Intelligent forklift path determination method and control system

Abstract

The invention provides an intelligent forklift path determination method and a control system. Determining a target intelligent forklift and a plurality of candidate paths through the target task; obtaining respective dynamic evaluation values of the plurality of candidate paths through respective task execution durations, traffic jam cost values and space conflict cost values of the plurality of candidate paths, load cost values of the target intelligent forklifts and priority cost values of the target tasks; and determining a target path through the respective dynamic evaluation values of the plurality of candidate paths. The scheduling strategy and the path planning are quickly adjusted by sensing the change of the working load and the traffic state in real time, so that the system adapts to the complicated and changeable working environment; through load balance scheduling, trafficability of a driving space and optimal path planning, the empty driving time, waiting time and invalid driving of the intelligent forklift are reduced, and the overall transportation efficiency is improved; automatic task allocation, automatic path planning and dynamic adjustment are realized, and manual intervention is reduced.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and more specifically, to a method and control system for determining the path of an intelligent forklift. Background Technology

[0002] Intelligent Guided Vehicles (IGVs) are unmanned transport devices used in automated terminals and smart factories. They achieve autonomous navigation and obstacle avoidance through technologies such as the BeiDou Navigation Satellite System, LiDAR, and visual SLAM. They possess high flexibility, intelligent navigation, and superior performance. IGVs typically follow navigation information provided by QR code strips or matrix QR codes.

[0003] In large automobile manufacturing enterprises, the production workshops and warehouse environments are complex, and forklift operations are frequent. Manually operated forklifts are inefficient, often resulting in congestion and waiting times, leading to delayed material delivery and disrupting the normal operation of the production line. Therefore, large automobile manufacturers are increasingly adopting IGV-based intelligent forklift systems, which can significantly improve operational efficiency.

[0004] However, intelligent forklifts are subject to many limitations due to their large size (such as space, load, and energy consumption). If they are still based on manual operation or the static scheduling rules of ordinary IGVs, it is difficult to cope with complex and dynamic changes in production needs.

[0005] Therefore, this application provides a method for determining the path of an intelligent forklift to solve the above-mentioned technical problems. Summary of the Invention

[0006] The purpose of this application is to provide a method and control system for determining the path of an intelligent forklift, which can solve at least one of the technical problems mentioned above. The specific solution is as follows: According to a specific embodiment of this application, in a first aspect, this application provides a method for determining the path of an intelligent forklift, comprising: Obtain the target task; Based on the target task, a target intelligent forklift and multiple candidate paths are determined, along with the full-process road condition information for each of the multiple candidate paths; The task execution time of each of the multiple candidate paths is determined; the traffic congestion cost of each candidate path is obtained based on the full-process road condition information of each candidate path; the spatial conflict cost of each candidate path is obtained based on the full-process road condition information of each candidate path and the status information of the target intelligent forklift; the load cost of the target intelligent forklift is obtained based on the status information of the target intelligent forklift; and the priority cost of the target task is determined. The dynamic evaluation values ​​of the multiple candidate paths are obtained based on the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost. The target path is determined based on the dynamic evaluation values ​​of each of the multiple candidate paths.

[0007] Optionally, the dynamic evaluation value of each of the multiple candidate paths is obtained based on the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost, including the following formula: ； in, Cost_i Indicates the first i The dynamic evaluation value of each candidate path, T_i Indicates the first i The task execution time for each candidate path; C_i Indicates the first i The traffic congestion cost of each candidate route; Q_i Indicates the first i The spatial conflict value of each candidate path; W This indicates the load-carrying capacity of the target intelligent forklift; P This represents the priority cost of the target task, and the priority cost of the target task is inversely proportional to the priority of the target task. P Less than or equal to 1; w1, w2, w3, w4 and w5 These represent the dynamic weighting coefficients for the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost, respectively.

[0008] Optionally, the dynamic weighting coefficient is obtained by looking up the number of congestion markers in the full-process traffic information.

[0009] Optionally, obtaining the spatial conflict cost of each candidate path based on the full-process road condition information of each of the multiple candidate paths and the state information of the target intelligent forklift includes: Based on the driving space information in the full road condition information of any candidate path and the external space information of the target intelligent forklift, multiple spatial conflict locations and the spatial conflict level of each of the multiple spatial conflict locations are obtained. The number of spatial conflicts at each spatial conflict level is obtained based on the spatial conflict level of each of the multiple spatial conflict locations. The percentage of spatial conflict for each of the various spatial conflict levels is obtained based on the number of spatial conflicts for each level. The spatial conflict level cost is obtained based on the spatial conflict percentage of each spatial conflict level and the preset spatial conflict weight value of the corresponding spatial conflict level. The spatial conflict cost of any candidate path is obtained based on the spatial conflict cost of each of the various spatial conflict levels.

[0010] Optionally, obtaining the load value of the target intelligent forklift based on its state information includes: Obtain the total number of tasks, total travel distance, and total power consumption of the target intelligent forklift within the preset statistical period to date; The load value of the target intelligent forklift is obtained based on the total number of tasks and the preset quantity weight value, the total travel distance and the preset distance weight value, and the total power consumption and the preset power consumption weight value.

[0011] Optionally, determining the task execution time of each of the plurality of candidate paths includes: The number of each of the various path feature tags in the corresponding candidate path is obtained based on the full road condition information of any candidate path. The total characteristic time of the corresponding candidate path is obtained based on the number of each of the various path feature markers in any candidate path and the preset average travel time of the corresponding path feature markers; Based on the various path feature markers in the full-process road condition information, any candidate path is divided into multiple straight road segments; The total travel time of the corresponding candidate path is obtained based on the travel length of each straight segment in any candidate path and the preset straight travel speed value. Calculate the sum of the total characteristic duration and the total travel time of any candidate path to obtain the task execution time of the corresponding candidate path.

[0012] Optionally, obtaining the traffic congestion cost of each candidate path based on its full-process traffic information includes: Based on the various path feature markers in the full-process traffic information of any candidate path, the candidate path is divided into multiple straight road segments. Obtain the number of intelligent forklifts for each of the multiple straight road segments; The vehicle density value of the corresponding straight road segment is obtained based on the number of intelligent forklifts on each of the multiple straight road segments and the length of the corresponding straight road segment. The congestion cost of the corresponding straight road segment is obtained based on the vehicle density value of each of the multiple straight road segments and the preset road segment weight value of the corresponding straight road segment. The traffic congestion cost of any candidate path is obtained based on the congestion cost of each of the multiple straight road segments.

[0013] Optionally, the various path feature markers include: turn markers, intersection markers, and congestion markers.

[0014] Optionally, the plurality of candidate paths includes: the candidate path with the shortest distance, the candidate path with the fewest congestion markers, and the candidate path with the highest security.

[0015] According to a specific embodiment of this application, in a second aspect, this application provides a control system, including: A group control device, which stores a computer program and is configured to execute the computer program to implement the method described above; The intelligent forklift is configured to travel along a target path determined by the method.

[0016] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects: This application provides a method and control system for determining intelligent forklift paths. The method determines a target intelligent forklift and multiple candidate paths based on the target task. It obtains dynamic evaluation values ​​for each candidate path based on their respective task execution time, traffic congestion cost, spatial conflict cost, target intelligent forklift load cost, and target task priority cost. The target path is then determined using these dynamic evaluation values. By real-time sensing of changes in workload and traffic conditions, the method rapidly adjusts scheduling strategies and path planning to adapt to complex and changing operating environments. Through load balancing scheduling, accessibility of driving space, and optimal path planning, it reduces the intelligent forklift's empty driving time, waiting time, and ineffective driving, improving overall transportation efficiency. It achieves automatic task allocation, automatic path planning, and dynamic adjustment, reducing manual intervention. Multi-source data fusion technology ensures the accuracy of the sensed data, and the closed-loop control system guarantees the effective execution of instructions, improving the system's reliability and stability. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a method for determining the path of an intelligent forklift according to an embodiment of this application is shown; Figure 2 A schematic block diagram of a control system according to an embodiment of this application is shown. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0022] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0023] It should also be noted that the terms "comprising," "including," or any variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0024] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0025] The following is in conjunction with the appendix Figure 1 Detailed description of optional embodiments of this application.

[0026] The embodiments provided in this application, namely a method for determining the path of an intelligent forklift, include: Step S101: Obtain the target task.

[0027] Step S102: Based on the target task, determine the target intelligent forklift and multiple candidate paths, as well as the full-process road condition information of each of the multiple candidate paths.

[0028] Intelligent forklifts can be either Automated Guided Vehicles (AGVs) or Ingeniously Guided Vehicles (IGVs).

[0029] Optionally, multiple candidate paths can be preprocessed to filter out invalid paths containing permanent obstacles or extremely narrow passages, thereby improving the efficiency of subsequent evaluation.

[0030] In some specific embodiments, the plurality of candidate paths includes: the candidate path with the shortest distance, the candidate path with the fewest congestion markers, and the candidate path with the highest security.

[0031] In traffic electronic maps, Dijkstra's algorithm or A* algorithm can be used to obtain the shortest candidate path; however, the shortest candidate path may pass through congested sections. Based on the shortest candidate path, through deviation and correction processes, the 2nd, 3rd, ..., Nth candidate paths are successively found. These candidate paths are topologically distinct, providing a basis for dynamic selection. The generated N candidate paths are preprocessed to filter out invalid paths (such as paths with permanent obstacles or extremely narrow paths), improving the efficiency of subsequent evaluation. After preprocessing, candidate paths are filtered based on congestion markers to bypass or minimize passage through congested sections, resulting in the candidate path with the fewest congestion markers. Similarly, based on the shortest distance candidate path, the preprocessed candidate paths are further filtered based on the historical frequency of obstacle occurrences and / or driving difficulty to bypass sections with high historical obstacle frequency and / or high driving difficulty (e.g., many turning markers, many narrow path markers), resulting in the candidate path with the fewest historical obstacle occurrences and / or the lowest driving difficulty, i.e., the safest candidate path. For example, if the shortest path ABCD is found using Dijkstra's algorithm, and if the path deviates from node A to nearby node E, and then corrects from node E to node D, resulting in a shortest distance ECD, then another candidate path AECD is obtained from node A to node D. This process continues, resulting in multiple candidate paths. From these multiple candidate paths, the candidate path with the shortest distance, the candidate path with the fewest congestion markers, and the candidate path with the highest safety are then selected.

[0032] Step S103: Determine the task execution time of each of the multiple candidate paths; obtain the traffic congestion cost of each candidate path based on the full-process road condition information of each candidate path; obtain the spatial conflict cost of each candidate path based on the full-process road condition information of each candidate path and the status information of the target intelligent forklift; obtain the load cost of the target intelligent forklift based on the status information of the target intelligent forklift; and determine the priority cost of the target task.

[0033] Task execution time characterizes the time required for an intelligent forklift to execute a target task through a candidate path. For example, the length of the candidate path and the average travel speed determine the basic execution time. Taking into account the interference of various influencing factors on the candidate path, the task execution time is obtained. Traffic congestion cost represents the density of intelligent forklifts on candidate paths, i.e. the degree of traffic congestion. For example, the traffic congestion cost is determined by the density of intelligent forklifts on the straight sections of candidate paths. Spatial conflict cost represents the difficulty of intelligent forklift driving on candidate paths. For example, spatial conflict level is determined based on the location of difficult spatial driving (such as many turning marks or many narrow path marks), and spatial conflict cost is determined based on the number of each spatial conflict level on the candidate path. Load cost value characterizes the frequency of tasks performed by an intelligent forklift. For example, it can be measured by the total number of tasks, total travel distance, and total power consumption. For instance, the task execution time of a candidate path can be obtained by calculating the travel length of a candidate path against a preset travel speed.

[0034] In some specific embodiments, determining the task execution time of each of the plurality of candidate paths includes: Step S103a-1: Based on the full-process road condition information of any candidate path, obtain the number of various path feature tags in the corresponding candidate path.

[0035] In some specific embodiments, the multiple path feature markers include: turn markers, intersection markers, and congestion markers.

[0036] For example, multiple turning markers include: right-angle turn markers, U-turn markers, and circular turn markers; multiple intersection markers include fork-in-the-road markers and crossroad markers; multiple turning markers and multiple intersection markers are obtained by measurement, and their respective markers are set at the corresponding locations when modeling the traffic electronic map; multiple congestion markers include: first congestion marker, second congestion marker, third congestion marker, ..., Nth congestion marker. Multiple congestion markers are set in the traffic electronic map according to the congestion length range at the real-time traffic location. By narrowing the congestion length range and increasing the number of corresponding congestion markers, the distortion of the total feature duration can be reduced, making the total feature duration closer to the true value.

[0037] This specific embodiment marks the static and dynamic information of the path in detail so as to accurately grasp the real-time detailed information of the path and thus accurately formulate the target path suitable for the intelligent forklift to drive.

[0038] Step S103a-2: Based on the number of each of the multiple path feature markers in any candidate path and the preset average travel time of the corresponding path feature markers, obtain the total feature time of the corresponding candidate path.

[0039] The preset average driving time is determined by the historical driving time of the most recent time period. The most recent time period can be configured by the user or adaptively set by the system according to the application scenario (e.g., 1 hour, 1 day).

[0040] Total feature duration represents the average time spent traversing all path feature markers in the candidate paths.

[0041] For example, calculate the product of the number of each path feature marker and the preset average travel time of the corresponding path feature marker, and then calculate the sum of the products of all path feature markers to obtain the total feature time.

[0042] Step S103a-3: Divide any candidate path into multiple straight road segments based on the multiple path feature markers in the full-process road condition information.

[0043] For example, if there is a turn mark and an intersection mark on the candidate path, then the distance from the starting point of the candidate path to the turn mark is divided into the first straight line segment, the distance from the turn mark to the intersection mark is divided into the second straight line segment, and the distance from the intersection mark to the ending point of the candidate path is divided into the third straight line segment.

[0044] Step S103a-4: Based on the driving length of each straight road segment in any candidate path and the preset straight driving speed value, obtain the total driving time of the corresponding candidate path segments.

[0045] Step S103a-5: Calculate the sum of the total characteristic duration and the total travel time of any candidate path to obtain the task execution time of the corresponding candidate path.

[0046] In this specific embodiment, the task execution time for each candidate path is calculated based on the road segment length and the number of various path feature markers. Multi-source data fusion technology ensures the accuracy of the perceived data, and changes in traffic conditions are perceived in real time through static and dynamic road condition information, enabling rapid adjustments to scheduling strategies and path planning to adapt to the complex and ever-changing operating environment of intelligent forklifts.

[0047] In some specific embodiments, obtaining the traffic congestion cost of each candidate path based on its full-process traffic information includes: Step S103b-1: Divide any candidate path into multiple straight road segments based on multiple path feature markers in the full-process road condition information of any candidate path.

[0048] Candidate paths are divided into multiple straight road segments using edge nodes (i.e., multiple path feature markers). For example, if a candidate path includes at least a first intersection marker and a second intersection marker, a straight road segment between the two intersection markers is extracted from the candidate path using the first intersection marker and the second intersection marker. If there is a congestion marker between the first intersection marker and the second intersection marker, a first straight road segment is extracted using the first intersection marker and the congestion marker, and a second straight road segment is extracted using the congestion marker and the second intersection marker.

[0049] Step S103b-2: Obtain the number of intelligent forklifts for each of the multiple straight road segments.

[0050] The system collects the real-time location information of all intelligent forklifts through a group control device, and calculates the number of intelligent forklifts on each straight road segment based on the traffic electronic map.

[0051] Step S103b-3: Obtain the vehicle density value of the corresponding straight road segment based on the number of intelligent forklifts on each of the multiple straight road segments and the length value of the corresponding straight road segment.

[0052] For example, if there are 10 intelligent forklifts on a straight road segment and the length of the straight road segment is 40m, then the vehicle density value of the straight road segment = 10 vehicles / 40m = 0.25 vehicles / m.

[0053] Step S103b-4: Obtain the congestion cost of the corresponding straight road segment based on the vehicle density value of each of the multiple straight road segments and the preset road segment weight value of the corresponding straight road segment.

[0054] The preset road segment weight value is an empirical value, set according to the frequency (i.e. importance) of straight road segments, and is used to adjust the importance of straight road segments in the cost of road segment congestion.

[0055] Step S103b-5: Obtain the traffic congestion cost of any candidate path based on the congestion cost of each of the multiple straight road segments.

[0056] For example, the sum of the congestion costs of multiple straight road segments can be used to obtain the traffic congestion cost, including the following formula:

[0057] in, C_i Indicates the first i The traffic congestion cost of each candidate route c ij Indicates the first i The first candidate path j The cost of congestion on a straight road segment n This indicates the number of straight road segments.

[0058] In this specific embodiment, the traffic congestion cost is calculated in real time using edge nodes (i.e., various path feature markers) and real-time status information (such as location) fed back by intelligent forklifts. The traffic congestion cost is a dynamically changing value that characterizes the density of intelligent forklifts on candidate paths, i.e., the degree of traffic congestion. The real-time status information fed back by the intelligent forklifts ensures the accuracy of the perceived data. Real-time changes in traffic conditions are perceived through dynamic road condition information, enabling rapid adjustments to scheduling strategies and route planning to adapt to the complex and ever-changing operating environment of the intelligent forklifts.

[0059] In some specific embodiments, obtaining the spatial conflict cost of each candidate path based on the full-process road condition information of each of the plurality of candidate paths and the state information of the target intelligent forklift includes: Step S103c-1: Based on the driving space information in the full road condition information of any candidate path and the external spatial information of the target intelligent forklift, obtain multiple spatial conflict locations and the spatial conflict level of each of the multiple spatial conflict locations.

[0060] Driving space information refers to the spatial information formed by the environmental information around the candidate path in the traffic electronic map. Driving space information can provide driving reference data for intelligent forklifts. For example, the environmental information around the candidate path includes: information about the process island, equipment information, and information about intelligent forklifts driving on other driving paths in the surrounding area. This specific embodiment is not limited to this. These information form the driving space information.

[0061] External spatial information refers to the outline information of the target intelligent forklift. External spatial information is part of the state information of the target intelligent forklift.

[0062] Spatial conflict location refers to the location where the environmental information around the candidate path may collide with or hinder the normal operation of the target intelligent forklift.

[0063] Spatial conflict level refers to the level at which a collision is likely between the target intelligent forklift and the surrounding environment of the candidate path at a spatial conflict location, or the level at which the target intelligent forklift's normal operation is hindered. For example, when a collision between the target intelligent forklift and the surrounding environment is certain, the spatial conflict location is at the highest conflict level; when the safe distance between the target intelligent forklift and the surrounding environment is 0-30cm, the spatial conflict location is at a high conflict level; when the safe distance is 30-50cm (excluding 30cm), the spatial conflict location is at a normal level; and when the safe distance is greater than 50cm (excluding 30cm), the spatial conflict location is at a lenient level. This specific embodiment has already eliminated the possibility that the target intelligent forklift cannot move when determining the candidate path.

[0064] Step S103c-2: Obtain the number of spatial conflicts for each spatial conflict level based on the spatial conflict level of each of the multiple spatial conflict locations.

[0065] Step S103c-3: Obtain the percentage of spatial conflicts for each of the various spatial conflict levels based on the number of spatial conflicts for each level.

[0066] For example, the total number of spatial conflicts can be obtained based on the number of spatial conflicts at each of the various spatial conflict levels. Then, the ratio of the number of spatial conflicts at each spatial conflict level to the total number of spatial conflicts can be calculated to obtain the percentage of spatial conflicts at the corresponding spatial conflict level.

[0067] Step S103c-4: Obtain the spatial conflict level cost for each spatial conflict level based on the spatial conflict percentage for each spatial conflict level and the preset spatial conflict weight value for the corresponding spatial conflict level.

[0068] The preset spatial conflict weight value is an empirical value, set according to the importance of the spatial conflict level on the candidate path, and is used to adjust the degree to which the spatial conflict level hinders the movement of the intelligent forklift.

[0069] Step S103c-5: Obtain the spatial conflict cost of any candidate path based on the spatial conflict cost of each of the multiple spatial conflict levels.

[0070] For example, the sum of the spatial conflict cost values ​​of the various spatial conflict levels is calculated to obtain the spatial conflict cost value, including the following formula:

[0071] in, Q_i Indicates the first i The spatial conflict value of each candidate path q ij Indicates the first i The first candidate path j The spatial conflict level and value of a straight road segment n This indicates the number of straight road segments.

[0072] In this specific embodiment, the spatial conflict cost of the candidate path is obtained by combining the driving space information from the overall road condition information with the external spatial information of the target intelligent forklift. The passability of the intelligent forklift is measured using static road condition information, providing a safe and reliable driving path to adapt to the complex and ever-changing operating environment of the intelligent forklift, increasing the robustness of the target path, and ensuring that the intelligent forklift can successfully complete the target task.

[0073] In some specific embodiments, obtaining the load value of the target intelligent forklift based on its state information includes: Step S103d-1: Obtain the total number of tasks, total travel distance, and total power consumption of the target intelligent forklift within the preset statistical time period to date.

[0074] The preset statistical duration can be one day, one month, or one production batch; this specific embodiment is not limited to these.

[0075] Step S103d-2: Based on the total number of tasks and the preset quantity weight value, the total travel distance and the preset distance weight value, and the total power consumption and the preset power consumption weight value, obtain the load cost of the target intelligent forklift.

[0076] For example, based on the total number of tasks and a preset quantity weight value, the total travel distance and a preset distance weight value, and the total power consumption and a preset power consumption weight value, the load cost of the target intelligent forklift is obtained, including the following formula:

[0077] in, W This indicates the load-carrying capacity of the target intelligent forklift. N r This indicates the total number of tasks. w r This indicates the preset quantity weight value. N d Indicates the total distance traveled. w d This indicates the preset distance weight value. N e Indicates total power consumption. w e This indicates the preset power consumption weight value.

[0078] In this specific embodiment, the optimization of a single task is considered, and the efficiency balance of the overall system is also taken into account through workload factors (total number of tasks, total travel distance, and total power consumption) to avoid overloading of the intelligent forklift. Through load balancing scheduling and optimal path planning, the empty driving time and waiting time of the intelligent forklift are reduced, thereby improving the overall transportation efficiency. Step S104: Based on the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost, obtain the dynamic evaluation values ​​of each of the multiple candidate paths.

[0079] In this embodiment, each candidate path is evaluated based on multiple factors on the candidate path, the target intelligent forklift's own factors, and the target task's own factors. The overall system efficiency balance is considered to obtain optimal path planning, reduce the intelligent forklift's empty run time and waiting time, and improve overall transportation efficiency.

[0080] In some specific embodiments, obtaining the dynamic evaluation values ​​of the multiple candidate paths based on the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost includes the following formula: ； in, Cost_i Indicates the firsti The dynamic evaluation value of each candidate path, T_i Indicates the first i The task execution time for each candidate path; C_i Indicates the first i The traffic congestion cost of each candidate route; Q_i Indicates the first i The spatial conflict value of each candidate path; W This indicates the load-carrying capacity of the target intelligent forklift; P This represents the priority cost of the target task, and the priority cost of the target task is inversely proportional to the priority of the target task. P Less than or equal to 1; w1, w2, w3, w4 and w5 These represent the dynamic weighting coefficients for the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost, respectively.

[0081] For example, the priorities of the target task include: highest priority, high priority, normal priority, low priority, and lowest priority; the highest priority is represented by the number "1", with a corresponding priority cost of 1; the high priority is represented by the number "2", with a corresponding priority cost of 0.5; the normal priority is represented by the number "3", with a corresponding priority cost of 1 / 3; the low priority is represented by the number "4", with a corresponding priority cost of 0.25; and the lowest priority is represented by the number "5", with a corresponding priority cost of 0.2.

[0082] The dynamic weighting coefficient is an empirical value.

[0083] In this specific embodiment, the impact of adverse factors (such as task execution time and traffic congestion cost) on the dynamic evaluation value is adjusted by prioritizing the cost. For example, the highest priority target task, due to its priority characteristics, can appropriately reduce the impact of adverse factors, ensuring that the intelligent forklift can complete the target task quickly.

[0084] In some specific embodiments, the dynamic weighting coefficient is obtained by looking up the number of congestion markers in the full-process traffic information.

[0085] In this specific embodiment, a dynamic weight table is established for the dynamic weight coefficients. This table stores a one-to-one correspondence between the range of congestion markers and multiple dynamic weight coefficients. For example, as shown in Table 1 below, if the maximum capacity of a congestion marker is 100, During peak traffic hours (i.e., the number of congestion markers is in the range of [70, 100]), the dynamic weighting coefficient for increasing the traffic congestion cost is 0.4, encouraging system diversion. During off-peak traffic hours (i.e., the number of congestion markers is in the range of [1, 20]), the dynamic weighting coefficient for increasing task execution time is 0.4, encouraging the selection of the shortest path. During normal traffic hours (i.e., the number of congestion markers is in the range of [20, 70]), all dynamic weighting coefficients are 0.2. By flexibly obtaining dynamic weighting coefficients according to different scenarios, adjustment strategies for off-peak hours, efficiency priority, and energy consumption priority are implemented. If the number of congestion markers exceeds 100, production is stopped.

[0086] Step S105: Determine the target path based on the dynamic evaluation values ​​of each of the multiple candidate paths.

[0087] For example, the target path can be determined based on the minimum dynamic evaluation value among multiple candidate paths.

[0088] This application's embodiments determine the target intelligent forklift and multiple candidate paths through a target task; obtain dynamic evaluation values ​​for each candidate path based on its task execution time, traffic congestion cost, spatial conflict cost, target intelligent forklift load cost, and target task priority cost; then determine the target path based on these dynamic evaluation values. By sensing changes in workload and traffic conditions in real time, the scheduling strategy and path planning are quickly adjusted to adapt to complex and changing operating environments; through load balancing scheduling, accessibility of driving space, and optimal path planning, the empty driving time, waiting time, and ineffective driving of the intelligent forklift are reduced, improving overall transportation efficiency; automatic task allocation, automatic path planning, and dynamic adjustment are achieved, reducing manual intervention; multi-source data fusion technology ensures the accuracy of the sensed data, and the closed-loop control system guarantees the effective execution of instructions, improving the system's reliability and stability.

[0089] like Figure 2 As shown, this application provides a control system, including: Group control device 21, which stores a computer program and is configured to execute the computer program to implement the method described above; The intelligent forklift 22 is configured to travel along a target path determined by the method.

[0090] In this embodiment, production tasks for each process island are generated based on the production plan, rolling production plan, and inventory bill of materials established by the Manufacturing Execution System (MES). These tasks include the work tasks for the intelligent forklift 22. The group control device 21 is integrated with the intelligent forklift 22, acquiring its status (e.g., running, stopped, faulty) and generating control commands based on the work tasks. The intelligent forklift 22 executes the work tasks according to the control commands.

[0091] The MES system communicates with the group control device 21 based on OPC UA (industrial standard protocol), mainly transmitting production plans and bills of materials, and synchronizing with inventory data. In special cases, historical data is compressed and transmitted.

[0092] The group control device 21 and the intelligent forklift 22 communicate using an improved UDP protocol to transmit control commands, equipment status, and location information.

[0093] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0094] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for determining the path of an intelligent forklift, characterized in that, include: Obtain the target task; Based on the target task, a target intelligent forklift and multiple candidate paths are determined, along with the full-process road condition information for each of the multiple candidate paths; The task execution time of each of the multiple candidate paths is determined; the traffic congestion cost of each candidate path is obtained based on the full-process road condition information of each candidate path; the spatial conflict cost of each candidate path is obtained based on the full-process road condition information of each candidate path and the status information of the target intelligent forklift; the load cost of the target intelligent forklift is obtained based on the status information of the target intelligent forklift; and the priority cost of the target task is determined. The dynamic evaluation values ​​of the multiple candidate paths are obtained based on the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost. The target path is determined based on the dynamic evaluation values ​​of each of the multiple candidate paths.

2. The method according to claim 1, characterized in that, The dynamic evaluation value of each of the multiple candidate paths is obtained based on the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost, including the following formula: ； in, Cost_i Indicates the first i The dynamic evaluation value of each candidate path, T_i Indicates the first i The task execution time for each candidate path; C_i Indicates the first i The traffic congestion cost of each candidate route; Q_i Indicates the first i The spatial conflict value of each candidate path; W This indicates the load-carrying capacity of the target intelligent forklift; P This represents the priority cost of the target task, and the priority cost of the target task is inversely proportional to the priority of the target task. P Less than or equal to 1; w1, w2, w3, w4 and w5 These represent the dynamic weighting coefficients for the task execution time, the traffic congestion cost, the spatial conflict cost, the load cost, and the priority cost, respectively.

3. The method according to claim 2, characterized in that, The dynamic weighting coefficient is obtained by looking up the number of congestion markers in the full-process road condition information.

4. The method according to any one of claims 1-3, characterized in that, The process of obtaining the spatial conflict cost of each candidate path based on the full-process road condition information of each of the multiple candidate paths and the state information of the target intelligent forklift includes: Based on the driving space information in the full road condition information of any candidate path and the external space information of the target intelligent forklift, multiple spatial conflict locations and the spatial conflict level of each of the multiple spatial conflict locations are obtained. The number of spatial conflicts at each spatial conflict level is obtained based on the spatial conflict level of each of the multiple spatial conflict locations. The percentage of spatial conflict for each of the various spatial conflict levels is obtained based on the number of spatial conflicts for each level. The spatial conflict level cost is obtained based on the spatial conflict percentage of each spatial conflict level and the preset spatial conflict weight value of the corresponding spatial conflict level. The spatial conflict cost of any candidate path is obtained based on the spatial conflict cost of each of the various spatial conflict levels.

5. The method according to any one of claims 1-3, characterized in that, The step of obtaining the load value of the target intelligent forklift based on its state information includes: Obtain the total number of tasks, total travel distance, and total power consumption of the target intelligent forklift within the preset statistical period to date; The load value of the target intelligent forklift is obtained based on the total number of tasks and the preset quantity weight value, the total travel distance and the preset distance weight value, and the total power consumption and the preset power consumption weight value.

6. The method according to claim 1, characterized in that, Determining the task execution time for each of the multiple candidate paths includes: The number of each of the various path feature tags in the corresponding candidate path is obtained based on the full road condition information of any candidate path. The total characteristic time of the corresponding candidate path is obtained based on the number of each of the various path feature markers in any candidate path and the preset average travel time of the corresponding path feature markers; Based on the various path feature markers in the full-process road condition information, any candidate path is divided into multiple straight road segments; The total travel time of the corresponding candidate path is obtained based on the travel length of each straight segment in any candidate path and the preset straight travel speed value. Calculate the sum of the total characteristic duration and the total travel time of any candidate path to obtain the task execution time of the corresponding candidate path.

7. The method according to claim 1, characterized in that, The process of obtaining the traffic congestion cost of each candidate path based on its full-process traffic information includes: Based on the various path feature markers in the full-process traffic information of any candidate path, the candidate path is divided into multiple straight road segments. Obtain the number of intelligent forklifts for each of the multiple straight road segments; The vehicle density value of the corresponding straight road segment is obtained based on the number of intelligent forklifts on each of the multiple straight road segments and the length of the corresponding straight road segment. The congestion cost of the corresponding straight road segment is obtained based on the vehicle density value of each of the multiple straight road segments and the preset road segment weight value of the corresponding straight road segment. The traffic congestion cost of any candidate path is obtained based on the congestion cost of each of the multiple straight road segments.

8. The method according to claim 6 or 7, characterized in that, The various path feature markers include: turn markers, intersection markers, and congestion markers.

9. The method according to claim 1, characterized in that, The multiple candidate paths include: the candidate path with the shortest distance, the candidate path with the fewest congestion markers, and the candidate path with the highest security.

10. A control system, characterized in that, include: A group control device, having a computer program stored thereon, configured to execute the computer program to implement the method as described in any one of claims 1 to 9; The intelligent forklift is configured to travel along a target path determined by the method.

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