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

【摘要】：探測未知環(huán)境是移動機器人領域的基礎問題,是機器人在未知環(huán)境中完成其他任務的必要條件。與單個機器人相比,多機器人系統(tǒng)具有更好的魯棒性、適應性、靈活性和擴展性,更加適用于探測未知環(huán)境,但是仍然在地圖融合和任務分配方面存在許多問題。地圖融合直接決定了多機器系統(tǒng)探測未知環(huán)境的準確性,任務分配影響著多機器人系統(tǒng)完成環(huán)境探測的效率。本文對多機器人協(xié)作探測未知環(huán)境中的地圖融合和任務分配兩個重要內容進行研究。針對地圖融合和任務分配中存在的技術難點,提出了解決方案,有效提高了探測效率。本文的主要工作如下:首先提出了一種幾何—拓撲混合式描述的地圖融合方法,將拓撲節(jié)點及其幾何特征作為地圖匹配的基本單元,將拓撲節(jié)點幾何特征的相似度作為拓撲節(jié)點相同的判斷依據(jù),為了增加地圖融合的可靠性,將拓撲節(jié)點與相鄰節(jié)點的歐式距離加入到節(jié)點相似度的計算當中。提出了有效的地圖融合指標和地圖融合次序。其次,提出了基于市場法的多機器人任務分配策略,利用經(jīng)濟市場的拍賣思想,利用合同網(wǎng)協(xié)議,根據(jù)機器人狀態(tài)隨時間變化,采用二次拍賣方法進行任務分配。為了避免機器人過多的集中于同一區(qū)域,對常見的投標計算方式進行改進,利用排斥信息素進行投標計算,減少機器人對相同區(qū)域的重復探索。最后,在仿真實驗平臺對本文提出的地圖融合算法和任務分配算法進行實驗,通過設置不同的機器人數(shù)量和環(huán)境范圍驗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.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242

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本文編號：1604620