發(fā)布時間：2018-04-12 22:01

  本文選題：路徑規(guī)劃 + 蟻群算法　； 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：隨著世界經(jīng)濟的發(fā)展趨勢的不斷變化,人類的工作、生活,教育已經(jīng)逐漸進入到智能時代。作為“智能制造”的一個重要分支,智能算法的應(yīng)用與發(fā)展方興未艾,而把智能算法與路徑規(guī)劃相結(jié)合,發(fā)展出在TSP問題、航海導(dǎo)航以及無人機編隊等方面具有廣泛應(yīng)用前景的智能路徑規(guī)劃方法也是未來的“智能制造”的重要的發(fā)展方向之一。蟻群算法作為智能算法里重要的一種方法,因其算法易懂以及魯棒性強等獨特的優(yōu)越性被廣泛應(yīng)用。因此以路徑規(guī)劃為應(yīng)用背景,以蟻群算法為研究手段,將二者相結(jié)合的思想正受到各國學(xué)者和機構(gòu)重視,已逐步成為當(dāng)前的研究熱點之一。本文針對傳統(tǒng)蟻群算法在動態(tài)路徑規(guī)劃中存在的動態(tài)特性較差等問題,結(jié)合貪婪算法等多種智能算法思想,對其進行改良與優(yōu)化,使之滿足不同動態(tài)環(huán)境下的路徑規(guī)劃要求。主要研究內(nèi)容如下:1)通過對傳統(tǒng)蟻群算法的改進,分別研究以區(qū)域分割和時間分割為指標的定半徑局部信息動態(tài)路徑規(guī)劃問題�？紤]在尋優(yōu)環(huán)境障礙按照分區(qū)域變化和時間變化的兩種變化方式前提下,設(shè)計出兩種動態(tài)改進蟻群算法。在這兩種算法中,機器人具有固定搜索范圍視野,并采用“邊走邊規(guī)劃”的策略對尋優(yōu)環(huán)境進行動態(tài)路徑規(guī)劃。最后通過實例對改進蟻群算法與傳統(tǒng)蟻群算法尋優(yōu)效果進行比較以驗證其有效性。2)研究基于改進蟻群算法的變半徑動態(tài)局部信息路徑規(guī)劃。針對定半徑局部信息動態(tài)路徑規(guī)劃過程中不能隨著尋優(yōu)環(huán)境的變化而自動改變搜索范圍視野的局限性,提出了基于改進蟻群算法的變半徑動態(tài)局部信息路徑規(guī)劃方法。該算法在動態(tài)路徑規(guī)劃過程中,機器人每次的搜索范圍視野會隨著尋優(yōu)環(huán)境變化而變化,并在以時間步長為指標情況下,進行動態(tài)路徑的變半徑和定半徑的改進算法的對比研究。3)探討基于蟻群算法的動態(tài)路徑規(guī)劃算法在多智能體編隊中的應(yīng)用研究。將本文所提出的基于改進蟻群算法的動態(tài)路徑規(guī)劃方法應(yīng)用到多智能體自主編隊問題中,考慮多智能體編隊在未知環(huán)境中進行動態(tài)路徑自主規(guī)劃與編隊協(xié)同問題,進一步驗證了所提算法的有效性與實用性。
[Abstract]:With the development trend of the world economy, human work, life and education have gradually entered the intelligent age.As an important branch of "intelligent manufacturing", the application and development of intelligent algorithms are in the ascendant, and the combination of intelligent algorithms and path planning is developed in the TSP problem.The intelligent path planning method, which has wide application prospect in navigation navigation and UAV formation, is also one of the important development directions of intelligent manufacturing in the future.Ant colony algorithm (ACA), as an important method in intelligent algorithm, is widely used because of its unique advantages such as easy to understand and strong robustness.Therefore, with path planning as the application background and ant colony algorithm as the research means, the idea of combining the two is paid more and more attention by scholars and institutions all over the world, and has gradually become one of the current research hotspots.Aiming at the problem of poor dynamic characteristics of traditional ant colony algorithm in dynamic path planning, this paper improves and optimizes it in combination with greedy algorithm and other intelligent algorithms, so that it can meet the requirements of path planning in different dynamic environments.The main research contents are as follows: (1) by improving the traditional ant colony algorithm, the dynamic path planning problem with constant radius and local information based on region segmentation and time segmentation is studied respectively.Two dynamic improved ant colony algorithms are designed on the premise of optimizing environment obstacle according to two changing modes of sub-region change and time change.In these two algorithms, the robot has a fixed search field, and adopts the strategy of "edge planning" to carry out dynamic path planning for the optimal environment.Finally, the improved ant colony algorithm is compared with the traditional ant colony algorithm to verify its effectiveness. 2) the variable radius dynamic local information path planning based on the improved ant colony algorithm is studied.In view of the limitation that the dynamic path planning of local information with constant radius can not automatically change the scope of search with the change of optimization environment, a method of path planning with variable radius dynamic local information based on improved ant colony algorithm is proposed.In the process of dynamic path planning, the range of search field of the robot changes with the optimization environment, and the time step is taken as the index.This paper discusses the application of dynamic path planning algorithm based on ant colony algorithm in multi-agent formation.The dynamic path planning method based on improved ant colony algorithm proposed in this paper is applied to multi-agent autonomous formation problem, and the dynamic path autonomous planning and formation coordination problem in unknown environment are considered.The validity and practicability of the proposed algorithm are further verified.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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