Industrial logistics unmanned warehouse AGV scheduling method based on energy consumption management

Abstract

The invention provides an industrial logistics unmanned warehouse AGV scheduling method based on energy consumption management, and the method comprises the steps: collecting the map information of an unmanned warehouse, the parameters of an AGV vehicle and a logistics information task in real time, and constructing an AGV vehicle energy consumption evaluation model; s3, judging whether the order task can be executed or not, if the order task cannot be executed, not making a response, otherwise, continuing to judge whether an idle AGV exists or not, if the idle AGV does not exist, temporarily converting the order task into a waiting state, and if the idle AGV does not exist, executing S3; the number of idle AGVs is judged, if the number of the idle AGVs is single, route energy consumption evaluation is carried out in combination with an AGV energy consumption evaluation model, multi-AGV collaborative optimal scheme planning is carried out, and an AGV scheduling scheme is output; and if there are multiple idle AGVs, route energy consumption evaluation of the multiple idle AGVs is calculated, an AGV scheduling scheme is output, and an order execution task is issued according to the AGV scheduling scheme. The method has important practical significance and application value in development and construction of industrial logistics smart factories.

Description

Technical Field

[0001] The present invention relates to the technical field of planning and scheduling, and in particular to a method for scheduling an unmanned warehouse AGV in industrial logistics based on energy consumption management. Background Art

[0002] With the advent of the Industrial 4.0 era, the automation and intelligence of industrial logistics are constantly improving. In industrial production, the efficient operation of logistics links plays a vital role in reducing costs, improving production efficiency and quality. As an emerging concept in industrial logistics, unmanned warehouses aim to achieve the storage, handling and management of goods through automated equipment and intelligent systems, reduce manual intervention, and improve the accuracy and timeliness of logistics operations.

[0003] AGV (Automated Guided Vehicle) has been widely used in industrial logistics due to its flexibility, efficiency and high degree of automation. However, with the expansion of unmanned warehouses and the increase in business complexity, how to effectively schedule AGV has become a key issue. Traditional AGV scheduling methods often focus on task allocation and path planning, but rarely consider energy consumption factors. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method for AGV scheduling in an industrial logistics unmanned warehouse based on energy consumption management, so as to solve the technical problem that the traditional AGV scheduling method in the prior art often focuses on task allocation and path planning, but pays less attention to energy consumption factors.

[0005] The present invention provides a method for dispatching AGVs in an industrial logistics unmanned warehouse based on energy consumption management, comprising:

[0006] S1. Collect the map information of the unmanned warehouse, the parameters of the AGV vehicles and the logistics information tasks in real time, and build the AGV vehicle energy consumption evaluation model;

[0007] S2, determine whether the order task is executable, if not, do not react, otherwise continue to determine whether there is an idle AGV vehicle, if there is no idle AGV vehicle, temporarily turn the order task to a waiting state, otherwise execute S3;

[0008] S3. Determine the number of idle AGV vehicles. If there is only one idle AGV vehicle, perform route energy consumption evaluation in combination with the AGV vehicle energy consumption evaluation model, perform multi-AGV collaborative optimal solution planning, and output the AGV scheduling solution;

[0009] S4. If there are multiple idle AGV vehicles, the route energy consumption evaluations of the multiple idle AGV vehicles are calculated respectively, and an AGV scheduling plan is output, and an execution order task is issued according to the AGV scheduling plan.

[0010] Optionally, the map information of the unmanned warehouse, the performance parameters of the AGV and the logistics information tasks include:

[0011] The map information of the unmanned warehouse includes at least the shelf location, obstacle location, and charging station location in the AGV driving path node;

[0012] The parameters of the AGV vehicle include at least the AGV's power information, location information, energy required when idle, energy consumed when moving when empty and energy consumed when loaded, driving speed and load information;

[0013] The logistics information task at least includes the weight of the goods and the shelf location information of the goods.

[0014] Optionally, the constructing of the AGV vehicle energy consumption evaluation model includes:

[0015] E consume =E unload_move +E load_move +E idle

[0016] Among them, E consume is the total energy consumed by the AGV vehicle, E unload_move is the energy consumed by the AGV when it moves empty, E load_move The energy consumed by the AGV when it moves with a load, E idle The energy required by the AGV when it is idle and based on the relationship between energy and the duration of energy consumption, the total energy consumed by the AGV vehicle is expressed as:

[0017] E consume =k unload_move ×t unload +k load_move ×t load +E idle ×t idle

[0018] Among them, K unload_move Indicates the energy consumption coefficient of AGV when it moves empty, t unload Indicates the energy consumption time when the AGV moves empty, K load_move is the energy consumption coefficient of AGV when loaded, t load Indicates the energy consumption time when the AGV moves empty, K idle is the energy consumption coefficient of AGV when it is idle, K idle It is the energy consumption time when the AGV moves empty.

[0019] Optionally, the step of performing route energy consumption evaluation in combination with the AGV vehicle energy consumption evaluation model includes:

[0020] The energy consumption evaluation of the route is expressed as:

[0021] F(n)=G(n)+H(n)+E consume (n)

[0022] Among them, F(n) is the total estimated energy cost of the path through node n, and G(n) is the time cost from the starting node to node n, expressed as:

[0023] G(n)=d / v

[0024] d=|x0-x1|+|y0-y1|

[0025] Among them, (x0, y0) is the starting node coordinate of the AGV vehicle, (x1, y1) is the current node coordinate of the AGV vehicle, and v is the actual speed of the AGV;

[0026] H(n) is the heuristic estimated cost from node n to the target node, expressed as:

[0027] H(n)=|x1-x2|+|y1-y2|

[0028] Among them, (x1, y1) is the current node coordinate, (x2, y2) is the target node coordinate, E consume (n) is the energy cost of AGV moving to node n.

[0029] Optionally, the multi-AGV collaborative optimal solution planning and output of the AGV scheduling solution include:

[0030] It is determined again whether there is an AGV vehicle that is about to complete the task within the set time. If not, a single idle AGV vehicle is directly designated to execute the AGV vehicle scheduling plan. Otherwise, the route energy consumption evaluation is repeatedly calculated for the interval time of the AGV vehicles that are about to complete the task within the set time, and the AGV vehicle with the lowest energy consumption evaluation among the AGVs that are about to complete the task within the set time and the AGV vehicle with the lowest energy consumption evaluation among the single idle AGV vehicles are selected, and they are designated to execute the AGV vehicle scheduling plan.

[0031] Optionally, respectively calculating route energy consumption evaluations of a plurality of idle AGV vehicles and outputting an AGV scheduling plan includes:

[0032] The route energy consumption evaluations of multiple idle AGV vehicles are calculated respectively, and the AGV vehicle with the lowest route energy consumption evaluation is selected and designated to execute the AGV vehicle scheduling plan.

[0033] Optionally, on the contrary, the route energy consumption evaluation is repeatedly calculated for the interval time of AGV vehicles that are about to complete the task within the set time, including:

[0034] The set time is 10S, and the route energy consumption evaluation is repeatedly calculated every 3S for the AGV vehicle that is about to complete the task within 10S.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] 1. Constructing a more accurate AGV vehicle energy consumption assessment model, combined with vehicle routes and vehicle loads, can significantly reduce vehicle energy consumption, which has important practical significance and application value for the development and construction of industrial logistics smart factories.

[0037] 2. The proposed scheduling algorithm can reduce the idle time and conflicts of AGVs, enabling them to complete logistics tasks more efficiently. It can also cope with the complex and changing environment and task requirements in industrial logistics unmanned warehouses. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other implementation cases obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The functional units with the same labels in the examples of the present invention have the same and similar structures and functions.

[0042] See also Figure 1 The present invention provides a method for dispatching AGVs in an industrial logistics unmanned warehouse based on energy consumption management, comprising:

[0043] S1. Collect the map information of the unmanned warehouse, the parameters of the AGV vehicles and the logistics information tasks in real time, and build the AGV vehicle energy consumption evaluation model;

[0044] S2, determine whether the order task is executable, if not, do not react, otherwise continue to determine whether there is an idle AGV vehicle, if there is no idle AGV vehicle, temporarily turn the order task to a waiting state, otherwise execute S3;

[0045] S3. Determine the number of idle AGV vehicles. If there is only one idle AGV vehicle, perform route energy consumption evaluation in combination with the AGV vehicle energy consumption evaluation model, perform multi-AGV collaborative optimal solution planning, and output the AGV scheduling solution;

[0046] S4. If there are multiple idle AGV vehicles, the route energy consumption evaluations of the multiple idle AGV vehicles are calculated respectively, and an AGV scheduling plan is output, and an execution order task is issued according to the AGV scheduling plan.

[0047] In this embodiment, S1, real-time collection of map information of the unmanned warehouse, parameters of the AGV vehicle and logistics information tasks, and construction of an AGV vehicle energy consumption evaluation model;

[0048] The map information of the unmanned warehouse includes at least the shelf location (x H ,y H ), obstacle position (x H ,y H )、Charging pile location (x E ,y E );

[0049] The parameters of the AGV vehicle include at least the AGV's power information, location information, energy required when idle, energy consumed when moving when empty and energy consumed when loaded, driving speed and load information;

[0050] The logistics information task at least includes the weight of the goods and the shelf location information of the goods (x M ,y M ).

[0051] And build an AGV vehicle energy consumption evaluation model, including:

[0052] E consume =E unload_move +E load_move +E idle

[0053] Among them, E consume is the total energy consumed by the AGV vehicle, E unload_move is the energy consumed by the AGV when it moves empty, E load_move The energy consumed by the AGV when it moves with a load, E idle The energy required by the AGV when it is idle and based on the relationship between energy and the duration of energy consumption, the total energy consumed by the AGV vehicle is expressed as:

[0054] E consume =k unload_move ×t unload +k load_move ×t load +E idle ×t idle

[0055] Among them, K unload_move Indicates the energy consumption coefficient of AGV when it moves empty, t unload Indicates the energy consumption time when the AGV moves empty, K load_move is the energy consumption coefficient of AGV when loaded, t load Indicates the energy consumption time when the AGV moves empty, K idle is the energy consumption coefficient of AGV when it is idle, K idle It is the energy consumption time when the AGV moves empty.

[0056] The total energy consumed by the AGV vehicle calculated through the above steps can achieve a more accurate energy consumption assessment.

[0057] S2, determine whether the order task is executable. If it is not executable, do not react. Otherwise, continue to determine whether there is an idle AGV vehicle. If there is no idle AGV vehicle, temporarily turn the order task to a waiting state. Otherwise, execute S3.

[0058] According to the order task, the most suitable vehicle for executing the task is assigned. Specifically, first determine whether the order is empty (i.e. whether the order can be executed), and then determine whether there is an idle vehicle. If there is no idle vehicle, it will switch to the waiting state (wait for 5 seconds) and re-determine. If there is an idle vehicle, proceed to the next step.

[0059] S3. Determine the number of idle AGV vehicles. If there is only one idle AGV vehicle, perform route energy consumption evaluation in combination with the AGV vehicle energy consumption evaluation model, perform multi-AGV collaborative optimal plan planning, and output the AGV scheduling plan.

[0060] When the aforementioned AGV vehicle energy consumption evaluation model is introduced into the A* algorithm, the route energy consumption evaluation is expressed as:

[0061] F(n)=G(n)+H(n)+E consume (n)

[0062] Among them, F(n) is the total estimated energy cost of the path through node n, and G(n) is the time cost from the starting node to node n, expressed as:

[0063] G(n)=d / v

[0064] d=|x0-x1|+|y0-y1|

[0065] Among them, (x0, y0) is the starting node coordinate of the AGV vehicle, (x1, y1) is the current node coordinate of the AGV vehicle, and v is the actual speed of the AGV;

[0066] H(n) is the heuristic estimated cost from node n to the target node, expressed as:

[0067] H(n)=|x1-x2|+|y1-y2|

[0068] Among them, (x1, y1) is the current node coordinate, (x2, y2) is the target node coordinate, E consume (n) is the energy consumption cost of AGV moving to node n.

[0069] In addition, the total energy consumed by the AGV vehicle is E consume (n) is the energy cost at node n. For example, in a robot moving scenario, if the robot moves on different terrains, the energy consumption is different. Moving on flat ground has lower energy consumption, while climbing a slope has higher energy consumption. Suppose at a certain node n, the robot needs to climb a slope, and the energy consumption of climbing is E consume (n), the energy consumption is obtained by obtaining the energy consumption coefficient and the operating time.

[0070] Therefore, first determine the number of idle AGV vehicles. If there is a single idle AGV vehicle, perform route energy consumption evaluation in combination with the AGV vehicle energy consumption evaluation model, and determine again whether there is an AGV vehicle that is about to complete the task within the set time. If not, directly specify a single idle AGV vehicle to execute the AGV vehicle scheduling plan. Otherwise, repeat the route energy consumption evaluation for the interval time of the AGV vehicles that are about to complete the task within the set time, and select the AGV vehicle with the lowest energy consumption evaluation among the AGVs that are about to complete the task within the set time and the AGV vehicle with the lowest energy consumption evaluation among the single idle AGV vehicles, and specify them to execute the AGV vehicle scheduling plan. The set time can be preferably 10S, and the route energy consumption evaluation is repeated every 3S for the AGV vehicles that are about to complete the task within 10S.

[0071] S4. If there are multiple idle AGV vehicles, the route energy consumption evaluations of the multiple idle AGV vehicles are calculated respectively, and an AGV scheduling plan is output, and an execution order task is issued according to the AGV scheduling plan.

[0072] If the route energy consumption of multiple idle AGV vehicles is evaluated, the AGV vehicle with the lowest route energy consumption evaluation is selected and designated to execute the AGV vehicle scheduling plan.

[0073] Among them, the scheduling method for AGV also includes dynamic adjustment. Task preemption requires calculating whether the vehicle at the other task end point closest to the current task point can preempt the current task and calculating the energy consumption E required by other vehicles. seize is smaller, the current car should allow the task to be preempted, and the task should be transferred to other cars. That is:

[0074] E consume <E seize

[0075] In addition, the power of the AGV is monitored at any time to monitor the remaining power:

[0076] Remaining power percentage = (current power / total power) × 100%

[0077] When the remaining power percentage is lower than the preset charging threshold (for example, 20%), the charging demand is triggered, that is, no new tasks are issued to it. If there are tasks, charging will be started after completing the tasks, otherwise charging will be started immediately.

[0078] At the same time, set the charging priority of AGV

[0079] Priority = (urgentness of remaining tasks / remaining battery) * weight

[0080] The charging priority of the AGV is determined by comprehensively considering the urgency of the remaining tasks and the percentage of remaining power, and assigning different weights. The urgency of the remaining tasks is obtained by issuing tasks by Wes, and the value is (1-100). The remaining power is obtained through the real-time heartbeat between the AGV and RCS, and the value is (1-100). The weight is inversely proportional to the result of the urgency of the remaining tasks / the remaining power.

[0081] The present invention constructs a more accurate AGV vehicle energy consumption assessment model, which can significantly reduce the vehicle's energy consumption in combination with the vehicle route and vehicle load, and has important practical significance and application value for the development and construction of industrial logistics smart factories.

[0082] It should be noted that, in this article, relational terms such as "first" and "second" 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. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0083] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for dispatching AGVs in unmanned warehouses in industrial logistics based on energy consumption management. It is characterized in that include: S1. Collect the map information of the unmanned warehouse, the parameters of the AGV vehicles and the logistics information tasks in real time, and build the AGV vehicle energy consumption evaluation model; S2, determine whether the order task is executable, if not, do not react, otherwise continue to determine whether there is an idle AGV vehicle, if there is no idle AGV vehicle, temporarily turn the order task to a waiting state, otherwise execute S3; S3. Determine the number of idle AGV vehicles. If there is only one idle AGV vehicle, perform route energy consumption evaluation in combination with the AGV vehicle energy consumption evaluation model, perform multi-AGV collaborative optimal solution planning, and output the AGV scheduling solution; S4. If there are multiple idle AGV vehicles, the route energy consumption evaluations of the multiple idle AGV vehicles are calculated respectively, and an AGV scheduling plan is output, and an execution order task is issued according to the AGV scheduling plan.

2. The method for dispatching AGVs in an industrial logistics unmanned warehouse based on energy consumption management as claimed in claim 1, It is characterized in that The map information of the unmanned warehouse, the performance parameters of the AGV and the logistics information tasks include: The map information of the unmanned warehouse includes at least the shelf location, obstacle location, and charging pile location in the AGV driving path node; The parameters of the AGV vehicle include at least the AGV's power information, location information, energy required when idle, energy consumed when moving when empty and energy consumed when loaded, driving speed and load information; The logistics information task at least includes the weight of the goods and the shelf location information of the goods.

3. The method for dispatching AGVs in an industrial logistics unmanned warehouse based on energy consumption management as claimed in claim 2, It is characterized in that The construction of the AGV vehicle energy consumption evaluation model includes: AND consume =And unload_move +E load_move +E idle Among them, E consume is the total energy consumed by the AGV vehicle, E unload_move is the energy consumed by the AGV when it moves empty, E load_move The energy consumed by the AGV when it moves with a load, E idle The energy required by the AGV when it is idle and based on the relationship between energy and the duration of energy consumption, the total energy consumed by the AGV vehicle is expressed as: E consume =k unload_move ×t unload +k load_move ×t load +E idle ×t idle Among them, K unload_move Indicates the energy consumption coefficient of AGV when it moves empty, t unload Indicates the energy consumption time when the AGV moves empty, K load_move is the energy consumption coefficient of AGV when loaded, t load Indicates the energy consumption time when the AGV moves empty, K idle is the energy consumption coefficient of AGV when it is idle, K idle It is the energy consumption time when the AGV moves empty.

4. The method for dispatching AGVs in an industrial logistics unmanned warehouse based on energy consumption management as claimed in claim 3, It is characterized in that The energy consumption evaluation of the route is performed in combination with the AGV vehicle energy consumption evaluation model, including: The energy consumption evaluation of the route is expressed as: F(n)=G(n)+H(n)+E consume (n) Among them, F(n) is the total estimated energy cost of the path through node n, and G(n) is the time cost from the starting node to node n, expressed as: G(n)=d / v d=|x 0 -x 1 |+|y 0 -y 1 | Among them, (x 0 ,y 0 ) is the starting node coordinate of the AGV vehicle, (x 1 ,y 1 ) is the current node coordinate of the AGV vehicle, and v is the actual speed of the AGV; H(n) is the heuristic estimated cost from node n to the target node, expressed as: H(n)=|x 1 -x 2 |+|y 1 -y 2 | Among them, (x 1 ,y 1 ) is the current node coordinate, (x 2 ,y 2 ) is the target node coordinate, E consume (n) is the energy cost of AGV moving to node n.

5. The method for dispatching AGVs in an industrial logistics unmanned warehouse based on energy consumption management as claimed in claim 4, It is characterized in that The multi-AGV collaborative optimal solution planning is performed and the AGV scheduling solution is output, including: It is determined again whether there is an AGV vehicle that is about to complete the task within the set time. If not, a single idle AGV vehicle is directly designated to execute the AGV vehicle scheduling plan. Otherwise, the route energy consumption evaluation is repeatedly calculated for the interval time of the AGV vehicles that are about to complete the task within the set time, and the AGV vehicle with the lowest energy consumption evaluation among the AGVs that are about to complete the task within the set time and the AGV vehicle with the lowest energy consumption evaluation among the single idle AGV vehicles are selected, and they are designated to execute the AGV vehicle scheduling plan.

6. The method for dispatching AGVs in an industrial logistics unmanned warehouse based on energy consumption management as claimed in claim 5, It is characterized in that The step of calculating the route energy consumption evaluation of multiple idle AGV vehicles and outputting the AGV scheduling plan includes: The route energy consumption evaluations of multiple idle AGV vehicles are calculated respectively, and the AGV vehicle with the lowest route energy consumption evaluation is selected and designated to execute the AGV vehicle scheduling plan.

7. The method for dispatching AGVs in an industrial logistics unmanned warehouse based on energy consumption management as claimed in claim 5, It is characterized in that Otherwise, the route energy consumption evaluation is repeatedly calculated for the interval time of the AGV vehicles that are about to complete the task within the set time, including: The set time is 10S, and the route energy consumption evaluation is repeatedly calculated every 3S for the AGV vehicle that is about to complete the task within 10S.

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