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

【摘要】：隨著社會的不斷進步,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)更是獨樹一幟,理論和實踐都自成一個體系。同時,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)新點之一。如今,對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,運動狀態(tài)也從靜態(tài)過渡到了動態(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,從路徑規(guī)劃角度出發(fā),針對不規(guī)則障礙物,就如何實現(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,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,定義了自動識別凸形化的方法,并在融入子登陸點法完成避障的同時,實現(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、多個靜態(tài)不規(guī)則障礙物�？紤]到現(xiàn)實環(huán)境中障礙物的多樣性,利用自動識別凸形化規(guī)則對多個不規(guī)則障礙物環(huán)境進行了數(shù)據(jù)的集中處理,并改進子登陸點的概念,同時增加了輔助尋跡線作出瞬時路徑規(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)角速度及最小運動速度等參數(shù)的定義,選擇以避障為優(yōu)先完成目標追蹤的路徑規(guī)劃。3、單個勻速直線運動的不規(guī)則障礙物。為實現(xiàn)動態(tài)的不規(guī)則障礙物避障算法的嘗試,將使用自動識別凸形化規(guī)則之后的障礙物采用組合運動的方式實現(xiàn)勻速直線運動;同時,加入碰撞預(yù)測和邊緣運動策略,從而保證避障的優(yōu)先性,并重新進行路徑的規(guī)劃,體現(xiàn)了環(huán)境的動態(tài)性和規(guī)劃的智能性。4、實地測試。為實現(xiàn)算法的理論仿真與實際運動路徑的比較,進行了智能車的實地測試,通過兩者之間的比較,驗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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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