本文選題：柔性制造 + 多機器人��； 參考：《廣東工業(yè)大學(xué)》2016年博士論文

【摘要】：隨著我國智能制造戰(zhàn)略的實施,機器人柔性制造單元被越來越多的制造企業(yè)所采用。針對模具這類單件小批定制生產(chǎn)問題,機器人柔性制造單元需要進行實時調(diào)度以適應(yīng)外部環(huán)境變化以及模具零件類型、加工工藝和工時等隨訂單變化的需求。目前柔性制造單元的理論和方法面臨以下挑戰(zhàn),模型的結(jié)構(gòu)和規(guī)模隨著工件、機床或機器人的變化而變化導(dǎo)致建模繁瑣復(fù)雜；大量工時短、交貨急的工件所需要的柔性搬運對單軌上多移動機器人的爭奪混亂；給出初始生產(chǎn)計劃后,缺少含死鎖控制和糾正計劃功能的仿真系統(tǒng)進行評估和反饋等方面。本文在國家自然科學(xué)基金(51105082)的資助下研究單軌約束條件下多移動機器人柔性制造單元的建模和調(diào)度問題。結(jié)合模具制造的特點,提出隨機令牌智能變遷(STITPN)模型理論,探討單軌上多機器人運動沖突消解問題,實現(xiàn)包含死鎖控制策略和糾正計劃功能的狀態(tài)推演仿真算法,創(chuàng)造多維空間偵察機制及算法優(yōu)化生產(chǎn)性能,單軌多機器人柔性制造單元決策支持系統(tǒng)的實施,有助于提高制造單元效率。本文的主要工作包含以下幾個方面：1、分析模具零部件生產(chǎn)的特點,抽象出單軌約束條件下多機器人柔性制造單元的問題,針對主要的不確定因素進行分析,考慮了工件離散隨機到達、工序加工時間隨機分布、機床可用和休整的隨機性等因素,最后提出研究的框架。2、針對傳統(tǒng)Petri網(wǎng)模型難以表達模型結(jié)構(gòu)和規(guī)模隨工件、機床或機器人的變化而變化的問題,提出隨機令牌智能變遷petri網(wǎng)(STITPN)理論。首先,將工件、機床和機器人及其組合建模為令牌,接著根據(jù)情景演化路徑建模庫所和變遷,然后建立系統(tǒng)狀態(tài)推演機制,Petri網(wǎng)中令牌攜帶隨機實時信息并可以停留在變遷中,變遷根據(jù)令牌的實時信息進行觸發(fā)�；赟TITPN理論的制造單元模型,以后只需不斷輸入生產(chǎn)任務(wù)及配置參數(shù)即可推演得到符合約束條件的調(diào)度方案。本文用實例演示建模和狀態(tài)推演過程,輸入經(jīng)典案例數(shù)據(jù)進行計算驗證模型理論的有效性。3、在柔性制造單元的STITPN模型中,多機器人令牌需要主動決定去向,然而在單軌約束條件下的多機器人柔性搬運存在沖突問題。為此建立多機器人的運動方程模型、沖突判據(jù)模型和消解策略。當驅(qū)動事件發(fā)生時實時規(guī)劃機器人運動方程,通過聯(lián)立多個方程求解來判斷是否有沖突。提出沖突消解策略,即機器人的實時規(guī)劃路徑有沖突時需廢棄等待下一驅(qū)動事件,多個機器人同時啟動但有沖突時按工件優(yōu)先級選擇執(zhí)行,對沖突路徑中的靜止機器人規(guī)劃運動并循環(huán)檢測沖突等策略。最后,用實例計算和案例應(yīng)用驗證方法的有效性。4、由于初步的總體生產(chǎn)計劃普遍只考慮工件及機床,沒有考慮機器人的搬運,需要柔性制造單元的STITPN模型進行推演仿真。在資源高度共享中存在死鎖阻塞問題,計劃也可能需要糾正。針對該實際問題,提出變遷的智能函數(shù)、死鎖控制策略和狀態(tài)推演等算法。在變遷的智能函數(shù)中,通過檢測令牌的可移動方向判斷是否滿足觸發(fā)條件,然后按優(yōu)先級移動令牌；為了避免死鎖,工件令牌先預(yù)測資源占用情況再決定是否進入搬運變遷；根據(jù)確定好工序選擇的機床和各工序的處理次序,在模型運行中根據(jù)期望時間觸發(fā)事件,使能變遷并觸發(fā),由狀態(tài)推演路徑得到完善后的機床加工計劃和機器人搬運規(guī)劃。5、在生產(chǎn)計劃優(yōu)化方面,由于工序選擇機床和工序先后排列存在組合爆炸性問題,本文采用元啟發(fā)式智能算法,由偵察隊伍評估和搜索目標所啟發(fā),提出多維空間偵察機制及優(yōu)化算法。在評估目標方面,將多維空間點解碼為車間計劃,輸入柔性制造單元的STITPN模型中進行推演得到的生產(chǎn)性能為適應(yīng)度值。在快速收斂方面,在各士官所代表的優(yōu)化點的各個方向進行隨機擾動尋求最優(yōu)值。在避免過早落入局部最優(yōu)值,擴大搜索范圍方面,求出多個信息交互指揮官之間的中心點,在代表指揮官、中心、上限和下限的四個點之間形成新偵察點。本文把經(jīng)典案例數(shù)據(jù)作為生產(chǎn)任務(wù)進行優(yōu)化計算,并與典型的算法DE(差分進化算法),ElitistES(進化策略),gGA(遺傳算法)和PSO(粒子群優(yōu)化算法)等進行比較,本文提出的算法在平均最優(yōu)值方面比DE和PSO好；在最優(yōu)值標準差方面比ElitistES,gGA和PSO好,說明本文提出的偵察算法是很有競爭力的。6、分析了企業(yè)應(yīng)用背景和制造單元動態(tài)調(diào)度策略,設(shè)計了系統(tǒng)的功能模塊和數(shù)據(jù)庫,開發(fā)了一套單軌多機器人柔性制造單元決策支持系統(tǒng),用于合作企業(yè)輔助生產(chǎn)車間活動,應(yīng)用效果良好。
[Abstract]:With the implementation of China's intelligent manufacturing strategy, more and more manufacturing enterprises are adopted by the flexible manufacturing unit of robot. Aiming at the problem of small batch production of single piece of mold, the robot flexible manufacturing unit needs real-time scheduling to adapt to the change of the external environment and the type of the die zero, the processing technology and the working hours and so on with the order change. At present, the theory and methods of FMS are facing the following challenges. The structure and size of the model changes with the changes of the workpiece, machine tool or robot, which leads to complicated and complicated modeling; the flexible handling required by a large number of workhours and quick delivery of the workpiece is scramble for the chaos of the multi shift robot on the monorail; the initial production meter is given. After delimit, the simulation system with the function of deadlock control and correction plan is lack of evaluation and feedback. Under the support of the National Natural Science Foundation (51105082), this paper studies the modeling and scheduling problem of the flexible manufacturing unit of a multi mobile robot under the condition of monorail constraints. ITPN) model theory, discuss the problem of multi robot movement conflict resolution on Monorail, realize the state inference simulation algorithm including the deadlock control strategy and the correction plan function, create multi-dimensional space reconnaissance mechanism and algorithm to optimize the production performance. The implementation of the decision support system for the flexible manufacturing unit of monorail robot is helpful to improve the manufacturing unit. The main work of this paper includes the following aspects: 1, analyzing the characteristics of the production of mould parts and parts, abstracting the problem of the multi robot flexible manufacturing unit under the condition of monorail constraint, analyzing the main uncertain factors, considering the discrete random arrival of the workpiece, the random distribution of the processing time of the working sequence, the available and the rest of the machine tool. In the end, the frame.2 of the research is put forward. In view of the problem that the traditional Petri net model is difficult to express the model structure and the scale change with the workpiece, machine tool or robot, the stochastic token intelligent change Petri net (STITPN) theory is proposed. First, the workpiece, machine tool and robot and their combinations are modeled as token, and then the scene is performed. The transformation of the path modeling library and the change, and then establish the system state inference mechanism, the token carrying random real-time information in the Petri network can stay in the change, the change is triggered according to the token's real-time information. Based on the STITPN theory, the manufacturing unit model can be deduced to be conformed only by the continuous input of the production task and configuration parameters. In this paper, an example is used to demonstrate the modeling and state inference process, and the validity of the model theory is.3. In the STITPN model of FMS, the multi robot token needs to decide the direction actively, but there is a conflict in the flexible handling of the multi robot under the condition of the monorail. To solve this problem, the motion equation model of multi robot, the conflict criterion model and the resolution strategy are established. When the driving event occurs, the motion equations of the robot are planned in real time and the conflict is determined by solving the multiple equations. The conflict resolution strategy is proposed, that is, the next driver is abandoned and waiting to be abandoned when the robot's real-time routing path is conflict. At the same time, a number of robots are started at the same time but are chosen to execute according to the priority of the workpiece in conflict, plan the motion of the static robot in the conflict path and circulate the conflict detection strategy. Finally, the validity of the method is.4 with example calculation and case application. Because the initial overall production plan is generally only considered the workpiece and the machine tool, there is no consideration. The handling of the robot requires the STITPN model of FMS to carry out the simulation. There is a deadlock blocking problem in the high sharing of resources. The plan may also need to be corrected. For the actual problem, an intelligent function, a deadlock control strategy and a state inference algorithm are proposed. In the changing intelligent function, the detection token is detected. The moving direction determines whether the trigger condition is satisfied and then the token is moved by priority; in order to avoid the deadlock, the work token first predicts the occupancy of the resource and determines whether it enters the transfer change; according to the processing order of the machine tool and the various processes selected for the selection of the good procedure, the event is triggered in the model running according to the expected time, so that the change can be changed and Triggered by the state deduction path, the machine tool processing plan and robot handling planning.5 are improved. In the aspect of production planning optimization, because of the problem of combinatorial explosions in the process selection of machine tools and processes, this paper uses the meta heuristic intelligent algorithm, which is inspired by the evaluation of the reconnaissance team and the search target, and puts forward the multi-dimensional space reconnaissance. The multi-dimensional space point is decoded into a workshop plan, and the production performance obtained in the STITPN model of the flexible manufacturing unit is considered as the fitness value. In the fast convergence, the optimal value is sought in the direction of the optimization points represented by the officers in each direction. In terms of local optimal value and enlarging the range of search, the center points between multiple information interactive commanders are found, and new reconnaissance points are formed between four points representing commanders, centers, upper limits and lower limits. This paper optimizes the classic case data as a production task, and the typical algorithm DE (differential evolution algorithm), ElitistES (Evolution). Strategy), compared with gGA (genetic algorithm) and PSO (particle swarm optimization algorithm), the algorithm proposed in this paper is better than DE and PSO in the average optimal value. It is better than ElitistES, gGA and PSO on the standard deviation of the optimal value. It shows that the reconnaissance algorithm proposed in this paper is a competitive.6, and the enterprise application background and the dynamic scheduling policy of the manufacturing unit are analyzed. The function module and database of the system are designed, and a set of decision support system for MP flexible manufacturing unit is developed, which is used in the cooperative enterprise to assist the production workshop, and the application effect is good.

【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP242;TH165

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