Industrial logistics AGV intersection area traffic flow collaborative time sequence planning and scheduling method

Abstract

The invention provides an industrial logistics AGV intersection area traffic flow collaborative time sequence planning and scheduling method, and the method comprises the steps: carrying out the collaborative consideration of a global traffic flow, carrying out the marking and time sequence combination calculation of an intersection point of an AGV intersection area, and building an initial solution, and carrying out safety interval, intersection area processing strategy and driving route priority constraint on the AGV, continuously circulating until no solution is finally obtained, and obtaining a final approximate optimal solution, so that the AGV can safely and efficiently pass through the intersection area without staying and congestion. The method has important reference significance for AGV and driving route management and intersection area traffic flow collaborative planning.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV planning and scheduling, and in particular to a method for coordinated timing planning and scheduling of traffic flow in an industrial logistics AGV intersection area. Background Art

[0002] As the level of intelligence and automation in the manufacturing industry continues to improve, the importance of industrial logistics in the production process has become increasingly prominent. In large-scale, high-efficiency production environments, AGV (Automated Guided Vehicle) as an intelligent material handling equipment is widely used in factory workshops, warehouses and other places.

[0003] However, with the increase in the number of AGVs and the complexity of logistics tasks, the traffic conflict problem of AGVs in the intersection area has gradually become a key factor restricting the efficiency and safety of industrial logistics systems.

[0004] Traditional AGV scheduling methods usually use simple rules or fixed priority-based strategies, which cannot effectively cope with complex and changing traffic conditions. For example, when multiple AGVs arrive at the intersection area at the same time, deadlocks and collisions may occur, leading to stagnation of the logistics system and production delays. In addition, traditional methods often lack coordinated consideration of global traffic flow, making it difficult to achieve the optimal configuration of system resources, thus affecting the operating efficiency and cost-effectiveness of the entire industrial logistics system. Summary of the invention

[0005] In response to the problems existing in the prior art, the present invention provides a method for coordinated timing planning and scheduling of traffic flow in the intersection area of ​​industrial logistics AGVs, so as to solve the technical problems in the prior art in some single scenarios such as follow-up rotation, where the rotation accuracy is not high, resulting in increased warehouse maintenance time and maintenance costs, reduced warehouse operation efficiency, and even more seriously threatening the personal safety of employees.

[0006] The present invention provides a method for coordinated time sequence planning and scheduling of traffic flow in an industrial logistics AGV intersection area, comprising:

[0007] S1. Obtain the driving route of each AGV from the starting point to the end point through map data, calculate the driving time of the AGV from the starting point to the end point using the driving speed of the AGV, mark the intersection points of the AGV intersection area and calculate the AGV exit time series set of each intersection point;

[0008] S2. Randomly generate n mutually unequal numbers that meet the fixed time, use them as a set of initial solutions, and generate a total of m sets of initial solutions to form an m×n array;

[0009] S3, combining the time series set to perform safety interval, intersection area processing strategy and driving route priority constraints on the m×n array, and storing the solutions that meet the constraints into a feasible solution set;

[0010] S4, comprehensively optimizing the feasible solution set to find an approximate optimal solution;

[0011] S5. Gradually compress the fixed time range and repeat steps S2-S4 until there is no solution. The last near-optimal solution is used as the timing plan of the AGV.

[0012] Optionally, the randomly generating n mutually unequal numbers within a fixed time includes:

[0013] The fixed time needs to meet the following intervals:

[0014] t start_earlist ≤t dj_start ≤t start_latest

[0015] Among them, t dj_start represents the departure time of the jth AGV from starting point d, t start_earlist Indicates the earliest allowed AGV exit time from the starting point, t start_latest It indicates the latest allowed AGV exit time from the starting point, and n is the total number of AGVs exiting.

[0016] Optionally, the safety interval includes:

[0017] The time interval between two AGVs driving out from the same starting point must exceed the safety interval ΔT d , and expressed as:

[0018] t dj+1_start -t dj_start ≥ΔT d

[0019] Among them, t dj+1_start Represents the departure time of the j+1th AGV from the starting point d in the time series set.

[0020] Optionally, the intersection area processing strategy further includes:

[0021] The time difference between any two AGVs passing the intersection point cannot be less than the safe intersection time ΔT dj×d'j'_cross , and expressed as:

[0022] (t dj_start +t dj_cross_driving )-(t d'j'_start +t d'j'_cross_driving )|≥ΔT dj×d'j'_cross

[0023] Among them, t dj_cross_driving It is represented as the travel time of the jth AGV from the AGV starting point d in the time series set to reach the intersection, t d'j'_start represents the departure time of the j'th AGV from the starting point d' in the time series set, t d'j'_cross_driving It is expressed as the travel time of the j'th AGV from the d'th AGV starting point to the intersection in the time series set, ΔT dj×d'j'_cross It represents the safe driving time of the jth AGV starting from the starting point d and the j'th AGV starting from the starting point d' to meet at the intersection point, where the intersection point is generated by the intersection of the driving route of the jth AGV from the dth starting point and the driving route of the j'th AGV from the d'th AGV starting point.

[0024] Optionally, the driving route priority includes:

[0025] According to the driving speed of the AGV, the distance l from the current position of the AGV to the intersection point and the tonnage of the AGV body T o Prioritize driving routes:

[0026]

[0027] Among them, ω is the weight coefficient, and the driving route priority of the AGV is determined according to the weight coefficient.

[0028] Optionally, the set of feasible solutions is comprehensively optimized to find a near-optimal solution, including:

[0029] The situation in which all AGVs ensure that they complete the task in the shortest time is taken as the approximate optimal solution, which is expressed as:

[0030] t=min(max{t dj_start})

[0031] Among them, t is the departure time of the last AGV of all intersections.

[0032] Compared with the prior art, the present invention has the following advantages:

[0033] By taking the global traffic flow into consideration in a coordinated manner, the intersection points in the AGV intersection area are marked and calculated in time series. An initial solution is established and then subjected to safety intervals, intersection area processing strategies, and driving route priority constraints. The solution is continuously cycled until no solution is found. The final approximate optimal solution is obtained, which enables the AGV to pass through the intersection area safely and efficiently without stopping or congestion. This has important reference significance for the management of AGVs and driving routes and the coordinated planning of traffic flow in intersection areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0036] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0037] Figure 2 This is a schematic diagram of AGV driving in the intersection area of ​​the present invention;

[0038] Figure 3 It is a schematic diagram of the initial solution of the AGV exit time series in the present invention;

[0039] Figure 4 It is a schematic diagram of the intersection point of the driving routes in the intersection area in the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other implementation cases obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The functional units with the same labels in the examples of the present invention have the same and similar structures and functions.

[0041] See also Figure 1 The present invention provides a method for coordinating and scheduling traffic flow in an industrial logistics AGV intersection area, comprising:

[0042] S1. Obtain the driving route of each AGV from the starting point to the end point through map data, calculate the driving time of the AGV from the starting point to the end point using the driving speed of the AGV, mark the intersection points of the AGV intersection area and calculate the AGV exit time series set of each intersection point;

[0043] S2. Randomly generate n mutually unequal numbers that meet the fixed time, use them as a set of initial solutions, and generate a total of m sets of initial solutions to form an m×n array;

[0044] S3, combining the time series set to perform safety interval, intersection area processing strategy and driving route priority constraints on the m×n array, and storing the solutions that meet the constraints into a feasible solution set;

[0045] S4, comprehensively optimizing the feasible solution set to find an approximate optimal solution;

[0046] S5. Gradually compress the fixed time range and repeat steps S2-S4 until there is no solution. The last near-optimal solution is used as the timing plan of the AGV.

[0047] In this embodiment, S1, the driving route of each AGV from the starting point to the end point is obtained through map data, the driving speed of the AGV is used to calculate the driving time of the AGV from the starting point to the end point, and the intersection points of the AGV intersection area are marked and the AGV exit time series set of each intersection point is calculated.

[0048] Refer to 2. Use map data to quickly obtain the distance from each AGV's starting point to the end point, and use the average driving speed of the AGV to calculate the driving time of the AGV from the starting point to the end point. Mark the intersection points of the intersection area and calculate the two time series sets of each intersection point (i.e., time = distance / speed).

[0049] See also Figure 4 , 3 AGVs drive out from each starting point, and a total of 24 AGVs drive out as an example. Assuming that all AGVs are of the same type, size, and performance, and the average speed of AGVs in the east-west direction is 1.2m / s, and the average speed of AGVs in the north-south direction is 1.1m / s, the distance and time traveled by each AGV from the starting point to the end point are shown in Table 1.

[0050]

[0051] Table 1

[0052] After calculation, the eight driving routes form a total of 16 driving route intersections. Each intersection has two time attributes (combined to form a time series set), that is, the time it takes for the AGV to travel from the two starting points to the driving route intersection is shown in Table 2.

[0053]

[0054] Table 2

[0055] S2. Randomly generate n mutually unequal numbers that satisfy a fixed time, use them as a set of initial solutions, and generate a total of m sets of initial solutions to form an m×n array.

[0056] Randomly generate a set of n mutually unequal numbers within a fixed time (set according to requirements), which is an initial solution. n is the total number of AGVs driving out of each intersection. Use the same method to randomly generate m sets of initial solutions and store them in an m×n array. Then reset the initial solutions to zero. Subtract the smallest number from each set of random numbers to ensure that each set of random numbers has an AGV driving out at time 0.

[0057] See also Figure 3 , m groups are generated, representing the initial solution scale, each group contains the time series of n AGVs, ensuring that each group of time series corresponds to a group of AGV exit timing planning schemes at the intersection.

[0058] The fixed time needs to meet the following intervals:

[0059] t start_earlist ≤t dj_start ≤t start_latest

[0060] Among them, t dj_start represents the departure time of the jth AGV from starting point d, t start_earlist Indicates the earliest allowed AGV exit time from the starting point, t start_latest It indicates the latest allowed AGV exit time from the starting point, and n is the total number of AGVs exiting.

[0061] S3. Combining the time series set, the m×n array is subjected to safety interval, intersection area processing strategy and driving route priority constraints, and the solutions satisfying the constraints are stored in the feasible solution set.

[0062] The safety interval, intersection area processing strategy and driving route priority constraints are respectively applied to the m×n array, where:

[0063] The time interval between two AGVs driving out from the same starting point must exceed the safety interval ΔT d , and expressed as:

[0064] t dj+1_start -t dj_start ≥ΔT d

[0065] Among them, t dj+1_start Represents the departure time of the j+1th AGV from the starting point d in the time series set.

[0066] Then, the time difference between any two AGVs passing the intersection point cannot be less than the safe intersection time ΔT dj×d'j'_cross , and expressed as:

[0067] (t dj_start +t dj_cross_driving )-(t d'j'_start +td'j'_cross_driving )|≥ΔT dj×d'j'_cross

[0068] Among them, t dj_cross_driving It is represented as the travel time of the jth AGV from the AGV starting point d in the time series set to reach the intersection, t d'j'_start represents the departure time of the j'th AGV from the starting point d' in the time series set, t d'j'_cross_driving It is expressed as the travel time of the j'th AGV from the d'th AGV starting point to the intersection in the time series set, ΔT dj×d'j'_cross It represents the safe driving time of the jth AGV starting from the starting point d and the j'th AGV starting from the starting point d' to meet at the intersection point, where the intersection point is generated by the intersection of the driving route of the jth AGV from the dth starting point and the driving route of the j'th AGV from the d'th AGV starting point.

[0069] Finally, according to the AGV's driving speed, the distance l from the AGV's current position to the intersection point, and the AGV's body tonnage T o Prioritize driving routes:

[0070]

[0071] Among them, ω is the weight coefficient, and the driving route priority of the AGV is determined according to the weight coefficient.

[0072] D represents the set of AGV starting points, d∈D and d∈N * ;

[0073] J dj represents the jth AGV that leaves from the AGV starting point d, and j∈N *

[0074] T represents the AGV destination set.

[0075] Based on the above, to ensure that each AGV can safely pass through the intersection area of ​​the driving route, the AGV starting point needs to meet the following time constraints when sending out the AGV:

[0076] (1)ΔT d : The shortest time interval ΔT when each AGV starting point sends out two AGVs in succession d ≥3s, and all AGVs should be driven out of the starting point within one day (24h).

[0077] (2) The time interval between two AGVs passing through the intersection of their routes must be no less than 2 seconds, that is, ΔT dj×d'j'_cross ≥2s.

[0078] S4. Comprehensively optimize the feasible solution set to find an approximate optimal solution.

[0079] According to the driving speed of the AGV, the distance l from the current position of the AGV to the intersection point and the tonnage of the AGV body T o Prioritize driving routes:

[0080]

[0081] Among them, ω is the weight coefficient, and the driving route priority of the AGV is determined according to the weight coefficient.

[0082] S5. Gradually compress the fixed time range and repeat steps S2-S4 until there is no solution. The last near-sighted optimal solution is used as the timing planning of the AGV.

[0083] Based on the above operations, the initial solution scale is 4000 groups, and the maximum number of iterations is 1000. The model and method can solve the feasible solution of the traffic flow exit time series of each driving route, as shown in Table 3 (all AGV exit time series before and after optimization). Before optimizing the feasible solution, it can be found that two AGVs can exit at time 0, namely AGV No. 3 of D1 and AGV No. 15 of D5. AGV No. 12 of D4 exits last, and the algorithm searches to find that the shortest time for all AGVs to exit from each is 726s. After optimizing the feasible solution, it can be found that two AGVs can exit at time 0, namely AGV No. 2 of D1 and AGV No. 16 of D6. AGV No. 11 of D4 exits last, and the algorithm searches to find that the shortest time for all AGVs to exit from each is compressed to 563s.

[0084]

[0085]

[0086] Table 3

[0087] Statistical analysis software (IBM SPSS Statistics) was used to analyze the feasible solutions of the time series before and after optimization. The t-test was used to test the significance of the difference between the time series before and after optimization, and the significance test value of the difference between the time series before and after optimization was p = 0.012 < α = 0.05, which means that the quality of the time series of AGV collaborative driving after optimization has been significantly improved.

[0088] The results of the example show that the model and method of the paper can solve the time series of coordinated traffic flow in the intersection area, and further optimize the time series, so that the AGV can pass through the intersection area without stopping, congestion, safety and efficiency. It has important reference significance for the management of AGV and driving routes and the coordinated planning of traffic flow in the intersection area.

[0089] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0090] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for coordinated time sequence planning and scheduling of traffic flow in an industrial logistics AGV intersection area, characterized in that: include: S1. Obtain the driving route of each AGV from the starting point to the end point through map data, calculate the driving time of the AGV from the starting point to the end point using the driving speed of the AGV, mark the intersection points of the AGV intersection area and calculate the AGV exit time series set of each intersection point; S2. Randomly generate n mutually unequal numbers that meet the fixed time, use them as a set of initial solutions, and generate a total of m sets of initial solutions to form an m×n array; S3, combining the time series set to perform safety interval, intersection area processing strategy and driving route priority constraints on the m×n array, and storing the solutions that meet the constraints into a feasible solution set; S4, comprehensively optimizing the feasible solution set to find an approximate optimal solution; S5. Gradually compress the fixed time range and repeat steps S2-S4 until there is no solution. The last near-optimal solution is used as the timing plan of the AGV.

2. The method for coordinated time sequence planning and scheduling of traffic flow in the industrial logistics AGV intersection area as claimed in claim 1 is characterized in that: The random generation satisfies n mutually unequal numbers within a fixed time, including: The fixed time needs to meet the following intervals: t start_earlist ≤t dj_start ≤t start_latest Among them, t dj_start represents the departure time of the jth AGV from starting point d, t start_earlist Indicates the earliest allowed AGV exit time from the starting point, t start_latest It indicates the latest allowed AGV exit time from the starting point, and n is the total number of AGVs exiting.

3. The method for coordinated time sequence planning and scheduling of traffic flow in the industrial logistics AGV intersection area as claimed in claim 2 is characterized in that: The safety interval includes: The time interval between two AGVs driving out from the same starting point must exceed the safety interval ΔT d , and expressed as: t dj+1_start -t dj_start ≥ΔT d Among them, t dj+1_start Represents the departure time of the j+1th AGV from the starting point d in the time series set.

4. The method for coordinated time sequence planning and scheduling of traffic flow in the industrial logistics AGV intersection area as claimed in claim 3 is characterized in that: The intersection area processing strategy also includes: The time difference between any two AGVs passing the intersection point cannot be less than the safe intersection time ΔT dj×d'j'_cross , and expressed as: (t dj_start +t dj_cross_driving )-(t d'j'_start +t d'j'_cross_driving )|≥ΔT dj×d'j'_cross Among them, t dj_cross_driving It is represented as the travel time of the jth AGV from the AGV starting point d in the time series set to reach the intersection, t d'j'_start represents the departure time of the j'th AGV from the starting point d' in the time series set, t d'j'_cross_driving It is expressed as the travel time of the j'th AGV from the d'th AGV starting point to the intersection in the time series set, ΔT dj×d'j'_cross It represents the safe driving time of the jth AGV starting from the starting point d and the j'th AGV starting from the starting point d' to meet at the intersection point, where the intersection point is generated by the intersection of the driving route of the jth AGV from the dth starting point and the driving route of the j'th AGV from the d'th AGV starting point.

5. The method for coordinated time sequence planning and scheduling of traffic flow in the industrial logistics AGV intersection area as claimed in claim 4, characterized in that: The driving route priority includes: According to the driving speed of the AGV, the distance l from the current position of the AGV to the intersection point and the tonnage of the AGV body T o Prioritize driving routes: Among them, ω is the weight coefficient, and the driving route priority of the AGV is determined according to the weight coefficient.

6. The method for coordinated time sequence planning and scheduling of traffic flow in the industrial logistics AGV intersection area as claimed in claim 5, characterized in that: The feasible solution set is comprehensively optimized to find an approximate optimal solution, including: The situation in which all AGVs ensure that they complete the task in the shortest time is taken as the approximate optimal solution, which is expressed as: t=min(max{t dj_start }) Among them, t is the departure time of the last AGV of all intersections.

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