本文選題：多機(jī)器人系統(tǒng)　切入點(diǎn)：協(xié)作探索　出處：《南京理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：探測(cè)未知環(huán)境是移動(dòng)機(jī)器人領(lǐng)域的基礎(chǔ)問題,是機(jī)器人在未知環(huán)境中完成其他任務(wù)的必要條件。與單個(gè)機(jī)器人相比,多機(jī)器人系統(tǒng)具有更好的魯棒性、適應(yīng)性、靈活性和擴(kuò)展性,更加適用于探測(cè)未知環(huán)境,但是仍然在地圖融合和任務(wù)分配方面存在許多問題。地圖融合直接決定了多機(jī)器系統(tǒng)探測(cè)未知環(huán)境的準(zhǔn)確性,任務(wù)分配影響著多機(jī)器人系統(tǒng)完成環(huán)境探測(cè)的效率。本文對(duì)多機(jī)器人協(xié)作探測(cè)未知環(huán)境中的地圖融合和任務(wù)分配兩個(gè)重要內(nèi)容進(jìn)行研究。針對(duì)地圖融合和任務(wù)分配中存在的技術(shù)難點(diǎn),提出了解決方案,有效提高了探測(cè)效率。本文的主要工作如下:首先提出了一種幾何—拓?fù)浠旌鲜矫枋龅牡貓D融合方法,將拓?fù)涔?jié)點(diǎn)及其幾何特征作為地圖匹配的基本單元,將拓?fù)涔?jié)點(diǎn)幾何特征的相似度作為拓?fù)涔?jié)點(diǎn)相同的判斷依據(jù),為了增加地圖融合的可靠性,將拓?fù)涔?jié)點(diǎn)與相鄰節(jié)點(diǎn)的歐式距離加入到節(jié)點(diǎn)相似度的計(jì)算當(dāng)中。提出了有效的地圖融合指標(biāo)和地圖融合次序。其次,提出了基于市場(chǎng)法的多機(jī)器人任務(wù)分配策略,利用經(jīng)濟(jì)市場(chǎng)的拍賣思想,利用合同網(wǎng)協(xié)議,根據(jù)機(jī)器人狀態(tài)隨時(shí)間變化,采用二次拍賣方法進(jìn)行任務(wù)分配。為了避免機(jī)器人過多的集中于同一區(qū)域,對(duì)常見的投標(biāo)計(jì)算方式進(jìn)行改進(jìn),利用排斥信息素進(jìn)行投標(biāo)計(jì)算,減少機(jī)器人對(duì)相同區(qū)域的重復(fù)探索。最后,在仿真實(shí)驗(yàn)平臺(tái)對(duì)本文提出的地圖融合算法和任務(wù)分配算法進(jìn)行實(shí)驗(yàn),通過設(shè)置不同的機(jī)器人數(shù)量和環(huán)境范圍驗(yàn)證本文方法的有效性。
[Abstract]:Detection of unknown environments is a fundamental problem in the field of mobile robots and a necessary condition for robots to accomplish other tasks in unknown environments. Compared with a single robot, multi-robot systems have better robustness and adaptability. Flexibility and expansibility are more suitable for detecting unknown environments, but there are still many problems in map fusion and task assignment. Map fusion directly determines the accuracy of multi-machine systems in detecting unknown environments. Task assignment affects the efficiency of environment detection in multi-robot systems. In this paper, two important contents of map fusion and task assignment in multi-robot cooperative detection unknown environment are studied. Technical difficulties in matching, The main work of this paper is as follows: firstly, a map fusion method is proposed, in which topological nodes and their geometric features are taken as the basic unit of map matching. In order to increase the reliability of map fusion, the similarity of geometric features of topological nodes is taken as the basis for judging the same topological nodes. The Euclidean distance between topological nodes and adjacent nodes is added to the computation of node similarity. An effective map fusion index and map fusion order are proposed. Secondly, a multi-robot task allocation strategy based on market approach is proposed. Using the auction idea of economic market, using contract net protocol, according to the change of robot state with time, the second auction method is used to assign the task. In order to avoid the robot being too concentrated in the same area, The common bidding calculation method is improved, and the bidding calculation is done by using repellent pheromone to reduce the repeated exploration of the same area by the robot. Finally, The proposed map fusion algorithm and task assignment algorithm are tested on the simulation platform, and the effectiveness of the proposed method is verified by setting the number of robots and the range of environment.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

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本文編號(hào)：1604620