技术领域Technical Field
本发明涉及智能化仓库在面对多机器人多任务时的任务分配技术领域，是一种基于拍卖算法的仓库任务分配方法。The present invention relates to the technical field of task allocation in an intelligent warehouse facing multiple robots and multiple tasks, and is a warehouse task allocation method based on an auction algorithm.
背景技术Background Art
随着时代的不断发展，存在于之前人类梦想中的科幻场景已经逐一实现。人类的军事，社会生活等各个方面都已经步入智能化时代。由于网络的迅速发展，网购也逐渐代替了实体店行业，所以物流等产业也得到飞速的发展。传统的仓库需要人们逐个分类并依靠人力运送货物。仓库的现代化则让机器逐渐取代人类，不仅效率更高且节省劳动力。而智能化仓库则需要合理的任务分配，以便高效的完成任务。因此设计一种快速高效的任务分配方法来合理的将任务分配给合适的对象很有必要。With the continuous development of the times, the science fiction scenes that existed in the dreams of mankind have been realized one by one. All aspects of human military, social life, etc. have entered the era of intelligence. Due to the rapid development of the Internet, online shopping has gradually replaced the physical store industry, so industries such as logistics have also developed rapidly. Traditional warehouses require people to sort and transport goods one by one. The modernization of warehouses allows machines to gradually replace humans, which is not only more efficient but also saves labor. Intelligent warehouses require reasonable task allocation in order to complete tasks efficiently. Therefore, it is necessary to design a fast and efficient task allocation method to reasonably assign tasks to appropriate objects.
拍卖算法最早被提出用于解决分配问题的分布式算法。其本质是模拟拍卖会机制。拍卖会产生于人类社会，具有较为完善的制度和方式。拍卖算法就是投标者之间互相竞争，考虑自身最终获得利益而进行出价，直到得出最高价，将竞拍物品分配给胜利者。传统的拍卖算法更倾向于算法理论分配，为了将其利用于实际，更加贴合仓库环境。对其进行了改进。The auction algorithm was first proposed as a distributed algorithm to solve the allocation problem. Its essence is to simulate the auction mechanism. Auctions originated in human society and have relatively complete systems and methods. The auction algorithm is that bidders compete with each other and bid based on their own ultimate interests until the highest price is reached and the auctioned items are allocated to the winner. The traditional auction algorithm is more inclined to algorithmic theoretical allocation. In order to apply it in practice and make it more suitable for the warehouse environment, it has been improved.
发明内容Summary of the invention
本发明为克服现有技术的不足，本发明针对目前智能化仓库系统效率不高等问题进行解决。目前多为立体货架以及有轨巷道或者传送带模式，运输路径较为固定，自由度不高，由此产生效率低下等问题。The present invention is to overcome the shortcomings of the prior art and solve the problems of low efficiency of the current intelligent warehouse system. Currently, most of them are three-dimensional shelves and rail lanes or conveyor belt modes, and the transportation path is relatively fixed and the degree of freedom is not high, which leads to problems such as low efficiency.
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
本发明提供了一种基于拍卖算法的仓库任务分配方法，本发明提供了以下技术方案：The present invention provides a warehouse task allocation method based on an auction algorithm, and the present invention provides the following technical solutions:
一种基于拍卖算法的仓库任务分配方法，所述方法包括以下步骤：A warehouse task allocation method based on an auction algorithm, the method comprising the following steps:
步骤1：对任务，仓库环境以及机器人进行参数初始化；Step 1: Initialize the parameters of the task, warehouse environment and robot;
步骤2：通过拍卖算法进行任务分配；Step 2: Assign tasks through auction algorithm;
步骤3：派遣机器人执行任务。Step 3: Dispatch the robot to perform the task.
优选地，所述步骤1具体为：Preferably, the step 1 is specifically:
所述参数初始化将数字化实际环境；将机器人以及物品进行建模，定义机器人集合为R＝{r1,r2,…,rn},机器人数量为n，每个机器人最多可以执行Ni个任务，且同时只能执行一个任务；机器人在运行过程中，保持匀速行驶，移动速度不会发生改变；定义任务集合为T＝{t1,t2,…,tm},任务数量为m，保证每个任务一定会分配给机器人，且只能分配给一个机器人，不能由多个机器人同时执行相同的任务。The parameter initialization will digitize the actual environment; the robots and objects are modeled, and the robot set is defined as R = {r1 , r2 , …, rn }, the number of robots is n, each robot can perform at most Ni tasks, and can only perform one task at the same time; during the operation, the robot maintains a constant speed and the moving speed does not change; the task set is defined as T = {t1 , t2 , …, tm }, the number of tasks is m, to ensure that each task will be assigned to a robot and can only be assigned to one robot, and multiple robots cannot perform the same task at the same time.
优选地，在拍卖算法前建立目标函数，进行任务分配时的目标函数为Preferably, an objective function is established before the auction algorithm, and the objective function for task allocation is:
其中，aij为收益函数，bij为成本函数。Among them,aij is the benefit function andbij is the cost function.
优选地，将收益最高作为首要选择标准。将从所有的任务矩阵中选择收益最大的，并将其分配给对应的机器人。Preferably, the highest benefit is used as the primary selection criterion. The task with the highest benefit is selected from all task matrices and assigned to the corresponding robot.
优选地，除了要求收益最高外，还要求具备足够的能源，需要设置能源限制，机器人需要具备足够的能源去完成此次任务；当能量小于完成任务所需要能量时，则需要放弃此次任务，对可以执行的任务进行拍卖。Preferably, in addition to requiring the highest profit, sufficient energy is also required. Energy limits need to be set, and the robot needs to have enough energy to complete the task. When the energy is less than the energy required to complete the task, the task needs to be abandoned and the executable tasks must be auctioned.
优选地，按照国际快递计重方式将货物按照重量从小到大分为A,B,C三种类型；机器人也根据自身所能运输的货物重量不同而分为α，β，γ三种类型。具体分类方式为：Preferably, the goods are classified into three types: A, B, and C according to the weight from small to large according to the international express weight measurement method; the robots are also divided into three types: α, β, and γ according to the weight of the goods they can transport. The specific classification method is:
机器人根据自己的位置计算自己与每个任务点之间的距离，将距离数据存入价值矩阵中；The robot calculates the distance between itself and each task point based on its own position and stores the distance data in the value matrix;
机器人通过数据计算与任务点之间的距离；采用欧式距离进行计算，计算机器人与任务点之间的真实距离，计算公式为：The robot calculates the distance between the robot and the task point through data; the Euclidean distance is used to calculate the actual distance between the robot and the task point. The calculation formula is:
拍卖开始，按照任务入库顺序对其逐个通过进行拍卖，由于机器人类型的不同，针对不同类型的任务某些机器人无法进行竞拍，机器人需要计算自身所得利益对可竞标物品进行出价；The auction begins, and tasks are auctioned one by one in the order they are put into the database. Due to the different types of robots, some robots cannot bid for different types of tasks. Robots need to calculate their own benefits and bid for the biddable items.
拍卖开始，机器人根据任务与机器人出发点距离以及与任务的匹配度开始出价。匹配度越高，距离越远，则报价越高，反之，则报价越低；计算得到最新的价值矩阵，将更新后的数据代替原本价值矩阵中的数据；出价公式为：At the beginning of the auction, the robot starts bidding based on the distance between the task and the robot's starting point and the matching degree with the task. The higher the matching degree and the longer the distance, the higher the bid, and vice versa. The latest value matrix is calculated and the updated data replaces the data in the original value matrix. The bidding formula is:
P＝k·dijP = k·dij
经过出价之后，拍卖方通过机器人序号和任务序号找到每个机器人对应的任务以及坐标位置；将机器人的位置移动到被分配任务的位置，并将该任务从任务列表中删除；After bidding, the auctioneer finds the task and coordinate position corresponding to each robot through the robot serial number and task serial number; moves the robot to the position of the assigned task and deletes the task from the task list;
当存在最高价相同的情况，器人重新出价进行拍卖；当最高价相同，则任务不被分配，重新返回任务列表中等待下一轮拍卖；机器人继续竞拍接下来的任务；When the highest prices are the same, the robot will bid again for the auction; when the highest prices are the same, the task will not be assigned and will be returned to the task list to wait for the next round of auction; the robot will continue to bid for the next task;
为了避免单个机器人分配得过多任务，在机器人成功分配得一个任务后，应适当增加其拍卖下一个任务的代价；针对机器人已经分配任务的数量m，从而取值不同的增长代价ε；随着机器人分配任务的增加，机器人所能执行任务数量越来越少，ε的增长越来越慢，所以采用对数函数，对数增长，曲率无限趋近于0，其中，a为自定义系数，来控制函数增长的幅度，b为函数基础数值；In order to avoid a single robot being assigned too many tasks, after a robot successfully assigns a task, the price of its next task auction should be appropriately increased; different growth costs ε are taken for the number of tasks m that the robot has assigned; as the number of tasks assigned to the robot increases, the number of tasks that the robot can perform decreases, and the growth of ε becomes slower and slower, so a logarithmic function is used, with logarithmic growth and curvature infinitely approaching 0, where a is a custom coefficient to control the amplitude of the function growth, and b is the basic value of the function;
ε＝logam+bε＝loga m+b
每轮拍卖结束时，机器人计算本轮收益，并准备进行下一轮拍卖；在一轮拍卖结束后，一部分任务已经被分配至机器人，机器人则计算每轮拍卖所得的收益，并累加起来，收益为每个任务与机器人的匹配度与距离的倒数以及重量之和；没有分配的任务则返回重新进行拍卖；At the end of each round of auction, the robot calculates the revenue of this round and prepares for the next round of auction. After a round of auction, some tasks have been assigned to the robot, and the robot calculates the revenue of each round of auction and adds them up. The revenue is the sum of the reciprocal of the matching degree and distance between each task and the robot and the weight. Tasks that have not been assigned are returned for re-auction.
拍卖结束，机器人执行自身任务；计算自身所处位置，并更新价值矩阵，准备进行下一轮拍卖。After the auction is over, the robot performs its own tasks; calculates its own position and updates the value matrix, preparing for the next round of auctions.
优选地，针对不同的任务类型与机器人类型，有不同的匹配度，不同的组合将拥有不同的出价策略，对于不同的组合拥有不同的匹配度；对于机器人能量不足以执行对应任务的，则匹配度为0。Preferably, there are different matching degrees for different task types and robot types, and different combinations will have different bidding strategies, and different matching degrees for different combinations; if the robot energy is insufficient to perform the corresponding task, the matching degree is 0.
一种基于拍卖算法的仓库任务分配系统，所述系统包括：A warehouse task allocation system based on an auction algorithm, the system comprising:
初始化模块，所述初始化模块对任务，仓库环境以及机器人进行参数初始化；An initialization module, which initializes the parameters of the task, warehouse environment and robot;
任务分配模块，所述任务分配模块通过拍卖算法进行任务分配；A task allocation module, wherein the task allocation module performs task allocation through an auction algorithm;
派遣模块，所述派遣模块派遣机器人执行任务。A dispatching module is used to dispatch robots to perform tasks.
一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行，以用于实现一种基于拍卖算法的仓库任务分配方法A computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement a warehouse task allocation method based on an auction algorithm
一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现一种基于拍卖算法的仓库任务分配方法A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements a warehouse task allocation method based on an auction algorithm when executing the computer program
本发明具有以下有益效果：The present invention has the following beneficial effects:
本发明与现有技术相比：Compared with the prior art, the present invention has the following advantages:
本发明从实际出发，针对现代化仓库自由度较低，不够灵活，应对突发事故能力弱等问题，将拍卖算法应用于仓库中对于任务的选择中，将效率最大化。为了更加符合实际情况，并将拍卖算法进行了改进，使其更加智能化，针对不同重量的任务采取不同的措施，降低了能耗。由于该算法基于分布式，有较高的鲁棒性，每个个体独立行动，所以当遇到突发情况时，拥有较好的应对能力。The present invention is based on reality, and aims to address the problems of low degree of freedom, lack of flexibility, and weak ability to deal with emergencies in modern warehouses. The auction algorithm is applied to the selection of tasks in the warehouse to maximize efficiency. In order to be more in line with the actual situation, the auction algorithm is improved to make it more intelligent, and different measures are taken for tasks of different weights to reduce energy consumption. Since the algorithm is based on distribution and has high robustness, each individual acts independently, so when encountering emergencies, it has better coping capabilities.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
图1为本发明的基于拍卖算法的仓库任务分配方法流程图；FIG1 is a flow chart of a warehouse task allocation method based on an auction algorithm according to the present invention;
图2为机器人与任务位置示意图；Figure 2 is a schematic diagram of the robot and task positions;
图3为任务分配路径轨迹图。Figure 3 is a task allocation path trajectory diagram.
具体实施方式DETAILED DESCRIPTION
下面将结合附图对本发明的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
在本发明的描述中，需要说明的是，术语“中心”、“上”、“下”、“左”、“右”、“竖直”、“水平”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，术语“第一”、“第二”、“第三”仅用于描述目的，而不能理解为指示或暗示相对重要性。In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.
在本发明的描述中，需要说明的是，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以具体情况理解上述术语在本发明中的具体含义。In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
此外，下面所描述的本发明不同实施方式中所涉及的技术特征只要彼此之间未构成冲突就可以相互结合。In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.
以下结合具体实施例，对本发明进行了详细说明。The present invention is described in detail below in conjunction with specific embodiments.
具体实施例一：Specific embodiment one:
根据图1至图3所示，本发明为解决上述技术问题采取的具体优化技术方案是：本发明涉及一种基于拍卖算法的仓库任务分配方法，本发明针对目前智能化仓库系统效率不高等问题进行解决。目前多为立体货架以及有轨巷道或者传送带模式，运输路径较为固定，自由度不高，由此产生效率低下等问题。As shown in Figures 1 to 3, the specific optimization technical solution adopted by the present invention to solve the above technical problems is: The present invention relates to a warehouse task allocation method based on an auction algorithm, and the present invention solves the problem that the current intelligent warehouse system is inefficient. At present, most of them are three-dimensional shelves and rail lanes or conveyor belt modes, and the transportation path is relatively fixed and the degree of freedom is not high, which leads to problems such as low efficiency.
一种基于拍卖算法的仓库任务分配方法，所述方法包括以下步骤：A warehouse task allocation method based on an auction algorithm, the method comprising the following steps:
步骤1：对任务，仓库环境以及机器人进行参数初始化；Step 1: Initialize the parameters of the task, warehouse environment and robot;
步骤2：通过拍卖算法进行任务分配；Step 2: Assign tasks through auction algorithm;
步骤3：派遣机器人执行任务。Step 3: Dispatch the robot to perform the task.
具体实施例二：Specific embodiment 2:
本申请实施例二与实施例一的区别仅在于：The difference between the second embodiment of the present application and the first embodiment is that:
所述步骤1具体为：The step 1 is specifically as follows:
所述参数初始化将数字化实际环境；将机器人以及物品进行建模，定义机器人集合为R＝{r1,r2,…,rn},机器人数量为n，每个机器人最多可以执行Ni个任务，且同时只能执行一个任务；机器人在运行过程中，保持匀速行驶，移动速度不会发生改变；定义任务集合为T＝{t1,t2,…,tm},任务数量为m，保证每个任务一定会分配给机器人，且只能分配给一个机器人，不能由多个机器人同时执行相同的任务。The parameter initialization will digitize the actual environment; the robots and objects are modeled, and the robot set is defined as R = {r1 , r2 , …, rn }, the number of robots is n, each robot can perform at most Ni tasks, and can only perform one task at the same time; during the operation, the robot maintains a constant speed and the moving speed does not change; the task set is defined as T = {t1 , t2 , …, tm }, the number of tasks is m, to ensure that each task will be assigned to a robot and can only be assigned to one robot, and multiple robots cannot perform the same task at the same time.
具体实施例三：Specific embodiment three:
本申请实施例三与实施例二的区别仅在于：The difference between the third embodiment of the present application and the second embodiment is that:
在拍卖算法前建立目标函数，进行任务分配时的目标函数为The objective function is established before the auction algorithm, and the objective function for task allocation is
其中，aij为收益函数，bij为成本函数。Among them,aij is the benefit function andbij is the cost function.
具体实施例四：Specific embodiment four:
本申请实施例四与实施例三的区别仅在于：The difference between the fourth embodiment of the present application and the third embodiment is that:
将收益最高作为首要选择标准，将从所有的任务矩阵中选择收益最大的，并将其分配给对应的机器人。Taking the highest benefit as the primary selection criterion, the task with the highest benefit will be selected from all the task matrices and assigned to the corresponding robot.
具体实施例五：Specific embodiment five:
本申请实施例五与实施例四的区别仅在于：The difference between the fifth embodiment of the present application and the fourth embodiment is that:
除了要求收益最高外，还要求具备足够的能源，需要设置能源限制，机器人需要具备足够的能源去完成此次任务；当能量小于完成任务所需要能量时，则需要放弃此次任务，对可以执行的任务进行拍卖。In addition to requiring the highest profit, sufficient energy is also required. Energy limits need to be set, and the robot needs to have enough energy to complete the task. When the energy is less than the energy required to complete the task, the task needs to be abandoned and the tasks that can be executed must be auctioned.
具体实施例六：Specific embodiment six:
本申请实施例六与实施例五的区别仅在于：The difference between the sixth embodiment of the present application and the fifth embodiment is that:
按照国际快递计重方式将货物按照重量从小到大分为A,B,C三种类型；机器人也根据自身所能运输的货物重量不同而分为α，β，γ三种类型；具体分类方式为：According to the international express weight measurement method, the goods are divided into three types: A, B, and C according to their weight from small to large; the robots are also divided into three types: α, β, and γ according to the weight of the goods they can transport; the specific classification method is:
机器人根据自己的位置计算自己与每个任务点之间的距离，将距离数据存入价值矩阵中；The robot calculates the distance between itself and each task point based on its own position and stores the distance data in the value matrix;
机器人通过数据计算与任务点之间的距离；采用欧式距离进行计算，计算机器人与任务点之间的真实距离，计算公式为：The robot calculates the distance between the robot and the task point through data; the Euclidean distance is used to calculate the actual distance between the robot and the task point. The calculation formula is:
拍卖开始，按照任务入库顺序对其逐个通过进行拍卖，由于机器人类型的不同，针对不同类型的任务某些机器人无法进行竞拍，机器人需要计算自身所得利益对可竞标物品进行出价；The auction begins, and tasks are auctioned one by one in the order they are put into the database. Due to the different types of robots, some robots cannot bid for different types of tasks. Robots need to calculate their own benefits and bid for the biddable items.
拍卖开始，机器人根据任务与机器人出发点距离以及与任务的匹配度开始出价。匹配度越高，距离越远，则报价越高，反之，则报价越低；计算得到最新的价值矩阵，将更新后的数据代替原本价值矩阵中的数据；出价公式为：At the beginning of the auction, the robot starts bidding based on the distance between the task and the robot's starting point and the matching degree with the task. The higher the matching degree and the longer the distance, the higher the bid, and vice versa. The latest value matrix is calculated and the updated data replaces the data in the original value matrix. The bidding formula is:
P＝k·dijP = k·dij
经过出价之后，拍卖方通过机器人序号和任务序号找到每个机器人对应的任务以及坐标位置；将机器人的位置移动到被分配任务的位置，并将该任务从任务列表中删除；After bidding, the auctioneer finds the task and coordinate position corresponding to each robot through the robot serial number and task serial number; moves the robot to the position of the assigned task and deletes the task from the task list;
当存在最高价相同的情况，器人重新出价进行拍卖；当最高价相同，则任务不被分配，重新返回任务列表中等待下一轮拍卖；机器人继续竞拍接下来的任务；When the highest prices are the same, the robot will bid again for the auction; when the highest prices are the same, the task will not be assigned and will be returned to the task list to wait for the next round of auction; the robot will continue to bid for the next task;
为了避免单个机器人分配得过多任务，在机器人成功分配得一个任务后，应适当增加其拍卖下一个任务的代价；针对机器人已经分配任务的数量m，从而取值不同的增长代价ε；随着机器人分配任务的增加，机器人所能执行任务数量越来越少，ε的增长越来越慢，所以采用对数函数，对数增长，曲率无限趋近于0，其中，a为自定义系数，来控制函数增长的幅度，b为函数基础数值；In order to avoid a single robot being assigned too many tasks, after a robot successfully assigns a task, the price of its next task auction should be appropriately increased; different growth costs ε are taken for the number of tasks m that the robot has assigned; as the number of tasks assigned to the robot increases, the number of tasks that the robot can perform decreases, and the growth of ε becomes slower and slower, so a logarithmic function is used, with logarithmic growth and a curvature that infinitely approaches 0, where a is a custom coefficient to control the amplitude of the function growth, and b is the basic value of the function;
ε＝logam+bε＝loga m+b
每轮拍卖结束时，机器人计算本轮收益，并准备进行下一轮拍卖；在一轮拍卖结束后，一部分任务已经被分配至机器人，机器人则计算每轮拍卖所得的收益，并累加起来，收益为每个任务与机器人的匹配度与距离的倒数以及重量之和；没有分配的任务则返回重新进行拍卖；At the end of each round of auction, the robot calculates the revenue of this round and prepares for the next round of auction. After a round of auction, some tasks have been assigned to the robot, and the robot calculates the revenue of each round of auction and adds them up. The revenue is the sum of the reciprocal of the matching degree and distance between each task and the robot and the weight. Tasks that have not been assigned are returned for re-auction.
拍卖结束，机器人执行自身任务；计算自身所处位置，并更新价值矩阵，准备进行下一轮拍卖。After the auction is over, the robot performs its own tasks; calculates its own position and updates the value matrix, preparing for the next round of auctions.
具体实施例七：Specific embodiment seven:
本申请实施例七与实施例六的区别仅在于：The difference between the seventh embodiment of the present application and the sixth embodiment is that:
针对不同的任务类型与机器人类型，有不同的匹配度，不同的组合将拥有不同的出价策略，对于不同的组合拥有不同的匹配度；对于机器人能量不足以执行对应任务的，则匹配度为0。There are different matching degrees for different task types and robot types. Different combinations will have different bidding strategies and different matching degrees for different combinations. If the robot's energy is insufficient to perform the corresponding task, the matching degree is 0.
具体实施例八：Specific embodiment eight:
本申请实施例八与实施例七的区别仅在于：The difference between the eighth embodiment of the present application and the seventh embodiment is that:
本发明提供一种基于拍卖算法的仓库任务分配系统，所述系统包括：The present invention provides a warehouse task allocation system based on an auction algorithm, the system comprising:
初始化模块，所述初始化模块对任务，仓库环境以及机器人进行参数初始化；An initialization module, which initializes the parameters of the task, warehouse environment and robot;
任务分配模块，所述任务分配模块通过拍卖算法进行任务分配；A task allocation module, wherein the task allocation module performs task allocation through an auction algorithm;
派遣模块，所述派遣模块派遣机器人执行任务。A dispatching module is used to dispatch robots to perform tasks.
具体实施例九：Specific embodiment nine:
本申请实施例九与实施例八的区别仅在于：The difference between the ninth embodiment of the present application and the eighth embodiment is that:
本发明提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行，以用于实现如一种基于拍卖算法的仓库任务分配方法The present invention provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement a warehouse task allocation method based on an auction algorithm.
一种基于改进拍卖算法的仓库任务分配方法，包括以下步骤：A warehouse task allocation method based on an improved auction algorithm comprises the following steps:
步骤1：初始化数据信息，根据机器人所能运输货物的不同将其分为α，β，γ三类。将任务类型也按照国际快递计重标准分为A,B,C三类。Step 1: Initialize the data information and classify the robots into three categories: α, β, and γ according to the different goods they can transport. The task types are also divided into three categories: A, B, and C according to the international express weight standard.
针对现场环境的初始化。具体包括：根据实际环境中，任务地点与机器人所在地点，建立平面坐标系，将坐标形式记为(xi，yi)，以矩阵的方式储存。Initialization of the on-site environment. Specifically, it includes: establishing a plane coordinate system based on the actual environment, the task location and the robot location, recording the coordinates in the form of (xi ,yi ), and storing them in the form of a matrix.
步骤2：机器人根据自己的位置计算自己与每个任务点之间的距离。将距离数据存入价值矩阵中。Step 2: The robot calculates the distance between itself and each task point based on its own position and stores the distance data in the value matrix.
机器人通过数据计算与任务点之间的距离。采用欧式距离进行计算，计算机器人与任务点之间的真实距离。The robot calculates the distance between the robot and the task point through data. The Euclidean distance is used to calculate the actual distance between the robot and the task point.
具体计算公式为：The specific calculation formula is:
步骤3：拍卖开始，按照任务入库顺序对其逐个通过进行拍卖，由于机器人类型的不同，针对不同类型的任务某些机器人无法进行竞拍，机器人需要计算自身所得利益对可竞标物品进行出价。Step 3: The auction begins. Tasks are auctioned one by one in the order they were put into the database. Due to the different types of robots, some robots cannot bid for different types of tasks. Robots need to calculate their own benefits and bid for the biddable items.
拍卖开始，机器人根据任务与机器人出发点距离以及与任务的匹配度开始出价。匹配度越高，距离越远，则报价越高，反之，则报价越低。计算得到最新的价值矩阵，将更新后的数据代替原本价值矩阵中的数据。When the auction begins, the robot starts bidding based on the distance between the task and the robot's starting point and the matching degree with the task. The higher the matching degree and the longer the distance, the higher the bid, and vice versa. The latest value matrix is calculated and the updated data replaces the data in the original value matrix.
出价公式为：The bidding formula is:
P＝k·dijP = k·dij
经过出价之后，拍卖方通过机器人序号和任务序号找到每个机器人对应的任务以及坐标位置。然后将机器人的位置移动到被分配任务的位置，并将该任务从任务列表中删除。After bidding, the auctioneer finds the task and coordinate position corresponding to each robot through the robot serial number and task serial number, then moves the robot to the position of the assigned task and deletes the task from the task list.
步骤4：如果存在最高价相同的情况，则返回步骤3，机器人重新出价进行拍卖。如果最高价相同，则任务不被分配，重新返回任务列表中等待下一轮拍卖。机器人继续竞拍接下来的任务。Step 4: If the highest prices are the same, return to step 3 and the robot will bid again for the auction. If the highest prices are the same, the task will not be assigned and will return to the task list to wait for the next round of auction. The robot will continue to bid for the next task.
步骤5：为了避免单个机器人分配得过多任务，在机器人成功分配得一个任务后，应适当增加其拍卖下一个任务的代价。以此来平衡任务分配的合理性。针对机器人已经分配任务的数量m，从而取值不同的增长代价ε。随着机器人分配任务的增加，机器人所能执行任务数量越来越少，ε的增长越来越慢，所以采用对数函数，对数增长，曲率无限趋近于0，其中，a为自定义系数，来控制函数增长的幅度，b为函数基础数值。Step 5: In order to avoid too many tasks being assigned to a single robot, after a robot successfully assigns a task, the price of the next task to be auctioned should be appropriately increased. This is to balance the rationality of task assignment. According to the number of tasks m that the robot has assigned, different growth costs ε are taken. As the number of tasks assigned to the robot increases, the number of tasks that the robot can perform decreases, and the growth of ε becomes slower and slower. Therefore, a logarithmic function is used, which grows logarithmically and the curvature approaches 0 infinitely. Among them, a is a custom coefficient to control the amplitude of the function growth, and b is the basic value of the function.
ε＝logam+bε＝loga m+b
步骤6：每轮拍卖结束时，机器人计算本轮收益，并准备进行下一轮拍卖。在一轮拍卖结束后，一部分任务已经被分配至机器人，机器人则计算每轮拍卖所得的收益，并累加起来，收益为每个任务与机器人的匹配度与距离的倒数以及重量之和。没有分配的任务则返回步骤3重新进行拍卖。Step 6: At the end of each round of auction, the robot calculates the revenue of this round and prepares for the next round of auction. After a round of auction, some tasks have been assigned to the robot. The robot calculates the revenue of each round of auction and adds them up. The revenue is the sum of the reciprocal of the matching degree and distance between each task and the robot and the weight. Tasks that have not been assigned return to step 3 and re-auction.
步骤7：拍卖结束，机器人执行自身任务。计算自身所处位置，并更新价值矩阵，准备进行下一轮拍卖。Step 7: The auction ends and the robot performs its own tasks, calculates its own position, and updates the value matrix to prepare for the next round of auctions.
具体实施例十：Specific embodiment ten:
本申请实施例十与实施例九的区别仅在于：The difference between the tenth embodiment of the present application and the ninth embodiment is that:
本发明提供一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，其所述处理器执行所述计算机程序时实现一种基于拍卖算法的仓库任务分配方法The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements a warehouse task allocation method based on an auction algorithm when executing the computer program.
算法流程图如图1所示。The algorithm flow chart is shown in Figure 1.
机器人数量为n个，分别为三种不同的类型以应对不同的任务型号，成员坐标记为(xi,yi)，任务坐标记为(xj,yj)。There are n robots, which are of three different types to cope with different task models. The member coordinates are marked as (xi , yi ) and the task coordinates are marked as (xj , yj ).
初始化数据。机器人数量记为n个，任务数量记为m个。Initialize the data. The number of robots is n, and the number of tasks is m.
构造任务矩阵与机器人矩阵。Construct the task matrix and robot matrix.
将任务类型按照重量分为A,B,C三种类型，机器人也分为α，β，γ三种不同的型号。对于不同的机器人种类和任务类型，具有不同的匹配度系数。A型任务对α，β，γ三种类型机器人的匹配度系数k取值矩阵为[1,0.6,0.3]，B型任务对α，β，γ三种类型机器人的匹配度系数k取值矩阵为[0,1,0.6]，C型任务对α，β，γ三种类型机器人的匹配度系数k取值矩阵为[0,0,1]。The task types are divided into three types according to weight: A, B, and C. The robots are also divided into three different models: α, β, and γ. Different robot types and task types have different matching coefficients. The matching coefficient k value matrix of type A tasks for α, β, and γ robots is [1, 0.6, 0.3], the matching coefficient k value matrix of type B tasks for α, β, and γ robots is [0, 1, 0.6], and the matching coefficient k value matrix of type C tasks for α, β, and γ robots is [0, 0, 1].
计算每个任务点与每辆车之间的欧式距离，价值矩阵为Calculate the Euclidean distance between each task point and each vehicle, and the value matrix is
计算每辆车与每个任务之间的匹配度，将匹配度系数k加入价值矩阵Calculate the matching degree between each vehicle and each task, and add the matching coefficient k to the value matrix
valuenew≡k·valueoldvaluenew ≡ k·valueold
将已经拍卖的任务从任务列表中删除。并将小车位置更新到分配的任务所在的地点。Delete the tasks that have been auctioned from the task list and update the location of the trolley to the location of the assigned task.
利用matlab进行仿真验证。Matlab is used for simulation verification.
机器人数量设置为3，分别属于不同的类型，任务数量设置为20，也分为三种类型。机器人参数见表1，任务参数见表2。The number of robots is set to 3, each belonging to a different type, and the number of tasks is set to 20, also divided into three types. The robot parameters are shown in Table 1, and the task parameters are shown in Table 2.
仿真结果Simulation Results
系数矩阵如表3所示，距离矩阵如表4所示，价值矩阵如表5所示。The coefficient matrix is shown in Table 3, the distance matrix is shown in Table 4, and the value matrix is shown in Table 5.
表格3Table 3
表格4Table 4
表格5Table 5
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或N个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本发明的描述中，“N个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更N个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本发明的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本发明的实施例所属技术领域的技术人员所理解。在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或N个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。应当理解，本发明的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，N个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。In the description of this specification, the description of reference terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction. In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. Any process or method description in the flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code including one or more executable instructions for implementing the steps of a custom logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, in which functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by a person skilled in the art of the art to which the embodiments of the present invention belong. The logic and/or steps represented in the flowchart or otherwise described herein, for example, may be considered as a sequenced list of executable instructions for implementing a logic function, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program may be printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or processing in other suitable ways as necessary, and then stored in a computer memory. It should be understood that various parts of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiment, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
以上所述仅是一种基于拍卖算法的仓库任务分配方法的优选实施方式，一种基于拍卖算法的仓库任务分配方法的保护范围并不仅局限于上述实施例，凡属于该思路下的技术方案均属于本发明的保护范围。应当指出，对于本领域的技术人员来说，在不脱离本发明原理前提下的若干改进和变化，这些改进和变化也应视为本发明的保护范围。The above is only a preferred implementation of a warehouse task allocation method based on an auction algorithm. The protection scope of a warehouse task allocation method based on an auction algorithm is not limited to the above embodiment. All technical solutions under this idea belong to the protection scope of the present invention. It should be pointed out that for those skilled in the art, several improvements and changes without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.
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| CN202310813733.6ACN116596447B (en) | 2023-07-05 | 2023-07-05 | A warehouse task allocation method based on auction algorithm |
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| CN202310813733.6ACN116596447B (en) | 2023-07-05 | 2023-07-05 | A warehouse task allocation method based on auction algorithm |
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| CN116596447Atrue CN116596447A (en) | 2023-08-15 |
| CN116596447B CN116596447B (en) | 2023-10-03 |
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| CN202310813733.6AActiveCN116596447B (en) | 2023-07-05 | 2023-07-05 | A warehouse task allocation method based on auction algorithm |
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| CN116911573A (en)* | 2023-09-07 | 2023-10-20 | 南京邮电大学 | A multi-task collaboration method for supply chain logistics providers oriented to intelligent manufacturing |
| CN119378940A (en)* | 2024-12-27 | 2025-01-28 | 浙江爱达科技有限公司 | A multi-robot multi-task scheduling method based on dynamic auction algorithm and its application |
| CN120471252A (en)* | 2025-07-11 | 2025-08-12 | 中国建筑第四工程局有限公司 | Optimization method of construction robot operation path |
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| CN109426560A (en)* | 2017-08-28 | 2019-03-05 | 杭州海康机器人技术有限公司 | Method for allocating tasks, device and computer readable storage medium |
| CN109919431A (en)* | 2019-01-28 | 2019-06-21 | 重庆邮电大学 | A task assignment method for heterogeneous multi-robots based on auction algorithm |
| CN111222764A (en)* | 2019-12-27 | 2020-06-02 | 西安羚控电子科技有限公司 | A UAV swarm task assignment algorithm based on distributed collaborative auction |
| CN112785132A (en)* | 2021-01-14 | 2021-05-11 | 北京理工大学 | Task allocation method for multi-robot mobile shelf for intelligent warehouse |
| CN115423243A (en)* | 2022-07-18 | 2022-12-02 | 北京邮电大学 | Task allocation method, device, equipment and storage medium |
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| US5315222A (en)* | 1992-07-03 | 1994-05-24 | Daihen Corporation | Control apparatus for industrial robot |
| CN109426560A (en)* | 2017-08-28 | 2019-03-05 | 杭州海康机器人技术有限公司 | Method for allocating tasks, device and computer readable storage medium |
| CN109919431A (en)* | 2019-01-28 | 2019-06-21 | 重庆邮电大学 | A task assignment method for heterogeneous multi-robots based on auction algorithm |
| CN111222764A (en)* | 2019-12-27 | 2020-06-02 | 西安羚控电子科技有限公司 | A UAV swarm task assignment algorithm based on distributed collaborative auction |
| CN112785132A (en)* | 2021-01-14 | 2021-05-11 | 北京理工大学 | Task allocation method for multi-robot mobile shelf for intelligent warehouse |
| CN115423243A (en)* | 2022-07-18 | 2022-12-02 | 北京邮电大学 | Task allocation method, device, equipment and storage medium |
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| CN116911573A (en)* | 2023-09-07 | 2023-10-20 | 南京邮电大学 | A multi-task collaboration method for supply chain logistics providers oriented to intelligent manufacturing |
| CN116911573B (en)* | 2023-09-07 | 2023-11-14 | 南京邮电大学 | A multi-task collaboration method for supply chain logistics providers oriented to intelligent manufacturing |
| CN119378940A (en)* | 2024-12-27 | 2025-01-28 | 浙江爱达科技有限公司 | A multi-robot multi-task scheduling method based on dynamic auction algorithm and its application |
| CN119378940B (en)* | 2024-12-27 | 2025-05-27 | 浙江爱达科技有限公司 | A multi-robot multi-task scheduling method based on dynamic auction algorithm and its application |
| CN120471252A (en)* | 2025-07-11 | 2025-08-12 | 中国建筑第四工程局有限公司 | Optimization method of construction robot operation path |
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