Ring track RGV multi-vehicle cooperative intelligent scheduling method and system

Abstract

This invention relates to the field of RGV intelligent scheduling technology, and particularly to a method and system for collaborative intelligent scheduling of multiple RGVs on a circular track. The method includes: real-time acquisition of the position, speed, and task status of each RGV vehicle; execution of a scheduling cycle within a specified time interval; wherein: the avoidance position of idle vehicles is obtained based on a mathematical model of integer linear programming; avoidance scheduling instructions are sent to idle vehicles; freight transport tasks are assigned to RGV vehicles for execution based on initial task allocation rules; and dynamic adjustment is performed on RGV vehicles currently executing tasks based on a dynamic adjustment mechanism. This invention solves the problem of collaborative scheduling of multiple RGVs on a circular track by using an integer linear programming mathematical model to achieve collaborative avoidance among multiple vehicles, setting initial task allocation rules to achieve reasonable allocation and scheduling of freight transport tasks, and using a dynamic adjustment mechanism to dynamically adjust RGV vehicles currently executing tasks.

Description

Technical Field

[0001] This invention relates to the field of RGV intelligent scheduling technology, and in particular to a method and system for intelligent scheduling of multiple RGV vehicles on a circular track. Background Technology

[0002] In modern logistics automation systems, RGVs (Rail Guided Vehicles) are widely used in scenarios such as workshop material distribution, tobacco logistics, pharmaceutical distribution, and e-commerce warehousing. Compared with linear tracks, RGVs have advantages such as higher utilization and more flexible scheduling options, but they also bring more complex multi-vehicle scheduling and obstacle avoidance problems: RGVs run clockwise in one direction, and vehicles cannot travel in reverse. When the path frequently crosses the starting point (zero point) of the loop, traditional linear distance calculation methods become cumbersome; when multiple vehicles operate in parallel, phenomena such as path contention, deadlock, and task backlog are prone to occur; in addition, when a vehicle approaches the target platform, it may cross the platform for various reasons and continue driving, but because this cannot be detected, it leads to task backlog and idle vehicle running, resulting in wasted transport capacity.

[0003] The current mainstream RGV scheduling technology for circular tracks mainly suffers from the following problems: First, scheduling methods based on simple rules (such as nearest distance priority, first-come first-served, etc.) are simple to implement and have low computational load, but they cannot take into account constraints such as multi-vehicle path conflicts, distance calculation across zero points on circular tracks, and task priorities. This can easily lead to multi-vehicle collisions, unreasonable avoidance leading to increased total running time, and some tasks being delayed.

[0004] Secondly, among optimization methods based on traditional mathematical models and heuristic algorithms (such as integer programming, genetic algorithms, and greedy algorithms), genetic algorithms are slow to solve problems and difficult to tune parameters, making them unsuitable for real-time scheduling. Greedy algorithms only consider local optima and cannot guarantee global efficiency. Some solutions using commercial solvers lack a complete model of the special characteristics of circular tracks, particularly regarding the handling of "circular closure constraints" and "path crossing zero-point intervals," which leave technical gaps. Furthermore, in existing technologies, each scheduling stage (task allocation, obstacle avoidance planning, station detection, unloading decisions, etc.) typically operates independently, lacking a unified collaborative optimization mechanism.

[0005] Third, at the task execution level, the existing multi-vehicle dispatching system lacks an effective detection and automatic correction mechanism for platform overruns. Once a vehicle crosses the target platform, it cannot automatically return, resulting in task backlog and requiring manual intervention. At the same time, there is no effective error correction mechanism after task allocation, and the task assignment cannot be dynamically adjusted according to the real-time vehicle position, which can easily lead to unreasonable phenomena such as nearby vehicles waiting empty while distant vehicles carrying tasks take detours.

[0006] In summary, the existing technology has the following drawbacks: ① The circular track path modeling is incomplete, and there are errors in the calculation of the distance across the zero point; ② The multi-vehicle avoidance planning lacks a global optimal guarantee; ③ There is a lack of effective detection and automatic correction mechanisms for passing target stations; ④ The task allocation and avoidance planning lack linkage optimization; ⑤ The independent operation of each scheduling link fails to form a collaborative system.

[0007] Therefore, it is necessary to provide a method and system for intelligent scheduling of multiple RGV vehicles on a circular track to solve the above-mentioned technical problems. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention proposes a method and system for intelligent collaborative scheduling of multiple RGV vehicles on a circular track. This method overcomes the difficulty of collaborative scheduling of multiple RGV vehicles in existing track environments.

[0009] In a first aspect, the present invention provides a method for intelligent scheduling of multiple RGVs on a circular track, which collects the position, speed, and task status of each RGV in real time, executes a scheduling cycle within a specified time interval, and sends scheduling instructions to each RGV. The process of one scheduling cycle includes: S1: Solve the mathematical model based on integer linear programming to obtain the avoidance position of the idle car, and send the avoidance scheduling instruction to the idle car; S2: Assign cargo transportation tasks to RGV vehicles for execution based on the initial task allocation rules; S3: Dynamically adjust the RGV vehicles performing tasks based on the dynamic adjustment mechanism.

[0010] Optionally, in S1, the avoidance position of the idle car is obtained by solving a mathematical model based on integer linear programming. The process includes: Establish a mathematical model for integer linear programming, preprocess the coordinates, linearize the tangent point, cut the circular track at this point and straighten it into a linear coordinate system; Set constraints, including kinematic constraints, sequence preservation constraints, and avoidance soft constraints; Establish an objective function. The goal of the model is to minimize the total cost, which includes movement cost, circling cost, and blocking penalty. A solver is used to solve the mathematical model to obtain the avoidance strategy for idle vehicles.

[0011] Optionally, in S1, the process of linearizing the tangent point includes: Select the point with the smallest physical location among all current vehicles as the tangent point. , The point of tangency for the orbital unfolding, i.e. , This represents the physical coordinates of the vehicle's position before the coordinate transformation. This represents the collection of all online RGV vehicles; The physical coordinates of the vehicle's position are transformed to a linear coordinate system using a coordinate transformation function, expressed as follows: ; in, This is a coordinate transformation function used to convert physical coordinates. Mapped to linear coordinates originating from the point of tangency. mod represents the modulo operation. This represents the physical coordinates of the vehicle's position after coordinate transformation. L Indicates the track length; after conversion, the vehicle position satisfies ; Using expanded coordinate function The expression for handling the multi-turn expansion of a circular orbit is as follows: ,in, This represents the number of laps.

[0012] Optionally, in S1, the constraints specifically include: Kinematic constraints: The vehicle travels in one direction, and in a linear coordinate system, the target position is greater than or equal to the current position, and the target position is determined by the relative position and the number of revolutions. ; ; Definition of movement distance: ; in, Indicates vehicle The distance traveled; , represents the set of available cars; , indicating vehicle The relative position of the target; , indicating vehicle The number of loops; Indicates vehicle The absolute target position of the unfolded position. ; Sequence Preservation Constraint: The relative order of vehicles on the track remains unchanged; assume the vehicles are ordered according to their transformed positions as follows: ,but , , The minimum safe distance in the workshop; Avoidance soft constraints: Define idle vehicles With mission vehicle safe zone The relationship; if the idle vehicle successfully yields ( If the target location is outside the safe zone, then the target location must be outside the safe zone; for cross-zone zones... Perform segmentation processing; For the normal interval: ; When avoidance fails, This creates a blocking distance: ; ; in, It is a binary variable representing an idle vehicle. Does it obstruct the mission vehicle? The path; It is a continuous variable representing an idle vehicle. Blocking mission vehicle The remaining distance.

[0013] Optionally, in S1, the expression for the objective function is as follows: ; in: Indicates the weight of the movement distance; Indicates the penalty weight for circling; Indicates the weight of the penalty for obstructive behavior; This indicates the weight of the remaining distance to be blocked.

[0014] Optionally, in S1, the data model solution process is as follows: The solver is used to solve the avoidance strategy of the idle car, and a state snapshot is used to detect whether the input state has changed. If the state has not changed, the solution from the previous round is reused. Will , , The integer variables are mapped to solvers. Using the Big-M method, logical constraints are transformed into linear constraints. Solving time limits are set to find feasible or optimal solutions within a finite time.

[0015] Optionally, in S2, the initial task allocation rules include the nearest allocation rule, the task coverage rule, the dual-cargo consolidation processing rule, and the unloading decision rule, wherein: The nearest allocation rule is as follows: the pickup stations are numbered sequentially in a clockwise direction along the circular track. The available vehicles are allocated goods to the nearest pickup station in descending order of the pickup station number. The conditions for determining whether a vehicle is available are: there are no goods at the left or right workstations of the vehicle, and the vehicle is currently not on a task, or although it has a task, it has not reached the target station. Task coverage determination rules: For RGV vehicles with existing tasks, determine whether to switch to a new task based on the following conditions: Let the current mission target station be... The new mission objective platform is The current vehicle location is The clockwise distances from the current location to the two platforms are respectively and Switching is allowed when all of the following inequalities are met: ; in, This is a configurable distance threshold parameter; Double-cargo consolidation processing rules: The trigger condition for double-cargo consolidation is that two goods come from the same pickup station and their destination stations are different; the consolidation task is assigned to an RGV vehicle. back, The execution process is as follows: first, go to the pickup platform to load two goods in sequence, and then release the goods in sequence according to the distance to their respective destination stations; Unloading decision rule: When the RGV vehicle arrives at the unloading platform, a comprehensive decision is made based on the occupancy status of the two workstations on the left and right sides of the platform; let the workstation status be... 0, 1, 2, 3 represent empty on both sides, occupied only on the left side, occupied only on the right side, and occupied on both sides, respectively; The decision-making strategy for releasing goods is as follows: Place it directly at the target workstation; or When an RGV vehicle carries the same goods to two target stations, it requests a new target station to release the goods. When an RGV vehicle carries a single item, it considers the size data of the item and the items occupied on the target station, and places the item on an empty workstation while meeting collision safety requirements. If collision safety is not met, it requests a new target station to release the goods. When an RGV vehicle carries different items to two target stations, it considers the size data of the item with the current station as the target station, and releases the item while meeting collision safety requirements. If collision safety is not met, it requests a new target station to release the goods. Request a new destination station.

[0016] Optionally, in S3, the dynamic adjustment mechanism includes a platform-crossing detection and correction mechanism, wherein: For RGV vehicles performing tasks, a platform crossing detection is performed. The criteria for determining platform crossing are as follows: ; The vehicle's current location is The target platform location is The total length of the orbit is , and These are all configurable parameters used to set the upper and lower limits of the distance for calibration triggering. The clockwise distance is represented and calculated as follows: ; ; When it is determined that the RGV vehicle has passed the platform, the cargo associated with the task is returned to the queue and a task clearing instruction is written.

[0017] Optionally, in S3, the dynamic adjustment mechanism also includes a path yielding mechanism, wherein: Let the unmanned vehicle performing the pickup task be... Its target platform is The current location is ; no empty trucks The current location is ; Using a clockwise path determination method Determine if the vehicle is within the clockwise path between the two points: ; ; The conditions for triggering the transfer are as follows: ; Clear if satisfied The task is to return the goods to the queue, at which point the next round... Obtain the task.

[0018] In a second aspect, the present invention also provides a multi-vehicle cooperative intelligent scheduling system for a circular track RGV, including a task queue management module, a vehicle status analysis module, a task allocation module, a path transfer module, a past detection and correction module, a multi-vehicle cooperative obstacle avoidance planning module, and an unloading decision module. The task queue management module, vehicle status analysis module, task allocation module, path transfer module, overpass detection and correction module, multi-vehicle collaborative avoidance planning module, and unloading decision module work together to realize the above-mentioned circular track RGV multi-vehicle collaborative intelligent scheduling method.

[0019] The application of the technical solution of the present invention has at least the following beneficial effects: This invention provides a method and system for intelligent scheduling of multiple RGV vehicles on a circular track. By collecting the position, speed, and task status of each RGV vehicle in real time, a mathematical model based on integer linear programming is constructed to achieve cooperative avoidance among multiple vehicles. Furthermore, this invention sets up initial task allocation rules to achieve reasonable allocation and scheduling of cargo transportation tasks, and also uses a dynamic adjustment mechanism to dynamically adjust the RGV vehicles that are performing tasks. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the steps of the multi-vehicle cooperative intelligent scheduling method for a circular track RGV in a preferred embodiment of the present invention. Figure 2 This is a schematic diagram of a circular track in a preferred embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1: like Figure 1 As shown, this embodiment provides a method for intelligent scheduling of multiple RGV vehicles on a circular track. It collects the position, speed and task status of each RGV vehicle in real time, executes a scheduling cycle once within a specified time interval, and sends scheduling instructions to each RGV vehicle.

[0024] like Figure 2 As shown, this embodiment deploys two types of platforms on the circular track: pickup platforms and delivery platforms. The upper track is entirely composed of pickup platforms, while the lower track is entirely composed of delivery platforms. Pickup platforms are divided into two categories based on their carrying capacity: single-cargo pickup platforms (capable of holding only one item, with one workstation) and double-cargo pickup platforms (capable of holding two items simultaneously, with two independent workstations on the left and right). Delivery platforms are uniformly double-cargo delivery platforms, each with two independent delivery workstations on the left and right, used to receive goods from RGV vehicles.

[0025] Each RGV (Rail Guided Vehicle) has two independent cargo conveyor lines, designated as a left and right workstation, with each workstation capable of loading one item. This means that each RGV can carry a maximum of two items simultaneously. When performing a pickup task, an RGV can select the following pickup modes: 1) Single item pickup: Pick up one item from the pickup station and place it at the left or right workstation; 2) Simultaneous pickup of two goods: Two goods are picked up from the same pickup station at one time and placed in the left and right workstations respectively; 3) Dual-station pickup: Pick up one item from two different pickup stations in sequence and place them at the left and right workstations.

[0026] Goods will randomly appear at various pickup platforms, and each item has its unique destination platform determined at the time of dispatch. After picking up the goods, the RGV will proceed to the corresponding delivery platform to perform the delivery task based on the destination information on the goods. Therefore, an RGV can choose from the following delivery modes: 1) Single cargo placement: Take the cargo from the left or right workstation of the vehicle and place it at the left or right workstation of the cargo placement platform; 2) Dual cargo placement: Take cargo from the left and right workstations of the vehicle and place them at the left and right workstations of the cargo placement platform respectively; 3) Dual-station separate placement: Take goods from the left or right workstation of the vehicle and place them in the left or right workstation of the two placement stations in sequence.

[0027] The method in this embodiment achieves the scheduling of all RGV vehicles by executing a scheduling cycle once within a specified time interval. The process of one scheduling cycle includes (S1 to S3): S1: Solve the mathematical model based on integer linear programming to obtain the avoidance position of the idle car, and send the avoidance scheduling instruction to the idle car.

[0028] In this embodiment, the avoidance position of the idle car is obtained by solving a mathematical model based on integer linear programming. The process includes: Establish a mathematical model for integer linear programming, preprocess the coordinates, linearize the tangent point, cut the circular track at this point and straighten it into a linear coordinate system; Set constraints, including kinematic constraints, sequence preservation constraints, and avoidance soft constraints; Establish an objective function. The goal of the model is to minimize the total cost, which includes movement cost, circling cost, and blocking penalty. A solver is used to solve the mathematical model to obtain the avoidance strategy for idle vehicles.

[0029] Specifically, the process of tangent point linearization includes: Select the point with the smallest physical location among all current vehicles as the tangent point. , The point of tangency for the orbital unfolding, i.e. , This represents the physical coordinates of the vehicle's position before the coordinate transformation. This represents the collection of all online RGV vehicles; The physical coordinates of the vehicle's position are transformed to a linear coordinate system using a coordinate transformation function, expressed as follows: ; in, This is a coordinate transformation function used to convert physical coordinates. Mapped to linear coordinates originating from the point of tangency. mod represents the modulo operation. This represents the physical coordinates of the vehicle's position after coordinate transformation. L Indicates the track length; after conversion, the vehicle position satisfies ; Using expanded coordinate function The expression for handling the multi-turn expansion of a circular orbit is as follows: ,in, This represents the number of laps.

[0030] Optionally, in S1, the constraints specifically include: Kinematic constraints: The vehicle travels in one direction, and in a linear coordinate system, the target position is greater than or equal to the current position, and the target position is determined by the relative position and the number of revolutions. ; ; Definition of movement distance: ; in, Indicates vehicle The distance traveled; , represents the set of available cars; , indicating vehicle The relative position of the target; , indicating vehicle The number of loops; Indicates vehicle The absolute target position of the unfolded position. ; Sequence Preservation Constraint: The relative order of vehicles on the track remains unchanged to prevent overtaking; assuming the vehicles are ordered according to their transformed positions as follows: ,but , , The minimum safe distance in the workshop; Avoidance soft constraints: Define idle vehicles With mission vehicle safe zone The relationship; if the idle vehicle successfully yields ( If the target location is outside the safe zone, then the target location must be outside the safe zone; for cross-zone zones... Perform segmentation processing; For the normal interval: ; When avoidance fails, This creates a blocking distance: ; ; in, It is a binary variable representing an idle vehicle. Does it obstruct the mission vehicle? The path; It is a continuous variable representing an idle vehicle. Blocking mission vehicle The remaining distance.

[0031] Furthermore, the expression for the objective function is as follows: ; in: Indicates the weight of travel distance (energy consumption and time cost); This indicates the penalty weight for circling (to avoid meaningless detours); Indicates the weight of punishment for obstructive behavior (a core security indicator); This represents the weight of the remaining blocking distance (minimizing the blocking effect).

[0032] Furthermore, the data model solution process is as follows: The solver is used to solve the avoidance strategy of the idle car, and a state snapshot is used to detect whether the input state has changed. If the state has not changed, the solution from the previous round is reused. Will , , The integer variables are mapped to solvers. Using the Big-M method, logical constraints are transformed into linear constraints. Solving time limits are set to find feasible or optimal solutions within a finite time.

[0033] In this embodiment, an existing solver can be used, and this embodiment does not impose any restrictions.

[0034] S2: Assign cargo transportation tasks to RGV vehicles for execution based on the initial task assignment rules.

[0035] In this embodiment, the initial task allocation rules include the nearest allocation rule, the task coverage rule, the dual-cargo consolidation processing rule, and the unloading decision rule, wherein: The nearest allocation rule is as follows: the pickup stations are numbered sequentially in a clockwise direction along the circular track. The available vehicles are allocated goods to the nearest pickup station in descending order of the pickup station number. The conditions for determining whether a vehicle is available are: there are no goods at the left or right workstations of the vehicle, and the vehicle is currently not on a task, or although it has a task, it has not reached the target station. Task coverage determination rules: For RGV vehicles with existing tasks, determine whether to switch to a new task based on the following conditions: Let the current mission target station be... The new mission objective platform is The current vehicle location is The clockwise distances from the current location to the two platforms are respectively and Switching is allowed when all of the following inequalities are met: ; in, This is a configurable distance threshold parameter; Double-cargo consolidation processing rules: The trigger condition for double-cargo consolidation is that two goods come from the same pickup station and their destination stations are different; the consolidation task is assigned to an RGV vehicle. back, The execution process is as follows: first, go to the pickup platform to load two goods in sequence, and then release the goods in sequence according to the distance to their respective destination stations; Unloading decision rule: When the RGV vehicle arrives at the unloading platform, a comprehensive decision is made based on the occupancy status of the two workstations on the left and right sides of the platform; let the workstation status be... 0, 1, 2, 3 represent empty on both sides, occupied only on the left side, occupied only on the right side, and occupied on both sides, respectively; The decision-making strategy for releasing goods is as follows: Place it directly at the target workstation; or When an RGV vehicle carries the same goods to two target stations, it requests a new target station to release the goods. When an RGV vehicle carries a single item, it considers the size data of the item and the items occupied on the target station, and places the item on an empty workstation while meeting collision safety requirements. If collision safety is not met, it requests a new target station to release the goods. When an RGV vehicle carries different items to two target stations, it considers the size data of the item with the current station as the target station, and releases the item while meeting collision safety requirements. If collision safety is not met, it requests a new target station to release the goods. Request a new destination station.

[0036] It should be noted that specific collision safety requirements can be determined based on the type of cargo.

[0037] S3: Dynamically adjust the RGV vehicles performing tasks based on the dynamic adjustment mechanism.

[0038] In this embodiment, the dynamic adjustment mechanism includes a platform-crossing detection and correction mechanism, wherein: For RGV vehicles performing tasks, a platform crossing detection is performed. The criteria for determining platform crossing are as follows: ; The vehicle's current location is The target platform location is The total length of the orbit is , and These are all configurable parameters used to set the upper and lower limits of the distance for calibration triggering. The clockwise distance is represented and calculated as follows: ; ; When it is determined that the RGV vehicle has passed the platform, the cargo associated with the task is returned to the queue and a task clearing instruction is written.

[0039] Furthermore, the dynamic adjustment mechanism also includes a path transfer mechanism, in which: Let the unmanned vehicle performing the pickup task be... Its target platform is The current location is ; no empty trucks The current location is ; Using a clockwise path determination method Determine if the vehicle is within the clockwise path between the two points: ; ; The conditions for triggering the transfer are as follows: ; Clear if satisfied The task is to return the goods to the queue, at which point the next round... Obtain the task.

[0040] This embodiment provides a method and system for intelligent scheduling of multiple RGV vehicles on a circular track. By collecting the position, speed, and task status of each RGV vehicle in real time, a mathematical model based on integer linear programming is constructed to achieve cooperative avoidance among multiple vehicles. Furthermore, the method of this invention sets initial task allocation rules to achieve reasonable allocation and scheduling of cargo transportation tasks, and also dynamically adjusts the RGV vehicles that are performing tasks through a dynamic adjustment mechanism.

[0041] Example 2: This embodiment provides a circular track RGV multi-vehicle cooperative intelligent scheduling system, including a task queue management module, a vehicle status analysis module, a task allocation module, a path transfer module, a past detection and correction module, a multi-vehicle cooperative obstacle avoidance planning module, and an unloading decision module; The task queue management module, vehicle status analysis module, task allocation module, path transfer module, overpass detection and correction module, multi-vehicle collaborative avoidance planning module, and unloading decision module work together to implement the circular track RGV multi-vehicle collaborative intelligent scheduling method as in Example 1. The implementation method and technical effects of the system in this example can refer to the method in Example 1, and will not be repeated here.

[0042] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0043] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent scheduling of multiple vehicles in a circular track RGV system, characterized in that, The system collects the location, speed, and task status of each RGV vehicle in real time, executes a scheduling cycle at specified time intervals, and sends scheduling instructions to each RGV vehicle. One scheduling cycle includes: S1: Solve the mathematical model based on integer linear programming to obtain the avoidance position of the idle car, and send the avoidance scheduling instruction to the idle car; S2: Assign cargo transportation tasks to RGV vehicles for execution based on the initial task allocation rules; S3: Dynamically adjust the RGV vehicles performing tasks based on the dynamic adjustment mechanism.

2. The intelligent scheduling method for multi-vehicle cooperative operation of a circular track RGV according to claim 1, characterized in that, In S1, the avoidance position of the idle car is obtained by solving a mathematical model based on integer linear programming. The process includes: Establish a mathematical model for integer linear programming, preprocess the coordinates, linearize the tangent point, cut the circular track at this point and straighten it into a linear coordinate system; Set constraints, including kinematic constraints, sequence preservation constraints, and avoidance soft constraints; Establish an objective function. The goal of the model is to minimize the total cost, which includes movement cost, circling cost, and blocking penalty. A solver is used to solve the mathematical model to obtain the avoidance strategy for idle vehicles.

3. The circular track RGV multi-vehicle cooperative intelligent scheduling method according to claim 2, characterized in that, In S1, the process of linearizing the tangent point includes: Select the point with the smallest physical location among all current vehicles as the tangent point. , The point of tangency for the unfolding of the orbit, i.e. , This represents the physical coordinates of the vehicle's position before the coordinate transformation. This represents the collection of all online RGV vehicles; The physical coordinates of the vehicle's position are transformed to a linear coordinate system using a coordinate transformation function, expressed as follows: ； in, This is a coordinate transformation function used to convert physical coordinates. Mapped to linear coordinates originating from the point of tangency. mod represents the modulo operation. This represents the physical coordinates of the vehicle's position after coordinate transformation. L Indicates the track length; after conversion, the vehicle position satisfies ; Using expanded coordinate function The expression for handling the multi-turn expansion of a circular orbit is as follows: ,in, This represents the number of laps.

4. The intelligent scheduling method for multi-vehicle cooperative operation of a circular track RGV according to claim 3, characterized in that, In S1, the constraints specifically include: Kinematic constraints: The vehicle travels in one direction, and in a linear coordinate system, the target position is greater than or equal to the current position, and the target position is determined by the relative position and the number of revolutions. ； ； Definition of movement distance: ； in, Indicates vehicle The distance traveled; , represents the set of available cars; , indicating vehicle The relative position of the target; , indicating vehicle The number of loops; Indicates vehicle The absolute target position of the unfolded position. ; Sequence Preservation Constraint: The relative order of vehicles on the track remains unchanged; assume the vehicles are ordered according to their transformed positions as follows: ,but , , The minimum safe distance in the workshop; Avoidance soft constraints: Define idle vehicles With mission vehicle safe zone The relationship; if the idle vehicle successfully yields ( If the target location is outside the safe zone, then the target location must be outside the safe zone; for cross-zone zones... Perform segmentation processing; For the normal interval: ; When avoidance fails, This creates a blocking distance: ； ； in, It is a binary variable representing an idle vehicle. Does it obstruct the mission vehicle? The path; It is a continuous variable representing an idle vehicle. Blocking mission vehicle The remaining distance.

5. The intelligent scheduling method for multi-vehicle cooperative operation of a circular track RGV according to claim 4, characterized in that, In S1, the objective function is expressed as follows: ； in: Indicates the weight of the distance traveled; Indicates the penalty weight for circling; Indicates the weight of the penalty for obstructive behavior; This indicates the weight of the remaining distance to be blocked.

6. The intelligent scheduling method for multi-vehicle cooperative operation of a circular track RGV according to claim 5, characterized in that, In S1, the data model solution process is as follows: The solver is used to solve the avoidance strategy of the idle car, and a state snapshot is used to detect whether the input state has changed. If the state has not changed, the solution from the previous round is reused. Will , , The integer variables are mapped to solvers. Using the Big-M method, logical constraints are transformed into linear constraints. Solving time limits are set to find feasible or optimal solutions within a finite time.

7. The intelligent scheduling method for multi-vehicle cooperative operation of a circular track RGV according to claim 1, characterized in that, In S2, the initial task allocation rules include the nearest allocation rule, task coverage rule, dual-cargo consolidation processing rule, and unloading decision rule, among which: The nearest allocation rule is as follows: the pickup stations are numbered sequentially in a clockwise direction along the circular track. The available vehicles are allocated goods to the nearest pickup station in descending order of the pickup station number. The conditions for determining whether a vehicle is available are: there are no goods at the left or right workstations of the vehicle, and the vehicle is currently not on a task, or although it has a task, it has not reached the target station. Task coverage determination rules: For RGV vehicles with existing tasks, determine whether to switch to a new task based on the following conditions: Let the current mission target station be... The new mission objective platform is The current vehicle location is The clockwise distances from the current location to the two platforms are respectively and Switching is allowed when all of the following inequalities are met: ； in, This is a configurable distance threshold parameter; Double-cargo consolidation processing rules: The trigger condition for double-cargo consolidation is that two goods come from the same pickup station and their destination stations are different; the consolidation task is assigned to an RGV vehicle. back, The execution process is as follows: first, go to the pickup platform to load two goods in sequence, and then release the goods in sequence according to the distance to their respective destination stations; Unloading decision rule: When the RGV vehicle arrives at the unloading platform, a comprehensive decision is made based on the occupancy status of the two workstations on the left and right sides of the platform; let the workstation status be... 0, 1, 2, 3 represent empty on both sides, occupied only on the left side, occupied only on the right side, and occupied on both sides, respectively; The decision-making strategy for releasing goods is as follows: Place it directly at the target workstation; or When an RGV vehicle carries the same goods to two target stations, it requests a new target station to release the goods. When an RGV vehicle carries a single item, it considers the size data of the item and the items occupied on the target station, and places the item on an empty workstation while meeting collision safety requirements. If collision safety is not met, it requests a new target station to release the goods. When an RGV vehicle carries different items to two target stations, it considers the size data of the item with the current station as the target station, and releases the item while meeting collision safety requirements. If collision safety is not met, it requests a new target station to release the goods. Request a new destination station.

8. The intelligent scheduling method for multi-vehicle cooperative operation of a circular track RGV according to claim 1, characterized in that, In S3, the dynamic adjustment mechanism includes a platform-crossing detection and correction mechanism, wherein: For RGV vehicles performing tasks, a platform crossing detection is performed. The criteria for determining platform crossing are as follows: ； The vehicle's current location is The target platform location is The total length of the orbit is , and These are all configurable parameters used to set the upper and lower limits of the distance for calibration triggering. The clockwise distance is represented and calculated as follows: ； ； When it is determined that the RGV vehicle has passed the platform, the cargo associated with the task is returned to the queue and a clear task instruction is written.

9. The intelligent scheduling method for multi-vehicle cooperative operation of a circular track RGV according to claim 8, characterized in that, In S3, the dynamic adjustment mechanism also includes a path transfer mechanism, in which: Let the unmanned vehicle performing the pickup task be... Its target platform is The current location is ; no empty trucks The current location is ; Using a clockwise path determination method Determine if the vehicle is within the clockwise path between the two points: ； ； The conditions for triggering the transfer are as follows: ； Clear if satisfied The task is to return the goods to the queue, at which point the next round... Obtain the task.

10. A multi-vehicle cooperative intelligent scheduling system for a circular track RGV, characterized in that, It includes a task queue management module, a vehicle status analysis module, a task allocation module, a path transfer module, a past detection and correction module, a multi-vehicle cooperative obstacle avoidance planning module, and an unloading decision module; The task queue management module, vehicle status analysis module, task allocation module, path transfer module, overpass detection and correction module, multi-vehicle collaborative avoidance planning module, and unloading decision module work together to implement the circular track RGV multi-vehicle collaborative intelligent scheduling method as described in any one of claims 1-9.

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