CN114973781A - Airport scene unmanned aerial vehicle collision risk detection method and device and computer equipment - Google Patents
Airport scene unmanned aerial vehicle collision risk detection method and device and computer equipment Download PDFInfo
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- G08—SIGNALLING
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- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/04—Anti-collision systems
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The application provides a collision risk detection method, device and computer equipment for an unmanned aerial vehicle on an airport scene, relates to the technical field of aviation, and is used for improving the accuracy of collision risk detection of the unmanned aerial vehicle on the airport scene. The method mainly comprises the following steps: acquiring running data of a target unmanned aerial vehicle and running data of aircrafts and vehicles in a preset range of the position of the target unmanned aerial vehicle in real time; predicting estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to the running data and the path planning information corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively and the airport map; determining whether a target unmanned aerial vehicle has a junction with the aircraft and the vehicle or not by predicting a driving track; if the junction exists, predicting the arrival time of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction; and if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk.
Description
Technical Field
The application relates to the technical field of aviation, in particular to a collision risk detection method and device for an unmanned aerial vehicle in an airport scene, computer equipment and a storage medium.
Background
In the professional fields of logistics, energy, police, fire fighting, communication, medical treatment, water conservancy, homeland and the like, the large-scale fixed wing unmanned aerial vehicle plays an increasingly important role. The large-scale fixed wing unmanned aerial vehicle has a great deal of advantages in the aspects of long voyage, high industry load, cluster operation efficiency and the like, but the running and sliding operation mode of the large-scale fixed wing unmanned aerial vehicle has higher requirements on take-off and landing airports, an airport built repeatedly is necessarily not favorable for intensive development, and a general airport and a branch airport have low utilization rate, so that the unmanned aerial vehicle and the unmanned aerial vehicle can be used together. Therefore, the mixed operation of unmanned aerial vehicles and manned vehicles on airport scenes is a future development trend.
After the unmanned aerial vehicle is merged into an airport scene to operate, the differences of the unmanned aerial vehicle and a manned driving mode and a technical system bring the problems of space-time difference of perception information, difference of autonomous control capability, weak cross-platform interconnection and intercommunication capability and the like, and safety risks can be caused. Therefore, one of the key problems of safe and efficient operation of airport surfaces in a hybrid operation scene is: how unmanned aerial vehicles and manned machines cooperate to pass and avoid collision, how to realize that airport scene unmanned aerial vehicle collides the risk detection becomes the problem that needs to solve at present urgently.
Disclosure of Invention
The embodiment of the application provides a collision risk detection method and device for an airport surface unmanned aerial vehicle, computer equipment and a storage medium, and is used for improving the accuracy of collision risk detection of the airport surface unmanned aerial vehicle.
The embodiment of the invention provides a collision risk detection method for an unmanned aerial vehicle in an airport scene, which comprises the following steps:
acquiring running data of a target unmanned aerial vehicle and running data of aircrafts and vehicles within a preset range of the position of the target unmanned aerial vehicle in real time, wherein the running data at least comprises acquisition time, a horizontal position and a horizontal speed;
predicting estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to running data and path planning information corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively and an airport map;
determining whether the target unmanned aerial vehicle has an intersection with the aircraft or the vehicle according to the estimated running track;
if the junction exists, predicting arrival times of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction;
and if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk.
The embodiment of the invention provides an airport scene unmanned aerial vehicle collision risk detection device, which comprises:
the acquisition module is used for acquiring the running data of the target unmanned aerial vehicle and the running data of aircrafts and vehicles in a preset range of the position of the target unmanned aerial vehicle in real time, wherein the running data at least comprises acquisition time, a horizontal position and a horizontal speed;
the prediction module is used for predicting the estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle according to the running data, the path planning information and the airport map corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle;
the determining module is used for determining whether the target unmanned aerial vehicle has an intersection with the aircraft or the vehicle according to the estimated running track;
the prediction module is further configured to predict arrival times of the target unmanned aerial vehicle, the aircraft and the vehicle at the intersection if the intersection exists;
the determining module is further configured to determine that the target unmanned aerial vehicle has a collision risk if an absolute value of a time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the above-mentioned collision risk detection method for airport scene unmanned aerial vehicles when executing the computer program.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the above-mentioned method for collision risk detection of airport scene drones.
The invention provides a collision risk detection method and device for an unmanned aerial vehicle on an airport scene, computer equipment and a storage medium, wherein the method comprises the steps of firstly acquiring running data of a target unmanned aerial vehicle in real time and running data of aircrafts and vehicles within a preset range of the position of the target unmanned aerial vehicle; predicting estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to the running data and the path planning information corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively and an airport map; determining whether a target unmanned aerial vehicle has an intersection with the aircraft and the vehicle or not by predicting a running track; if the junction exists, predicting the arrival time of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction; and if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk. Therefore, the collision risk detection of the unmanned aerial vehicle on the airport scene is realized, and the accuracy of the collision risk detection of the unmanned aerial vehicle on the airport scene is improved.
Drawings
Fig. 1 is a flowchart of a collision risk detection method for an unmanned aerial vehicle on an airport scene provided by the present application;
FIG. 2 is a schematic view of a sliding window provided herein;
FIG. 3 is a schematic diagram of conflict recognition based on intersection provided herein;
FIG. 4 is a schematic diagram of autonomous conflict resolution based on hybrid operation rules provided herein;
fig. 5 is a flow chart of collision risk detection of an unmanned aerial vehicle in an airport scene provided by the present application;
fig. 6 is a schematic structural diagram of the collision risk detection device for the unmanned aerial vehicle on the airport surface provided by the present application;
fig. 7 is a schematic diagram of a computer device provided in the present application.
Detailed Description
In order to better understand the technical solutions described above, the technical solutions of the embodiments of the present application are described in detail below with reference to the drawings and the specific embodiments, and it should be understood that the specific features of the embodiments and the embodiments of the present application are detailed descriptions of the technical solutions of the embodiments of the present application, and are not limitations of the technical solutions of the present application, and the technical features of the embodiments and the embodiments of the present application may be combined with each other without conflict.
Referring to fig. 1, a method for detecting collision risk of an unmanned aerial vehicle in an airport scene provided in an embodiment of the present invention is applied to an airport taxiing stage of the unmanned aerial vehicle, and the method specifically includes steps S101 to S105:
and S101, acquiring the running data of the target unmanned aerial vehicle and the running data of the aircrafts and vehicles within a preset range of the position of the target unmanned aerial vehicle in real time.
Wherein the driving data at least comprises acquisition time, horizontal position and horizontal speed. Specifically, IN the embodiment, the information such as the real-time position, the heading, the speed and the like of the surrounding aircraft/vehicle is acquired through the ADS-BIIN technology, and the important information such as the horizontal position, the horizontal position precision, the horizontal speed precision, the ground speed, the heading, the identification code and the like of the surrounding aircraft/vehicle is acquired through ADS-BIIN decoding.
In an optional embodiment provided by the present invention, the present embodiment provides specific requirements for data that affect the accuracy of trajectory prediction and collision recognition, such as horizontal position accuracy, horizontal velocity accuracy, and heading accuracy. Specifically, the method for acquiring the running data of the target unmanned aerial vehicle and the running data of the aircrafts and vehicles in the preset range of the position of the target unmanned aerial vehicle comprises the following steps:
step S1011, acquiring horizontal position precision, horizontal speed precision, course precision, horizontal position confidence, horizontal speed confidence and course confidence corresponding to the aircraft and the vehicle respectively in a preset range of positions of the target unmanned aerial vehicle and the target unmanned aerial vehicle.
Specifically, the horizontal position, horizontal speed, and heading requirement in this embodiment may be as follows:
(1) horizontal position accuracy requirement
The horizontal position accuracy represents a horizontal position accuracy value after positioning source combination such as GNSS sources and inertial navigation carried by surrounding aircrafts/vehicles is positioned. The accuracy of the horizontal position of the surrounding aircraft/vehicle is required to be better than 10 meters (95% confidence) in this embodiment.
(2) Horizontal velocity accuracy requirement
In this embodiment the accuracy of the horizontal velocity of the surrounding aircraft/vehicle is required to be better than 3m/s (95% confidence).
(3) Course accuracy requirement
The heading accuracy of the surrounding aircraft/vehicle is required to be better than ± 8 ° (with 95% confidence) in this embodiment.
Step S1012, determining the driving data in which the horizontal position accuracy, the horizontal speed accuracy, and the heading accuracy are higher than the preset accuracy, and the horizontal position confidence, the horizontal speed confidence, and the heading confidence are higher than the preset confidence, as the driving data of the target drone or the driving data of the aircraft or vehicle within the preset range of the location of the target drone.
In this embodiment, the preset confidence degrees corresponding to the horizontal position confidence degree, the horizontal speed confidence degree, and the heading accuracy confidence degree may be set according to actual requirements, and the preset confidence degrees may be uniform numerical values, such as the confidence degrees of 98% and 96%, or different numerical values, which is not specifically limited in this embodiment.
And S102, predicting the estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to the running data and the path planning information corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively and the airport map.
It should be noted that if only the driving data corresponding to the target unmanned aerial vehicle, the target aircraft, and the target vehicle are obtained, but the operation states of the target unmanned aerial vehicle, the target aircraft, and the target vehicle are not estimated, when a conflict occurs, the target unmanned aerial vehicle has insufficient time to take a risk avoidance measure, and therefore estimated driving tracks corresponding to the target unmanned aerial vehicle, the target aircraft, and the target vehicle need to be predicted according to the driving data, the path planning information, and the airport map. The accuracy of prediction of the estimated running track determines the accuracy of conflict recognition and the timeliness of conflict resolution. Specifically, after the driving data corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle are obtained, it is determined whether the relative motion between the target unmanned aerial vehicle and the surrounding aircraft or vehicle is far away, if so, collision recognition is not required (prediction of the predicted driving track relatively far away from the aircraft or vehicle is not required), otherwise, the respective predicted driving tracks of the target unmanned aerial vehicle and the surrounding aircraft or vehicle are predicted.
In this embodiment, the estimated travel track may be predicted specifically according to the travel data, the route planning information, and the airport map. The path planning information is a predicted driving path set by the target unmanned aerial vehicle, the aircraft or the vehicle, and predicted driving tracks corresponding to the predicted target unmanned aerial vehicle, the aircraft and the vehicle can be predicted respectively through the path planning information, the latest driving data and the airport map.
It should be noted that, if the target unmanned aerial vehicle, aircraft or vehicle deviates from the preset predicted travel path during the travel process, the embodiment may predict the predicted travel track by combining the travel data and the airport map.
Furthermore, because the moving path of the airport surface is relatively fixed, the moving trend of the target unmanned aerial vehicle can be obtained by integrating the current position, path planning, airport map and other information of the target unmanned aerial vehicle, the moving intention of the target unmanned aerial vehicle is calculated by combining the course information of the target unmanned aerial vehicle, if the target unmanned aerial vehicle slides in a straight line, the track of 60 seconds is predicted by adopting a uniform velocity linear extrapolation model, and if the target unmanned aerial vehicle slides in a turn, the track of 60 seconds is predicted by adopting a uniform turning rate model. The predicted trajectories are all 1 second apart.
In an embodiment provided by the present invention, after acquiring the driving data of the target drone and the driving data of the aircrafts and vehicles within the preset range of the location of the target drone, the method further includes: acquiring running data in preset window areas corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively, wherein the preset window areas comprise the recently acquired running data with preset number; and determining whether the running data in the preset window area is valid data or not according to the running data in the preset window area corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively. The preset number may be 3, 4, or 5, which is not specifically limited in this embodiment.
For example, preset window regions with the number of 3 are preset, then sliding is performed according to the specified step length, the driving data in each preset window region is obtained, then the driving data in the window regions are preset, and whether the driving data in the preset window regions are valid data or not is determined.
Correspondingly, the predicting the estimated travel tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to the travel data, the path planning information and the airport map corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively comprises: and if the running data in the preset window areas respectively corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle are valid data, predicting the estimated running tracks respectively corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle according to the running data and the path planning information in the preset window areas respectively corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle and an airport map.
Wherein, the determining whether the driving data in the preset window area is valid data according to the driving data in the preset window area corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively comprises: determining whether the time interval between every two adjacent driving data in the preset window region is smaller than a preset interval or not, and determining whether the time interval between the acquisition time of the latest driving data in the preset window region and the current time of the system is smaller than the preset interval or not; and if the time interval of every two adjacent running data in the preset window region is smaller than a preset interval, and the time interval between the acquisition time of the latest running data in the preset window region and the current time of the system is smaller than the preset interval, determining that the running data in the preset window region is valid data.
For example, based on the rule that the update rate of ADS-B and GPS is 1 second and the maximum allowed number of missing points is 2, the validity determination criterion of the target trace in fig. 2 is defined as:
1) the difference between the time of every two adjacent driving data in the preset window area is less than 3 seconds, and
2) and the difference between the acquisition time of the latest driving data in the preset window area and the current system time is less than 3 seconds.
And step S103, determining whether the target unmanned aerial vehicle has an intersection with the aircraft or the vehicle by estimating the driving track.
Wherein, the junction point is a point where the estimated running tracks are intersected. Specifically, the determining whether the target drone has an intersection with the aircraft or the vehicle through the estimated travel track includes: and if the distance between the target unmanned aerial vehicle and the aircraft or the vehicle is determined to be gradually reduced according to the estimated running track, and the intersection point between the target unmanned aerial vehicle and the aircraft or the vehicle is determined to exist, determining that the intersection point exists between the target unmanned aerial vehicle and the aircraft or the vehicle.
And step S104, if the junction exists, predicting the arrival time of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction.
Step S105, if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk.
As shown in fig. 3, the arrival times, i.e., T, of the predicted target drone, the aircraft, and the vehicle to the intersection are predicted separately Machine for making book (target drone) and T Other machine (aircraft and vehicle). Setting a threshold value phi if T Machine for making book And T Other machine If the absolute value of the difference is less than phi, the existence of the collision risk is judged. The size of the threshold phi is related to factors such as the size of the target outline, the relative speed, the current airport management and control strategy and the like.
The embodiment of the invention provides a collision risk detection method for an unmanned aerial vehicle on an airport scene, which comprises the steps of firstly, acquiring running data of a target unmanned aerial vehicle in real time and running data of aircrafts and vehicles in a preset range of the position of the target unmanned aerial vehicle; then, predicting estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to the running data and the path planning information corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively and an airport map; determining whether a target unmanned aerial vehicle has an intersection with the aircraft and the vehicle or not by predicting a running track; if the junction exists, predicting the arrival time of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction; and if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk. Therefore, the collision risk detection of the unmanned aerial vehicle on the airport scene is realized, and the accuracy of the collision risk detection of the unmanned aerial vehicle on the airport scene is improved.
Referring to fig. 4, in an embodiment of the present invention, after determining that the target drone is at a collision risk, the method may further include: and controlling the target unmanned aerial vehicle, the aircraft or the vehicle to run according to the rules in the hybrid operation rule base. Wherein, the rule base of mixing operation includes at least: a first-in first-out rule that an unmanned aircraft has priority over an unmanned aircraft rule; and (4) a task urgency rule.
In the mixed operation process of the manned vehicle and the unmanned vehicle in the airport scene, if the target unmanned vehicle is determined to have collision risk, the target unmanned vehicle needs to be controlled to run according to the rules in the mixed operation rule base and the positions, the speeds and the intentions of the surrounding aircrafts/vehicles. Wherein, the first-in first-out rule: priority is given to aircraft/vehicles entering the intersection first.
For example, if aircraft a is supposed to be passing aircraft B but is performing an emergency mission as specified in the hybrid operating rule base, then aircraft B needs to be adjusted to pass aircraft a. Specifically, the driving speed, the driving angle, and the like of the aircraft B can be controlled according to the positions and the speeds of the aircraft a and the aircraft B, so that the aircraft B gives way to the aircraft a.
As shown in fig. 4, on the one hand, the airport scene manned and unmanned aerial vehicle hybrid operation intelligent management and control platform makes airport scene hybrid operation rules according to factors such as airport scene operation state, operation execution progress, task emergency degree and the like, broadcasts the airport scene hybrid operation rules to unmanned aerial vehicles in scene operation, and when the unmanned aerial vehicles predict conflict risks, conflict resolution strategies are rapidly obtained through a conflict resolution strategy module based on road rights. On the other hand, the game-based dynamic adjustment module for the conflict resolution strategy determines the dynamic adjustment strategy for conflict resolution under real-time operation by analyzing the position, speed and intention of surrounding aircrafts/vehicles. Meanwhile, a conflict resolution strategy based on the road right and a conflict resolution dynamic adjustment strategy based on the game are sent to the unmanned aerial vehicle speed dynamic adjustment module, a conflict resolution optimal strategy is obtained, and the speed (acceleration, deceleration, parking and the like) of the unmanned aerial vehicle is adjusted based on the pair.
As shown in fig. 5, the present invention provides an airport surface unmanned aerial vehicle collision risk detection system, which comprises: unmanned mixed operation intelligent management and control platform, unmanned aerial vehicle remote control platform, unmanned aerial vehicle airport scene are current in coordination with collision avoidance machine carries system. Specifically, the unmanned aerial vehicle remote control console sends the position of the unmanned aerial vehicle to the intelligent management and control platform for unmanned and unmanned hybrid operation of the airport surface, identity task information, then the intelligent management and control platform for unmanned and unmanned hybrid operation of the airport surface plans the unmanned aerial vehicle path, surrounding airplanes, the rule for hybrid operation of the vehicle path, the airport surface and the vehicle path is sent to the unmanned aerial vehicle remote control platform, then the unmanned aerial vehicle remote control platform sends the rule to the system for cooperation passage and collision avoidance of the airport surface of the unmanned aerial vehicle, so that the system for cooperation passage and collision avoidance of the airport surface of the unmanned aerial vehicle executes the method for detecting collision risk of the unmanned aerial vehicle of the airport surface in the embodiment, and the risk detection of collision of the unmanned aerial vehicle of the airport surface is realized. As shown in fig. 1 and 5, the unmanned aerial vehicle airport scene collaborative passage and collision avoidance airborne system predicts estimated traveling tracks respectively corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle according to the traveling data, the path planning information and the airport map respectively corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle; determining whether the target unmanned aerial vehicle has an intersection with the aircraft or the vehicle according to the estimated running track; if the junction exists, predicting arrival times of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction; and if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, an airport surface unmanned aerial vehicle collision risk detection device is provided, and the airport surface unmanned aerial vehicle collision risk detection device corresponds to the airport surface unmanned aerial vehicle collision risk detection method in the above embodiments one to one. As shown in fig. 6, the functional modules of the collision risk detection device for the unmanned aerial vehicle on the airport surface are described in detail as follows:
the acquisition module 61 is used for acquiring the running data of the target unmanned aerial vehicle and the running data of the aircrafts and vehicles within a preset range of the position of the target unmanned aerial vehicle in real time, wherein the running data at least comprises acquisition time, a horizontal position and a horizontal speed;
a prediction module 62, configured to predict, according to the driving data, the path planning information and the airport map that correspond to the target unmanned aerial vehicle, the aircraft and the vehicle, estimated driving tracks that correspond to the target unmanned aerial vehicle, the aircraft and the vehicle, respectively;
a determining module 63, configured to determine whether the target drone has an intersection with the aircraft or the vehicle according to the estimated travel track;
the prediction module 62 is further configured to predict arrival times of the target drone, the aircraft, and the vehicle at the intersection if the intersection exists;
the determining module 63 is further configured to determine that the target drone has a collision risk if an absolute value of a time difference between an arrival time of the target drone and an arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value.
In an optional embodiment, the obtaining module 61 is specifically configured to:
acquiring horizontal position precision, horizontal speed precision and course precision, horizontal position confidence, horizontal speed confidence and course confidence which correspond to the aircraft and the vehicle respectively in a preset range of positions of the target unmanned aerial vehicle and the target unmanned aerial vehicle;
and determining the running data of which the horizontal position accuracy, the horizontal speed accuracy and the course accuracy are higher than preset accuracy and the horizontal position confidence coefficient, the horizontal speed confidence coefficient and the course confidence coefficient are higher than preset confidence coefficient as the running data of the target unmanned aerial vehicle or the running data of the aircraft or the vehicle in the preset range of the position of the target unmanned aerial vehicle.
In an optional embodiment, the obtaining module 61 is further configured to obtain driving data in preset window regions respectively corresponding to the target unmanned aerial vehicle, the aircraft, and the vehicle, where the preset window regions include a preset number of driving data that are obtained recently;
the determining module 63 is further configured to determine whether the driving data in the preset window area is valid data according to the driving data in the preset window area corresponding to the target unmanned aerial vehicle, the aircraft, and the vehicle;
the prediction module 62 is further configured to, if the driving data in the preset window areas respectively corresponding to the target unmanned aerial vehicle, the aircraft, and the vehicle are valid data, predict estimated driving tracks respectively corresponding to the target unmanned aerial vehicle, the aircraft, and the vehicle according to the driving data, the path planning information, and the airport map in the preset window areas respectively corresponding to the target unmanned aerial vehicle, the aircraft, and the vehicle.
In an optional embodiment, the determining module 63 is specifically configured to:
determining whether the time interval of every two adjacent running data in the preset window area is smaller than a preset interval or not, and determining whether the time interval between the acquisition time of the latest running data in the preset window area and the current time of the system is smaller than the preset interval or not;
and if the time interval of every two adjacent running data in the preset window region is smaller than a preset interval, and the time interval between the acquisition time of the latest running data in the preset window region and the current time of the system is smaller than the preset interval, determining that the running data in the preset window region is valid data.
In an optional embodiment, the determining module 63 is specifically configured to:
and if the distance between the target unmanned aerial vehicle and the aircraft or the vehicle is gradually reduced and the intersection point between the target unmanned aerial vehicle and the aircraft or the vehicle is determined according to the estimated running track, determining that the intersection point exists between the target unmanned aerial vehicle and the aircraft or the vehicle.
In an alternative embodiment, the apparatus further comprises a control module 64;
a control module 64 configured to control the target drone, the aircraft, or the vehicle to travel according to rules in a hybrid operation rule base.
In an optional embodiment, the hybrid-operation rule base includes at least: a first-in first-out rule, a rule that an unmanned aircraft is prioritized over an unmanned aircraft, and a mission emergency rule.
For specific limitations of the collision risk detection device for the unmanned aerial vehicle on the airport scene, reference may be made to the above limitations on the collision risk detection method for the unmanned aerial vehicle on the airport scene, and details are not repeated here. The various modules in the above-described apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a collision risk detection method for unmanned aerial vehicles in airport surfaces.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring running data of a target unmanned aerial vehicle and running data of aircrafts and vehicles in a preset range of the position of the target unmanned aerial vehicle in real time, wherein the running data at least comprises acquisition time, a horizontal position and a horizontal speed;
predicting estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to running data and path planning information corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively and an airport map;
determining whether the target unmanned aerial vehicle has an intersection with the aircraft or the vehicle according to the estimated running track;
if the junction exists, predicting arrival times of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction;
and if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring running data of a target unmanned aerial vehicle and running data of aircrafts and vehicles in a preset range of the position of the target unmanned aerial vehicle in real time, wherein the running data at least comprises acquisition time, a horizontal position and a horizontal speed;
predicting estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to running data and path planning information corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively and an airport map;
determining whether the target unmanned aerial vehicle has an intersection with the aircraft or the vehicle according to the estimated running track;
if the junction exists, predicting arrival times of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction;
and if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include both non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. An airport surface unmanned aerial vehicle collision risk detection method is characterized by comprising the following steps:
acquiring running data of a target unmanned aerial vehicle and running data of aircrafts and vehicles in a preset range of the position of the target unmanned aerial vehicle in real time, wherein the running data at least comprises acquisition time, a horizontal position and a horizontal speed;
predicting estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to running data and path planning information corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively and an airport map;
determining whether the target unmanned aerial vehicle has an intersection with the aircraft or the vehicle according to the estimated running track;
if the junction exists, predicting arrival times of the target unmanned aerial vehicle, the aircraft and the vehicle to the junction;
and if the absolute value of the time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value, determining that the target unmanned aerial vehicle has a collision risk.
2. The method according to claim 1, wherein the acquiring of the driving data of the target drone and the driving data of the aircrafts and vehicles within the preset range of the location of the target drone comprises:
acquiring horizontal position precision, horizontal speed precision, course precision, horizontal position confidence coefficient, horizontal speed confidence coefficient and course confidence coefficient which correspond to the aircraft and the vehicle respectively in a preset range of positions of the target unmanned aerial vehicle and the target unmanned aerial vehicle;
and determining the running data of the target unmanned aerial vehicle or the running data of the aircraft or the vehicle in the preset range of the position of the target unmanned aerial vehicle, wherein the horizontal position precision, the horizontal speed precision and the course precision are higher than the preset precision, and the horizontal position confidence, the horizontal speed confidence and the course confidence are higher than the preset confidence.
3. The method according to claim 1 or 2, wherein after acquiring the traveling data of the target drone and the traveling data of the aircraft and the vehicle within the preset range of the location, the method further comprises:
acquiring running data in preset window areas corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively, wherein the preset window areas comprise the recently acquired running data with preset number;
determining whether the running data in the preset window area is valid data or not according to the running data in the preset window area corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively;
the predicting the estimated travel tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively according to the travel data, the path planning information and the airport map corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively comprises the following steps:
and if the running data in the preset window areas respectively corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle are valid data, predicting the estimated running tracks respectively corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle by the airport map according to the running data and the path planning information in the preset window areas respectively corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle.
4. The method of claim 3, wherein the determining whether the driving data in the preset window area is valid data according to the driving data in the preset window area corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle respectively comprises:
determining whether the time interval of every two adjacent running data in the preset window area is smaller than a preset interval or not, and determining whether the time interval between the acquisition time of the latest running data in the preset window area and the current time of the system is smaller than the preset interval or not;
and if the time interval of every two adjacent running data in the preset window region is smaller than a preset interval, and the time interval between the acquisition time of the latest running data in the preset window region and the current time of the system is smaller than the preset interval, determining that the running data in the preset window region is valid data.
5. The method of claim 4, wherein determining whether the target drone has an intersection with the aircraft or the vehicle via the estimated travel trajectory includes:
and if the distance between the target unmanned aerial vehicle and the aircraft or the vehicle is gradually reduced and the intersection point between the target unmanned aerial vehicle and the aircraft or the vehicle is determined according to the estimated running track, determining that the intersection point exists between the target unmanned aerial vehicle and the aircraft or the vehicle.
6. The method of claim 1, wherein after determining that the target drone is at risk of collision, the method further comprises:
controlling the travel of the target drone, the aircraft, or the vehicle according to rules in a hybrid operational rule base.
7. The method of claim 6, wherein the hybrid-run rule base comprises at least: first-in first-out rules, manned aircraft over unmanned aircraft rules, mission emergency rules.
8. The utility model provides an airport surface unmanned aerial vehicle collision risk detection device which characterized in that, the device includes:
the acquisition module is used for acquiring the running data of the target unmanned aerial vehicle and the running data of aircrafts and vehicles in a preset range of the position of the target unmanned aerial vehicle in real time, wherein the running data at least comprises acquisition time, a horizontal position and a horizontal speed;
the prediction module is used for predicting the estimated running tracks corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle according to the running data, the path planning information and the airport map corresponding to the target unmanned aerial vehicle, the aircraft and the vehicle;
the determining module is used for determining whether the target unmanned aerial vehicle has an intersection with the aircraft or the vehicle according to the estimated running track;
the prediction module is further configured to predict arrival times of the target unmanned aerial vehicle, the aircraft and the vehicle at the intersection if the intersection exists;
the determining module is further configured to determine that the target unmanned aerial vehicle has a collision risk if an absolute value of a time difference between the arrival time of the target unmanned aerial vehicle and the arrival time of the aircraft or the vehicle at the intersection is smaller than a preset value.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the method of collision risk detection of airport surface drones according to any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the method of collision risk detection of airport surfaces drone according to any one of claims 1 to 7.
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