研究為后面機(jī)器人小車的運(yùn)動(dòng)控制研究做了鋪墊。 接著,將各種改進(jìn)的BP算法與PID相結(jié)合,得出新的控制算法,并對各種算法的性能一一比較,仿真結(jié)果表明,這種改進(jìn)方案與其他幾種PID控制相比,超調(diào)量小、調(diào)節(jié)速度快、調(diào)整時(shí)間短,說明其具有更好的控制特性;另外,穩(wěn)態(tài)誤差也較小。所以,改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)PID控制的控制精度更高,從而會獲得令人滿意的效果。 因此,將BP神經(jīng)網(wǎng)絡(luò)運(yùn)用于PID控制中,能夠有效克服經(jīng)典PID控制器在被控對象具有非線性、時(shí)變不確定性和難以建立精確的數(shù)學(xué)模型時(shí)出現(xiàn)的參數(shù)整定不良和性能欠佳等缺陷。 本文還研究了基于BP神經(jīng)網(wǎng)絡(luò)的PID控制器結(jié)構(gòu)和算法,利用改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)對PID控制參數(shù)進(jìn)行在線自整定,構(gòu)造一個(gè)具有參數(shù)自整定能力、穩(wěn)定的PID控制器。將這種改進(jìn)的BP PID控制器應(yīng)用到機(jī)器人小車到定點(diǎn)運(yùn)動(dòng)及圓周圓周運(yùn)動(dòng)中,通過仿真平臺及MATLAB實(shí)驗(yàn),從實(shí)驗(yàn)結(jié)果里我們看到這種新型的PID控制器在一定程度上提高了系統(tǒng)的魯棒性,使小車的運(yùn)動(dòng)更加穩(wěn)定和軌跡更加平滑,明顯提高了小車的控制性能。 路勁規(guī)劃問題一直是足球機(jī)器人研究的熱點(diǎn)和難點(diǎn),機(jī)器人在有障礙物的情況下,尋找一條恰當(dāng)?shù)穆窂?能從給定起點(diǎn)到終點(diǎn),使機(jī)器人在運(yùn)動(dòng)過程中能安全、無碰撞地繞過所有的障礙物。因此,在很大程度上,路徑規(guī)劃問題就是避障問題。 路徑規(guī)劃成功,機(jī)器人能快速完成給定任務(wù),但是如果失敗,機(jī)器人的行動(dòng)受阻,動(dòng)作難以完成,甚至影響整個(gè)策略的實(shí)現(xiàn),直接影響比賽結(jié)果。所以說路徑規(guī)劃任務(wù)在足球機(jī)器人系統(tǒng)中占有很重要的地位。 本文設(shè)計(jì)了一種應(yīng)用SVM模式識別分類技術(shù)進(jìn)行機(jī)器人路徑規(guī)劃的方法。支持向量機(jī)是一種基于小樣本統(tǒng)計(jì)理論的學(xué)習(xí)機(jī),具有完備的理論基礎(chǔ)和嚴(yán)格的理論體系,支持向量機(jī)是能夠提高學(xué)習(xí)機(jī)的泛化能力,此外,存在全局唯一最優(yōu)解。 我們把障礙物分為兩類,SVM在滿足最慈 ?嗉涓艫奶跫鋿?產(chǎn)生一個(gè)非線性分類面,從而產(chǎn)生一個(gè)安全的平滑的路徑,本文利用SVM這個(gè)性質(zhì)進(jìn)行路徑規(guī)劃研究。首先,得到一組小車陣型,將小車離散化為樣本點(diǎn),然后設(shè)置一些樣本引導(dǎo)點(diǎn)和向?qū)c(diǎn),下一步就是尋找一條可行的路徑,通過MATLAB仿真,我們將得出一條避開障礙物的路徑。 對于不同的障礙物模式,起始點(diǎn)和目標(biāo)點(diǎn)可能處于不同的中間區(qū)域,因此當(dāng)搜索步數(shù)大于某一個(gè)閾值的時(shí)候我們終止搜索。在下一步的搜索中當(dāng)找不到符合條件的下一個(gè)使V 1的安全點(diǎn)時(shí),也終止搜索,因?yàn)檫@時(shí)候在中間區(qū)域兩邊的障礙物的距離太小,而不能安全越過障礙物。 經(jīng)過多重搜索,我們得出幾條路徑曲線,通過比較,我們將選擇路徑最短、最平滑的曲線作為小車的實(shí)際路徑。 因此,實(shí)驗(yàn)仿真證明了利用SVM取得很好的效果,機(jī)器人能夠?qū)ふ业揭粭l最優(yōu)路徑,為實(shí)際機(jī)器人足球比賽提供了很好的理論基礎(chǔ)。 最后,對本文的工作做了總結(jié),指出了工作的成果意義及不足,并對今后的進(jìn)一步工作進(jìn)行了展望。
[Abstract]:Soccer robot is a highly challenging and high technology intensive project in the field of artificial intelligence and robot. It is also an ideal breakthrough point of artificial intelligence technology. It covers many fields such as robotics, artificial intelligence and intelligent control. It has become an important experimental platform for the research of the application technology of multi-agent system and artificial intelligence. Robot playing football, seemingly game, actually shows the comprehensive strength of a country's information and automation technology.
As an auxiliary part of developing the real robot soccer system, the simulation system has attracted the attention of its people for its economic and flexible characteristics.
Real soccer robot is difficult to control because of its difficulty in control, high real-time performance and a series of hardware problems.
For the simulation competition, because the platform is close to the ideal, unlike the physical competition easily affected by the surrounding environment, and the platform is a unified platform provided by the official platform, it has better objectivity and more convenient for the competition to carry out. The advanced control method is more easily applied and tested in the simulation of soccer robot competition than in the physical robot competition.
This paper takes the robot soccer game as the background and takes the The Robot Soccer Simulator as the simulation platform to study the motion control system of the soccer robot, and carries out a series of improvements in algorithm, performance and application, and can be used as the basis for other simulation and decision making system development.
This paper first reviewed the soccer robot competition, analyzed the key technology of the soccer robot, the research status at home and abroad, the research significance and the application. Then, the simulation platform and the simulation environment were introduced, and the kinematics model of the soccer robot was deduced, which provided the model foundation and platform environment for the follow-up study of this paper.
In order to make the soccer robot realize the tactical strategy, it must have good motion control first. The conventional PID algorithm is widely used in the control of soccer robot. However, the mechanism of the control process of the soccer robot is complex and it is difficult to determine the precise mathematical model. There are different degrees of non linear, time-varying and other uncertainties, at the same time, with the control of the robot. The requirement of the system is further improved. It is difficult to satisfy the system requirements by using conventional PID control.
As a very popular interdisciplinary subject, neural network has been widely used in control field with its powerful nonlinear mapping ability, parallel processing capability and self-learning ability.
In this paper, the BP neural network is studied and analyzed, and the BP neural network is a kind of neural network structure which is widely used at present. It is a kind of neural network with excellent performance. It mainly studies the mathematical theory of BP neural network, analyzes the advantages and disadvantages of several popular BP neural network algorithms and its improved BP algorithm. These studies are the rear machines. The research on the motion control of the human car has been paved.
Then, a variety of improved BP algorithms are combined with PID to get a new control algorithm and compare the performance of all kinds of algorithms. The simulation results show that, compared with several other PID controls, the overshoot is small, the adjustment speed is fast, the adjustment time is short, and the control characteristics are better. In addition, the steady-state error is also small. The improved BP neural network PID control has higher control accuracy, and it will achieve satisfactory results.
Therefore, the application of BP neural network to PID control can effectively overcome the defects of the classical PID controller, such as the parameter setting and the poor performance, when the controlled object is nonlinear, time-varying and difficult to establish an accurate mathematical model.
In this paper, the structure and algorithm of PID controller based on BP neural network are also studied. The improved BP neural network is used to self-tuning PID control parameters online, and a PID controller with parameter self-tuning ability and stability is constructed. This improved BP PID controller is applied to the robot car to fixed point motion and circumference circle motion. Through the simulation platform and the MATLAB experiment, we see from the experimental results that this new PID controller improves the robustness of the system to a certain extent, makes the motion of the car more stable and smooth, and obviously improves the control performance of the car.
The problem of road strength planning has always been a hot and difficult point in the research of soccer robot. In the case of obstacles, the robot can find a proper path, which can make the robot safe and avoid all obstacles in the process of motion from a given starting point to the end. Therefore, to a great extent, the path planning problem is the obstacle avoidance.
If the path planning is successful, the robot can complete the given task quickly, but if the robot fails, the action of the robot is blocked, the action is difficult to complete, and even the realization of the whole strategy is affected, and the result of the game is directly affected. Therefore, the path planning task plays an important role in the soccer robot system.
In this paper, a method of robot path planning using SVM pattern recognition classification technology is designed. Support vector machine is a learning machine based on small sample statistics theory. It has complete theoretical basis and strict theoretical system. Support vector machine can improve the generalization ability of the learning machine. In addition, there is a global unique optimal solution.
We divide the obstacles into two categories, and the SVM is satisfied with the most kind. We produce a nonlinear classification surface to produce a safe and smooth path. In this paper, we use the property of SVM to study the path planning. First, we get a group of small car formations, scatter the car into a sample point, and then set some sample guide points and directions. The next step is to find a feasible path. Through MATLAB simulation, we will get a path to avoid obstacles.
For different barrier patterns, the starting point and the target point may be in different middle regions, so we terminate the search when the search step is greater than a certain threshold. In the next search, when the next secure point for V 1 is not found, the search is terminated, because the barrier on both sides of the middle area is at this time. The distance between obstructions is too small to cross the barrier safely.
After multiple searches, we get several path curves. By comparison, we will choose the shortest path and the smoothest curve as the actual path of the car.
Therefore, the experimental simulation proves that the use of SVM has achieved good results. The robot can find an optimal path and provide a good theoretical basis for the actual robot soccer game.
Finally, the work of this paper is summarized, the significance and shortcomings of the work are pointed out, and the further work in the future is prospected.

【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2009
【分類號】：TP242

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 劉崇翔;基于ARM的智能小車的設(shè)計(jì)與研究[D];江南大學(xué);2012年

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本文編號：1878857