A multi-vehicle path planning method and system equipment for airport baggage check-in

Abstract

This invention discloses a multi-vehicle path planning method and system for airport baggage check-in. S1: Areas within the airport requiring baggage check-in are divided and prioritized to generate a two-dimensional map. S2: Starting and destination points are marked on the two-dimensional map. The baggage check-in fleet continuously learns through interaction with surrounding entities, iterating towards the highest-priority direction initially defined by the algorithm in the two-dimensional map, searching for an unobstructed path until the fleet reaches the destination. This method solves the problem of path-finding for mobile objects and achieves higher transportation efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of path planning and relates to a multi-vehicle path planning method and system equipment applied to airport baggage consignment. Background Art

[0002] With the rapid development of the AI-powered autonomous driving market, various intelligent path planning algorithms have also made corresponding progress. These algorithms leverage existing technologies to perceive, make decisions, and control intelligent agents in complex scenarios, finding a collision-free path between a starting point and a destination. Currently, there are many different path planning algorithms, including A_star, Q-learning, ant colony algorithms, genetic algorithms, temporal difference algorithms, actor-critic algorithms, and RRT algorithms.

[0003] Airport baggage handling with multiple vehicles also falls under the purview of intelligent path planning. Unlike highways, urban roads, and special operations sites, airport environments possess unique characteristics: fewer obstacles, more sparsely distributed obstacles, a lower likelihood of pedestrians on the ground, and relatively fixed paths for movable obstacles. Currently, airport baggage handling still relies on manually driven vehicles from designated locations to designated locations. This is inefficient due to the traditional vehicle structure, high mass, long length, and limited steering flexibility. Furthermore, labor costs are rapidly rising, and the rising transportation costs for baggage handlers are detrimental to airport operations. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a multi-vehicle path planning method and system equipment for airport baggage check-in, which can solve the problem of mobile objects finding paths and achieve higher transportation efficiency.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A multi-vehicle path planning method for airport baggage check-in includes the following steps:

[0007] S1: Divide the areas within the airport that require baggage check-in, set regional priorities, and generate a two-dimensional map;

[0008] S2: Mark the starting point and destination point on the two-dimensional map. The baggage check-in fleet continuously learns through its own situational interactions with surrounding entities, it iterates towards the direction with the highest priority initially defined by the algorithm in the two-dimensional map, and searches for an obstacle-free path until the baggage check-in fleet reaches the destination point.

[0009] Preferably, the method for dividing the area in S1 is: using the existing cameras at the airport to identify and analyze the global obstacle positions, based on the principle that the number of obstacles is positively correlated with the probability of an infeasible path, where obstacles are dense, the probability of an infeasible path being predicted is high, and the area is coarsely divided into grids; where obstacles are scattered, the probability of an infeasible path being predicted is low, and the area is subdivided into grids.

[0010] Preferably, the regional priorities in S1 are quantified as follows:

[0011] factor_0: the value of the distance from the target point;

[0012] factor_1: the value of the accumulated distance;

[0013] factor_2: the sum of the distances from the target point and the starting point;

[0014] factor_3: The area is an obstacle or boundary, and the value is negative infinity;

[0015] On this basis, the corresponding priority factors α, β, γ, and η are set, where -0.6=<α<=-0.4, -2.0=<β<=-1.2, and -0.6=<γ<=-0.4. The value of η is 1.

[0016] Preferably, the baggage check-in fleet in S2 continuously learns from its situational interactions with surrounding entities. Every time the baggage check-in fleet wants to take a step, the environment judges the behavior according to the pre-set priority, and performs positive feedback or reverse feedback, and finally forms a situational interaction feedback table. After multiple path searches, the value of the priority factor in the priority is continuously changed according to the feedback table, and the optimal path is continuously optimized.

[0017] Preferably, in S2, the vehicles in the baggage handling fleet identify the surrounding environment, mark it on the initialized map, and proceed to the area with the highest priority by considering the current optimal option in combination with the pre-set priority;

[0018] Create a line between the current point and the target point, and use the number of upper and lower triangle obstacles to solve the problem of equal priority in the area;

[0019] Determine whether the forward point is the target point. If it is not the target point, continue to search for the next point. If it is the target point, stop searching.

[0020] Preferably, when the baggage consignment team is moving, if the front part of the vehicle senses that a moving object is approaching within a certain range, it will slow down. If the moving object stops moving, the team will continue to move forward. If the moving object continues to move, the team will stop and wait.

[0021] When the middle part senses that a moving object is approaching within a certain range, the baggage check-in fleet separates from the check-in vehicle closest to the moving object. The front fleet continues to move forward, and the rear fleet repeats the above-mentioned vehicle head judgment process. When the moving object leaves the fleet, the rear fleet acts as the new baggage check-in fleet to find an obstacle-free path again.

[0022] Furthermore, when a vehicle in the baggage check-in fleet senses that a moving object is approaching within a certain range and slows down or stops, communication between vehicles can be used to instruct the following vehicles to slow down or stop to varying degrees.

[0023] A multi-vehicle path planning system for airport baggage check-in, comprising:

[0024] Map generation module: used to divide the areas within the airport that require baggage check-in, set regional priorities, and generate a two-dimensional map;

[0025] Travel module: Used to mark the starting point and destination point on the two-dimensional map. The baggage check-in fleet relies on its own situational interaction with surrounding entities to continuously self-learn, iterates in the direction of the highest priority initially defined by the algorithm in the two-dimensional map, and searches for an obstacle-free path until the baggage check-in fleet reaches the destination point.

[0026] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the above-described multi-vehicle path planning methods for airport baggage check-in are implemented.

[0027] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned multi-vehicle path planning methods for airport baggage check-in.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] During the path planning process of the present invention, perception iteration is performed according to regional priority to find an obstacle-free path, which can solve the problem of mobile objects finding paths and achieve higher transportation efficiency. In addition, the starting point is movable, which is conducive to improving the local path planning effect on the basis of macro path planning and realizing reasonable macro and local planning.

[0030] Furthermore, quantifying the connection between the next point of the starting point and the target point is beneficial for updating the path on the one hand, and on the other hand, it can reduce the blindness in the learning process, thereby reducing the time spent on planning.

[0031] Furthermore, the baggage handling fleet's strategy during travel has a high degree of local flexibility, which can enhance the fleet's planning and response capabilities to local obstacles. This allows the fleet to balance flexibility with minimal computational effort in route planning, achieving the goal of a fully automated baggage handling fleet. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of the multi-vehicle path planning method for airport baggage check-in of the present invention;

[0033] Figure 2 This is a schematic diagram of a 10*10 map simulation of the present invention. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the accompanying drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0037] The multi-vehicle path planning method for airport baggage check-in of the present invention is as follows: Figure 1 As shown, step one: using existing high-precision map technology, locate the areas within the airport that require baggage check-in. After area division and area priority initialization, the areas are input into the storage module of each vehicle in the baggage check-in fleet and saved. At the same time, each vehicle in the baggage check-in fleet must be installed with an environmental perception module under existing technical conditions and a vehicle-to-vehicle communication module.

[0038] Step 2: Mark the starting point and destination point on the saved map, and control the baggage check-in fleet to approach the destination point.

[0039] The specific process of step 2 is:

[0040] SP1: Initialize the current point and assign the location information of the starting point to the current point.

[0041] SP2: Determine whether the condition flag is 0 and start the loop.

[0042] SP3: Explore several areas around the current point and add the surrounding areas to an array according to the corresponding rules. Through the reward table generated by the continuous interaction between the vehicle and the environment, the vehicle continuously learns and updates the priority factor. The rules are as follows:

[0043] If a perceived area is outside the map, discard it. If a surrounding area already appears in the path array, discard it. Add the position and priority information of the remaining perceived non-obstruction areas to the temporary array. The priority information of a certain area is calculated as follows:

[0044] factor_0: The distance value from the target point. The distance from the remaining points to the target point is estimated using the straight-line distance formula between two points, and priority information is assigned in turn.

[0045]

[0046] factor_1: The value of the cumulative distance. The cumulative distance is calculated by summing the absolute values of the coordinate differences of each point in the existing path, and priority information is assigned to each point in turn.

[0047] D2=|x1-x2|+|y1-y2|+|x2-x3|+|y2-y3|+...+|x n-1 -x n |+|y n-1 -y n |

[0048] factor_2: The sum of the distances to the target point and the starting point, estimated using the straight-line distance formula between two points.

[0049]

[0050] factor_3: The area is an obstacle or boundary, with the lowest priority, and D4 is negative infinity.

[0051] Finally, the priorities are weighted:

[0052]

[0053] SP4: Determine whether there is a way to go. If the starting point or the target point is surrounded by obstacles, it means there is no way to go. A prompt "No way to go" is issued. Wait and execute the algorithm again. If the problem is still not solved, perform manual intervention. If there is a way to go, proceed to the next step.

[0054] SP5: Find the point with the highest priority in the surrounding environment perceived by the current point, iterate to the next current point, put the coordinates of this point into the path array, and control the baggage check-in team to move to this point; if the priority levels are the same, execute the min_value function to draw a straight line between the current point and the target point:

[0055]

[0056] Count the number of obstacles in the rectangular space with the current point and the target point as the diagonal angles. Classify the obstacles above the line into up_obstacle and the obstacles below the line into down_obstacle. Compare the sizes of the two and choose the direction with fewer obstacles.

[0057] SP6: Determine whether the point is the target point. If it is not the target point, continue the loop; if it is the target point, assign the flag value to 0 and output the path array.

[0058] SP7: The baggage handling fleet arrives at the destination and the traveled path is visualized.

[0059] During the movement of a baggage consignment team, the following three situations are inevitable: obstacles are stationary throughout the journey, a moving object approaches a certain range and then stops, and a moving object approaches a certain range and then continues to move forward. The present invention adopts the following four measures:

[0060] SSP1: If all obstacles along the way are stationary, the entire baggage handling fleet can simply follow the trajectory of the lead vehicle.

[0061] SSP2: In the event that a moving object approaches within a certain range and then stops, the fleet uses sensing to determine each vehicle's real-time distance from the moving object. When a vehicle reaches a certain distance from the moving object, it decelerates and, through vehicle-to-vehicle communication, instructs the following vehicles to decelerate to varying degrees. If the moving object continues to approach the baggage consignment fleet to a different distance range, the vehicle closest to the moving object stops and waits. Through vehicle-to-vehicle communication, the following vehicles are instructed to decelerate to varying degrees, or even stop. Since the moving object will eventually stop in this situation, the consignment vehicle closest to the moving object becomes the lead vehicle after it stops, and the following fleet resumes its original speed and associated movements.

[0062] SSP3: For situations where a moving object approaches within a certain range and then continues forward, the baggage delivery fleet uses sensing to determine the real-time distance between each vehicle and the moving object. When a vehicle reaches a certain distance from the moving object, it decelerates and, through vehicle-to-vehicle communication, instructs the following vehicles to decelerate to varying degrees. If the moving object continues to approach the convoy to a different distance range, the vehicle closest to the moving object stops and waits. Through vehicle-to-vehicle communication, the following vehicles are instructed to decelerate to varying degrees, or even stop. In this case, the moving object will pass through the convoy. If it stops within a certain distance after passing through the convoy, the baggage delivery vehicle closest to the moving object becomes the lead vehicle behind it. The following convoy resumes its journey after a few seconds, and the algorithm continues. If the moving object continues to move after passing through the convoy, the baggage delivery vehicle closest to the moving object becomes the lead vehicle behind it. The following convoy resumes its commuting journey once the moving object leaves the area, and the algorithm continues.

[0063] SSP4: The baggage handling fleet determines whether to send an alarm signal to the control tower via the airport's internal communications and turn on the vehicle's hazard lights via broadcasting within the fleet, based on whether the vehicle itself and its cargo have been damaged and whether its waiting time is greater than a set value. The control tower responds to the relevant alarm and takes over the baggage handling fleet's actions through the airport's internal command system.

[0064] The convoy strategy is to simulate the movement of a human team and conduct multi-vehicle collaboration and cooperation.

[0065] 1. Obedience. Each vehicle in a convoy can sense its surroundings, including those ahead and to the sides. However, the impact of its observations on the entire convoy varies. The front vehicle has a higher weight and, when faced with movable or immovable obstacles, can determine whether it and the vehicles behind it move forward. Conversely, the rear vehicle cannot determine the direction of the vehicle in front.

[0066] Second: High local flexibility. Although the decisions of the rear vehicle cannot affect the vehicle in front, when faced with movable or immovable obstacles in the middle of the platoon, the rear vehicle can decide whether to separate itself and the vehicles behind it from the vehicle in front, and then form a team and move towards the destination after the dangerous situation is resolved.

[0067] Compliance significantly reduces the computational workload for the entire vehicle during path planning. The lead vehicle has a higher computational workload, while the following vehicles have a lower workload. High local flexibility enhances the fleet's ability to plan and respond to local obstacles. This allows the fleet to achieve both flexibility and minimal computational workload during path planning.

[0068] The simulation results of the present invention are shown in the attached Figure 2As shown in FIG, the simulation results show that the multi-vehicle path planning algorithm for airport baggage check-in proposed in the present invention can solve the problems of low transportation efficiency and high labor costs faced by airport baggage check-in vehicles.

[0069] The following are device embodiments of the present invention, which can be used to perform the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0070] In another embodiment of the present invention, a multi-vehicle path planning system for airport baggage check-in is provided. The multi-vehicle path planning system for airport baggage check-in can be used to implement the above-mentioned multi-vehicle path planning method for airport baggage check-in. Specifically, the multi-vehicle path planning system for airport baggage check-in includes a map generation module and a travel module.

[0071] Among them, the map generation module is used to divide the areas within the airport that require baggage check-in, set regional priorities, and generate a two-dimensional map.

[0072] The travel module is used to mark the starting point and destination point on the two-dimensional map. The baggage check-in fleet relies on its own situational interaction with surrounding entities to continuously learn, iterates in the direction of the highest priority initially defined by the algorithm in the two-dimensional map, and searches for an obstacle-free path until the baggage check-in fleet reaches the destination point.

[0073] In yet another embodiment of the present invention, a terminal device is provided. The terminal device includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal and is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to implement corresponding method processes or corresponding functions. The processor described in the embodiment of the present invention can be used for the operation of a multi-vehicle path planning method for airport baggage check-in, including: S1: dividing areas within the airport requiring baggage check-in, setting area priorities, and generating a two-dimensional map; S2: marking a starting point and a destination point on the two-dimensional map. The baggage check-in fleet relies on its own situational interaction with surrounding entities to continuously self-learn, iterate towards the direction with the highest priority initially defined by the algorithm in the two-dimensional map, and search for an obstacle-free path until the baggage check-in fleet reaches the destination point.

[0074] In another embodiment, the present invention further provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0075] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the multi-vehicle path planning method applied to airport baggage check-in in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and execute the following steps: S1: Divide the areas within the airport that require baggage check-in, set regional priorities, and generate a two-dimensional map; S2: Mark the starting point and target point on the two-dimensional map. The baggage check-in fleet relies on its own situational interaction with surrounding entities to continuously self-learn, perceive and iterate in the direction with the highest priority initially defined by the algorithm in the two-dimensional map, and find an obstacle-free path until the baggage check-in fleet reaches the target point.

[0076] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0077] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0081] It should be understood that the above description is for illustrative purposes only and is not intended to be limiting. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of the present teachings should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of completeness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein from the preceding claims is not a disclaimer of such subject matter, nor should it be considered that the applicants did not consider such subject matter to be part of the disclosed inventive subject matter.

Claims

1. A multi-vehicle path planning method for airport baggage check-in, characterized in that: The following processes are included: S1: Divide the areas within the airport that require baggage check-in, set regional priorities, and generate a two-dimensional map; Regional priorities are quantified as follows: factor_0: the value of the distance to the target point; factor_1: the value of the accumulated distance; factor_2: the sum of the distances from the target point and the starting point; factor_3: The area is an obstacle or boundary, and the value is negative infinity; Set the corresponding priority factor based on this 、 、 、 , -0.6=< <=-0.4, -2.0=< <=-1.2, -0.6=< <=-0.4, The value of is 1; S2: Mark the starting and destination points on a two-dimensional map. The baggage handling fleet continuously learns through interaction with surrounding entities, iterating towards the highest priority direction initially defined by the algorithm in the two-dimensional map, searching for an obstacle-free path until the baggage handling fleet reaches the destination. The baggage handling fleet continuously learns through its interactions with surrounding entities. Every time the baggage handling fleet takes a step, the environment judges its behavior based on pre-set priorities and provides positive or negative feedback, ultimately forming a scenario interaction feedback table. After multiple attempts to find a path, the priority factor in the priority table is continuously adjusted based on the feedback table to continuously optimize the optimal path. Vehicles in the baggage handling fleet identify their surroundings, mark them on the initialized map, and then, based on pre-set priorities and the current optimal option, proceed to the area with the highest priority. Create a line between the current point and the target point, and use the number of upper and lower triangle obstacles to solve the problem of equal priority in the area; Determine whether the forward point is the target point. If it is not the target point, continue to search for the next point. If it is the target point, stop searching.

2. The multi-vehicle path planning method for airport baggage check-in according to claim 1 is characterized in that: The method for dividing areas in S1 is as follows: using the existing cameras at the airport, the global obstacle positions are identified and analyzed. Based on the principle that the number of obstacles is positively correlated with the probability of infeasible paths, areas with dense obstacles have a higher probability of infeasible paths being predicted, and the area is coarsely divided by gridding. Areas with scattered obstacles have a lower probability of infeasible paths being predicted, and the area is subdivided by gridding.

3. The multi-vehicle path planning method for airport baggage check-in according to claim 1 is characterized in that: When a baggage handling team senses a moving object approaching within a certain range, the team will slow down. If the moving object stops, the team will continue to move forward. If the moving object continues to move, the team will stop and wait. When the middle part senses that a moving object is approaching within a certain range, the baggage check-in fleet separates from the check-in vehicle closest to the moving object. The front fleet continues to move forward, and the rear fleet repeats the above-mentioned vehicle head judgment process. When the moving object leaves the fleet, the rear fleet acts as the new baggage check-in fleet to find an obstacle-free path again.

4. The multi-vehicle path planning method for airport baggage check-in according to claim 3 is characterized in that: When a vehicle in the baggage handling fleet senses that a moving object is approaching within a certain range and slows down or stops, the vehicle-to-vehicle communication will cause the following vehicle to slow down or stop to varying degrees.

5. A multi-vehicle path planning system for airport baggage check-in, characterized in that: include: Map generation module: used to divide the areas within the airport that require baggage check-in, set regional priorities, and generate a two-dimensional map; Regional priorities are quantified as follows: factor_0: the value of the distance from the target point; factor_1: the value of the accumulated distance; factor_2: the sum of the distances from the target point and the starting point; factor_3: The area is an obstacle or boundary, and the value is negative infinity; Set the corresponding priority factor based on this 、 、 、 , -0.6=< <=-0.4, -2.0=< <=-1.2, -0.6=< <=-0.4, The value of is 1; The travel module is used to mark the starting and destination points on a two-dimensional map. The baggage handling fleet continuously learns through interaction with surrounding entities, iterating towards the highest priority direction initially defined by the algorithm on the two-dimensional map, searching for an obstacle-free path until the baggage handling fleet reaches the destination. The baggage handling fleet continuously learns through its interactions with surrounding entities. Every time the baggage handling fleet takes a step, the environment judges its behavior based on pre-set priorities and provides positive or negative feedback, ultimately forming a scenario interaction feedback table. After multiple attempts to find a path, the priority factor in the priority table is continuously adjusted based on the feedback table to continuously optimize the optimal path. Vehicles in the baggage handling fleet identify their surroundings, mark them on the initialized map, and then, based on pre-set priorities and the current optimal option, proceed to the area with the highest priority. Create a line between the current point and the target point, and use the number of upper and lower triangle obstacles to solve the problem of equal priority in the area; Determine whether the forward point is the target point. If it is not the target point, continue to search for the next point. If it is the target point, stop searching.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the multi-vehicle path planning method applied to airport baggage check-in as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the multi-vehicle path planning method for airport baggage check-in as claimed in any one of claims 1 to 4 are implemented.

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