Airport unmanned vehicle cooperative control method and device and electronic equipment

Abstract

The invention belongs to the technical field of airport unmanned vehicle cooperative control, and relates to an airport unmanned special vehicle cooperative control method and device and electronic equipment. The method comprises the steps of environment perception and data collection, task allocation, information sharing, path planning and emergency alarm. The environment perception and data collection comprises collection of airport unmanned vehicle environment data and vehicle state data; task allocation comprises task allocation according to the collected airport unmanned vehicle environment data and vehicle state data; the information sharing comprises sharing of task information and vehicle state information between airport unmanned vehicles and between the airport unmanned vehicles and airport infrastructures; the path planning comprises the step of constructing a path decision algorithm to carry out path planning. According to the invention, the flight guarantee efficiency can be improved, and the operation efficiency and collaborative operation capability of unmanned vehicles in an airport are improved; meanwhile, the airport traffic flow is optimized, and the overall operation efficiency, operation safety and service quality can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of cooperative control of unmanned vehicles at airports, and in particular, relates to a method, device and electronic equipment for cooperative control of unmanned vehicles at airports. Background Art

[0002] Airports, as a highly complex and busy transportation hub, have a wide variety of vehicles, including shuttle buses, guide vehicles, bird-scaring vehicles, lawn mowers, food delivery vehicles, and aircraft tractors. As airport unmanned vehicles are gradually penetrating into every corner of the airport, facing the unique complex and changing environment of the airport, how to ensure efficient coordination between airport unmanned vehicles, between airport unmanned vehicles and aircraft, and between airport unmanned vehicles and airport infrastructure has become a technical problem that needs to be solved urgently.

[0003] At present, although unmanned vehicles have been initially applied in airports, most of them are still in an isolated operation state, lacking a comprehensive and efficient information sharing and coordination mechanism. Existing collaborative control strategies are mostly limited to the internal optimization of unmanned systems, and fail to fully consider the particularity of the airport as an overall service ecosystem, that is, all ground vehicle activities must be closely centered on the smooth operation of aircraft, and aircraft have become the core driver of this complex service network. This situation not only greatly limits the potential for improving the overall operational efficiency of the airport, but also may hide security risks that cannot be ignored due to the phenomenon of information islands. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an airport unmanned vehicle collaborative control method, device and electronic equipment.

[0005] In a first aspect, the present invention provides a collaborative control method for unmanned vehicles at an airport, including environmental perception and data collection, task allocation, information sharing, path planning and emergency alarm;

[0006] Environmental perception and data collection, including: collecting environmental data and vehicle status data of airport unmanned vehicles; using the integrated sensor array to monitor the surrounding environment in real time at 360 degrees, while capturing and collecting the location data, obstacle distribution data and road condition data of all airport unmanned vehicles, and obtaining all aircraft dynamic data; vehicle status data includes power, load and speed;

[0007] Task allocation, including: task allocation based on collected airport unmanned vehicle environment data and vehicle status data;

[0008] Information sharing, including: establishing a real-time communication system between airport unmanned vehicles, using wireless communication methods to share mission information and vehicle status information between airport unmanned vehicles and between airport unmanned vehicles and airport infrastructure; mission information includes starting position, end position and airport unmanned vehicle operation range; vehicle status information includes position information and speed information;

[0009] Path planning, including: the central control system builds a path decision algorithm based on shared mission information and vehicle status information to plan the paths of unmanned vehicles at each airport;

[0010] Emergency alarm, including: the central control system performs abnormality detection and emergency alarm based on the collected airport unmanned vehicle environment data and vehicle status data;

[0011] Construct a path decision algorithm, including:

[0012] According to the horizontal distance from each airport unmanned vehicle to each flight support position, a functional expression of the airport unmanned vehicle and flight support efficiency is constructed;

[0013] Determine the objective function for maximum flight support efficiency;

[0014] The flight support scenario is converted into a directed graph, where each node represents a flight support location and an edge represents the path cost between two flight support locations, thus obtaining a graph model.

[0015] Initialize the path, select the garage location node as the starting point, and initialize the path to a list containing only the starting point;

[0016] Starting from the last node of the current path, select the next node with the shortest distance according to the greedy principle;

[0017] Update the path, add the selected next node to the path list to form an updated path list;

[0018] Evaluate the path according to the objective function and calculate the current flight support efficiency;

[0019] Update the optimal path.

[0020] In a second aspect, the present invention provides an airport unmanned vehicle cooperative control device, including an environment perception and data collection unit, a task allocation unit, an information sharing unit, a path planning unit and an emergency alarm unit;

[0021] Environmental perception and data collection unit, used to collect environmental data and vehicle status data of airport unmanned vehicles; using the integrated sensor array to monitor the surrounding environment in real time at 360 degrees, and at the same time capture and collect the location data, obstacle distribution data and road condition data of all airport unmanned vehicles, and obtain all aircraft dynamic data; vehicle status data includes power, load and speed;

[0022] A task allocation unit, used for allocating tasks according to the collected airport unmanned vehicle environment data and vehicle status data;

[0023] An information sharing unit is used to establish a real-time communication system between airport unmanned vehicles, and to share mission information and vehicle status information between airport unmanned vehicles and between airport unmanned vehicles and airport infrastructure by wireless communication; mission information includes starting position, end position and operation range of airport unmanned vehicles; vehicle status information includes position information and speed information;

[0024] The path planning unit is used to build a path decision algorithm based on the shared mission information and vehicle status information, and plan the paths of the unmanned vehicles at each airport;

[0025] Emergency alarm unit, used by the central control system to detect anomalies and issue emergency alarms based on the collected airport unmanned vehicle environment data and vehicle status data;

[0026] Construct a path decision algorithm, including:

[0027] According to the horizontal distance from each airport unmanned vehicle to each flight support position, a functional expression of the airport unmanned vehicle and flight support efficiency is constructed;

[0028] Determine the objective function for maximum flight support efficiency;

[0029] The flight support scenario is converted into a directed graph, where each node represents a flight support location and an edge represents the path cost between two flight support locations, thus obtaining a graph model.

[0030] Initialize the path, select the garage location node as the starting point, and initialize the path to a list containing only the starting point;

[0031] Starting from the last node of the current path, select the next node with the shortest distance according to the greedy principle;

[0032] Update the path, add the selected next node to the path list to form an updated path list;

[0033] Evaluate the path according to the objective function and calculate the current flight support efficiency;

[0034] Update the optimal path.

[0035] In a third aspect, the present invention provides an electronic device, comprising:

[0036] Processor and memory;

[0037] The memory is used to store computer operation instructions;

[0038] The processor is used to execute the airport unmanned vehicle collaborative control method by calling the computer operation instruction.

[0039] Based on the above technical solution, the present invention can also be improved as follows.

[0040] Furthermore, the sensor array includes lidar, millimeter-wave radar and high-definition cameras.

[0041] Further, initializing the path includes selecting the garage location node as a starting point and initializing the path to be a list including the starting point;

[0042] Let V F Flight support efficiency for vehicles, d 01 is the horizontal distance from the vehicle position to the first flight support position, d ij is the horizontal distance from the i-th vehicle position to the j-th target support position; d ji is the horizontal distance from the jth vehicle position to the ith target support position, d n1 is the horizontal distance from the nth vehicle position to the target support position, then the function expression of the airport unmanned vehicle and flight support efficiency is:

[0043]

[0044] Furthermore, starting from the last node of the current path, the next node with the shortest distance is selected according to the greedy principle, including: in each step of selecting the next target position to be protected, the priority of each target protection position is calculated, and the target positions are selected in descending order according to the priority. Let λ i is the priority of the i-th target flight; x′ o is the horizontal coordinate of the target flight support position, y′ o is the vertical coordinate of the target flight support position, x a is the horizontal coordinate of the airport unmanned vehicle, y a is the ordinate of the airport unmanned vehicle, d{(x′ o ,y′ o ),(x a ,y a )} is the distance between the target flight support position and the airport unmanned vehicle; mi The weight of the flight to ensure the target position; i To ensure the speed of the flight, we need:

[0045]

[0046] Furthermore, the greedy algorithm is improved by backtracking and pruning to update the optimal path, including:

[0047] Selection: Starting from the current node, select the next feasible node according to the greedy strategy; if a feasible node is found, add it to the current path; if all neighbor nodes of the current node have been visited or there are no feasible neighbor nodes, perform a backtracking operation;

[0048] Backtracking: Go back to the previous node and reselect another feasible node;

[0049] Path evaluation: When the backtracking operation can no longer find a feasible node, the current path cost is calculated and compared with the recorded optimal path;

[0050] Path update: If the evaluation result of the current path is better than the previously recorded optimal path, that is, the path cost is lower and the effect is better, then the optimal path is updated and the currently found optimal solution is recorded;

[0051] Pruning: When reselecting a path after backtracking, if the length of the current path exceeds the length of the previously recorded optimal path, the current path cannot find a better solution.

[0052] Output result: The optimal path is used as the optimal path for vehicle flight support.

[0053] Furthermore, let cost be the comprehensive cost of the path, pathlength be the total length of the current path, that is, the cumulative distance that the vehicle flight guarantees pass through the nodes; heuristic be the heuristic function value, then the path cost calculation formula is:

[0054] cost=pathlength+heuristic.

[0055] The beneficial effects of the present invention are as follows: the present invention can improve flight support efficiency, improve the operating efficiency and collaborative operation capability of airport unmanned vehicles, reduce waiting time and conflict, enhance the safety and reliability of airport unmanned vehicles, and reduce accident risks through environmental perception and data collection, task allocation, information sharing, path planning and emergency alarm; at the same time, it can optimize airport traffic flow, which is conducive to improving overall operational efficiency and service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1A schematic diagram of the airport unmanned vehicle cooperative control method provided in Example 1 of the present invention;

[0057] Figure 2 A schematic diagram of a cooperative control device for unmanned vehicles at an airport provided in Example 2 of the present invention;

[0058] Figure 3 This is a schematic diagram of an electronic device provided in Example 3 of the present invention.

[0059] Icons: 30 - electronic device; 310 - processor; 320 - bus; 330 - memory; 340 - transceiver. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0061] Example 1

[0062] As an example, Figure 1 As shown, in order to solve the above technical problems, this embodiment provides an airport unmanned vehicle collaborative control method, including environment perception and data collection, task allocation, information sharing, path planning and emergency alarm;

[0063] Environmental perception and data collection, including: collecting environmental data and vehicle status data of airport unmanned vehicles; using the integrated sensor array to monitor the surrounding environment in real time at 360 degrees, while capturing and collecting the location data, obstacle distribution data and road condition data of all airport unmanned vehicles, and obtaining all aircraft dynamic data; vehicle status data includes power, load and speed;

[0064] Task allocation, including: task allocation based on collected airport unmanned vehicle environment data and vehicle status data;

[0065] Information sharing, including: establishing a real-time communication system between airport unmanned vehicles, using wireless communication methods to share mission information and vehicle status information between airport unmanned vehicles and between airport unmanned vehicles and airport infrastructure; mission information includes starting position, end position and airport unmanned vehicle operation range; vehicle status information includes position information and speed information;

[0066] Path planning, including: the central control system builds a path decision algorithm based on shared mission information and vehicle status information to plan the paths of unmanned vehicles at each airport;

[0067] Emergency alarm, including: the central control system performs abnormality detection and emergency alarm based on the collected airport unmanned vehicle environment data and vehicle status data;

[0068] Construct a path decision algorithm, including:

[0069] According to the horizontal distance from each airport unmanned vehicle to each flight support position, a functional expression of the airport unmanned vehicle and flight support efficiency is constructed;

[0070] Determine the objective function for maximum flight support efficiency;

[0071] The flight support scenario is converted into a directed graph, where each node represents a flight support location and an edge represents the path cost between two flight support locations, thus obtaining a graph model.

[0072] Initialize the path, select the garage location node as the starting point, and initialize the path to a list containing only the starting point;

[0073] Starting from the last node of the current path, select the next node with the shortest distance according to the greedy principle;

[0074] Update the path, add the selected next node to the path list to form an updated path list;

[0075] Evaluate the path according to the objective function and calculate the current flight support efficiency;

[0076] Update the optimal path.

[0077] The path decision algorithm ensures efficient coordination between vehicles, effectively avoids potential conflicts and congestion, and improves overall operational efficiency. By establishing a real-time communication system between airport unmanned vehicles, information sharing between vehicles and airport infrastructure is achieved.

[0078] In order to accurately plan flight support tasks, determine the starting and end points of the tasks, clarify the specific location distribution of flights to be supported and the range of operations that airport unmanned vehicles can perform, and determine the objective function, that is, the standard that needs to be optimized.

[0079] Optionally, the sensor array includes lidar, millimeter wave radar and high-definition cameras.

[0080] Optionally, initializing the path, including selecting the garage location node as a starting point, and initializing the path to a list including the starting point;

[0081] Let VF Flight support efficiency for vehicles, d 01 is the horizontal distance from the vehicle position to the first flight support position, d ij is the horizontal distance from the i-th vehicle position to the j-th target support position; d ji is the horizontal distance from the jth vehicle position to the ith target support position, d n1 is the horizontal distance from the nth vehicle position to the target support position, then the function expression of the airport unmanned vehicle and flight support efficiency is:

[0082]

[0083] The support order in flight support is the key to improving the efficiency of vehicles in flight support. The flight support efficiency is converted into an equivalent function, and the maximization of flight support efficiency is taken as the goal. The above formula is expressed as the optimal control problem of the path of the airport unmanned vehicle from the garage to the designated area for flight support.

[0084] Optionally, starting from the last node of the current path, the next node with the shortest distance is selected according to the greedy principle, including: in each step of selecting the next target position to be protected, the priority of each target protection position is calculated, and the target positions are selected in descending order according to the priority. Let λ i is the priority of the i-th target flight; x′ o is the horizontal coordinate of the target flight support position, y′ o is the vertical coordinate of the target flight support position, x a is the horizontal coordinate of the airport unmanned vehicle, y a is the vertical coordinate of the airport unmanned special vehicle, d{(x′ o ,y′ o ),(x a ,y a )} is the distance between the target flight support position and the airport unmanned vehicle; m i The weight of the flight to ensure the target position; i To ensure the speed of the flight, we need:

[0085]

[0086] The priority is calculated based on factors such as the location, weight, and size of the guaranteed flight.

[0087] Optionally, improve the greedy algorithm by backtracking and pruning to update the optimal path, including:

[0088] Selection: Starting from the current node, select the next feasible node according to the greedy strategy; if a feasible node is found, add it to the current path; if all neighbor nodes of the current node have been visited or there are no feasible neighbor nodes, perform a backtracking operation;

[0089] Backtracking: Go back to the previous node and reselect another feasible node;

[0090] Path evaluation: When the backtracking operation can no longer find a feasible node, the current path cost is calculated and compared with the recorded optimal path;

[0091] Path update: If the evaluation result of the current path is better than the previously recorded optimal path, that is, the path cost is lower and the effect is better, then the optimal path is updated and the currently found optimal solution is recorded;

[0092] Pruning: When reselecting a path after backtracking, if the length of the current path exceeds the length of the previously recorded optimal path, the current path cannot find a better solution.

[0093] Output result: The optimal path is used as the optimal path for vehicle flight support.

[0094] By improving the greedy algorithm through backtracking and pruning, unreasonable paths can be discovered in time during the search process, and continuing to search along suboptimal paths can be avoided, thereby improving the efficiency of finding the optimal path.

[0095] Optionally, let cost be the comprehensive cost of the path, pathlength be the total length of the current path, that is, the cumulative distance that the vehicle flight guarantees pass through the nodes; heuristic be the heuristic function value, then the path cost calculation formula is:

[0096] cost=pathlength+heuristic.

[0097] Through environmental perception and data collection, task allocation, information sharing, path planning and emergency alarm, the present invention can improve flight support efficiency, improve the operating efficiency and collaborative operation capability of airport unmanned vehicles, reduce waiting time and conflict occurrence, enhance the safety and reliability of airport unmanned vehicles, and reduce accident risks; at the same time, it optimizes airport traffic flow, which is conducive to improving overall operational efficiency and service quality.

[0098] Example 2

[0099] Based on the same principle as the method shown in Example 1 of the present invention, as shown in the attached Figure 2As shown, an embodiment of the present invention also provides an airport unmanned vehicle cooperative control device, including an environment perception and data collection unit, a task allocation unit, an information sharing unit, a path planning unit and an emergency alarm unit;

[0100] Environmental perception and data collection unit, used to collect environmental data and vehicle status data of airport unmanned vehicles; using the integrated sensor array to monitor the surrounding environment in real time at 360 degrees, and at the same time capture and collect the location data, obstacle distribution data and road condition data of all airport unmanned vehicles, and obtain all aircraft dynamic data; vehicle status data includes power, load and speed;

[0101] A task allocation unit, used for allocating tasks according to the collected airport unmanned vehicle environment data and vehicle status data;

[0102] An information sharing unit is used to establish a real-time communication system between airport unmanned vehicles, and to share mission information and vehicle status information between airport unmanned vehicles and between airport unmanned vehicles and airport infrastructure by wireless communication; mission information includes starting position, end position and operation range of airport unmanned vehicles; vehicle status information includes position information and speed information;

[0103] The path planning unit is used to build a path decision algorithm based on the shared mission information and vehicle status information, and plan the paths of the unmanned vehicles at each airport;

[0104] Emergency alarm unit, used by the central control system to detect anomalies and issue emergency alarms based on the collected airport unmanned vehicle environment data and vehicle status data;

[0105] Construct a path decision algorithm, including:

[0106] According to the horizontal distance from each airport unmanned vehicle to each flight support position, a functional expression of the airport unmanned vehicle and flight support efficiency is constructed;

[0107] Determine the objective function for maximum flight support efficiency;

[0108] The flight support scenario is converted into a directed graph, where each node represents a flight support location and an edge represents the path cost between two flight support locations, thus obtaining a graph model.

[0109] Initialize the path, select the garage location node as the starting point, and initialize the path to a list containing only the starting point;

[0110] Starting from the last node of the current path, select the next node with the shortest distance according to the greedy principle;

[0111] Update the path, add the selected next node to the path list to form an updated path list;

[0112] Evaluate the path according to the objective function and calculate the current flight support efficiency;

[0113] Update the optimal path.

[0114] Optionally, the sensor array includes lidar, millimeter wave radar and high-definition cameras.

[0115] Optionally, initializing the path, including selecting the garage location node as a starting point, and initializing the path to a list including the starting point;

[0116] Let V F Flight support efficiency for vehicles, d 01 is the horizontal distance from the vehicle position to the first flight support position, d ij is the horizontal distance from the i-th vehicle position to the j-th target support position; d ji is the horizontal distance from the jth vehicle position to the ith target support position, d n1 is the horizontal distance from the nth vehicle position to the target support position, then the function expression of the airport unmanned vehicle and flight support efficiency is:

[0117]

[0118] Optionally, starting from the last node of the current path, the next node with the shortest distance is selected according to the greedy principle, including: in each step of selecting the next target position to be protected, the priority of each target protection position is calculated, and the target positions are selected in descending order according to the priority. Let λ i is the priority of the i-th target flight; x′ o is the horizontal coordinate of the target flight support position, y′ o is the vertical coordinate of the target flight support position, x a is the horizontal coordinate of the airport unmanned vehicle, y a is the ordinate of the airport unmanned vehicle, d{(x′ o ,y′ o ),(x a ,y a )} is the distance between the target flight support position and the vehicle; m i The weight of the flight to ensure the target position; i To ensure the speed of the flight, we need:

[0119]

[0120] Optionally, improve the greedy algorithm by backtracking and pruning to update the optimal path, including:

[0121] Selection: Starting from the current node, select the next feasible node according to the greedy strategy; if a feasible node is found, add it to the current path; if all neighbor nodes of the current node have been visited or there are no feasible neighbor nodes, perform a backtracking operation;

[0122] Backtracking: Go back to the previous node and reselect another feasible node;

[0123] Path evaluation: When the backtracking operation can no longer find a feasible node, the current path cost is calculated and compared with the recorded optimal path;

[0124] Path update: If the evaluation result of the current path is better than the previously recorded optimal path, that is, the path cost is lower and the effect is better, then the optimal path is updated and the currently found optimal solution is recorded;

[0125] Pruning: When reselecting a path after backtracking, if the length of the current path exceeds the length of the previously recorded optimal path, the current path cannot find a better solution.

[0126] Output result: The optimal path is used as the optimal path for vehicle flight support.

[0127] Optionally, let cost be the comprehensive cost of the path, pathlength be the total length of the current path, that is, the cumulative distance that the vehicle flight guarantees pass through the nodes; heuristic be the heuristic function value, then the path cost calculation formula is:

[0128] cost=pathlength+heuristic.

[0129] Example 3

[0130] Based on the same principle as the method shown in the embodiment of the present invention, an electronic device is also provided in the embodiment of the present invention, as shown in the attached Figure 3 As shown, the electronic device may include but is not limited to: a processor and a memory; the memory is used to store computer programs; the processor is used to execute the airport unmanned vehicle collaborative control shown in the embodiment of the present invention by calling the computer program.

[0131] In an optional embodiment, an electronic device is provided. Figure 3 The electronic device 30 shown includes: a processor 310 and a memory 330. The processor 310 and the memory 330 are connected, for example, via a bus 320.

[0132] Optionally, the electronic device 30 may further include a transceiver 340, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 340 is not limited to one, and the structure of the electronic device 30 does not constitute a limitation on the embodiments of the present invention.

[0133] The processor 310 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. The processor 310 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0134] The bus 320 may include a path to transmit information between the above components. The bus 320 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 320 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0135] The memory 330 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0136] The memory 330 is used to store application code (computer program) for executing the solution of the present invention, and is controlled to be executed by the processor 310. The processor 310 is used to execute the application code stored in the memory 330 to implement the contents shown in the above method embodiment.

[0137] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The airport unmanned vehicle cooperative control method is characterized by: Including environmental perception and data collection, task allocation, information sharing, path planning and emergency alarm; Environmental perception and data collection, including: collecting environmental data and vehicle status data of airport unmanned vehicles; using the integrated sensor array to monitor the surrounding environment in real time at 360 degrees, while capturing and collecting the location data, obstacle distribution data and road condition data of all airport unmanned vehicles, and obtaining all aircraft dynamic data; vehicle status data includes power, load and speed; Task allocation, including: task allocation based on collected airport unmanned vehicle environment data and vehicle status data; Information sharing, including: establishing a real-time communication system between airport unmanned vehicles, using wireless communication methods to share mission information and vehicle status information between airport unmanned vehicles and between airport unmanned vehicles and airport infrastructure; mission information includes starting position, end position and airport unmanned vehicle operation range; vehicle status information includes position information and speed information; Path planning, including: the central control system builds a path decision algorithm based on shared mission information and vehicle status information to plan the paths of unmanned vehicles at each airport; Emergency alarm, including: the central control system performs abnormality detection and emergency alarm based on the collected airport unmanned vehicle environment data and vehicle status data; Construct a path decision algorithm, including: According to the horizontal distance from each airport unmanned vehicle to each flight support position, a functional expression of the airport unmanned vehicle and flight support efficiency is constructed; Determine the objective function for maximum flight support efficiency; The flight support scenario is converted into a directed graph, where each node represents a flight support location and an edge represents the path cost between two flight support locations, thus obtaining a graph model. Initialize the path, select the garage location node as the starting point, and initialize the path to a list containing only the starting point; Starting from the last node of the current path, select the next node with the shortest distance according to the greedy principle; Update the path, add the selected next node to the path list to form an updated path list; Evaluate the path according to the objective function and calculate the current flight support efficiency; Update the optimal path.

2. The airport unmanned vehicle cooperative control method according to claim 1, characterized in that: The sensor array includes lidar, millimeter-wave radar and high-definition cameras.

3. The airport unmanned vehicle cooperative control method according to claim 1, characterized in that: Initialize the path, including selecting the garage location node as the starting point, and initialize the path to a list containing the starting point; Let V F Flight support efficiency for vehicles, d 01 is the horizontal distance from the vehicle position to the first flight support position, d ij is the horizontal distance from the i-th vehicle position to the j-th target support position; d ji is the horizontal distance from the jth vehicle position to the ith target support position, d n1 is the horizontal distance from the nth vehicle position to the target support position, then the function expression of the airport unmanned vehicle and flight support efficiency is:

4. The airport unmanned vehicle cooperative control method according to claim 1, characterized in that: Starting from the last node of the current path, the next node with the shortest distance is selected according to the greedy principle, including: in each step of selecting the next target position to be protected, the priority of each target protection position is calculated, and the target positions are selected in descending order according to the priority. Let λ i is the priority of the i-th target flight; x′ o is the horizontal coordinate of the target flight support position, y′ o is the vertical coordinate of the target flight support position, x a is the horizontal coordinate of the airport unmanned vehicle, y a is the ordinate of the airport unmanned vehicle, d{(x′ o ,y′ o ),(x a ,y a )} is the distance between the target flight support position and the airport unmanned vehicle; m i The weight of the flight to ensure the target position; i To ensure the speed of the flight, we need:

5. The airport unmanned vehicle cooperative control method according to claim 1, characterized in that: Improve the greedy algorithm through backtracking and pruning to update the optimal path, including: Selection: Starting from the current node, select the next feasible node according to the greedy strategy; if a feasible node is found, add it to the current path; if all neighbor nodes of the current node have been visited or there are no feasible neighbor nodes, perform a backtracking operation; Backtracking: Go back to the previous node and reselect another feasible node; Path evaluation: When the backtracking operation can no longer find a feasible node, the current path cost is calculated and compared with the recorded optimal path; Path update: If the evaluation result of the current path is better than the previously recorded optimal path, that is, the path cost is lower and the effect is better, then the optimal path is updated and the currently found optimal solution is recorded; Pruning: When reselecting a path after backtracking, if the length of the current path exceeds the length of the previously recorded optimal path, the current path cannot find a better solution. Output result: The optimal path is used as the optimal path for vehicle flight support.

6. The airport unmanned vehicle cooperative control method according to claim 1, characterized in that: Let cost be the comprehensive cost of the path, pathlength be the total length of the current path, i.e. the cumulative distance that the vehicle flight guarantees to pass through the nodes; heuristic be the heuristic function value, then the path cost calculation formula is: cost=pathlength+heuristic.

7. The airport unmanned vehicle cooperative control device is characterized by: It includes environment perception and data collection unit, task allocation unit, information sharing unit, path planning unit and emergency alarm unit; Environmental perception and data collection unit, used to collect environmental data and vehicle status data of airport unmanned vehicles; using the integrated sensor array to monitor the surrounding environment in real time at 360 degrees, and at the same time capture and collect the location data, obstacle distribution data and road condition data of all airport unmanned vehicles, and obtain all aircraft dynamic data; vehicle status data includes power, load and speed; A task allocation unit, used for allocating tasks according to the collected airport unmanned vehicle environment data and vehicle status data; An information sharing unit is used to establish a real-time communication system between airport unmanned vehicles, and to share mission information and vehicle status information between airport unmanned vehicles and between airport unmanned vehicles and airport infrastructure by wireless communication; the mission information includes the starting position, the end position and the operation range of the airport unmanned vehicles; Vehicle status information includes location information and speed information; The path planning unit is used to build a path decision algorithm based on the shared mission information and vehicle status information, and plan the paths of the unmanned vehicles at each airport; Emergency alarm unit, used by the central control system to detect anomalies and issue emergency alarms based on the collected airport unmanned vehicle environment data and vehicle status data; Construct a path decision algorithm, including: According to the horizontal distance from each airport unmanned vehicle to each flight support position, a functional expression of the airport unmanned vehicle and flight support efficiency is constructed; Determine the objective function for maximum flight support efficiency; The flight support scenario is converted into a directed graph, where each node represents a flight support location and an edge represents the path cost between two flight support locations, thus obtaining a graph model. Initialize the path, select the garage location node as the starting point, and initialize the path to a list containing only the starting point; Starting from the last node of the current path, select the next node with the shortest distance according to the greedy principle; Update the path, add the selected next node to the path list to form an updated path list; Evaluate the path according to the objective function and calculate the current flight support efficiency; Update the optimal path.

8. An electronic device, characterized in that: include: Processor and memory; The memory is used to store computer operation instructions; The processor is used to execute the airport unmanned vehicle collaborative control method according to any one of claims 1 to 6 by calling the computer operation instructions.

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