本文選題：多智能體　切入點(diǎn)：不規(guī)則　出處：《南京信息工程大學(xué)》2017年碩士論文

【摘要】：隨著社會(huì)的不斷進(jìn)步,科技的飛速發(fā)展,人工智能的重要性逐漸在科學(xué)研究與工程應(yīng)用中體現(xiàn)。其中的多智能體技術(shù)更是獨(dú)樹一幟,理論和實(shí)踐都自成一個(gè)體系。同時(shí),智能體的智能性、環(huán)境的復(fù)雜性和多樣性,為多智能體相關(guān)的系統(tǒng)進(jìn)行避障路徑規(guī)劃的研究提供了新的挑戰(zhàn),亦為熱門突破和創(chuàng)新點(diǎn)之一。如今,對(duì)于障礙物的研究已不僅僅只停留于有規(guī)律可循的規(guī)則形狀,運(yùn)動(dòng)狀態(tài)也從靜態(tài)過渡到了動(dòng)態(tài)。為解決普遍適應(yīng)規(guī)則的制定這一難題,論文假定利用現(xiàn)代通信技術(shù)可以鎖定跟蹤目標(biāo),從路徑規(guī)劃角度出發(fā),針對(duì)不規(guī)則障礙物,就如何實(shí)現(xiàn)多智能體避障路徑規(guī)劃展開了研究,內(nèi)容主要包括:1、單個(gè)靜態(tài)不規(guī)則障礙物避障路徑規(guī)劃。傳統(tǒng)的多智能體避障算法在考慮障礙物形狀時(shí),存在路徑冗余、能耗高等現(xiàn)象,不具普適性。為此,給出一種不規(guī)則障礙物避障路徑規(guī)劃的算法,定義了自動(dòng)識(shí)別凸形化的方法,并在融入子登陸點(diǎn)法完成避障的同時(shí),實(shí)現(xiàn)邊界最短路徑規(guī)劃,提高了障礙物處理的適用性和避障路徑規(guī)劃的智能性。2、多個(gè)靜態(tài)不規(guī)則障礙物�？紤]到現(xiàn)實(shí)環(huán)境中障礙物的多樣性,利用自動(dòng)識(shí)別凸形化規(guī)則對(duì)多個(gè)不規(guī)則障礙物環(huán)境進(jìn)行了數(shù)據(jù)的集中處理,并改進(jìn)子登陸點(diǎn)的概念,同時(shí)增加了輔助尋跡線作出瞬時(shí)路徑規(guī)劃,最后為避免“摩擦”碰撞現(xiàn)象的產(chǎn)生,增加了安全閾值、轉(zhuǎn)角速度及最小運(yùn)動(dòng)速度等參數(shù)的定義,選擇以避障為優(yōu)先完成目標(biāo)追蹤的路徑規(guī)劃。3、單個(gè)勻速直線運(yùn)動(dòng)的不規(guī)則障礙物。為實(shí)現(xiàn)動(dòng)態(tài)的不規(guī)則障礙物避障算法的嘗試,將使用自動(dòng)識(shí)別凸形化規(guī)則之后的障礙物采用組合運(yùn)動(dòng)的方式實(shí)現(xiàn)勻速直線運(yùn)動(dòng);同時(shí),加入碰撞預(yù)測和邊緣運(yùn)動(dòng)策略,從而保證避障的優(yōu)先性,并重新進(jìn)行路徑的規(guī)劃,體現(xiàn)了環(huán)境的動(dòng)態(tài)性和規(guī)劃的智能性。4、實(shí)地測試。為實(shí)現(xiàn)算法的理論仿真與實(shí)際運(yùn)動(dòng)路徑的比較,進(jìn)行了智能車的實(shí)地測試,通過兩者之間的比較,驗(yàn)證了算法的可行性和有效性。
[Abstract]:With the continuous progress of society and the rapid development of science and technology, the importance of artificial intelligence is gradually reflected in scientific research and engineering applications. The intelligence of the agent, the complexity and diversity of the environment provide a new challenge for the research of obstacle avoidance path planning in multi-agent related systems, and also one of the hot breakthroughs and innovations. In order to solve the problem of making rules of universal adaptation, the study of obstacles has not only been in the regular shape, but also in the state of motion from static to dynamic. Based on the assumption that the tracking target can be locked by using modern communication technology, this paper studies how to realize obstacle avoidance path planning of multi-agent from the point of view of path planning and irregular obstacles. The main contents include: 1, single static irregular obstacle avoidance path planning. The traditional multi-agent obstacle avoidance algorithm has the phenomenon of path redundancy and high energy consumption when considering the shape of obstacle. In this paper, an algorithm of obstacle avoidance path planning for irregular obstacles is presented, and the method of automatically recognizing convexity is defined, and the shortest path planning of boundary is realized by incorporating the sub-landing point method to avoid obstacles. The applicability of obstacle processing and the intelligence of obstacle avoidance path planning are improved. 2. Multiple static irregular obstacles. Considering the diversity of obstacles in real environment, In this paper, the data of several irregular obstacle environments are processed by using the automatic recognition convexation rule, and the concept of sub-landing point is improved, and the instantaneous path planning of auxiliary tracing line is added. Finally, in order to avoid the phenomenon of "friction" collision, the definitions of safety threshold, angular velocity and minimum velocity are added. The path planning. 3, which takes obstacle avoidance as the priority to complete target tracking, is chosen, and a single irregular obstacle with uniform velocity linear motion is chosen. In order to realize the algorithm of obstacle avoidance of dynamic irregular obstacle, The obstacles after automatic recognition of convex rules will be combined to realize uniform linear motion. At the same time, collision prediction and edge motion strategies will be added to ensure the priority of obstacle avoidance, and path planning will be carried out again. It reflects the dynamic nature of the environment and the intelligence of planning. 4, field testing. In order to realize the comparison between the theoretical simulation of the algorithm and the actual movement path, the field test of the intelligent vehicle is carried out, and through the comparison between the two, The feasibility and effectiveness of the algorithm are verified.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

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