An AGV intelligent yarn transport vehicle transportation path planning method and system

Abstract

The present invention relates to the technical field of path planning, and particularly relates to a method and system for transporting path planning of an AGV intelligent yarn transport vehicle. Based on the initial transport paths of each AGV intelligent yarn transport vehicle, a shared path segment is determined. According to the relationship between the times taken by any two AGV intelligent yarn transport vehicles to reach the shared path segment, it is determined whether there will be a path conflict between any two AGV intelligent yarn transport vehicles. If a path conflict occurs, the waiting cost generated by any two AGV intelligent yarn transport vehicles waiting to yield and the detour cost generated by detouring are determined, and the minimum cost among the waiting cost and the detour cost and its corresponding scheduling scheme are selected. In this way, even if the number of AGV intelligent yarn transport vehicles is large, in the case of a path conflict, a scheduling scheme for the AGV intelligent yarn transport vehicle with the minimum cost can be determined, thereby avoiding the situation of mutual blockage between AGV intelligent yarn transport vehicles and improving the transport efficiency of AGV intelligent yarn transport vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and particularly to a method and system for planning the transportation path of an AGV intelligent yarn transport vehicle. Background Art

[0002] In a spinning factory, after the production line of yarn products is completed, the finished products need to be transported to the next process (such as a sorting and packing workshop, etc.), and the demand for yarn transportation operations is relatively large. Nowadays, with the rapid development of AGV (Automated Guided Vehicle) automated guided vehicles, using them as transportation vehicles greatly reduces labor costs and speeds up work efficiency. When using an AGV intelligent yarn transport vehicle to transport yarn products, only by making a reasonable transportation path planning for it can it quickly and efficiently carry out transportation operations.

[0003] Currently, when planning the transportation path of an AGV intelligent yarn transport vehicle, some common shortest path planning algorithms (such as the A* algorithm, Dijkstra algorithm, etc.) are used to make the transport vehicle transport along the shortest path, greatly reducing the time consumed by the transport vehicle for transportation and increasing its transportation efficiency. However, in a spinning factory with a large number of product productions, the demand for transportation volume is extremely large, and often a large number of transport vehicles need to run in parallel simultaneously to meet the transportation volume demand. When the number of transport vehicles increases, there may be a large number of common paths in the transportation paths of each transport vehicle obtained by the shortest path algorithm. During transportation, when multiple transport vehicles are in the same common path at the same time, it is easy to cause path conflicts and affect the transportation efficiency of a large number of transport vehicles. Therefore, the prior art often uses the shortest path algorithm to plan the transportation path of the transport vehicle, but does not consider the situation that when the number of transport vehicles is large, multiple transport vehicles may share the same section of the transportation path, resulting in mutual blockage between transport vehicles on the same path within the same time period and hindering the transportation efficiency of the transport vehicle. Summary of the Invention

[0004] In order to solve the technical problem that the existing transportation path planning method of an AGV intelligent yarn transport vehicle affects the transportation efficiency of the AGV intelligent yarn transport vehicle, the purpose of the present invention is to provide a method and system for planning the transportation path of an AGV intelligent yarn transport vehicle, and the specific technical solutions adopted are as follows:

[0005] In the first aspect of the present invention, a method for planning the transportation path of an AGV intelligent yarn transport vehicle is provided, including:

[0006] Determining a common path segment based on the initial transportation paths of each AGV intelligent yarn transport vehicle;

[0007] Determining whether there will be a path conflict between any two AGV intelligent yarn transport vehicles according to the relationship between the times taken by any two AGV intelligent yarn transport vehicles to reach the common path segment;

[0008] If a path conflict occurs, determine the waiting cost generated by any two AGV intelligent yarn transport vehicles waiting for right-of-way, and the detour cost generated by detouring.

[0009] Select the minimum cost among the waiting cost and the detour cost, and determine the scheduling plan corresponding to the minimum cost.

[0010] In an exemplary embodiment, the path conflict includes a same-direction path conflict and an oncoming path conflict.

[0011] In an exemplary embodiment, determining whether any two AGV intelligent yarn transport vehicles will have a path conflict includes:

[0012] If any two AGV intelligent yarn transport vehicles are traveling in the same direction, then if the driving time period of one of the two AGV intelligent yarn transport vehicles in the shared path segment contains the driving time period of the other AGV intelligent yarn transport vehicle in the shared path segment, the two AGV intelligent yarn transport vehicles will have a same-direction path conflict.

[0013] If any two AGV intelligent yarn transport vehicles are traveling in opposite directions, then if the entry time point of one of the two AGV intelligent yarn transport vehicles into the shared path segment is within the driving time period of the other AGV intelligent yarn transport vehicle in the shared path segment, the two AGV intelligent yarn transport vehicles will have an oncoming path conflict.

[0014] In an exemplary embodiment, if a same-direction path conflict occurs, the process of obtaining the waiting cost includes:

[0015] The waiting cost of the AGV intelligent yarn transport vehicle that enters the shared path segment later is equal to: the duration between the exit time point of the AGV intelligent yarn transport vehicle that enters the shared path segment later leaving the shared path segment and the exit time point of the other AGV intelligent yarn transport vehicle leaving the shared path segment.

[0016] In an exemplary embodiment, if an oncoming path conflict occurs, the process of obtaining the waiting cost includes:

[0017] The waiting cost of the AGV intelligent yarn transport vehicle to be yielded is equal to: the sum of the time required for the AGV intelligent yarn transport vehicle to be yielded to yield from the original route, the time required for the other AGV intelligent yarn transport vehicle among the two AGV intelligent yarn transport vehicles to pass through the yield section, and the time required for the AGV intelligent yarn transport vehicle to be yielded to return to the original route; the AGV intelligent yarn transport vehicle to be yielded is any one of the two AGV intelligent yarn transport vehicles.

[0018] In an exemplary embodiment, the time required for the AGV intelligent yarn transport vehicle to yield from the original route and the time required for the AGV intelligent yarn transport vehicle to return to the original route are both equal to the ratio of the vehicle length of the AGV intelligent yarn transport vehicle to be yielded to the vehicle speed of the AGV intelligent yarn transport vehicle to be yielded; the time required for another AGV intelligent yarn transport vehicle to pass through the yielding section is equal to twice the ratio of the vehicle length of another AGV intelligent yarn transport vehicle to the vehicle speed of another AGV intelligent yarn transport vehicle.

[0019] In an exemplary embodiment, the process of obtaining the detour cost includes:

[0020] Obtain at least one re-planned shortest path with the path conflict point as the starting point and the final target position as the ending point for the AGV intelligent yarn transport vehicle to be detoured; the AGV intelligent yarn transport vehicle to be detoured is the AGV intelligent yarn transport vehicle that needs to detour among any two AGV intelligent yarn transport vehicles;

[0021] Based on the difference between the remaining transport path in the initial transport path and the re-planned shortest path, and combined with the speed of the AGV intelligent yarn transport vehicle to be detoured, obtain the additional time required for detouring corresponding to the re-planned shortest path;

[0022] Based on the additional time required for detouring corresponding to each re-planned shortest path, obtain the final detour cost.

[0023] In an exemplary embodiment, the process of obtaining the final detour cost includes:

[0024] Obtain the conflict incidence rate of each shared path segment in the re-planned shortest path, and thus obtain the first conflict incidence rate comprehensive feature of the re-planned shortest path; the conflict incidence rate is used to characterize the possibility of path conflict occurring on the shared path segment;

[0025] Obtain the conflict incidence rate of each shared path segment in the remaining transport path of the initial transport path, and thus obtain the second conflict incidence rate comprehensive feature of the remaining transport path;

[0026] Calculate the product of the additional time required for detouring corresponding to each re-planned shortest path and the detour difficulty coefficient to obtain the path detour cost corresponding to each re-planned shortest path; the detour difficulty coefficient is equal to the ratio of the first conflict incidence rate comprehensive feature to the second conflict incidence rate comprehensive feature;

[0027] Based on the path detour cost corresponding to each re-planned shortest path, obtain the final detour cost.

[0028] In an exemplary embodiment, the process of obtaining the conflict incidence rate includes:

[0029] The conflict occurrence rate is obtained from the number of overlapping transportation paths on the shared path segment, the length of the shared path segment, and the number of obstacles within the preset range of the shared path segment; the conflict occurrence rate is proportional to the density of AGV intelligent yarn carriers and the number of obstacles within the preset range of the shared path segment, and the density of AGV intelligent yarn carriers is the ratio of the number of overlapping transportation paths on the shared path segment to the length of the shared path segment.

[0030] In a second aspect of the present invention, there is provided an AGV intelligent yarn carrier transportation path planning system, including: a memory and a processor; the memory is connected to the processor; the memory is used for storing program instructions; the processor is used for implementing the above-mentioned AGV intelligent yarn carrier transportation path planning method when the program instructions are executed.

[0031] The present invention has the following beneficial effects: when the number of simultaneously operating AGV intelligent yarn carriers is large, the shared path segment is determined according to the initial transportation paths of each AGV intelligent yarn carrier, and then, according to the relationship between the times taken by any two AGV intelligent yarn carriers to reach the shared path segment, it is determined whether there will be a path conflict between these two AGV intelligent yarn carriers. In the case of a path conflict, the waiting cost generated by the AGV intelligent yarn carrier waiting to yield and the detour cost generated by detouring among these two AGV intelligent yarn carriers are determined, and finally, the minimum cost among the waiting cost and the detour cost is selected to obtain the scheduling scheme corresponding to the minimum cost. In this way, even when the number of AGV intelligent yarn carriers is large, in the case of a path conflict, the minimum cost and its AGV intelligent yarn carrier scheduling scheme can be obtained, thereby avoiding the situation of mutual blockage among AGV intelligent yarn carriers and improving the transportation efficiency of AGV intelligent yarn carriers. Description of the Drawings

[0032] Figure 1 is a flowchart of an AGV intelligent yarn carrier transportation path planning method provided by an embodiment of the present invention;

[0033] Figure 2 is a flowchart for obtaining the detour cost provided by an embodiment of the present invention;

[0034] Figure 3 is a flowchart for obtaining the final detour cost provided by an embodiment of the present invention. Detailed Embodiments

[0035] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The data information collected in this application is obtained with full consent and authorization, and the collection, use, and processing of relevant information need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0037] As Figure 1 shown, this embodiment provides a method for planning the transportation path of an AGV intelligent yarn transport vehicle, including:

[0038] Step S1: Determine the common path segment based on the initial transportation paths of each AGV intelligent yarn transport vehicle;

[0039] Step S2: Determine whether there is a path conflict between any two AGV intelligent yarn transport vehicles according to the relationship between the times taken by any two AGV intelligent yarn transport vehicles to reach the common path segment;

[0040] Step S3: If there is a path conflict, determine the waiting cost generated by any two AGV intelligent yarn transport vehicles waiting to yield, and the detour cost generated by detouring;

[0041] Step S4: Select the minimum cost among the waiting cost and the detour cost, and determine the scheduling scheme corresponding to the minimum cost.

[0042] The following, in conjunction with the accompanying drawings, specifically describes each step.

[0043] Step S1: Determine the common path segment based on the initial transportation paths of each AGV intelligent yarn transport vehicle.

[0044] Obtain the transportation environment in the yarn factory and the transportation requirements. Taking the transportation from the finished products to the sorting room as an example, there are several product production lines and sorting rooms in the transportation requirements of this embodiment, and several AGV intelligent yarn transport vehicles are allocated for transportation operations. The reason it is called an AGV intelligent yarn transport vehicle is that the AGV intelligent yarn transport vehicle has the functions of remote communication with the background and being remotely monitored by the background.

[0045] Each AGV intelligent yarn transport vehicle is equipped with a positioning sensor for obtaining the real-time position signal of the AGV intelligent yarn transport vehicle, and each AGV intelligent yarn transport vehicle is also equipped with a speed sensor, etc., for obtaining the vehicle speed of the AGV intelligent yarn transport vehicle. It should be understood that the vehicle speeds of each AGV intelligent yarn transport vehicle are not necessarily the same, and moreover, the vehicle lengths of each AGV intelligent yarn transport vehicle are not necessarily the same.

[0046] The background monitoring system pre-obtains information such as the coordinates of the entire driving area of the AGV intelligent yarn transport vehicle, and pre-obtains the positions of each product production line and sorting room in the entire driving area, as well as information such as the occupied space. Moreover, environmental impact factors during transportation (certain obstacles that will block the progress of the transport vehicle, etc.) are pre-entered into the background monitoring system. The background monitoring system assigns the end point of the transportation operation, that is, the final target position, to each AGV intelligent yarn transport vehicle, and obtains the shortest path between the starting position and the final target position of each AGV intelligent yarn transport vehicle as the initial transportation path according to existing algorithms such as the shortest path algorithm.

[0047] Initially, each AGV intelligent yarn transport vehicle travels according to the corresponding initial transportation path, and the background monitoring system can know the real-time position of each AGV intelligent yarn transport vehicle in the corresponding initial transportation path.

[0048] Due to the large demand for product transportation flow in the spinning factory, multiple AGV intelligent yarn transport vehicles are often required to perform transportation operations in parallel. When multiple operations are scheduled in parallel, route conflicts between AGV intelligent yarn transport vehicles will inevitably occur, such as: multiple AGV intelligent yarn transport vehicles passing through a point at the same time resulting in congestion; two AGV intelligent yarn transport vehicles moving towards each other resulting in occupying each other's roads, etc. To ensure transportation efficiency, each AGV intelligent yarn transport vehicle monitors in real time whether there is a phenomenon that the transportation route is occupied by a path conflict and takes corresponding measures in a timely manner.

[0049] It should be understood that there may be an overlap of transportation paths between multiple initial transportation paths, that is, a certain path segment is a path segment where at least two initial transportation paths overlap, that is, this path segment is the overlapping path segment of at least two initial transportation paths. Based on this, each common path segment existing is determined according to the initial transportation paths of each AGV intelligent yarn transport vehicle. For any common path segment, it may be formed by the overlap of two initial transportation paths, or may be formed by the overlap of more initial transportation paths.

[0050] Then, obtain the lengths of each common path segment and the number of overlapping initial transportation paths on each common path segment. Moreover, in this embodiment, the number of obstacles within the preset range of each common path segment is also obtained. For any common path segment, its preset range can be a circular area obtained by taking the midpoint of the common path segment as the center point and a preset length as the radius to draw a circle, where the preset length is set according to actual needs. For example, the preset length is equal to 2 times the length of the common path segment. Here, an obstacle refers to an immovable fixed object existing in the entire driving area, such as a storage rack, etc. The more the number of obstacles, the greater the difficulty for the AGV intelligent yarn transport vehicle to bypass, and the more likely it is to cause conflicts.

[0051] Step S2: Determine whether any two AGV intelligent yarn transport vehicles will have a path conflict according to the relationship between the times taken by any two AGV intelligent yarn transport vehicles to reach the common path segment.

[0052] Taking any two of the multiple AGV intelligent yarn transport vehicles as an example, by determining the common path segment corresponding to the initial transportation paths of these two AGV intelligent yarn transport vehicles, it is possible to determine whether these two AGV intelligent yarn transport vehicles will have a path conflict according to the relationship between the times taken by these two AGV intelligent yarn transport vehicles to reach the common path segment. In this embodiment, the path conflict includes two situations, namely: same-direction path conflict and opposite-direction path conflict. It should be understood that in this embodiment, the common path segment reached by these two AGV intelligent yarn transport vehicles is the nearest common path segment on the driving route, that is, the first common path segment reached.

[0053] The closer the times taken by these two AGV intelligent yarn transport vehicles to reach the common path segment, the more likely they are to meet within the common path segment and cause a conflict.

[0054] In an exemplary embodiment, determining that these two AGV intelligent yarn transport vehicles have a path conflict includes two situations, namely: these two AGV intelligent yarn transport vehicles will have a same-direction path conflict and will have an opposite-direction path conflict. The following will separately describe these two situations.

[0055] Set the two endpoints of the common path segment to be and . If these two AGV intelligent yarn transport vehicles are traveling in the same direction, then both of these AGV intelligent yarn transport vehicles enter the common path segment from the same endpoint and exit the common path segment from the same endpoint. For example: both of these AGV intelligent yarn transport vehicles enter the common path segment from and exit the common path segment from .

[0056] For any one of the two AGV intelligent yarn carriers, calculate the ratio of the distance from the current position of the AGV intelligent yarn carrier to to the vehicle speed of the AGV intelligent yarn carrier to obtain the time required for the AGV intelligent yarn carrier to enter the shared path segment, thereby obtaining the time point when the AGV intelligent yarn carrier enters the shared path segment, which is the entry time point; calculate the ratio of the distance from the current position of the AGV intelligent yarn carrier to to the vehicle speed of the AGV intelligent yarn carrier to obtain the time required for the AGV intelligent yarn carrier to exit the shared path segment, thereby obtaining the time point when the AGV intelligent yarn carrier exits the shared path segment, which is the exit time point. And the driving time period of the AGV intelligent yarn carrier in the shared path segment is composed of the entry time point, the exit time point, and the time length between the entry time point and the exit time point. Thus, the driving time period of each AGV intelligent yarn carrier among the two AGV intelligent yarn carriers is obtained.

[0057] If the driving time period of one of the two AGV intelligent yarn carriers in the shared path segment includes the driving time period of the other AGV intelligent yarn carrier in the shared path segment, that is, the driving time period of one AGV intelligent yarn carrier in the shared path segment is within the time range of the driving time period of the other AGV intelligent yarn carrier in the shared path segment, then the two AGV intelligent yarn carriers will have a same-direction path conflict. Specifically: Set the two AGV intelligent yarn carriers as the first AGV intelligent yarn carrier and the second AGV intelligent yarn carrier. The first entry time point and the first exit time point are the time points when the first AGV intelligent yarn carrier enters and exits the shared path segment respectively, and the second entry time point and the second exit time point are the time points when the second AGV intelligent yarn carrier enters and exits the shared path segment respectively. Then, if the first entry time point is earlier than the second entry time point and the first exit time point is later than the second exit time point, that is: compared with the second AGV intelligent yarn carrier, the first AGV intelligent yarn carrier enters the shared path segment earlier but exits later. Then, the second AGV intelligent yarn carrier will be blocked by the first AGV intelligent yarn carrier, and there must be a contact situation between the first AGV intelligent yarn carrier and the second AGV intelligent yarn carrier in the shared path segment, so the two AGV intelligent yarn carriers will have a same-direction path conflict.

[0058] Example: If the first entry time point of the first AGV intelligent yarn transport vehicle into the shared path segment is 10:05, the first exit time point of the first AGV intelligent yarn transport vehicle from the shared path segment is 10:10, the second entry time point of the second AGV intelligent yarn transport vehicle into the shared path segment is 10:06, and the second exit time point of the second AGV intelligent yarn transport vehicle from the shared path segment is 10:09, then the first entry time point is earlier than the second entry time point, the first exit time point is later than the second exit time point, the first AGV intelligent yarn transport vehicle enters the shared path segment earlier, but exits the shared path segment later. The second AGV intelligent yarn transport vehicle will be blocked by the first AGV intelligent yarn transport vehicle, and there must be a contact situation between the first AGV intelligent yarn transport vehicle and the second AGV intelligent yarn transport vehicle in the shared path segment. Then, these two AGV intelligent yarn transport vehicles will have a same-direction path conflict.

[0059] It should be understood that the setting of the first AGV intelligent yarn transport vehicle and the second AGV intelligent yarn transport vehicle in this embodiment is not specifically referred to, but generally defines these two AGV intelligent yarn transport vehicles as the first AGV intelligent yarn transport vehicle and the second AGV intelligent yarn transport vehicle respectively. Therefore, when comparing the above, it is necessary to compare whether the driving time period of one of the AGV intelligent yarn transport vehicles in the shared path segment is within the time range of the driving time period of the other AGV intelligent yarn transport vehicle in the shared path segment.

[0060] If these two AGV intelligent yarn transport vehicles are traveling in opposite directions, then these two AGV intelligent yarn transport vehicles enter the shared path segment from different endpoints and exit the shared path segment from different endpoints. For example: One of the AGV intelligent yarn transport vehicles is from entering the shared path segment and from exiting the shared path segment; the other AGV intelligent yarn transport vehicle is from entering the shared path segment and from exiting the shared path segment.

[0061] If these two AGV intelligent yarn transport vehicles are traveling in opposite directions, then if the time point when one of the two AGV intelligent yarn transport vehicles enters the shared path segment is within the driving time period of the other AGV intelligent yarn transport vehicle in the shared path segment, then these two AGV intelligent yarn transport vehicles will have an opposite-direction path conflict. Specifically: If the entry time point of the first AGV intelligent yarn transport vehicle into the shared path segment is later than the entry time point of the second AGV intelligent yarn transport vehicle into the shared path segment and earlier than the exit time point of the second AGV intelligent yarn transport vehicle from the shared path segment, that is, when the first AGV intelligent yarn transport vehicle enters the shared path segment, the second AGV intelligent yarn transport vehicle is traveling in the shared path segment, then the first AGV intelligent yarn transport vehicle and the second AGV intelligent yarn transport vehicle will surely meet in the shared path segment. Then, these two AGV intelligent yarn transport vehicles will have an opposite-direction path conflict.

[0062] For example: if the first entry time point of the first AGV intelligent yarn transport vehicle into the shared path segment is 10:05, the second entry time point of the second AGV intelligent yarn transport vehicle into the shared path segment is 10:04, and the second exit time point of the second AGV intelligent yarn transport vehicle out of the shared path segment is 10:09, these two AGV intelligent yarn transport vehicles will have a conflict in opposite paths.

[0063] It should be understood that when making the above comparison, it is necessary to compare whether the time point when one of the AGV intelligent yarn transport vehicles enters the shared path segment is within the driving time period of the other AGV intelligent yarn transport vehicle in the shared path segment, so as to determine whether the two AGV intelligent yarn transport vehicles will have a conflict in the opposite paths.

[0064] Step S3: If a path conflict occurs, determine the waiting cost incurred by any two AGV intelligent yarn transport vehicles waiting to give way, as well as the detour cost incurred by the detour.

[0065] If the above step S2 determines that a path conflict may occur, it is necessary to perform a plan scheduling in advance to deal with the possible path conflict.

[0066] Because the ultimate goal of route scheduling is fast and efficient transportation, after a route conflict, the AGVs can be scheduled to wait for a change of direction or take a detour to reduce the impact of the route conflict on transportation efficiency. By quantifying the transportation efficiency of various scheduling options, we can compare and determine the scheduling solution that resolves the route conflict. Correspondingly, if a route conflict is determined, we need to determine the waiting cost incurred by the two AGVs waiting for a change of direction and the detour cost incurred by the two AGVs taking a detour.

[0067] Therefore, the waiting cost generated by waiting for yielding needs to be determined based on the occurrence of same-direction path conflicts and opposite-direction path conflicts.

[0068] If a same-direction path conflict occurs, the AGV intelligent yarn transport vehicle that enters the shared path section earlier will hinder the AGV intelligent yarn transport vehicle that enters the shared path section later, thereby affecting the transportation efficiency of the AGV intelligent yarn transport vehicle that enters the shared path section later. Therefore, if a same-direction path conflict occurs, only the scheduling plan of the AGV intelligent yarn transport vehicle that enters the shared path section later (that is, the AGV intelligent yarn transport vehicle that exits the shared path section earlier) is considered.

[0069] Since the AGV intelligent yarn transport vehicle that enters the shared path segment late will exit the shared path segment early, the minimum waiting cost is when two AGV intelligent yarn transport vehicles exit the shared path segment simultaneously. Then, the waiting cost of the AGV intelligent yarn transport vehicle that enters the shared path segment late is equal to: the duration between the exit time point when the AGV intelligent yarn transport vehicle that enters the shared path segment late exits the shared path segment and the exit time point when another AGV intelligent yarn transport vehicle (i.e., the AGV intelligent yarn transport vehicle that exits the shared path segment late) exits the shared path segment.

[0070] For example: If the first entry time point of the first AGV intelligent yarn transport vehicle into the shared path segment is 10:05, the first exit time point of the first AGV intelligent yarn transport vehicle from the shared path segment is 10:10, the second entry time point of the second AGV intelligent yarn transport vehicle into the shared path segment is 10:06, and the second exit time point of the second AGV intelligent yarn transport vehicle from the shared path segment is 10:09, then the waiting cost of the second AGV intelligent yarn transport vehicle is equal to: the duration between 10:10 and 10:09, that is, 1 minute.

[0071] If an oncoming path conflict occurs, any one of these two AGV intelligent yarn transport vehicles can be the AGV intelligent yarn transport vehicle to be yielded, and yield to the other AGV intelligent yarn transport vehicle. The AGV intelligent yarn transport vehicle to be yielded needs to temporarily avoid first, and then continue to drive along the original route after yielding to the other AGV intelligent yarn transport vehicle. Then, the yielding process is: the AGV intelligent yarn transport vehicle to be yielded yields a vehicle body length position, and then allows the other AGV intelligent yarn transport vehicle to continue driving. After the other AGV intelligent yarn transport vehicle drives two vehicle body lengths, the AGV intelligent yarn transport vehicle to be yielded returns to its original position. Then, the time consumed in the yielding process is the waiting cost of the AGV intelligent yarn transport vehicle to be yielded. The waiting cost of the AGV intelligent yarn transport vehicle to be yielded includes: the time required for the AGV intelligent yarn transport vehicle to be yielded to yield from the original route, the time required for the other AGV intelligent yarn transport vehicle to pass through the yielding section, and the time required for the AGV intelligent yarn transport vehicle to be yielded to return to the original route. The waiting cost of the AGV intelligent yarn transport vehicle to be yielded is equal to: the sum of the time required for the AGV intelligent yarn transport vehicle to be yielded to yield from the original route, the time required for the other AGV intelligent yarn transport vehicle among any two AGV intelligent yarn transport vehicles to pass through the yielding section, and the time required for the AGV intelligent yarn transport vehicle to be yielded to return to the original route.

[0072] Among them, the time required for the AGV intelligent yarn transport vehicle to yield from the original route is equal to the ratio of the vehicle length of the AGV intelligent yarn transport vehicle to be yielded to the vehicle speed of the AGV intelligent yarn transport vehicle to be yielded. The time required for the other AGV intelligent yarn transport vehicle to pass through the yielding section is equal to the ratio of twice the vehicle length of the other AGV intelligent yarn transport vehicle to the vehicle speed of the other AGV intelligent yarn transport vehicle. The time required for the AGV intelligent yarn transport vehicle to be yielded to return to the original route is equal to the ratio of the vehicle length of the AGV intelligent yarn transport vehicle to be yielded to the vehicle speed of the AGV intelligent yarn transport vehicle to be yielded.

[0073] Regardless of whether the path conflict is in the same direction or in the opposite direction, a detour plan can be selected to directly detour to the final target position, and a certain detour cost will be generated after the detour.

[0074] In an exemplary embodiment, as Figure 2 shown, a specific acquisition process of the detour cost is given as follows:

[0075] Step S3-1: Obtain at least one re-planned shortest path with the path conflict point as the starting point and the final target position as the ending point for the AGV intelligent yarn transport vehicle to be detoured.

[0076] For ease of explanation, assume that the AGV intelligent yarn transport vehicle to be detoured is the AGV intelligent yarn transport vehicle that needs to detour among the two AGV intelligent yarn transport vehicles. Among them, for the path conflict in the same direction, the AGV intelligent yarn transport vehicle to be detoured is the AGV intelligent yarn transport vehicle that enters the shared path section later; for the path conflict in the opposite direction, the AGV intelligent yarn transport vehicle to be detoured can be any one of the two AGV intelligent yarn transport vehicles, and it is necessary to analyze with one of the AGV intelligent yarn transport vehicles as the AGV intelligent yarn transport vehicle to be detoured respectively.

[0077] First, it is necessary to confirm the path conflict point, which is the starting point for the AGV intelligent yarn transport vehicle to be detoured to start detouring. In an exemplary embodiment, the path conflict point is the entry point where the AGV intelligent yarn transport vehicle to be detoured enters the shared path section, that is, among the two endpoints of the shared path section, the endpoint where the AGV intelligent yarn transport vehicle to be detoured enters.

[0078] Obtain at least one re-planned shortest path with the path conflict point as the starting point and the final target position as the ending point for the AGV intelligent yarn transport vehicle to be detoured. In an exemplary embodiment, the depth-first search algorithm is used to perform path search with the path conflict point as the starting point and the final target position as the ending point to obtain at least one path, which is defined as the re-planned shortest path. Further, when performing path search, the position of the other AGV intelligent yarn transport vehicle when the AGV intelligent yarn transport vehicle to be detoured travels to the path conflict point can also be used as an obstacle area.

[0079] Step S3-2: Based on the difference between the remaining transportation path in the initial transportation path and the re-planned shortest path, and combined with the speed of the AGV intelligent yarn transport vehicle to be bypassed, obtain the additional time required for bypassing corresponding to the re-planned shortest path.

[0080] Since the AGV intelligent yarn transport vehicle to be bypassed travels to the path conflict point for bypassing, then, taking the path conflict point as the segmentation point, the initial transportation path is divided into the traveled path and the remaining transportation path in the initial transportation path.

[0081] For the AGV intelligent yarn transport vehicle to be bypassed, it is necessary to compare the difference between the remaining transportation path in the initial transportation path and the re-planned shortest path. Since the re-planned shortest path is the path planned for bypassing, the location of another AGV intelligent yarn transport vehicle will be regarded as an obstacle area and needs to be bypassed, while the initial transportation path is the initially planned shortest path. Then, the path length of the re-planned shortest path must be greater than or equal to the length of the remaining transportation path in the initial transportation path. Correspondingly, according to the amount by which the path length of the re-planned shortest path exceeds the length of the remaining transportation path, and combined with the vehicle speed of the AGV intelligent yarn transport vehicle to be bypassed, obtain the additional time required for bypassing corresponding to each re-planned shortest path.

[0082] The calculation formula for the additional time required for bypassing corresponding to the re-planned shortest path is as follows:

[0083] ;

[0084] Wherein, represents the additional time required for bypassing the th re-planned shortest path, represents the path length of the th re-planned shortest path, represents the path length of the remaining transportation path in the initial transportation path of the AGV intelligent yarn transport vehicle to be bypassed, represents the vehicle speed of the AGV intelligent yarn transport vehicle to be bypassed, and b represents the AGV intelligent yarn transport vehicle to be bypassed.

[0085] Step S3-3: Based on the additional time required for bypassing corresponding to each re-planned shortest path, obtain the final bypass cost.

[0086] According to the additional time required for bypassing corresponding to each re-planned shortest path, obtain the final bypass cost. The selection principle of the final bypass cost is the minimum cost. Then, the minimum value can be directly selected from the additional time required for bypassing corresponding to each re-planned shortest path as the final bypass cost. Since in addition to the influence of the path length, the difficulty level of the re-planned shortest path also determines the bypass cost to a certain extent. Then, as a more preferred embodiment, such as Figure 3As shown below, a specific process for obtaining the final detour cost is given as follows:

[0087] Step S3-3-1: Obtain the conflict incidence rate of each shared path segment in the re-planned shortest path, so as to obtain the first conflict incidence comprehensive feature of the re-planned shortest path.

[0088] For any re-planned shortest path, by analyzing it with the initial transportation paths of other AGV intelligent yarn transport vehicles, each shared path segment in the re-planned shortest path is obtained, and thus the conflict incidence rate of each shared path segment is obtained. Among them, the conflict incidence rate is used to characterize the possibility of path conflicts occurring on the shared path segment.

[0089] In an exemplary embodiment, for any shared path segment, obtain the number of overlapping transportation paths on the shared path segment. Here, the overlapping transportation paths on the shared path segment include the re-planned shortest path and each other initial transportation path. It should be understood that among each other initial transportation paths, the initial transportation path replaced by the re-planned shortest path is not included.

[0090] Obtain the length of the shared path segment and the number of obstacles within the preset range of the shared path segment. The specific method for obtaining the number of obstacles within the preset range of the shared path segment is as described above.

[0091] The conflict incidence rate is obtained from the number of overlapping transportation paths on the shared path segment, the length of the shared path segment, and the number of obstacles within the preset range of the shared path segment.

[0092] Obtain the AGV intelligent yarn transport vehicle density. The AGV intelligent yarn transport vehicle density is the ratio of the number of overlapping transportation paths on the shared path segment to the length of the shared path segment. The higher the AGV intelligent yarn transport vehicle density, the higher the possibility of path conflicts occurring on the shared path segment, and the higher the conflict incidence rate. Therefore, the conflict incidence rate is proportional to the AGV intelligent yarn transport vehicle density. The more the number of obstacles within the preset range of the shared path segment, the higher the possibility of path conflicts occurring during detouring, and the higher the conflict incidence rate. Therefore, the conflict incidence rate is proportional to the number of obstacles within the preset range of the shared path segment.

[0093] In an exemplary embodiment, a specific quantification method for the conflict incidence rate is given as follows:

[0094] [[ID=*]];

[0095] Wherein, represents the conflict incidence rate of the shared path segment, represents the number of overlapping transportation paths on the shared path segment, represents the length of the shared path segment, represents the AGV intelligent yarn transport vehicle density, Indicates the number of obstacles within the preset range of the shared path segment.

[0096] Indicates to perform normalization, and the normalization method here can be: sigmoid function.

[0097] For any re-planned shortest path, obtain the conflict incidence rate of each shared path segment in the re-planned shortest path. Then, based on the conflict incidence rate of each shared path segment in the re-planned shortest path, obtain the first comprehensive conflict incidence rate feature of the re-planned shortest path. In an exemplary embodiment, the first comprehensive conflict incidence rate feature is equal to the sum of the conflict incidence rates of each shared path segment.

[0098] Step S3-3-2: Obtain the conflict incidence rate of each shared path segment in the remaining transport path of the initial transport path, and thereby obtain the second comprehensive conflict incidence rate feature of the remaining transport path.

[0099] Obtain each shared path segment in the remaining transport path of the initial transport path corresponding to the re-planned shortest path, and adopt the calculation method of the conflict incidence rate in step S3-3-1 to obtain the conflict incidence rate of each shared path segment in the remaining transport path, and thereby obtain the second comprehensive conflict incidence rate feature of the remaining transport path. In an exemplary embodiment, the second comprehensive conflict incidence rate feature is equal to the sum of the conflict incidence rates of each shared path segment in the remaining transport path.

[0100] Step S3-3-3: Obtain the path detour cost corresponding to each re-planned shortest path according to the additional time required for detour, the first comprehensive conflict incidence rate feature, and the second comprehensive conflict incidence rate feature corresponding to each re-planned shortest path.

[0101] The first comprehensive conflict incidence rate feature characterizes the conflict incidence rate corresponding to the re-planned shortest path, and the second comprehensive conflict incidence rate feature characterizes the conflict incidence rate corresponding to the original transport path. Then, if the first comprehensive conflict incidence rate feature is greater than the second comprehensive conflict incidence rate feature, it means that the cost of path detour will increase compared with the original transport path, and the greater the first comprehensive conflict incidence rate feature is greater than the second comprehensive conflict incidence rate feature, the greater the increase in the cost of path detour; if the first comprehensive conflict incidence rate feature is less than the second comprehensive conflict incidence rate feature, it means that the cost of path detour will decrease compared with the original transport path, and the smaller the first comprehensive conflict incidence rate feature is less than the second comprehensive conflict incidence rate feature, the greater the decrease in the cost of path detour.

[0102] Therefore, for any re-planned shortest path, obtain the difficulty coefficient of detouring for the re-planned shortest path. The difficulty coefficient of detouring is equal to the ratio of the comprehensive feature of the first conflict incidence rate to the comprehensive feature of the second conflict incidence rate. Then, if the comprehensive feature of the first conflict incidence rate is greater than the comprehensive feature of the second conflict incidence rate, the difficulty coefficient of detouring is greater than 1; if the comprehensive feature of the first conflict incidence rate is less than the comprehensive feature of the second conflict incidence rate, the difficulty coefficient of detouring is less than 1; if the comprehensive feature of the first conflict incidence rate is equal to the comprehensive feature of the second conflict incidence rate, the difficulty coefficient of detouring is equal to 1.

[0103] Therefore, the difficulty coefficient of detouring characterizes the difficulty level of the detouring path relative to the original path. The smaller this value is, the easier it is to pass through the detour, and the lower the detouring cost. Conversely, the larger this value is, the more difficult it is to pass through the detour, and the higher the detouring cost.

[0104] Calculate the product of the additional time required for detouring and the difficulty coefficient of detouring corresponding to the re-planned shortest path. The result obtained is the path detouring cost corresponding to the re-planned shortest path. The larger the difficulty coefficient of detouring is, the greater the increase in the additional time required for detouring, and the higher the resulting path detouring cost.

[0105] Through the above method, obtain the path detouring costs of each re-planned shortest path of the AGV intelligent yarn transport vehicle to be detoured.

[0106] It should be understood that if there is no common path segment in the remaining transport path of the re-planned shortest path or the corresponding initial transport path, the processes in steps S3-3-1 to S3-3-3 are not performed, and directly use the obtained additional time required for detouring as the corresponding path detouring cost.

[0107] Step S3-3-4: Based on the path detouring costs corresponding to each re-planned shortest path, obtain the final detouring cost.

[0108] Based on the path detouring costs of each re-planned shortest path of the AGV intelligent yarn transport vehicle to be detoured, obtain the final detouring cost of the AGV intelligent yarn transport vehicle to be detoured. Since the principle for obtaining the scheduling plan is the minimum cost, therefore, take the minimum value among the path detouring costs of each re-planned shortest path of the AGV intelligent yarn transport vehicle to be detoured as the final detouring cost of the AGV intelligent yarn transport vehicle to be detoured.

[0109] Step S4: Select the minimum cost between the waiting cost and the detouring cost, and determine the scheduling plan corresponding to the minimum cost.

[0110] For the same - direction path conflict, select the minimum value from the waiting cost and the final detour cost of the AGV intelligent yarn - transporting vehicle to be detoured, and use the scheduling scheme corresponding to the obtained minimum value as the final scheduling scheme of the AGV intelligent yarn - transporting vehicle to be detoured. For example: If the waiting cost is greater than the final detour cost, it means that the detour cost is lower, then select the re - planned shortest path corresponding to the final detour cost as the final scheduling scheme of the AGV intelligent yarn - transporting vehicle to be detoured, and then control the AGV intelligent yarn - transporting vehicle to be detoured to drive according to this final scheduling scheme; if the waiting cost is less than the final detour cost, it means that the waiting cost is lower, and the cost of waiting for another AGV intelligent yarn - transporting vehicle to yield to it is lower, then select the scheduling method corresponding to the waiting cost (i.e., waiting to yield) as the final scheduling scheme of the AGV intelligent yarn - transporting vehicle to be detoured, and then control the AGV intelligent yarn - transporting vehicle to be detoured with this final scheduling scheme.

[0111] For the opposite - direction path conflict, first take one of the AGV intelligent yarn - transporting vehicles as the AGV intelligent yarn - transporting vehicle to be detoured, obtain the waiting cost and the final detour cost of this AGV intelligent yarn - transporting vehicle to be detoured, and then take the other AGV intelligent yarn - transporting vehicle as the AGV intelligent yarn - transporting vehicle to be detoured to obtain the waiting cost and the final detour cost. Therefore, two waiting costs and two final detour costs are finally obtained. Since it is necessary to consider the scheduling of these two AGV intelligent yarn - transporting vehicles simultaneously to achieve the optimal solution for both sides, select the minimum cost among these four costs, take the AGV intelligent yarn - transporting vehicle corresponding to this minimum cost as the final AGV intelligent yarn - transporting vehicle to be detoured, and the final scheduling scheme of the final AGV intelligent yarn - transporting vehicle to be detoured, and then control the AGV intelligent yarn - transporting vehicle to be detoured with this final scheduling scheme.

[0112] The above analyzes any two AGV intelligent yarn - transporting vehicles. For other AGV intelligent yarn - transporting vehicles in the application scenario, determine the final scheduling scheme of each AGV intelligent yarn - transporting vehicle according to this analysis process to realize the transportation path planning of the AGV intelligent yarn - transporting vehicle.

[0113] This embodiment also provides an AGV intelligent yarn - transporting vehicle transportation path planning system, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above - mentioned AGV intelligent yarn - transporting vehicle transportation path planning method embodiment when the program instructions are executed.

[0114] In an exemplary embodiment, the present invention provides a computer - readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the steps in the above - mentioned AGV intelligent yarn - transporting vehicle transportation path planning method embodiment.

[0115] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A method for planning the transportation path of an AGV intelligent yarn transport vehicle, characterized in that, Including: Determine the shared path segment based on the initial transportation paths of each AGV intelligent yarn transport vehicle; Determine whether any two AGV intelligent yarn transport vehicles will have a path conflict according to the relationship between the time taken by any two AGV intelligent yarn transport vehicles to reach the shared path segment; If a path conflict occurs, determine the waiting cost generated by any two AGV intelligent yarn transport vehicles waiting for yielding and the detour cost generated by detouring; Select the minimum cost between the waiting cost and the detour cost, and determine the scheduling scheme corresponding to the minimum cost; Wherein, the path conflict includes a same-direction path conflict and an opposite-direction path conflict; The process of obtaining the detour cost includes: Obtain at least one re-planned shortest path with the path conflict point as the starting point and the final target position as the ending point for the AGV intelligent yarn transport vehicle to be detoured; the AGV intelligent yarn transport vehicle to be detoured is the AGV intelligent yarn transport vehicle that needs to detour among any two AGV intelligent yarn transport vehicles; Based on the difference between the remaining transportation path in the initial transportation path and the re-planned shortest path, and combined with the speed of the AGV intelligent yarn transport vehicle to be detoured, obtain the additional time required for detouring corresponding to the re-planned shortest path; Based on the additional time required for detouring corresponding to each re-planned shortest path, obtain the final detour cost, including: Obtain the conflict incidence rate of each shared path segment in the re-planned shortest path, so as to obtain the first conflict incidence rate comprehensive feature of the re-planned shortest path; the conflict incidence rate is used to characterize the possibility of a path conflict occurring on the shared path segment; Obtain the conflict incidence rate of each shared path segment in the remaining transportation path in the initial transportation path, so as to obtain the second conflict incidence rate comprehensive feature of the remaining transportation path; Calculate the product of the additional time required for detouring corresponding to each re-planned shortest path and the detour difficulty coefficient to obtain the path detour cost corresponding to each re-planned shortest path; the detour difficulty coefficient is equal to the ratio of the first conflict incidence rate comprehensive feature to the second conflict incidence rate comprehensive feature; Based on the path detour costs corresponding to each re-planned shortest path, obtain the final detour cost.

2. The transportation path planning method of an AGV intelligent yarn transport vehicle as described in claim 1, characterized in that, Determining whether any two AGV intelligent yarn transport vehicles will have a path conflict includes: If any two AGV intelligent yarn transport vehicles are traveling in the same direction, then if the driving time period of one of the two AGV intelligent yarn transport vehicles in the shared path segment contains the driving time period of the other AGV intelligent yarn transport vehicle in the shared path segment, the two AGV intelligent yarn transport vehicles will have a same-direction path conflict; If any two AGV intelligent yarn transport vehicles are traveling in opposite directions, then if the entry time point of one of the two AGV intelligent yarn transport vehicles into the shared path segment is within the driving time period of the other AGV intelligent yarn transport vehicle in the shared path segment, the two AGV intelligent yarn transport vehicles will have an opposite-direction path conflict.

3. The transportation path planning method of an AGV intelligent yarn transport vehicle according to claim 1, characterized in that, If a same-direction path conflict occurs, the process of obtaining the waiting cost includes: The waiting cost of the AGV intelligent yarn transport vehicle that enters the shared path section late is equal to: the duration between the departure time point when the AGV intelligent yarn transport vehicle that enters the shared path section late departs from the shared path section and the departure time point when another AGV intelligent yarn transport vehicle departs from the shared path section.

4. The transportation path planning method of an AGV intelligent yarn transportation vehicle as described in claim 1, characterized in that, If an oncoming path conflict occurs, the process of obtaining the waiting cost includes: The waiting cost of the AGV intelligent yarn transport vehicle to be yielded is equal to: the sum of the time required for the AGV intelligent yarn transport vehicle to be yielded to yield from the original route, the time required for another AGV intelligent yarn transport vehicle among any two AGV intelligent yarn transport vehicles to pass through the yielding section, and the time required for the AGV intelligent yarn transport vehicle to be yielded to return to the original route; the AGV intelligent yarn transport vehicle to be yielded is any one of any two AGV intelligent yarn transport vehicles.

5. The method for planning the transportation path of an AGV intelligent yarn transport vehicle according to claim 4, characterized in that, The time required for the AGV intelligent yarn transport vehicle to be yielded to yield from the original route and the time required for the AGV intelligent yarn transport vehicle to be yielded to return to the original route are both equal to the ratio of the vehicle length of the AGV intelligent yarn transport vehicle to be yielded to the vehicle speed of the AGV intelligent yarn transport vehicle to be yielded; the time required for another AGV intelligent yarn transport vehicle to pass through the yielding section is equal to twice the ratio of the vehicle length of another AGV intelligent yarn transport vehicle to the vehicle speed of another AGV intelligent yarn transport vehicle.

6. The transportation path planning method of an AGV intelligent yarn transport vehicle according to claim 1, characterized in that The process of obtaining the conflict incidence rate includes: The conflict incidence rate is obtained from the number of overlapping transport paths on the shared path section, the length of the shared path section, and the number of obstacles within the preset range of the shared path section; the conflict incidence rate is proportional to the AGV intelligent yarn transport vehicle density and the number of obstacles within the preset range of the shared path section, and the AGV intelligent yarn transport vehicle density is the ratio of the number of overlapping transport paths on the shared path section to the length of the shared path section.

7. An AGV intelligent yarn transportation vehicle transportation path planning system, characterized by comprising: A memory and a processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is used to implement the AGV intelligent yarn transport vehicle transport path planning method according to any one of claims 1-6 when the program instructions are executed.

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