WO2021031272A1 - Agv scheduling method based on dynamic path planning - Google Patents

Agv scheduling method based on dynamic path planning Download PDF

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WO2021031272A1
WO2021031272A1 PCT/CN2019/106937 CN2019106937W WO2021031272A1 WO 2021031272 A1 WO2021031272 A1 WO 2021031272A1 CN 2019106937 W CN2019106937 W CN 2019106937W WO 2021031272 A1 WO2021031272 A1 WO 2021031272A1
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agv
task
node
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idle
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王柳婷
任涛
张钧桓
李天鹏
张妍
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东北大学
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  • Step 4.4 If there are idle AGVs, judge whether there are multiple idle AGVs;
  • Step 5.1.3 Select the node with the longest time interval from the last time, that is, the node with the smallest value as the next node;
  • Step 4.2 Get the starting point of the current task in the task list
  • Step 7 Update the task list, add new tasks, and cancel completed tasks

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Abstract

The present invention relates to the technical field of optimal scheduling, and provides an AGV scheduling method based on dynamic path planning. The strategy implements initialization of an AGV working site, scheduling with different precision, initialization of a working map, and simulation of an actual map, and can solve the multi-unit multi-task problem in any scene. An adjacency matrix and a distance matrix are established on the basis of a Floyd-Warshall algorithm; an AGV selection process and the obstacle avoidance of the AGV in the execution process are optimally scheduled, the execution route of the AGV is dynamically planned in a single step, the AGV moving time is shortened, the working efficiency is improved, and the method has an important function in converting industrial automatic production into a flexible production mode.

Description

一种基于动态路径规划的AGV调度方法An AGV scheduling method based on dynamic path planning 技术领域Technical field
本发明涉及优化调度技术领域，尤其涉及一种基于动态路径规划的AGV调度方法。The present invention relates to the technical field of optimized scheduling, in particular to an AGV scheduling method based on dynamic path planning.
背景技术Background technique
随着自动化的发展，制造业对仓储管理、物流运输提出了更高的要求，AGV凭借其诸多优点成为该领域的关键设备，在自动化运输中起着无可替代的作用。AGV小车是实现柔性制造***的重要部分，而AGV调度作为AGV设计的核心技术，一直是国内外制造业、自动化领域的研究重点和难点。因此，进行对多AGV的调度方法、路径规划等问题的研究，对该领域有重要的意义。With the development of automation, the manufacturing industry has put forward higher requirements for warehousing management and logistics transportation. AGV has become a key equipment in this field with its many advantages and plays an irreplaceable role in automated transportation. AGV trolley is an important part of realizing flexible manufacturing system, and AGV scheduling, as the core technology of AGV design, has always been the research focus and difficulty in the field of manufacturing and automation at home and abroad. Therefore, research on the scheduling method and path planning of multiple AGVs is of great significance to this field.
对于复杂场景下多任务***的AGV调度方法由于存在路径冲突、资源利用率不高等问题，导致动态环境出现死锁的情况。现阶段在针对路径冲突问题方面，有学者提出一种基于速度调和几何路径调节的策略；还有学者以AGV运输车数量、任务满意度、行驶总距离为目标，建立多目标优化模型，并设计混合遗传算法进行求解；在排队场景方面，有学者引入***调度模型，选择排队队长与等待时间为优化指标，设计了一种调度策略；在路径规划方面，有学者提出一种有限理性自组织方法进行多AGV的路径规划，该方法可以有效解决任务资源的局部竞争问题。虽然上述方法可有效解决路径冲突及部分AGV利用率不高的问题，但需建立复杂的模型，计算周期长，且通过模型计算出的最优策略可能由于参数设置不当，导致模型规划出的仅为局部最优解。For the AGV scheduling method of the multi-task system in complex scenarios, there are problems such as path conflicts and low resource utilization, resulting in deadlocks in the dynamic environment. At this stage, in view of the path conflict problem, some scholars have proposed a strategy based on speed adjustment and geometric path adjustment; other scholars have established a multi-objective optimization model based on the number of AGV transport vehicles, task satisfaction, and total travel distance. Hybrid genetic algorithm is used to solve the problem; in terms of queuing scenarios, some scholars introduce a system scheduling model, select queue length and waiting time as optimization indicators, and design a scheduling strategy; in terms of path planning, some scholars propose a bounded rational self-organization method For path planning of multiple AGVs, this method can effectively solve the problem of local competition for task resources. Although the above methods can effectively solve the problem of path conflicts and low utilization of some AGVs, it is necessary to establish a complex model, the calculation cycle is long, and the optimal strategy calculated by the model may be improperly set due to improper parameter settings. It is the local optimal solution.
发明概述Summary of the invention
技术问题technical problem
问题的解决方案The solution to the problem
技术解决方案Technical solutions
针对上述现有技术的不足，提供一种基于动态路径规划的AGV调度方法。Aiming at the above shortcomings of the prior art, an AGV scheduling method based on dynamic path planning is provided.
本发明所采取的技术方案是一种基于动态路径规划的AGV调度方法，包括如下 步骤：The technical scheme adopted by the present invention is an AGV scheduling method based on dynamic path planning, which includes the following steps:
步骤1：对AGV工作场地进行初始化，将工作场地划分成具有一定长宽的矩形网格，网格线的交叉点称为节点，其中网格的长宽可调，以实现不同精度的调度；Step 1: Initialize the AGV work site and divide the work site into rectangular grids with a certain length and width. The intersections of the grid lines are called nodes, and the length and width of the grid are adjustable to achieve different precision scheduling;
步骤2：初始化工作地图，确定AGV工作节点，并对地图内所有节点进行编号为1，2，...，n，确定AGV数量为m，对其进行编号为1，2，...，m；Step 2: Initialize the work map, determine the working nodes of the AGV, and number all nodes in the map as 1, 2,..., n, determine the number of AGVs as m, and number them as 1, 2,..., m;
步骤3：通过弗洛伊德算法计算出任意两个工作节点的最短距离，生成相应的邻接矩阵和距离矩阵；Step 3: Calculate the shortest distance between any two working nodes by Freud algorithm, and generate the corresponding adjacency matrix and distance matrix;
步骤4：任务列表按任务的优先级进行排序，优先级最高的任务为当前任务。在任务列表中获得当前任务的起点，通过距离矩阵寻找工作场地中离任务起点最近的空闲AGV执行该项任务，其流程如图1所示；Step 4: The task list is sorted by task priority, and the task with the highest priority is the current task. Obtain the starting point of the current task in the task list, and use the distance matrix to find the idle AGV closest to the starting point of the task to execute the task. The process is shown in Figure 1;
步骤4.1：初始化AGV位置，其中小车占用位置为被占用节点，其余点为未被占用节点；Step 4.1: Initialize the AGV position, where the position occupied by the car is the occupied node, and the remaining points are the unoccupied nodes;
步骤4.2：在任务列表中获得当前任务的起点；Step 4.2: Get the starting point of the current task in the task list;
步骤4.3：判断是否有空闲AGV，如果没有则等待空闲AGV；Step 4.3: Determine whether there is an idle AGV, if not, wait for an idle AGV;
步骤4.4：如果有空闲AGV，则判断是否有多个空闲AGV；Step 4.4: If there are idle AGVs, judge whether there are multiple idle AGVs;
步骤4.5：如果当前仅有1辆空闲AGV，则选择该AGV执行任务；Step 4.5: If there is currently only one free AGV, select this AGV to perform the task;
步骤4.6：如果当前空闲AGV数量大于1，则通过弗洛伊德算法建立的距离矩阵，计算出所有空闲AGV到达任务起点的距离；Step 4.6: If the current number of idle AGVs is greater than 1, the distance matrix established by the Freud algorithm is used to calculate the distance between all idle AGVs to reach the starting point of the task;
步骤4.7：选择计算距离最近的空闲AGV执行此条任务；Step 4.7: Select the idle AGV with the closest calculation distance to execute this task;
步骤4.8：如果距离最近的空闲AGV数量大于1，则选择编号最小的AGV去执行任务。Step 4.8: If the number of closest idle AGVs is greater than 1, the AGV with the smallest number is selected to perform the task.
步骤5：动态规划AGV执行路线，避开被占用的节点，使执行任务的AGV可达到任务起点，其流程如图2所示；Step 5: Dynamically plan the execution route of the AGV to avoid occupied nodes, so that the AGV performing the task can reach the starting point of the task. The process is shown in Figure 2;
骤5.1：从相邻节点中选择一个曾经未走过的节点或者距离上次走过时间间隔最长的节点；Step 5.1: Select a node from the neighboring nodes that has not been walked before or the node with the longest time interval since the last walk;
步骤5.1.1：将AGV当前位置标记为1，其余相邻的工作节点标记为0；Step 5.1.1: Mark the current position of the AGV as 1, and mark the remaining adjacent working nodes as 0;
步骤5.1.2：当AGV选择移动时候，将相邻的工作节点都减1，然后将下一个即 将去的节点再改为1；Step 5.1.2: When the AGV chooses to move, reduce all adjacent working nodes by 1, and then change the next node that will go to 1 again;
步骤5.1.3：选择距离上次走过时间间隔最长的节点即数值最小的节点为下一个走的节点；Step 5.1.3: Select the node with the longest time interval from the last time, that is, the node with the smallest value as the next node;
步骤5.1.4：如果满足条件的点很多，则通过权利要求1所述的邻接矩阵和距离矩阵计算AGV到达相邻未被占用节点的距离与相邻节点到达任务起点的距离之和；Step 5.1.4: If there are many points that meet the condition, calculate the sum of the distance between the AGV to the adjacent unoccupied node and the distance from the adjacent node to the starting point of the task through the adjacency matrix and the distance matrix described in claim 1;
步骤5.1.5：选择距离之和最短的节点走，此时在选择的同时将下一步要走的节点标记为已占用。Step 5.1.5: Select the node with the shortest sum of distance to go. At this time, while selecting, mark the node to be taken as occupied.
步骤5.2：判断步骤5.1得到的工作节点是否被AGV占用，如果被AGV占用，则判断占用的AGV是否有在执行任务；Step 5.2: Determine whether the working node obtained in step 5.1 is occupied by an AGV, and if it is occupied by an AGV, determine whether the occupied AGV is performing tasks;
步骤5.3：如果占用的AGV处于空闲状态，则随机在未被占用的可达的点里选择一个工作节点让空闲AGV去执行，如果所有节点都不可达，则随机分配一个未被占用的点让空闲AGV去执行，同时当前执行任务的AGV选择步骤5.1得到的相邻工作节点移动；Step 5.3: If the occupied AGV is in the idle state, randomly select a working node among the unoccupied reachable points for the idle AGV to execute. If all nodes are unreachable, randomly assign an unoccupied point The idle AGV is executed, and the AGV currently executing the task selects the adjacent working node obtained in step 5.1 to move;
步骤5.4：如果占用的AGV处于工作状态，则当前执行任务的AGV选择一距离近的相邻节点移动；Step 5.4: If the occupied AGV is in working state, the AGV currently performing the task selects a nearby neighboring node to move;
步骤5.5：如果步骤5.1得到的工作节点未被AGV占用，则选择该工作节点移动。Step 5.5: If the working node obtained in Step 5.1 is not occupied by the AGV, select the working node to move.
步骤5.6：重复执行步骤5.1至步骤5.5实现单步动态规划AGV的执行路线，直到步骤5.1得到的相邻节点为任务起点，停止循环。Step 5.6: Repeat step 5.1 to step 5.5 to realize the single-step dynamic planning of the execution route of the AGV, until the adjacent node obtained in step 5.1 is the starting point of the task, stop the loop.
步骤6：从任务起点取货后，AGV重新动态规划执行路线，避开被占用的节点，到达任务终点；Step 6: After picking up the goods from the starting point of the task, the AGV dynamically re-plans the execution route, avoids the occupied nodes, and reaches the end of the task;
其中，任务终点视为步骤5中的任务起点，从取货点到达任务终点动态规划AGV执行路线的过程同步骤5中的过程一致；Among them, the end of the task is regarded as the starting point of the task in step 5, and the process of dynamically planning the AGV execution route from the pick-up point to the end of the task is the same as the process in step 5;
步骤7：更新任务列表，新增工作任务，取消已经执行完成的工作任务；Step 7: Update the task list, add new tasks, and cancel completed tasks;
步骤8：重复执行步骤4至步骤7，直到任务列表为空。Step 8: Repeat steps 4 to 7 until the task list is empty.
发明的有益效果The beneficial effects of the invention
有益效果Beneficial effect
采用上述技术方案所产生的有益效果在于：.The beneficial effects produced by using the above technical solutions are:
1.在选择AGV时，优先考虑空闲AGV，保证每一辆AGV的使用频率稳定；1. When choosing an AGV, give priority to idle AGVs to ensure that the frequency of use of each AGV is stable;
2.避障过程中，在车辆出发之前便考虑好车辆行驶路线，避免在运行过程中为了躲避其他车辆而更改路线。2. In the process of avoiding obstacles, consider the driving route of the vehicle before the vehicle starts, and avoid changing the route in order to avoid other vehicles during the operation.
3.可以对实际地图进行模拟，还可以通过调整网格的长宽，实现不同精度下的调度策略；3. It can simulate the actual map, and adjust the length and width of the grid to realize the scheduling strategy under different accuracy;
4.不仅仅限于工厂中的运输问题，而是对任意场景下的多单位多任务问题的普适性的方案，可以在生活中运用到更加广阔的方面。4. It is not only limited to the transportation problem in the factory, but a universal solution to the multi-unit and multi-task problem in any scene, which can be applied to a wider range of aspects in life.
对附图的简要说明Brief description of the drawings
附图说明Description of the drawings
图1为本发明AGV选择流程图；Figure 1 is a flowchart of the AGV selection of the present invention;
图2为本发明AGV路径规划流程图；Figure 2 is a flow chart of the AGV path planning of the present invention;
图3为本发明实施例中工作场地网格化分图；Figure 3 is a grid sub-map of the workplace in the embodiment of the present invention;
图4为本发明实施例中初始化工作地图，对工作节点进行编号图；FIG. 4 is a diagram of initializing a working map and numbering working nodes in an embodiment of the present invention;
图5为本发明实施例中调度前AGV所在工作节点位置图；FIG. 5 is a diagram of the working node position of the AGV before scheduling in the embodiment of the present invention;
图6为本发明实施例中调度后AGV所在工作节点位置图。Fig. 6 is a diagram of the working node position of the AGV after scheduling in the embodiment of the present invention.
发明实施例Invention embodiment
本发明的实施方式Embodiments of the invention
下面结合附图和实施例，对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明，但不用来限制本发明的范围。The specific embodiments of the present invention will be described in further detail below in conjunction with the drawings and embodiments. The following examples are used to illustrate the present invention, but not to limit the scope of the present invention.
本实施例将大小为1600px*800px的区域作为工作场地，该场地中AGV的数量为4。In this embodiment, an area with a size of 1600px*800px is used as a work site, and the number of AGVs in the site is 4.
本实施例的方法如下所述：The method of this embodiment is as follows:
步骤1：对大小为1600px*800px的工作场地进行初始化，将工作场地划分成100px*100px的矩形网格，如图3所示，网格线的交叉点称为节点，其中网格的长宽可调，以实现不同精度的调度；Step 1: Initialize the work area with a size of 1600px*800px and divide the work area into a rectangular grid of 100px*100px. As shown in Figure 3, the intersection of the grid lines is called a node, and the length and width of the grid are Adjustable to achieve different precision scheduling;
步骤2：初始化工作地图，确定AGV工作节点，并对地图内所有节点进行编号为1，2，...，22，如图4所示，确定AGV数量为4，对其进行编号为1，2，3，4；Step 2: Initialize the work map, determine the AGV work nodes, and number all nodes in the map as 1, 2,..., 22, as shown in Figure 4, determine the number of AGVs as 4, and number them as 1. 2, 3, 4;
步骤3：通过弗洛伊德算法计算出任意两个工作节点的最短距离，生成相应的邻接矩阵和距离矩阵；Step 3: Calculate the shortest distance between any two working nodes by Freud algorithm, and generate the corresponding adjacency matrix and distance matrix;
步骤4：任务列表按任务的优先级进行排序，优先级最高的任务为当前任务，任务列表如表1所示；Step 4: The task list is sorted according to the priority of the task. The task with the highest priority is the current task. The task list is shown in Table 1;
表1按优先级排序的任务列表Table 1 Task list sorted by priority
[Table 1][Table 1]
优先级priority 任务起点Mission starting point 任务终点End of mission
11 33 22twenty two
22 44 1818
33 1414 2020
44 1313 1111
在任务列表中获得当前任务的起点，通过距离矩阵寻找工作场地中离任务起点最近的空闲AGV执行该项任务，其流程如图1所示；Obtain the starting point of the current task in the task list, and use the distance matrix to find the idle AGV closest to the starting point of the task to execute the task. The process is shown in Figure 1;
步骤4.1：初始化AGV位置，4辆AGV的位置如图5所示，其中小车占用位置为被占用节点，其余点为未被占用节点；Step 4.1: Initialize the position of the AGV. The positions of the 4 AGVs are shown in Figure 5. The position occupied by the car is the occupied node, and the remaining points are the unoccupied nodes;
步骤4.2：在任务列表中获得当前任务的起点；Step 4.2: Get the starting point of the current task in the task list;
步骤4.3：判断是否有空闲AGV，如果没有则等待空闲AGV；Step 4.3: Determine whether there is an idle AGV, if not, wait for an idle AGV;
步骤4.4：如果有空闲AGV，则判断是否有多个空闲AGV；Step 4.4: If there are idle AGVs, judge whether there are multiple idle AGVs;
步骤4.5：如果当前仅有1辆空闲AGV，则选择该AGV执行任务；Step 4.5: If there is currently only one free AGV, select this AGV to perform the task;
步骤4.6：如果当前空闲AGV数量大于1，则通过弗洛伊德算法建立的距离矩阵，计算出所有空闲AGV到达任务起点的距离；Step 4.6: If the current number of idle AGVs is greater than 1, the distance matrix established by the Freud algorithm is used to calculate the distance between all idle AGVs to reach the starting point of the task;
步骤4.7：选择计算距离最近的空闲AGV执行此条任务；Step 4.7: Select the idle AGV with the closest calculation distance to execute this task;
步骤4.8：如果距离最近的空闲AGV数量大于1，则选择编号最小的AGV去执行任务。Step 4.8: If the number of closest idle AGVs is greater than 1, the AGV with the smallest number is selected to perform the task.
步骤5：动态规划AGV执行路线，避开被占用的节点，使执行任务的AGV可达到任务起点，其流程如图2所示；Step 5: Dynamically plan the execution route of the AGV to avoid occupied nodes, so that the AGV performing the task can reach the starting point of the task. The process is shown in Figure 2;
骤5.1：从相邻节点中选择一个曾经未走过的节点或者距离上次走过时间间隔最 长的节点；Step 5.1: Select a node from the adjacent nodes that has not been traversed or the node with the longest time interval since the last traverse;
步骤5.1.1：将AGV当前位置标记为1，其余相邻的工作节点标记为0；Step 5.1.1: Mark the current position of the AGV as 1, and mark the remaining adjacent working nodes as 0;
步骤5.1.2：当AGV选择移动时候，将相邻的工作节点都减1，然后将下一个即将去的节点再改为1；Step 5.1.2: When the AGV chooses to move, reduce all adjacent working nodes by 1, and then change the next upcoming node to 1;
步骤5.1.3：选择距离上次走过时间间隔最长的节点即数值最小的节点为下一个走的节点；Step 5.1.3: Select the node with the longest time interval from the last time, that is, the node with the smallest value as the next node;
步骤5.1.4：如果满足条件的点很多，则通过权利要求1所述的邻接矩阵和距离矩阵计算AGV到达相邻未被占用节点的距离与相邻节点到达任务起点的距离之和；Step 5.1.4: If there are many points that meet the condition, calculate the sum of the distance between the AGV to the adjacent unoccupied node and the distance from the adjacent node to the starting point of the task through the adjacency matrix and the distance matrix described in claim 1;
步骤5.1.5：选择距离之和最短的节点走，此时在选择的同时将下一步要走的节点标记为已占用。Step 5.1.5: Select the node with the shortest sum of distance to go. At this time, while selecting, mark the node to be taken as occupied.
步骤5.2：判断步骤5.1得到的工作节点是否被AGV占用，如果被AGV占用，则判断占用的AGV是否有在执行任务；Step 5.2: Determine whether the working node obtained in step 5.1 is occupied by an AGV, and if it is occupied by an AGV, determine whether the occupied AGV is performing tasks;
步骤5.3：如果占用的AGV处于空闲状态，则随机在未被占用的可达的点里选择一个工作节点让空闲AGV去执行，如果所有节点都不可达，则随机分配一个未被占用的点让空闲AGV去执行，同时当前执行任务的AGV选择步骤5.1得到的相邻工作节点移动；Step 5.3: If the occupied AGV is in the idle state, randomly select a working node among the unoccupied reachable points for the idle AGV to execute. If all nodes are unreachable, randomly assign an unoccupied point The idle AGV is executed, and the AGV currently executing the task selects the adjacent working node obtained in step 5.1 to move;
步骤5.4：如果占用的AGV处于工作状态，则当前执行任务的AGV选择一距离近的相邻节点移动；Step 5.4: If the occupied AGV is in working state, the AGV currently performing the task selects a nearby neighboring node to move;
步骤5.5：如果步骤5.1得到的工作节点未被AGV占用，则选择该工作节点移动。Step 5.5: If the working node obtained in Step 5.1 is not occupied by the AGV, select the working node to move.
步骤5.6：重复执行步骤5.1至步骤5.5实现单步动态规划AGV的执行路线，直到步骤5.1得到的相邻节点为任务起点，停止循环。Step 5.6: Repeat step 5.1 to step 5.5 to realize the single-step dynamic planning of the execution route of the AGV, until the adjacent node obtained in step 5.1 is the starting point of the task, stop the loop.
步骤6：从任务起点取货后，AGV重新动态规划执行路线，避开被占用的节点，到达任务终点；Step 6: After picking up the goods from the starting point of the task, the AGV dynamically re-plans the execution route, avoids the occupied nodes, and reaches the end of the task;
其中，任务终点视为步骤5中的任务起点，从取货点到达任务终点动态规划AGV执行路线的过程同步骤5中的过程一致；Among them, the end of the task is regarded as the starting point of the task in step 5, and the process of dynamically planning the AGV execution route from the pick-up point to the end of the task is the same as the process in step 5;
如图6所示，选择编号为1的AGV执行优先级为1的任务，从初始节点1到任务起 点3取货后，往任务终点22送货，任务正在执行中；选择编号为3的AGV执行优先级为3的任务，从初始节点15到任务节点14取货后，往任务终点20送货，任务正在执行中。As shown in Figure 6, the AGV numbered 1 is selected to execute the task with priority 1. After picking up the goods from the initial node 1 to the task starting point 3, the goods are delivered to the task ending point 22. The task is being executed; select the AGV numbered 3 The task with priority 3 is executed. After picking up the goods from the initial node 15 to the task node 14, the goods are delivered to the task terminal 20, and the task is being executed.
步骤7：更新任务列表，新增工作任务，取消已经执行完成的工作任务；Step 7: Update the task list, add new tasks, and cancel completed tasks;
步骤8：重复执行步骤4至步骤7，直到任务列表为空。Step 8: Repeat steps 4 to 7 until the task list is empty.

Claims (7)

  1. 一种基于动态路径规划的AGV调度方法，其特征在于包括如下步骤：An AGV scheduling method based on dynamic path planning is characterized by including the following steps:
    步骤1：对AGV工作场地进行初始化，将工作场地划分成具有一定长宽的矩形网格，网格线的交叉点称为节点，其中网格的长宽可调，以实现不同精度的调度；Step 1: Initialize the AGV work site and divide the work site into rectangular grids with a certain length and width. The intersections of the grid lines are called nodes, and the length and width of the grid are adjustable to achieve different precision scheduling;
    步骤2：初始化工作地图，确定AGV工作节点，并对地图内所有节点进行编号为1，2，...，n，确定AGV数量为m，对其进行编号为1，2，...，m；Step 2: Initialize the work map, determine the working nodes of the AGV, and number all nodes in the map as 1, 2,..., n, determine the number of AGVs as m, and number them as 1, 2,..., m;
    步骤3：通过弗洛伊德算法计算出任意两个工作节点的最短距离，生成相应的邻接矩阵和距离矩阵；Step 3: Calculate the shortest distance between any two working nodes by Freud algorithm, and generate the corresponding adjacency matrix and distance matrix;
    步骤4：在任务列表中获得当前任务的起点，通过距离矩阵寻找工作场地中离任务起点最近的空闲AGV执行该项任务；Step 4: Obtain the starting point of the current task in the task list, and use the distance matrix to find the idle AGV closest to the task starting point in the work site to execute the task;
    步骤5：动态规划AGV执行路线，避开被占用的节点，使执行任务的AGV可达到任务起点；Step 5: Dynamically plan the execution route of the AGV to avoid occupied nodes so that the AGV performing the task can reach the starting point of the task;
    步骤6：从任务起点取货后，AGV重新动态规划执行路线，避开被占用的节点，到达任务终点；Step 6: After picking up the goods from the starting point of the task, the AGV dynamically re-plans the execution route, avoids the occupied nodes, and reaches the end of the task;
    步骤7：更新任务列表，新增工作任务，取消已经执行完成的工作任务；Step 7: Update the task list, add new tasks, and cancel completed tasks;
    步骤8：重复执行步骤4至步骤7，直到任务列表为空。Step 8: Repeat steps 4 to 7 until the task list is empty.
  2. 根据权利要求1所述的一种基于动态路径规划的AGV调度方法，其特征在于：所述步骤4中任务列表按任务的优先级进行排序，优先级最高的任务为当前任务。The AGV scheduling method based on dynamic path planning according to claim 1, wherein the task list in step 4 is sorted according to the priority of the task, and the task with the highest priority is the current task.
  3. 根据权利要求1所述的一种基于动态路径规划的AGV调度方法，其特征在于：所述步骤4的过程如下：The AGV scheduling method based on dynamic path planning according to claim 1, characterized in that: the process of step 4 is as follows:
    步骤4.1：初始化AGV位置，其中小车占用位置为被占用节点，其余点为未被占用节点；Step 4.1: Initialize the AGV position, where the position occupied by the car is the occupied node, and the remaining points are the unoccupied nodes;
    步骤4.2：在任务列表中获得当前任务的起点；Step 4.2: Get the starting point of the current task in the task list;
    步骤4.3：判断是否有空闲AGV，如果没有则等待空闲AGV；Step 4.3: Determine whether there is an idle AGV, if not, wait for an idle AGV;
    步骤4.4：如果有空闲AGV，则判断是否有多个空闲AGV；Step 4.4: If there are idle AGVs, judge whether there are multiple idle AGVs;
    步骤4.5：如果当前仅有1辆空闲AGV，则选择该AGV执行任务；Step 4.5: If there is currently only one free AGV, select this AGV to perform the task;
    步骤4.6：如果当前空闲AGV数量大于1，则通过弗洛伊德算法建立的距离矩阵，计算出所有空闲AGV到达任务起点的距离；Step 4.6: If the current number of idle AGVs is greater than 1, the distance matrix established by the Freud algorithm is used to calculate the distance between all idle AGVs to reach the starting point of the task;
    步骤4.7：选择计算距离最近的空闲AGV执行此条任务；Step 4.7: Select the idle AGV with the closest calculation distance to execute this task;
    步骤4.8：如果距离最近的空闲AGV数量大于1，则选择编号最小的AGV去执行任务。Step 4.8: If the number of closest idle AGVs is greater than 1, the AGV with the smallest number is selected to perform the task.
  4. 根据权利要求1所述的一种基于动态路径规划的AGV调度方法，其特征在于：所述步骤5中的过程如下：The AGV scheduling method based on dynamic path planning according to claim 1, characterized in that: the process in step 5 is as follows:
    步骤5.1：从相邻节点中选择一个曾经未走过的节点或者距离上次走过时间间隔最长的节点；Step 5.1: Select a node from the neighboring nodes that has not been walked before or the node with the longest time interval since the last walk;
    步骤5.2：判断步骤5.1得到的工作节点是否被AGV占用，如果被AGV占用，则判断占用的AGV是否有在执行任务；Step 5.2: Determine whether the working node obtained in step 5.1 is occupied by an AGV, and if it is occupied by an AGV, determine whether the occupied AGV is performing tasks;
    步骤5.3：如果占用的AGV处于空闲状态，则随机在未被占用的可达的点里选择一个工作节点让空闲AGV去执行，如果所有节点都不可达，则随机分配一个未被占用的点让空闲AGV去执行，同时当前执行任务的AGV选择步骤5.1得到的相邻工作节点移动；Step 5.3: If the occupied AGV is in the idle state, randomly select a working node among the unoccupied reachable points for the idle AGV to execute. If all nodes are unreachable, randomly assign an unoccupied point The idle AGV is executed, and the AGV currently executing the task selects the adjacent working node obtained in step 5.1 to move;
    步骤5.4：如果占用的AGV处于工作状态，则当前执行任务的AGV选择一距离近的相邻节点移动；Step 5.4: If the occupied AGV is in working state, the AGV currently performing the task selects a nearby neighboring node to move;
    步骤5.5：如果步骤5.1得到的工作节点未被AGV占用，则选择该工作节点移动；Step 5.5: If the working node obtained in Step 5.1 is not occupied by AGV, select the working node to move;
    步骤5.6：重复执行步骤5.1至步骤5.5实现单步动态规划AGV的执行路线，直到步骤5.1得到的相邻节点为任务起点，停止循环。Step 5.6: Repeat step 5.1 to step 5.5 to realize the single-step dynamic planning of the execution route of the AGV, until the adjacent node obtained in step 5.1 is the starting point of the task, stop the loop.
  5. 根据权利要求4所述的一种基于动态路径规划的AGV调度方法，其特征在于：所述步骤5.1的过程如下：The AGV scheduling method based on dynamic path planning according to claim 4, characterized in that: the process of step 5.1 is as follows:
    步骤5.1.1：将AGV当前位置标记为1，其余相邻的工作节点标记为0；Step 5.1.1: Mark the current position of the AGV as 1, and mark the remaining adjacent working nodes as 0;
    步骤5.1.2：当AGV选择移动时候，将相邻的工作节点都减1，然后将下一个即将去的节点再改为1；Step 5.1.2: When the AGV chooses to move, reduce all adjacent working nodes by 1, and then change the next upcoming node to 1;
    步骤5.1.3：选择距离上次走过时间间隔最长的节点即数值最小的节点为下一个走的节点；Step 5.1.3: Select the node with the longest time interval from the last time, that is, the node with the smallest value as the next node;
    步骤5.1.4：如果满足条件的点很多，则通过权利要求1所述的邻接矩阵和距离矩阵计算AGV到达相邻未被占用节点的距离与相邻节点到达任务起点的距离之和；Step 5.1.4: If there are many points that meet the condition, calculate the sum of the distance between the AGV to the adjacent unoccupied node and the distance from the adjacent node to the starting point of the task through the adjacency matrix and the distance matrix described in claim 1;
    步骤5.1.5：选择距离之和最短的节点走，此时在选择的同时将下一步要走的节点标记为已占用。Step 5.1.5: Select the node with the shortest sum of distance to go. At this time, while selecting, mark the node to be taken as occupied.
  6. 根据权利要求1所述的一种基于动态路径规划的AGV调度方法，其特征在于：所述步骤6中的任务终点视为步骤5中的任务起点，从取货点到达任务终点动态规划AGV执行路线的过程同权利要求1所述步骤5中的过程一致。The AGV scheduling method based on dynamic path planning according to claim 1, characterized in that: the task endpoint in step 6 is regarded as the task starting point in step 5, and the AGV executes dynamic planning from the pick-up point to the task endpoint. The process of the route is the same as the process in step 5 of claim 1.
  7. 根据权利要求4所述的一种基于动态路径规划的AGV调度方法，其特征在于：所述步骤5.4中如果占用的AGV处于工作状态，且该被占用工作节点为当前执行任务AGV的任务起点，则当前执行任务的AGV选择一距离近的相邻节点移动后，重新回到任务起点。The AGV scheduling method based on dynamic path planning according to claim 4, characterized in that: in said step 5.4, if the occupied AGV is in a working state, and the occupied working node is the task starting point of the currently executing task AGV, Then the AGV currently performing the task selects a nearby neighboring node to move, and then returns to the starting point of the task.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114326621A (en) * 2021-12-25 2022-04-12 长安大学 Group intelligent airport trolley dispatching method and system based on layered architecture

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113554250A (en) * 2020-04-23 2021-10-26 北京京东乾石科技有限公司 Information processing method and device for transport vehicle
CN111888774B (en) * 2020-07-03 2021-11-30 深圳怡丰自动化科技有限公司 Cabin control method and device and storage medium
CN112061657B (en) * 2020-09-11 2022-12-02 灵动科技（北京）有限公司 Method and device for guiding a robot to transport goods in a warehouse
CN113110330B (en) * 2021-04-15 2022-11-22 青岛港国际股份有限公司 AGV dynamic scheduling management method based on global optimal matching
CN113743739B (en) * 2021-08-11 2024-02-13 青岛港国际股份有限公司 AGV scheduling method based on mixed integer programming and combined optimization algorithm
CN113934217B (en) * 2021-12-15 2022-02-25 南京绛门信息科技股份有限公司 Intelligent scheduling processing system based on 5G
CN114580728A (en) * 2022-02-28 2022-06-03 北京京东乾石科技有限公司 Elevator dispatching method and device, storage medium and electronic equipment
CN115049096A (en) * 2022-03-31 2022-09-13 日日顺供应链科技股份有限公司 Warehouse operation efficiency improving method and system
CN117035372B (en) * 2023-10-09 2023-12-22 成都思越智能装备股份有限公司 OHT scheduling processing method and device

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040199415A1 (en) * 1998-04-01 2004-10-07 Ho William P.C. Method for scheduling transportation resources
CN107179078A (en) * 2017-05-24 2017-09-19 合肥工业大学(马鞍山)高新技术研究院 A kind of AGV paths planning methods optimized based on time window
CN109541634A (en) * 2018-12-28 2019-03-29 歌尔股份有限公司 A kind of paths planning method, device and mobile device
CN109934388A (en) * 2019-02-18 2019-06-25 上海东普信息科技有限公司 One kind sorting optimization system for intelligence

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102566576B (en) * 2012-02-24 2014-03-19 山东鲁能智能技术有限公司 Multiple inspection robot cooperative operation method for substation sequence control system
CN106251016B (en) * 2016-08-01 2019-05-07 江苏海事职业技术学院 A kind of parking system paths planning method based on dynamic time windows

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040199415A1 (en) * 1998-04-01 2004-10-07 Ho William P.C. Method for scheduling transportation resources
CN107179078A (en) * 2017-05-24 2017-09-19 合肥工业大学(马鞍山)高新技术研究院 A kind of AGV paths planning methods optimized based on time window
CN109541634A (en) * 2018-12-28 2019-03-29 歌尔股份有限公司 A kind of paths planning method, device and mobile device
CN109934388A (en) * 2019-02-18 2019-06-25 上海东普信息科技有限公司 One kind sorting optimization system for intelligence

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LIU, JINGYI: "Research and Implement of Route Planning about Automated Guided Vehicles in Automated Storage Scheduling System", CHINESE MASTER’S THESES FULL-TEXT DATABASE, INFORMATION SCIENCE AND TECHNOLOGY, no. 1, 15 January 2019 (2019-01-15), pages 1 - 69, XP055783362 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114326621A (en) * 2021-12-25 2022-04-12 长安大学 Group intelligent airport trolley dispatching method and system based on layered architecture
CN114326621B (en) * 2021-12-25 2023-11-14 长安大学 Group intelligent airport consignment car scheduling method and system based on layered architecture

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