Unmanned aerial vehicle airport cluster scheduling method, system and equipment

Abstract

The invention provides an unmanned aerial vehicle airport cluster scheduling method, system and equipment, and the method comprises the following steps: carrying out the parameter initialization of an airport nest, an unmanned aerial vehicle, a task execution starting point and a task execution terminal point based on a task instruction, and building the adjacency relation between the airport nest and the task execution starting point and the task execution terminal point; determining a first sub-path of the unmanned aerial vehicle from the initial nest to the task execution start point, determining a second sub-path of the unmanned aerial vehicle from the task execution end point to the return nest, and generating a third sub-path of the unmanned aerial vehicle from the task execution start point to the task execution end point; and based on the first branch path, the second branch path and the third branch path, synthesizing a total path of the unmanned aerial vehicle starting from the initial aircraft nest, passing through the task execution starting point and the task execution terminal point and returning to the aircraft nest when the unmanned aerial vehicle executes the task. According to the method, an effective scheduling scheme is provided for cross-nest ordered operation of the unmanned aerial vehicle, and the operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a method, system and equipment for dispatching clusters of UAVs at airports. Background Art

[0002] With the continuous expansion of drone application scenarios, such as logistics distribution, inspection, surveying and mapping, multi-hive collaborative operation has become an inevitable trend. However, the current scheduling of drones between different nests lacks efficient and accurate methods, and often faces problems such as large flight path overhead, idle or overloaded nest resources, which seriously affects the operation efficiency and cannot meet the complex and changeable multi-task requirements. Therefore, an optimized scheduling solution is urgently needed to achieve smooth operation of drones in the nest network. Summary of the invention

[0003] The technical problem to be solved by the embodiments of the present invention is that there is a lack of efficient and accurate method for adjusting drones between different nests in the prior art, resulting in low resource utilization efficiency and high overhead costs.

[0004] In view of this, the present invention provides a method for dispatching drone airport clusters.

[0005] The present invention also provides a UAV airport cluster dispatching system.

[0006] The invention also provides an electronic device.

[0007] The present invention further provides a readable storage medium.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] The method for dispatching a drone airport cluster according to the first embodiment of the present invention comprises the following steps:

[0010] Based on the mission instructions, the parameters of the machine nest, UAV, mission execution starting point and mission execution end point are initialized, and the adjacency relationship between the machine nest and the mission execution starting point and mission execution end point is established;

[0011] Determine the first sub-path of the UAV from the initial machine nest to the starting point of the mission execution, determine the second sub-path of the UAV from the mission execution end point to the return machine nest, and generate the third sub-path of the UAV from the mission execution start point to the mission execution end point;

[0012] Based on the first sub-path, the second sub-path and the third sub-path, the total path of the synthetic UAV when performing a task starts from the initial machine nest, passes through the task execution starting point, the task execution end point, and returns to the machine nest.

[0013] The drone airport cluster scheduling method according to an embodiment of the present invention may also include the following technical features:

[0014] Furthermore, the determining of the first sub-path of the UAV from the initial nest to the mission execution starting point specifically includes:

[0015] Optimize the objective function by solving integer linear programming:

[0016]

[0017] The corresponding constraints are:

[0018]

[0019] Among them, i and j are positive integers respectively;

[0020] u i It is the initial nest of the drone;

[0021] is the starting point for task execution of task j;

[0022] For a single drone, start from the initial nest u i Go to the starting point of the task expenses;

[0023] is the decision parameter. When the UAV moves from the initial nest u i Go to the starting point of the task hour, is 1, otherwise, is 0;

[0024] P1 is the initial nest u of the drone i Go to the starting point of the task total expenditure.

[0025] Furthermore, the minimum path method is used to calculate

[0026] Further, the determining of the second branch path of the UAV from the mission execution end point to the return to the machine nest specifically includes:

[0027] Optimize the objective function by solving integer linear programming:

[0028]

[0029] The corresponding constraints are:

[0030]

[0031] Wherein, k and j are positive integers respectively;

[0032] v k For the drone to return to the nest;

[0033] It is the task execution endpoint of task j;

[0034] For a single drone from the mission execution end point To return to the machine nest v k expenses;

[0035] is the decision parameter. When the UAV moves from the initial nest u i To the end of the task execution hour, is 1, otherwise, is 0;

[0036] P2 is the end point of the human-machine task execution To return to the machine nest v k total expenditure.

[0037] Furthermore, the minimum path method is used to calculate

[0038] The drone airport cluster dispatching system according to the second aspect of the present invention includes:

[0039] The modeling module initializes the parameters of the machine nest, UAV, the mission execution starting point and the mission execution end point based on the mission instruction, and establishes the adjacency relationship between the machine nest and the mission execution starting point and the mission execution end point;

[0040] The processing module determines a first sub-path of the UAV from the initial machine nest to the starting point of the mission execution, determines a second sub-path of the UAV from the mission execution end point to the return machine nest, and generates a third sub-path of the UAV from the mission execution start point to the mission execution end point;

[0041] The generation module, based on the first sub-path, the second sub-path and the third sub-path, synthesizes the total path of the UAV starting from the initial machine nest, passing through the task execution starting point, the task execution end point, and returning to the machine nest when performing the task.

[0042] The drone airport cluster dispatching system according to an embodiment of the present invention may also include the following technical features:

[0043] Furthermore, the determining of the first sub-path of the UAV from the initial nest to the mission execution starting point specifically includes:

[0044] Optimize the objective function by solving integer linear programming:

[0045]

[0046] The corresponding constraints are:

[0047]

[0048] Among them, i and j are positive integers respectively;

[0049] u i It is the initial nest of the drone;

[0050] is the starting point for task execution of task j;

[0051] For a single drone, start from the initial nest u i Go to the starting point of the task expenses;

[0052] is the decision parameter. When the UAV moves from the initial nest u i Go to the starting point of the task hour, is 1, otherwise, is 0;

[0053] P1 is the initial nest u of the drone i Go to the starting point of the task total expenditure.

[0054] Further, the determining of the second branch path of the UAV from the mission execution end point to the return to the machine nest specifically includes:

[0055] Optimize the objective function by solving integer linear programming:

[0056]

[0057] The corresponding constraints are:

[0058]

[0059]

[0060] Wherein, k and j are positive integers respectively;

[0061] v k For the drone to return to the nest;

[0062] It is the task execution endpoint of task j;

[0063] For a single drone from the mission execution end point To return to the machine nest v k expenses;

[0064] is the decision parameter. When the UAV moves from the initial nest ui To the end of the task execution hour, is 1, otherwise, is 0;

[0065] P2 is the end point of the human-machine task execution To return to the machine nest v k total expenditure.

[0066] According to an embodiment of the third aspect of the present invention, the electronic device includes: a processor, a communication interface, a communication bus, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the drone airport cluster scheduling method as described in the above embodiment is implemented.

[0067] According to the readable storage medium of the fourth aspect of the present invention, a program is stored on the readable storage medium, and when the program is executed by the processor, the drone airport cluster scheduling method as described in the above embodiment is implemented.

[0068] The method for scheduling the airport cluster of drones according to the embodiment of the present invention divides the work of drones into three stages: in stage one, based on the starting and ending positions of multiple tasks and the status of drones in the machine nest, with the goal of minimizing the system cost, integer linear programming is used to allocate drones from the initial docking machine nest to the starting position of the task through several machine nests for charging and relaying; in stage two, the drone performs the transportation task, loading the cargo from the starting point of the task to the end point, and the transportation route is determined by the starting and ending points of the task; in stage three, after completing the transportation task, the drone returns to the machine nest from the cargo unloading site, which is achieved by solving the assignment problem of multiple machine nests to drones performing tasks. This method provides an effective scheduling solution for the orderly operation of drones across machine nests, thereby improving the operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of a method for dispatching a drone airport cluster according to an embodiment of the present invention;

[0070] Figure 2 A framework diagram of a method for scheduling a drone airport cluster according to an embodiment of the present invention;

[0071] Figure 3 is a detailed flow chart of the method for dispatching drone airport clusters according to an embodiment of the present invention;

[0072] Figure 4 A schematic diagram of an application scenario in a method for dispatching a drone airport cluster according to an embodiment of the present invention;

[0073] Figure 5 The optimal path planning diagram for a single-task scenario in the method for scheduling a cluster of drones at an airport according to an embodiment of the present invention is shown in FIG.

[0074] Figure 6 It is an optimal path planning diagram for two mission scenarios in the method for scheduling a drone airport cluster according to an embodiment of the present invention;

[0075] Figure 7 It is an optimal path planning diagram for three mission scenarios in the drone airport cluster scheduling method according to an embodiment of the present invention;

[0076] Figure 8 A drone airport cluster dispatching system according to an embodiment of the present invention;

[0077] Fig. 9 FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present invention.

[0078] Reference numerals

[0079] UAV airport cluster dispatching system 100; modeling module 110; processing module 120; generation module 130;

[0080] Processor 10; communication interface 20; communication bus 30; memory 40. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0082] The following first describes in detail the drone airport cluster scheduling method according to an embodiment of the present invention with reference to the accompanying drawings.

[0083] like Figures 1 to 3 As shown, the drone airport cluster scheduling method according to an embodiment of the present invention includes the following steps:

[0084] S10, based on the task instruction, initializing the parameters of the machine nest, the UAV, the task execution starting point and the task execution end point, and establishing the adjacency relationship between the machine nest and the task execution starting point and the task execution end point;

[0085] S20, determining a first sub-path of the UAV from the initial machine nest to the starting point of the mission execution, determining a second sub-path of the UAV from the mission execution end point to the return machine nest, and generating a third sub-path of the UAV from the mission execution start point to the mission execution end point;

[0086] S30. Based on the first sub-path, the second sub-path and the third sub-path, a total path is synthesized for the UAV to start from the initial machine nest, pass through the task execution starting point, the task execution end point, and return to the machine nest when performing the task.

[0087] Specifically, if Figure 4 As shown, in step S10, the geographical area is gridded according to the UAV operation coverage radius, and a nest is deployed at the center of each grid. The total number of nests is N, and each nest is a node in the network, represented by v k =1,…,N means, the machine nest v k The position is p k =(x k ,y k ,z k ), if the distance between nest u and nest v is less than the fully charged flight distance of the drone, then there is a connecting edge (u,v) between the nests, and the edge cost is c u,v , set according to the distance between the machine nests.

[0088] The number of tasks is M, represented by j=1,…,M. Each task has a starting position and an ending position, which correspond to two nodes in the network respectively. and The starting position of task j is End position is There is an edge between the starting and ending positions of each task, and the edge cost Determined by the mission's flight distance.

[0089] If the starting point of task j is within the coverage of machine nest k, there is a connection edge between machine nest k and task j, and the edge cost is According to the distance between the nest and the mission location, it reflects the flight cost from nest k to the mission starting point. If the destination of mission j is within the coverage of nest k, there is a connection edge between mission j and nest k, and the edge cost is It is set according to the distance between the mission endpoint and the machine nest, reflecting the flight cost from the mission endpoint to the machine nest k.

[0090] The drone number is represented by i=1,…,N′, the number of drones is N′, the number of drones is less than or equal to the number of nests, that is, N′≤N, and the initial docking nest of drone i is u i , 1≤u i The number of drones is ≤N, and the landing place of drone i after completing the mission is v k , 1≤v k The number of ≤N.

[0091] To complete mission j, you need the initial drone nest u i 、Task j execution starting point Task j execution end point and return to the nest v k , and for the four-tuple With a given value

[0092] To mathematically model this problem, we introduce 0-1 integer variables:

[0093]

[0094] The objective function of problem 1 is

[0095]

[0096] If the number of drones is greater than or equal to the number of tasks, the number of nests is greater than or equal to the number of drones, and all tasks must be completed, then the corresponding constraints are:

[0097]

[0098] The decision variables of the above problem involve the task execution point, drone allocation and nest allocation, which is a typical multidimensional assignment problem. However, in general, the multidimensional assignment problem is a difficult integer programming problem, and there is no known polynomial algorithm. The method according to the embodiment of the present invention decomposes the working process of each drone into three stages. Stage 1: UAV i starts from the initial docking nest u. i Departure, passing through several machine nest charging relays to reach the starting position of task j Phase 2 is to execute mission j. UAV i starts from the starting position of mission j. Arrive at the end of task j Stage 3: From the end of Task J To return to the machine nest v k Dock.

[0099] For drones i from the machine nest u i Set out to perform mission j and then return to the machine nest v k In this complete working process, our goal is to find a set of drones, tasks, and nests with the lowest total cost given the drone set I, the task set J, and the nest set K, so as to reduce the total cost of the system to complete all tasks. In view of the above problem, the cost of a single task can be divided into:

[0100]

[0101] in, is the cost of the drone from the initial nest to the starting point of mission execution, is the cost of executing task j, which is a constant. It is the cost of the UAV from the end of the mission execution to the return to the nest.

[0102] like Figure 2As shown, P can be decomposed into two sub-problems: optimizing the total cost of the UAV from the initial nest to the starting point of the mission execution P1 and optimizing the total cost of the UAV from the end of the mission execution to the return nest P2.

[0103] Therefore, step S10 also includes: in the initial stage of the UAV cross-machine nest task scheduling, a basic path graph is constructed to determine the shortest path from each starting machine nest to all potential target task starting points. Through deep search in the graph, the cost required for all UAVs to reach the starting points of each target task from the starting machine nest is calculated, and the path cost and the shortest path are stored in a specific format, recording the detailed path of the UAV starting from the initial machine nest, passing through the possible relay machine nest sequence, and finally arriving at the task starting point, for example: UAV i initial machine nest => relay machine nest 1 => ... => relay machine nest 3 => task j starting point.

[0104] In step S20, based on the critical path cost and path information obtained, the integer linear programming algorithm is called to solve the optimal assignment of the UAV to the mission starting point in problem P1. The integer linear programming algorithm can search and optimize in the decision space based on the pre-set objective function (such as minimizing the total flight cost, the shortest mission start time, etc.) and a series of constraints (such as each mission starting point must and can only be responsible for one UAV, the load and endurance of each UAV, etc.). Evaluate and screen various possible combinations of UAVs and mission starting points, and finally determine the optimal assignment plan for UAVs to the mission starting point under the current mission requirements, machine nest resources and path conditions.

[0105] After completing the mission, the drone needs to return to the nest. In this step, we first need to calculate the cost from each mission end point to all nests. This process involves traversing the network of machine nests again, and by applying the distance calculation formula, the cost required for the drone to reach different machine nests from each mission end point is obtained. These costs not only reflect the distance cost, but also potentially include the cost converted from comprehensive factors such as the current resource availability of the machine nest.

[0106] After calculating the cost of the drone to the return nest, the integer linear programming algorithm is called again to solve the optimal assignment problem P2 from the mission endpoint to the return nest. Similarly, this link is also based on specific objective functions (such as minimizing the drone return time, balancing the capacity of each nest, etc.) and a series of constraints (such as the drone at each mission end point must and can only return to one nest, the number of drones allowed to dock at the nest, etc.), and uses the integer linear programming algorithm to select the optimal solution from multiple assignment combinations. Ensure that each drone can be assigned to a suitable return nest after completing the mission.

[0107] According to the above determined assignment result of the UAV to the mission starting point, the shortest path from the corresponding UAV to the mission starting point is searched in the shortest path table constructed in step S10. Then, according to the actual starting point and end point of the mission, a path from the mission starting point to the mission end point is generated. Then, according to the result obtained in step S20, a path from the UAV to the return nest is generated.

[0108] Finally, in step S30, these path segments are integrated to generate a complete path for all drones selected to perform the task. For example: drone i initial nest => relay nest 1 -> ... => relay nest n -> task j starting point => task j end point => return to nest k.

[0109] After the task assignment and path planning are completed, the task execution starts. Through a stable and efficient communication link, the task instructions and corresponding path information are sent to all selected drones. This includes the path planning of the drone from the initial starting nest, the relay nests along the way, to the task execution point and the final return nest during the entire mission.

[0110] Further, in step S20, determining the first sub-path of the UAV from the initial nest to the mission execution starting point specifically includes:

[0111] Optimize the objective function by solving integer linear programming:

[0112]

[0113] The corresponding constraints are:

[0114]

[0115] Among them, i and j are positive integers respectively;

[0116] u i It is the initial nest of the drone;

[0117] is the starting point for task execution of task j;

[0118] For a single drone, start from the initial nest u i Go to the starting point of the task expenses;

[0119] is the decision parameter. When the UAV moves from the initial nest u i Go to the starting point of the task hour, is 1, otherwise, is 0;

[0120] P1 is the initial nest u of the drone i Go to the starting point of the task total expenditure.

[0121] Preferably, the minimum path method is used to calculate For example, the classic Dijkstra, Bellman-Ford or A* algorithm with heuristic search characteristics can be selected, starting from the starting nest, and continuously searching for the node with the closest distance and undetermined shortest path with a greedy strategy until the entire graph is traversed to determine the shortest path from each starting nest to all potential target task starting points.

[0122] The method according to the embodiment of the present invention divides the work of the drone into three stages: in stage one, based on the starting and ending positions of multiple tasks and the status of the drone in the machine nest, with the goal of minimizing the system cost, the drone is assigned from the initial docking machine nest to the starting position of the task through several machine nests for charging and relaying; in stage two, the drone performs the transportation task, loading the cargo from the starting point of the task to the end point, and the transportation route is determined by the starting and ending points of the task; in stage three, after completing the transportation task, the drone returns to the machine nest from the cargo unloading site, which is achieved by solving the assignment problem of multiple machine nests to the drones performing the tasks. This method provides an effective scheduling solution for the orderly operation of drones across machine nests, thereby improving the operation efficiency.

[0123] Further, in step S20, determining the second branch path of the UAV from the mission execution end point to the return to the machine nest specifically includes:

[0124] Optimize the objective function by solving integer linear programming:

[0125]

[0126] The corresponding constraints are:

[0127]

[0128]

[0129] Wherein, k and j are positive integers respectively;

[0130] v k For the drone to return to the nest;

[0131] It is the task execution endpoint of task j;

[0132] For a single drone from the mission execution end point To return to the machine nest v k expenses;

[0133] is the decision parameter. When the UAV moves from the initial nest u i To the end of the task execution hour, is 1, otherwise, is 0;

[0134] P2 is the end point of the human-machine task execution To return to the machine nest v k total expenditure.

[0135] Preferably, similarly, the minimum path method is used to calculate

[0136] The drone airport cluster scheduling method according to the embodiment of the present invention has the following technical effects:

[0137] Significantly improve work efficiency: The mathematical optimization model constructed by the present invention comprehensively integrates key factors such as mission requirements, machine nest resources, and jump flight paths, accurately plans the optimal jump flight path and scheduling strategy, and effectively solves the problem of seamless crossing and orderly scheduling of drones between machine nests. By breaking down the work of drones into three stages, each stage uses scientific algorithms in a targeted manner. For example, in stage one, integer linear programming is used to achieve drone allocation that minimizes system costs, ensuring that the drone's path from the initial docking machine nest to the mission starting position is optimal, avoiding unnecessary flight consumption, greatly reducing the overall operation time, and enabling drones to efficiently participate in the mission execution link, which greatly improves the efficiency of cross-machine nest operations overall.

[0138] Accurate resource allocation: In view of the drawbacks of idle or overloaded nest resources in traditional UAV scheduling, this invention fully considers the decision variable of nest allocation and dynamically allocates nest resources according to the actual situation of the task. At each stage, whether the UAV is departing, executing a mission or returning, it can be reasonably arranged according to the real-time status of the nest, ensuring that the utilization rate of each nest is optimized, and realizing efficient use of nest resources.

[0139] Adapting to complex mission requirements: With the application of drones in multiple scenarios such as logistics distribution, inspection, and mapping, mission requirements are becoming increasingly complex and changeable. The orderly scheduling method provided by the present invention can calmly cope with the situation of multiple tasks being concurrent, and flexibly handle different mission starting points, end points, and different numbers of drones and machine nest combinations. Through scientific model construction and phased strategy implementation, no matter what kind of mission start and end point layout and machine nest distribution, an adaptive scheduling plan can be quickly formulated to ensure that drones can operate smoothly in a complex machine nest network, meeting a variety of practical application needs.

[0140] Reduce operating costs: By optimizing the flight path and reducing the flight path overhead, the additional energy consumption of the drone due to unreasonable scheduling is reduced, the single operation time of the drone is extended, the equipment loss and maintenance costs caused by frequent charging are reduced, and the economic benefits are improved.

[0141] According to an embodiment of the present invention, taking a regional logistics distribution as an example, there are 9 drone nests and 4 drones to complete the logistics distribution task together. In the initialization stage, the nest coordinates are fixed, and the 4 drones numbered 1-4 are located at nests 1, nests 6, nests 4 and nests 2 respectively. After the task is released, the scheduling system runs the algorithm.

[0142] Step 1: Initialize the parameters such as the machine nest position and initial machine nest.

[0143] The coordinates of the machine nest position are shown in Table 1.

[0144] Table 1 Machine nest position coordinates

[0145]

[0146]

[0147] The numbers of the drone landing nests before the algorithm is executed are shown in Table 2.

[0148] Table 2 UAV initial nest number

[0149] Drone Number Drone 1 Drone 2 Drone 3 Drone 4 Nest number 1 6 4 2

[0150] Step 2: Receive the task and initialize the task execution parameters.

[0151] The number of tasks in this implementation plan is 1, 2, and 3 respectively. Each task has a starting point and an end point for cargo transportation. The drone must first fly to the starting point of the task to load the cargo, and then transport the cargo to the end point of the task. The coordinates of the starting point and the end point of the task execution are shown in Tables 3 to 5.

[0152] Table 3 Task execution point location coordinates when the number of tasks is 1

[0153]

[0154] Table 4 Task execution point location coordinates when the number of tasks is 2

[0155]

[0156] Table 5 Task execution point location coordinates when the number of tasks is 3

[0157]

[0158] Step 3: According to the location of the machine nest and the task execution point, establish the adjacency relationship between the machine nest and the task node. The adjacency relationship between the machine nest and the task node is shown in Table 6-Table 8.

[0159] Table 6: Adjacency table of nests and task nodes when the number of tasks is 1 (unit: km)

[0160] serial number 1 2 3 4 5 6 7 8 9 10 11 1 5.00 5.00 2 5.00 5.00 5.00 5.00 3 5.00 5.00 5.00 4 5.00 5.00 5.00 5.00 3.19 5 5.00 5.00 5.00 5.00 5.00 5.00 4.20 2.29 6 5.00 5.00 5.00 5.00 4.77 7 5.00 5.00 5.00 1.83 3.91 8 5.00 5.00 5.00 5.00 2.79 9 5.00 5.00 10 3.19 4.20 1.83 4.03 11 2.29 4.77 3.91 2.79 4.03

[0161] Table 7: The number of tasks is 2, and the task node adjacency table (unit: km)

[0162]

[0163] Table 8: The number of tasks is 3, and the task node adjacency table (unit: km)

[0164]

[0165]

[0166] Step 4: Run the scheduling algorithm.

[0167] The scheduling algorithm divides the working process of each drone into three stages.

[0168] Phase 1: According to the locations of multiple mission start and end points and the current status of the drone at the machine nest, the drone is assigned to the mission start location. The system cost is minimized in this phase. Integer linear programming is used to complete the assignment of drones and transportation tasks.

[0169] The optimal assignment of drones and tasks when the number of tasks is 1 is shown in Table 9.

[0170] Table 9 Optimal assignment of drones and tasks when the number of tasks is 1

[0171] Drones Departure Nest Task Jumping Path Cost (km) Drone 3 Machine Nest 4 Mission 1 Starting Point Machine Nest 4->Starting point of Mission 1 3.19

[0172] The optimal assignment of drones and tasks when the number of tasks is 2 is shown in Table 10.

[0173] Table 10 Optimal assignment of drones and tasks when the number of tasks is 2

[0174]

[0175] The optimal assignment of drones and tasks when the number of tasks is 3 is shown in Table 11.

[0176] Table 11 Optimal assignment of drones and tasks when the number of tasks is 3

[0177]

[0178]

[0179] Phase 2: The drone performs the transport mission, loads the cargo from the mission starting point, and transports the cargo to the destination. The transport route in this phase is a fixed event, which is determined by the mission starting point and the endpoint. Its costs are shown in Tables 12 to 14.

[0180] Table 12 Task overhead for task scenario 1

[0181] Drones path Cost (km) Drone 3 Mission 1 End -> Machine Nest 5 4.03

[0182] Table 13 Task overhead for 2 task scenarios

[0183]

[0184] Table 14 Task overhead for 3 task scenarios

[0185]

[0186] Phase 3: After the drone completes the transport mission, it returns to the machine nest from the cargo unloading site. This process is obtained by solving the assignment problem from multiple machine nests to the drones performing the mission.

[0187] The cost of the drone returning to the nest when the number of missions is 1 is shown in Table 15.

[0188] Table 15: Cost of returning the drone to the nest in mission scenario 1

[0189] Drones path Cost (km) Drone 3 Mission 1 End -> Machine Nest 5 2.29

[0190] 2 The optimal hopping flight path of the UAV in the mission scenario is shown in Table 16.

[0191] Table 16: Cost of returning the drone to the nest in 2 mission scenarios

[0192]

[0193] The optimal hopping flight paths of the UAV in 3 mission scenarios are shown in Table 17.

[0194] Table 17 Cost of returning the drone to the nest in three mission scenarios

[0195]

[0196] Step 5: Obtain the complete hopping flight path of each mission UAV. The optimal hopping flight path of the UAV in mission scenario 1 is shown in Table 18.

[0197] Table 18 Optimal skipping path cost when the number of tasks is 1

[0198] Drones Jumping Path Cost (km) Drone 3 Machine Nest 4->Mission 1 starting point->Mission 1 end point->Machine Nest 5 9.51

[0199] 2 The optimal hopping flight path of the UAV in the mission scenario is shown in Table 19.

[0200] Table 19 Optimal skipping path cost when the number of tasks is 2

[0201]

[0202] The optimal hopping flight paths of the UAV in 3 mission scenarios are shown in Table 20.

[0203] Table 20 Optimal skipping path cost when the number of tasks is 3

[0204]

[0205]

[0206] Step 6: Send the mission and path planning information to all selected drones. The routes for the drones to perform the corresponding missions are as follows: Figures 5 to 7 shown.

[0207] like Figure 8 As shown, according to an embodiment of the second aspect of the present invention, a drone airport cluster scheduling system 100 is provided, including a modeling module 110, a processing module 120 and a generation module 130.

[0208] Specifically, the modeling module 110 initializes the parameters of the machine nest, the UAV, the task execution starting point and the task execution end point based on the task instruction, and establishes the adjacency relationship between the machine nest and the task execution starting point and the task execution end point; the processing module 120 determines the first sub-path of the UAV from the initial machine nest to the task execution starting point, determines the second sub-path of the UAV from the task execution end point to the return to the machine nest, and generates the third sub-path of the UAV from the task execution starting point to the task execution end point; the generation module 130 synthesizes the total path of the UAV starting from the initial machine nest, passing through the task execution starting point, the task execution end point, and returning to the machine nest when performing the task based on the first sub-path, the second sub-path and the third sub-path.

[0209] Preferably, determining the first sub-path of the UAV from the initial nest to the mission execution starting point specifically includes:

[0210] Optimize the objective function by solving integer linear programming:

[0211]

[0212] The corresponding constraints are:

[0213]

[0214] Among them, i and j are positive integers respectively;

[0215] u i It is the initial nest of the drone;

[0216] is the starting point for task execution of task j;

[0217] For a single drone, start from the initial nest u i Go to the starting point of the task expenses;

[0218] is the decision parameter. When the UAV moves from the initial nest u i Go to the starting point of the task hour, is 1, otherwise, is 0;

[0219] P1 is the initial nest u of the drone i Go to the starting point of the task total expenditure.

[0220] Further, the determining of the second branch path of the UAV from the mission execution end point to the return to the machine nest specifically includes:

[0221] Optimize the objective function by solving integer linear programming:

[0222]

[0223] The corresponding constraints are:

[0224]

[0225] Wherein, k and j are positive integers respectively;

[0226] v k For the drone to return to the nest;

[0227] It is the task execution endpoint of task j;

[0228] For a single drone from the mission execution end point To return to the machine nest v k expenses;

[0229] is the decision parameter. When the UAV moves from the initial nest u i To the end of the task execution hour, is 1, otherwise, is 0;

[0230] P2 is the end point of the human-machine task execution To return to the machine nest v k total expenditure.

[0231] According to another embodiment of the present invention, there is also provided an electronic device, such as Fig. 9 As shown, it includes a processor 10 , a communication interface 20 , a communication bus 30 and a memory 40 , wherein the processor 10 , the communication interface 20 and the memory interact with each other through the communication bus 30 .

[0232] Wherein, the memory 40 is used to store computer programs;

[0233] The processor 10 is used to execute the program stored in the memory 40. When the computer program is executed by the processor 10, it is used to initialize the parameters of the machine nest, the unmanned aerial vehicle, the task execution starting point and the task execution end point based on the task instruction, and establish an adjacency relationship between the machine nest and the task execution starting point and the task execution end point;

[0234] Determine the first sub-path of the UAV from the initial machine nest to the starting point of the mission execution, determine the second sub-path of the UAV from the mission execution end point to the return machine nest, and generate the third sub-path of the UAV from the mission execution start point to the mission execution end point;

[0235] Based on the first sub-path, the second sub-path and the third sub-path, the total path of the synthetic UAV when performing a task starts from the initial machine nest, passes through the task execution starting point, the task execution end point, and returns to the machine nest.

[0236] When the computer program is executed by the processor 10, it is also used for:

[0237] Optimize the objective function by solving integer linear programming:

[0238]

[0239] The corresponding constraints are:

[0240]

[0241] Among them, i and j are positive integers respectively;

[0242] u i It is the initial nest of the drone;

[0243] is the starting point for task execution of task j;

[0244] For a single drone, start from the initial nest u i Go to the starting point of the task expenses;

[0245] is the decision parameter. When the UAV moves from the initial nest u i Go to the starting point of the task hour, is 1, otherwise, is 0;

[0246] P1 is the initial nest u of the drone i Go to the starting point of the task total expenditure.

[0247] When the computer program is executed by the processor 10, it is also used for:

[0248] Calculated by the minimum path method

[0249] When the computer program is executed by the processor 10, it is also used for:

[0250] Optimize the objective function by solving integer linear programming:

[0251]

[0252] The corresponding constraints are:

[0253]

[0254] Wherein, k and j are positive integers respectively;

[0255] v k For the drone to return to the nest;

[0256] It is the task execution endpoint of task j;

[0257] For a single drone from the mission execution end point To return to the machine nest v k expenses;

[0258] is the decision parameter. When the UAV moves from the initial nest u i To the end of the task execution hour, is 1, otherwise, is 0;

[0259] P2 is the end point of the human-machine task execution To return to the machine nest v k total expenditure.

[0260] When the computer program is executed by the processor 10, it is also used for:

[0261] Calculated by the minimum path method

[0262] The communication bus 30 mentioned in the above electronic device can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The communication bus 30 can be divided into an address bus, a data bus, a control bus, etc. For ease of identification, only one thick line is used in the figure, but it does not mean that there is only one bus or one data type.

[0263] The communication interface 20 is used for communication between the above terminal and other devices.

[0264] The memory 40 may include a random access memory 40 (RAM), or may include a non-volatile memory 40 (non-volatile memory), such as at least one disk memory 40. Optionally, the memory 40 may also be at least one storage device located away from the processor 10.

[0265] The above-mentioned processor 10 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0266] In another embodiment provided by the present invention, a computer-readable storage medium is also provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the drone airport cluster scheduling method described in any of the above embodiments.

[0267] In another embodiment provided by the present invention, a computer program product containing instructions is also provided. When the computer is run on a computer, the computer executes the drone airport cluster scheduling method described in any of the above embodiments.

[0268] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0269] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0270] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for dispatching drone airport clusters, characterized in that: The following steps are involved: Based on the mission instructions, the parameters of the machine nest, UAV, mission execution starting point and mission execution end point are initialized, and the adjacency relationship between the machine nest and the mission execution starting point and mission execution end point is established; Determine the first sub-path of the UAV from the initial machine nest to the starting point of the mission execution, determine the second sub-path of the UAV from the mission execution end point to the return machine nest, and generate the third sub-path of the UAV from the mission execution start point to the mission execution end point; Based on the first sub-path, the second sub-path and the third sub-path, the total path of the synthetic UAV when performing a task starts from the initial machine nest, passes through the task execution starting point, the task execution end point, and returns to the machine nest.

2. The method for dispatching drone airport clusters according to claim 1, characterized in that: Determining the first sub-path of the UAV from the initial nest to the mission execution starting point specifically includes: Optimize the objective function by integer linear programming: P1: The corresponding constraints are: Among them, i and j are positive integers respectively; u i It is the initial nest of the drone; is the starting point for task execution of task j; For a single drone, start from the initial nest u i Go to the starting point of the task expenses; is the decision parameter. When the UAV moves from the initial nest u i Go to the starting point of the task hour, is 1, otherwise, is 0; P1 is the initial nest u of the drone i Go to the starting point of the task total expenditure.

3. The method for dispatching drone airport clusters according to claim 2, characterized in that: Calculated by the minimum path method 4. The method for dispatching drone airport clusters according to claim 1, characterized in that: Determining the second branch path of the UAV from the mission execution endpoint to the return to the machine nest specifically includes: Optimize the objective function by integer linear programming: P2: The corresponding constraints are: Wherein, k and j are positive integers respectively; v k For the drone to return to the nest; It is the task execution endpoint of task j; For a single drone from the mission execution end point To return to the machine nest v k expenses; is the decision parameter. When the UAV moves from the initial nest u i To the end of the task execution hour, is 1, otherwise, is 0; P2 is the end point of the human-machine task execution To return to the machine nest v k total expenditure.

5. The method for dispatching drone airport clusters according to claim 4, characterized in that: Calculated by the minimum path method 6. A drone airport cluster dispatching system, characterized in that: include: The modeling module initializes the parameters of the machine nest, UAV, the mission execution starting point and the mission execution end point based on the mission instruction, and establishes the adjacency relationship between the machine nest and the mission execution starting point and the mission execution end point; The processing module determines a first sub-path of the UAV from the initial machine nest to the starting point of the mission execution, determines a second sub-path of the UAV from the mission execution end point to the return machine nest, and generates a third sub-path of the UAV from the mission execution start point to the mission execution end point; The generation module, based on the first sub-path, the second sub-path and the third sub-path, synthesizes the total path of the UAV starting from the initial machine nest, passing through the task execution starting point, the task execution end point, and returning to the machine nest when performing the task.

7. The drone airport cluster dispatching system according to claim 6, characterized in that: Determining the first sub-path of the UAV from the initial nest to the mission execution starting point specifically includes: Optimize the objective function by integer linear programming: P1: The corresponding constraints are: Among them, i and j are positive integers respectively; u i It is the initial nest of the drone; is the starting point for task execution of task j; For a single drone, start from the initial nest u i Go to the starting point of the task expenses; is the decision parameter. When the UAV moves from the initial nest u i Go to the starting point of the task hour, is 1, otherwise, is 0; P1 is the initial nest u of the drone i Go to the starting point of the task total expenditure.

8. The drone airport cluster dispatching system according to claim 6, characterized in that: Determining the second branch path of the UAV from the mission execution endpoint to the return to the machine nest specifically includes: Optimize the objective function by integer linear programming: P2: The corresponding constraints are: Wherein, k and j are positive integers respectively; v k For the drone to return to the nest; It is the task execution endpoint of task j; For a single drone from the mission execution end point To return to the machine nest v k expenses; is the decision parameter. When the UAV moves from the initial nest u i To the end of the task execution hour, is 1, otherwise, is 0; P2 is the end point of the human-machine task execution To return to the machine nest v k total expenditure.

9. An electronic device, characterized in that: include: A processor, a communication interface, a communication bus, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the drone airport cluster scheduling method as described in any one of claims 1 to 5 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a program, and when the program is executed by the processor, the drone airport cluster scheduling method according to any one of claims 1 to 5 is implemented.

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