本文選題：蛇形機(jī)器人　切入點(diǎn)：損傷恢復(fù)　出處：《中山大學(xué)》2017年碩士論文

【摘要】：蛇形機(jī)器人由于其多自由度帶來的運(yùn)動(dòng)靈活性開始被人們研制利用。幾十年來,已經(jīng)有很多蛇形機(jī)器人樣機(jī)研制出來,甚至被應(yīng)用到實(shí)際應(yīng)用中。它們的機(jī)體結(jié)構(gòu)多種多樣,實(shí)現(xiàn)的運(yùn)動(dòng)形式也各有不同。并且有豐富的控制方法被提出及使用。但是對(duì)于蛇形機(jī)器人有一個(gè)問題是需要面對(duì)的,而目前人們對(duì)這個(gè)問題還沒有太多關(guān)注,就是當(dāng)蛇形機(jī)器人離開調(diào)試環(huán)境在復(fù)雜的自然環(huán)境中做自主運(yùn)動(dòng)時(shí)會(huì)不可避免的損壞。當(dāng)蛇形機(jī)器人損傷時(shí),現(xiàn)有的做法是要么停止當(dāng)前作業(yè)由科研人員維修調(diào)試,要么根據(jù)自身預(yù)設(shè)的損傷模型進(jìn)行檢測(cè)并以預(yù)設(shè)的應(yīng)對(duì)措施應(yīng)對(duì)。而預(yù)設(shè)的損傷檢測(cè)模型是有限的,人為的預(yù)想不可能覆蓋所有不可預(yù)知的損傷情況。本文引入一種較新的智能試錯(cuò)學(xué)習(xí)算法應(yīng)用在正交結(jié)構(gòu)的蛇形機(jī)器人上,可以在不預(yù)設(shè)損傷模型的情況下,以不斷的試錯(cuò)-學(xué)習(xí)過程指導(dǎo)蛇形機(jī)器人尋找更優(yōu)的補(bǔ)償行為。此算法目前被應(yīng)用在六足機(jī)器人及機(jī)械臂上,還未被應(yīng)用在蛇形機(jī)器人上。本文第一次將此算法應(yīng)用在蛇形機(jī)器人上并在仿真工具上做多種方式的驗(yàn)證。首先,本文闡述了蛇形機(jī)器人自主損傷恢復(fù)的意義,并對(duì)當(dāng)前蛇形機(jī)器人的研究現(xiàn)狀及機(jī)器人的損傷恢復(fù)方面的研究做了介紹。其次,本文對(duì)智能試錯(cuò)學(xué)習(xí)這一框架算法做了展開介紹,包括兩個(gè)重要的步驟—行為-性能映射表的建立、損傷恢復(fù)的適應(yīng)過程。并討論了蛇形機(jī)器人采用的一般控制方式蜿蜒曲線。同時(shí)介紹了本文要使用的集成有物理引擎的仿真工具。然后,我們對(duì)使用智能試錯(cuò)學(xué)習(xí)算法所需要的工作做了歸納總結(jié)。并對(duì)現(xiàn)有蛇形機(jī)器人平臺(tái)做了必要的總結(jié),包括正交結(jié)構(gòu)、傳感器系統(tǒng)等。然后,在歸納總結(jié)的框架下對(duì)我們將此框架算法應(yīng)用在正交結(jié)構(gòu)蛇形機(jī)器人上的工作及過程做了詳細(xì)的分析與闡述。包括控制器的定義、行為空間的定義及選擇、性能度量、算法內(nèi)部方法選擇、樣本采集過程及具體實(shí)現(xiàn)等。最后,我們?cè)诜抡婀ぞ呱蠈?duì)應(yīng)用了智能試錯(cuò)學(xué)習(xí)這一框架算法的蛇形機(jī)器人做了多種方式的驗(yàn)證。包括單關(guān)節(jié)損傷的多種不同損傷角度的情況、雙關(guān)節(jié)同時(shí)損傷的多種典型情況,及三關(guān)節(jié)、多關(guān)節(jié)等極端的損傷情況做了驗(yàn)證。
[Abstract]:Snake-like robots have been developed and utilized because of the flexibility of motion brought by their multi-degree of freedom. In recent decades, many snake robot prototypes have been developed and even applied to practical applications. And there are plenty of control methods that have been proposed and used. But there is a problem with snake robots that people don't pay much attention to. That is, when a snake robot leaves the debugging environment to do its own motion in a complex natural environment, it will inevitably be damaged. When the snake robot is damaged, the existing practice is to either stop the current operation and be repaired and debugged by the researchers. Either detect according to their own default damage model and respond with the default response. The default damage detection model is limited. This paper introduces a new intelligent trial and error learning algorithm which can be applied to a snake robot with orthogonal structure, without presupposing damage model. The algorithm is now applied to hexapod robots and robot manipulators to guide the snake robot to find better compensation behavior with continuous trial and error-learning process. This algorithm has not been applied to snake robot for the first time and has been verified in many ways by simulation tools. Firstly, the significance of self-damage recovery of snake robot is expounded. The present research situation of snake robot and the research of robot damage recovery are introduced. Secondly, this paper introduces the framework algorithm of intelligent trial and error learning. Consists of two important steps-the establishment of the Behavior-Performance Map, The adaptive process of damage recovery is discussed, and the general control method of serpentine robot is discussed. The simulation tools integrated with physical engine are also introduced in this paper. We make a summary of the work needed to use intelligent trial and error learning algorithm, and make a necessary summary of the existing snake robot platform, including orthogonal structure, sensor system and so on. In the framework of induction and summary, the work and process of applying this framework algorithm to the orthogonal snake robot are analyzed and explained in detail, including the definition of controller, the definition and choice of behavior space, the performance measurement, the definition of controller, the definition and selection of behavior space, and the measurement of performance, including the definition of controller, the definition and selection of behavior space. Algorithm internal method selection, sample collection process and specific implementation. Finally, In the simulation tool, we verify the snake robot with intelligent trial and error learning algorithm in many ways, including many different damage angles of single joint injury, and many typical cases of simultaneous joint damage. And three joints, multiple joints and other extreme injuries were verified.
【學(xué)位授予單位】：中山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 王健;魏武;;纜索檢測(cè)蛇形機(jī)器人控制系統(tǒng)設(shè)計(jì)[J];計(jì)算機(jī)測(cè)量與控制;2010年10期

2 李斌,馬書根,王越超,陳麗,汪洋;一種具有三維運(yùn)動(dòng)能力的蛇形機(jī)器人的研究[J];機(jī)器人;2004年06期

3 陳麗,王越超,馬書根,李斌;一種可重構(gòu)蛇形機(jī)器人的研究[J];中國(guó)機(jī)械工程;2003年16期

4 遲冬祥,顏國(guó)正,林良明;基于蚯蚓運(yùn)動(dòng)原理的腸道檢查微小機(jī)器人內(nèi)窺鏡系統(tǒng)[J];機(jī)器人;2002年03期

5 劉華,顏國(guó)正,丁國(guó)清;仿蛇變體機(jī)器人運(yùn)動(dòng)機(jī)理研究[J];機(jī)器人;2002年02期

6 崔顯世,顏國(guó)正,陳寅,林良明;一個(gè)微小型仿蛇機(jī)器人樣機(jī)的研究[J];機(jī)器人;1999年02期

7 呂恬生,王翔宇;蛇的爬行運(yùn)動(dòng)實(shí)驗(yàn)和運(yùn)動(dòng)中蛇體曲線的動(dòng)態(tài)模擬[J];上海交通大學(xué)學(xué)報(bào);1998年01期

相關(guān)碩士學(xué)位論文 前1條

1 張?chǎng)?自重構(gòu)機(jī)器人結(jié)構(gòu)設(shè)計(jì)及其自修復(fù)算法的研究[D];上海交通大學(xué);2008年

，

本文編號(hào)：1661213