CN112987794A

CN112987794A - Flight cluster simulator

Info

Publication number: CN112987794A
Application number: CN202110427554.XA
Authority: CN
Inventors: 于航; 耿萌; 范新强; 范成程; 张毅
Original assignee: Nanji Agricultural Machinery Research Institute Co ltd
Current assignee: Nanji Agricultural Machinery Research Institute Co ltd
Priority date: 2021-04-21
Filing date: 2021-04-21
Publication date: 2021-06-18

Abstract

The invention discloses a flight cluster simulator, comprising: the system comprises a plurality of flight simulators, a plurality of ground stations and a cluster algorithm and monitoring server; the ground station receives current attitude information sent by the connected flight simulator and sends flight control information to the connected flight simulator; the cluster algorithm and the monitoring server calculate and determine flight lines of the flight simulators according to task targets of the flight clusters and then send the flight lines to the ground stations, so that the ground stations send flight control information to the connected flight simulators according to flight plans, the collision probability between the flight simulators is judged according to the current attitude information of the flight simulators forwarded by the ground stations, the flight lines of the flight simulators with the collision probability larger than a preset threshold value are adjusted and then sent to the corresponding ground stations, and the ground stations send the adjusted flight control information to the connected flight simulators according to the adjusted flight lines. The flight cluster simulator disclosed by the invention provides a reliable flight simulation scheme of the unmanned aerial vehicle cluster.

Description

Flight cluster simulator

Technical Field

The embodiment of the invention relates to the unmanned aerial vehicle technology, in particular to a flight cluster simulator.

Background

At present, unmanned aerial vehicles are widely applied to various fields such as military, agriculture, film and television, police and the like. Unmanned aerial vehicle flies through the control of the remote controller or the ground station that are located ground, perhaps realizes automatic flight according to the flight circuit of predetermineeing. No matter which kind of flight control mode, the flight control to unmanned aerial vehicle all is realized the flight control to unmanned aerial vehicle by flight control ware.

Because the load of a single unmanned aerial vehicle is small, in order to adopt the unmanned aerial vehicle to execute a large-scale flight task, an unmanned aerial vehicle cluster can be adopted to execute the large-scale flight task. The unmanned aerial vehicle cluster is a system composed of a plurality of unmanned aerial vehicles and remote controllers or ground stations corresponding to the unmanned aerial vehicles one to one, and complex large-scale flight tasks can be executed by the unmanned aerial vehicle cluster by planning different flight tasks for each unmanned aerial vehicle. However, as the number of the unmanned aerial vehicles in the unmanned aerial vehicle cluster is large, and many flying tasks require the unmanned aerial vehicles to densely form a formation flying in the air, the large number of unmanned aerial vehicles generally need to take off and land at the same place, so that the unmanned aerial vehicle cluster is easy to collide in the air. In order to avoid collision danger when the unmanned aerial vehicle cluster executes tasks, flight simulation needs to be carried out on the unmanned aerial vehicle cluster before flight, but the current unmanned aerial vehicle flight simulation mainly adopts simulation on a single unmanned aerial vehicle, and a reliable scheme is urgently needed for the flight simulation of the unmanned aerial vehicle cluster at present.

Disclosure of Invention

The invention provides a flight cluster simulator and provides a reliable flight simulation scheme of an unmanned aerial vehicle cluster.

In a first aspect, an embodiment of the present invention provides a flight cluster simulator, including: the system comprises a plurality of flight simulators, a plurality of ground stations and a cluster algorithm and monitoring server;

the flight simulators correspond to the ground stations one by one, each flight simulator is connected with one ground station through wireless communication, and each ground station is used for receiving current attitude information sent by the connected flight simulator and sending flight control information to the connected flight simulator;

the cluster algorithm is connected with the monitoring server and the ground stations and used for calculating and determining flight lines of the flight simulators according to task targets of the flight clusters, sending the flight lines of the flight simulators to the ground stations, enabling the ground stations to send flight control information to the connected flight simulators according to a flight plan, receiving current attitude information of the flight simulators forwarded by the ground stations, judging collision probability among the flight simulators according to the current attitude information of the flight simulators, adjusting the flight lines of the flight simulators with the collision probability larger than a preset threshold value, and then sending the adjusted flight control information to the connected flight simulators by the ground stations according to the adjusted flight lines.

In a possible implementation manner of the first aspect, the flight lines of the plurality of flight simulators and the adjusted flight lines of the plurality of flight simulators satisfy at least one of the following conditions:

the sum of the flight distances of all the flight simulators is minimum;

the flight distances of all the flight simulators are the same;

the ratio of the effective flight distance to the total flight distance of each flight simulator is maximized.

In a possible implementation manner of the first aspect, adjusting a flight path of a flight simulator with a collision probability greater than a preset threshold includes:

and adjusting the flight lines of the flight simulators intersected among the flight lines, or adjusting the flight lines of the flight simulators with the distance smaller than a preset distance threshold value.

In a possible implementation manner of the first aspect, the clustering algorithm and the monitoring server are further configured to, when receiving the fault information of the flight simulator, modify the flight lines of the flight simulators or add a new flight simulator to replace the fault flight simulator and determine the flight lines of the flight simulators.

In a possible implementation manner of the first aspect, each flight simulator includes: the system comprises an IMU simulator, an FCU and a data transmission unit;

the IMU simulator is a software module running in an operating system environment and comprises at least one sensor module, an IMU module and at least one controlled unit module, and the FCU is a hardware module realized by a real-time embedded system;

the sensor module is used for generating at least one flight data simulating the unmanned aerial vehicle in flight and inputting the at least one flight data into the IMU module;

the IMU module is used for estimating the current attitude information of the simulated unmanned aerial vehicle according to at least one flight data and inputting the current attitude information of the simulated unmanned aerial vehicle into the FCU;

the FCU is used for calculating target attitude information of the simulated unmanned aerial vehicle according to current attitude information of the simulated unmanned aerial vehicle and flight control information of the simulated unmanned aerial vehicle, calculating control data of at least one controlled unit according to the target attitude information of the simulated unmanned aerial vehicle, and sending corresponding control data to the at least one controlled unit;

the at least one controlled unit is used for calculating and updating the current attitude of the simulated unmanned aerial vehicle according to the control data sent by the FCU;

the data transmission unit is used for receiving the flight control information sent by the ground station through wireless communication connection and sending the flight control information to the FCU; and receiving the current attitude information of the simulated unmanned aerial vehicle sent by the FCU, and sending the current attitude information of the simulated unmanned aerial vehicle to the ground station through wireless communication connection.

In a possible implementation manner of the first aspect, the FCU is further configured to obtain flight control information of the simulated drone from a preset flight control program.

In a possible implementation manner of the first aspect, the at least one sensor module is specifically configured to generate at least one of the following flight data that simulates that the unmanned aerial vehicle is in flight:

the system comprises the navigation satellite number, navigation positioning precision, longitude and latitude information, altitude information, motion course, simulation unmanned aerial vehicle attitude, acceleration, three-axis speed, three-axis acceleration, three-axis angular speed and radar information.

In a possible implementation manner of the first aspect, the sensor module is further configured to generate at least one fault event simulating the unmanned aerial vehicle in flight, and input at least one flight data and the at least one fault event into the IMU module;

and the IMU module is also used for estimating the current attitude information of the simulated unmanned aerial vehicle according to the at least one flight data and the at least one fault event and inputting the current attitude information of the simulated unmanned aerial vehicle into the FCU.

In a possible implementation manner of the first aspect, the at least one fault event includes at least one of:

the method comprises the following steps of loss of a navigation satellite, disconnection of the navigation satellite, abnormality of a magnetic compass, abnormality of a radar, insufficient power supply, insufficient load dosage, time of medicine break and abnormality of a remote controller.

In a possible implementation manner of the first aspect, the IMU module is specifically configured to establish a current attitude model of the simulated unmanned aerial vehicle according to current attitude information of the simulated unmanned aerial vehicle, and input the current attitude model of the simulated unmanned aerial vehicle into the FCU;

and the at least one controlled unit is specifically used for calculating and updating the current attitude model of the simulated unmanned aerial vehicle according to the control data sent by the FCU.

The flight cluster simulator provided by the embodiment of the invention comprises a plurality of flight simulators, a plurality of ground stations, a cluster algorithm and a monitoring server, wherein the plurality of flight simulators correspond to the plurality of ground stations one by one; the cluster algorithm is connected with the monitoring server and the ground stations, and is used for calculating and determining flight lines of the flight simulators according to task targets of the flight clusters, sending the flight lines of the flight simulators to the ground stations, so that the ground stations send flight control information to the connected flight simulators according to flight plans, receiving current attitude information of the flight simulators forwarded by the ground stations, judging collision probability among the flight simulators according to the current attitude information of the flight simulators, adjusting the flight lines of the flight simulators with the collision probability larger than a preset threshold value, sending the flight lines to the corresponding ground stations, so that the ground stations send the adjusted flight control information to the connected flight simulators according to the adjusted flight lines, and performing flight simulation on the unmanned plane clusters by using the flight cluster simulator provided by the embodiment, so as to avoid the influence of collision of unmanned planes in the unmanned plane clusters on flight safety and flight task tasks during flight The flight simulation scheme of a reliable unmanned aerial vehicle cluster is provided.

Drawings

FIG. 1 is a schematic structural diagram of a first embodiment of flight cluster simulation provided by an embodiment of the present invention

Fig. 2 is a flow chart of flight simulation performed by applying the flight cluster simulator provided by the embodiment of the invention;

fig. 3 is a schematic diagram of a typical drone architecture;

fig. 4 is a schematic diagram of an architecture of a flight controller in a typical drone;

FIG. 5 is a schematic structural diagram of a flight simulator provided in an embodiment of the present invention;

fig. 6 is a schematic view of the control of the driving motor of the simulated drone.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

In the processes of development, production, testing and the like of the unmanned aerial vehicle, in order to ensure that the unmanned aerial vehicle can complete a predetermined flight task, the flight simulation of the unmanned aerial vehicle needs to be performed by using a flight simulator before actual flight. Because unmanned aerial vehicle's volume is less, and load is also less, and single unmanned aerial vehicle has been difficult to accomplish large-scale flight task, needs to use the unmanned aerial vehicle flight cluster that many unmanned aerial vehicles constitute to accomplish complicated flight task jointly. Present flight simulator only carries out the flight simulation to single unmanned aerial vehicle, and numerous unmanned aerial vehicle in the unmanned aerial vehicle flight cluster generally all need form intensive formation flight, and the flight circuit between each unmanned aerial vehicle produces the interact easily, especially takes off and descends the stage, extremely easily influences flight safety because the interact between the unmanned aerial vehicle.

In order to solve the above problems, embodiments of the present invention provide a flight cluster simulator, which is used for performing flight simulation on a flight cluster including multiple unmanned aerial vehicles.

Fig. 1 is a schematic structural diagram of a first embodiment of flight cluster simulation provided in an embodiment of the present invention, and as shown in fig. 1, a flight cluster simulator provided in an embodiment of the present invention includes:

a plurality of flight simulators 11, a plurality of ground stations 12 and a clustering algorithm and monitoring server 13. The number of flight simulators 11 and ground stations 12 is the same, and fig. 1 illustrates two flight simulators 11 and two ground stations 12 as an example, but the number of flight simulators 11 and ground stations 12 is not limited to this.

The plurality of flight simulators 11 correspond to the plurality of ground stations 12 one to one, each flight simulator 11 is connected with one ground station 12 through wireless communication, and each ground station 12 is used for receiving current attitude information sent by the connected flight simulator 11 and sending flight control information to the connected flight simulator 11. Each flight simulator 11 is for simulating the flight of a drone, and each ground station 12 is for simulating a ground control terminal of a drone, such as a remote control or flight control system of a drone.

The cluster algorithm is connected to the monitoring server 13 and the plurality of ground stations 12, and is configured to calculate and determine a flight line of each flight simulator 11 according to a task target of a flight cluster, send the flight line of each flight simulator 11 to each ground station 12, so that each ground station 12 sends flight control information to the connected flight simulator 11 according to a flight plan, receive current attitude information of each flight simulator 11 forwarded by the plurality of ground stations 12, determine collision probability between each flight simulator 11 according to the current attitude information of each flight simulator 11, adjust the flight line of the flight simulator 11 having collision probability greater than a preset threshold value, and send the flight control information to the corresponding ground station 12, so that each ground station 12 sends flight control information to the connected flight simulator 11 according to the adjusted flight line.

The traditional unmanned aerial vehicle flight simulator can only simulate the flight state of a single unmanned aerial vehicle and can only simulate the interaction between one flight simulator and one ground station. However, the number of unmanned aerial vehicles in an unmanned aerial vehicle flying cluster may be hundreds or thousands, and numerous unmanned aerial vehicles may also need to be densely formed into a formation to fly, and may complete various tasks through loads in flight, so that the flying lines of the unmanned aerial vehicles may intersect to cause collision. Consequently when simulating unmanned aerial vehicle flight cluster, need carry out analysis and judgement to the influence each other between each unmanned aerial vehicle in the unmanned aerial vehicle cluster, when the discovery influences the safe condition of flight, adjust relevant unmanned aerial vehicle's flight circuit to avoid the influence each other between the flight circuit of each unmanned aerial vehicle in the unmanned aerial vehicle flight cluster.

In order to comprehensively judge the flight lines of a plurality of unmanned aerial vehicles in the unmanned aerial vehicle flight cluster, a cluster algorithm and a monitoring server 13 are arranged, and the cluster algorithm and the monitoring server 13 are connected with a plurality of ground stations 12. Firstly, the flight circuit of each flight simulator 11 in the flight cluster is calculated by the cluster algorithm and the monitoring server 13 according to the task target of the flight cluster, and then the calculated flight circuit of each flight simulator 11 is sent to each corresponding ground station 12, so that the ground station 12 receiving the flight circuit can send flight control information to the flight simulator 11 connected with the ground station 12 according to the received flight plan to control each flight simulator 11 to simulate flight according to the flight plan. In the process of simulating the flight by the flight simulator 11, the current attitude information is fed back to the ground station 12 in real time, and the current attitude information includes the current position information and the flight speed and direction of the flight simulator 11, various states of the flight simulator 11, and the like. Each ground station 12 sends the received current attitude information of each flight simulator 11 to the clustering algorithm and monitoring server 13. The clustering algorithm and the monitoring server 13 can determine the collision probability between the flight simulators 11 according to the current attitude information of the flight simulators 11. Wherein the collision probability between the flight simulators 11 is the probability that a collision between the flight simulators 11 may occur, and includes, for example, that the flight lines of the flight simulators 11 intersect within a preset time range, that the distance between the flight simulators 11 is smaller than a preset distance threshold value, and the like. When the cluster algorithm and the monitoring server 13 find that the collision probability between the flight simulators 11 is greater than the preset threshold value, the flight line of the flight simulator 11 with the collision probability greater than the preset threshold value is adjusted, and the adjusted flight line is sent to the corresponding ground station 12, so that the ground station 12 receiving the adjusted flight line can send adjusted flight control information to the connected flight simulator 11 according to the adjusted flight line. In this way, it is avoided that the flight safety of the flight cluster is affected by collisions between the flight simulators 11 in the flight cluster.

Wherein, the flight circuit of the flight simulator 11, of which the adjusted collision probability is greater than the preset threshold value, includes: adjusting the flight lines of the flight simulators 11 intersecting between the flight lines, or adjusting the flight lines of the flight simulators 11 having a distance less than a preset distance threshold.

After the flight simulator provided by this embodiment simulates the flight of each flight simulator 11 in the flight cluster, the flight lines of each flight simulator 11 are adjusted to avoid collision. Therefore, the flight of the actual unmanned aerial vehicle cluster is controlled by the flight lines of the flight simulators 11 simulated by the flight cluster simulator, so that the unmanned aerial vehicles in the unmanned aerial vehicle cluster can not collide in the actual flight process, and the flight safety of the unmanned aerial vehicle cluster is ensured.

In an embodiment, the adjustment of the flight lines of the flight simulators 11 in the flight cluster by the cluster algorithm and the monitoring server 13 is not limited to avoid collision between the flight simulators 11, but can further achieve the purpose of optimizing the flight lines of all the flight simulators 11 in the flight cluster. No matter the unmanned aerial vehicle adopts battery drive or fuel drive, its flight energy of using is all limited, therefore flight time is also limited. Therefore, when planning and adjusting the flight line of the unmanned aerial vehicle, the flight time limit of the unmanned aerial vehicle needs to be considered. And unmanned aerial vehicles in the unmanned aerial vehicle cluster are numerous, are in the consideration of cost control and environmental protection, and also need to save energy consumption as far as possible when flying. And when charging or fuel supplement for unmanned aerial vehicle in the unmanned aerial vehicle cluster, the electric quantity or the fuel quantity of each unmanned aerial vehicle are the same and then can reach higher efficiency. Therefore, if when the unmanned aerial vehicle cluster flies, the energy consumption balance of each unmanned aerial vehicle is ensured, and the service efficiency of the unmanned aerial vehicle cluster can be improved.

The flight paths of the plurality of flight simulators and the adjusted flight paths of the plurality of flight simulators may thus satisfy at least one of the following conditions: the sum of the flight distances of all the flight simulators is minimum; the flight distances of all the flight simulators are the same; the ratio of the effective flight distance to the total flight distance of each flight simulator is maximized.

In consideration of energy saving, when the sum of the flight distances of all the flight simulators 11 in the flight simulator cluster is minimum, the total energy consumed by the whole flight cluster during flight can be ensured to be minimum. In consideration of balancing the energy consumption of each flight simulator 11, it is possible to ensure that the flight distances of each flight simulator 11 are the same. In addition, in some flight tasks of the flight cluster, many unmanned aerial vehicles need to form a formation and then uniformly fly after taking off, and need to be separated from the formation and then land after completing the flight tasks. And many unmanned aerial vehicles need take off in proper order, descend and can guarantee flight safety, and the unmanned aerial vehicle that takes off earlier then must be longer than the unmanned aerial vehicle's that takes off later flight duration, and flight distance is longer. The flight of the unmanned aerial vehicle in the takeoff and landing stages may not be effective flight, and the waste of redundant energy caused by the ineffective flight of the unmanned aerial vehicle in the unmanned aerial vehicle cluster is avoided as much as possible. Therefore, when the flight paths of the flight simulators 11 in the flight cluster are adjusted, the ratio of the effective flight distance of the flight simulators 11 to the total flight distance can be maximized.

The three conditions can meet one of the requirements or meet any combination of the three conditions according to the use requirements. Therefore, the flight performance of the flight cluster can be further improved by each flight simulator 11 in the flight cluster on the premise of ensuring the flight safety.

Additionally, in one embodiment. The clustering algorithm and monitoring server 13 is further configured to, upon receiving the fault information of the flight simulators 11, modify the flight lines of each flight simulator 11 or add a new flight simulator 11 to replace the fault flight simulator 11 and determine the flight lines of each flight simulator 11. Since the unmanned aerial vehicle may generate flight faults due to self faults or external factors during the flight process, the flight simulator 11 may also simulate various faults generated by the unmanned aerial vehicle, such as various mechanical faults, electronic faults or environmental factors. When the flight simulator 11 has a fault, the flight attitude may be affected, so the clustering algorithm and monitoring server 13 may receive the fault information of the flight simulator 11, determine whether the flight line of the faulty flight simulator 11 will change, then modify the flight lines of each flight simulator 11, or when the fault causes the flight simulator 11 to fail to complete the flight mission, the clustering algorithm and monitoring server 13 may further add a new flight simulator 11 to replace the faulty flight simulator 11 and further determine the flight line of each flight simulator 11. Therefore, various fault conditions of the flight cluster can be fully simulated, and the flight adjustment strategy under various fault conditions can be obtained. When various faults occur in the actual flight process of the unmanned aerial vehicle cluster, various simulated strategies can be applied to deal with the faults, so that the unmanned aerial vehicle cluster can complete flight tasks.

The flight cluster simulator provided by the embodiment comprises a plurality of flight simulators, a plurality of ground stations, a cluster algorithm and a monitoring server, wherein the plurality of flight simulators correspond to the plurality of ground stations one by one, each flight simulator is connected with one ground station through wireless communication, and each ground station is used for receiving current attitude information sent by the connected flight simulators and sending flight control information to the connected flight simulators; the cluster algorithm is connected with the monitoring server and the ground stations, and is used for calculating and determining flight lines of the flight simulators according to task targets of the flight clusters, sending the flight lines of the flight simulators to the ground stations, so that the ground stations send flight control information to the connected flight simulators according to flight plans, receiving current attitude information of the flight simulators forwarded by the ground stations, judging collision probability among the flight simulators according to the current attitude information of the flight simulators, adjusting the flight lines of the flight simulators with the collision probability larger than a preset threshold value, sending the flight lines to the corresponding ground stations, so that the ground stations send the adjusted flight control information to the connected flight simulators according to the adjusted flight lines, and performing flight simulation on the unmanned plane clusters by using the flight cluster simulator provided by the embodiment, so as to avoid the influence of collision of unmanned planes in the unmanned plane clusters on flight safety and flight task tasks during flight The flight simulation scheme of a reliable unmanned aerial vehicle cluster is provided.

Fig. 2 is a flow chart of flight simulation performed by applying the flight cluster simulator provided in the embodiment of the present invention, and as shown in fig. 2, the flight simulation flow provided in this embodiment includes:

step S201, each flight simulator 11 and ground station 12 are connected to the clustering algorithm and detection server 13, and each ground station 12 reports information such as the identifier, the aircraft type, and the size of each flight simulator 11 and ground station 12 to the clustering algorithm and detection server 13.

Step S202, the clustering algorithm and detection server 13 determines the identifications of the flight simulators 11 in the flight cluster, determines the takeoff location of each flight simulator 11, and then sends the latitude and longitude information of the takeoff location to each flight simulator 11.

Step S203, the clustering algorithm and detection server 13 plans the flight line of each flight simulator 11 according to the flight mission and algorithm of the flight cluster, and then sends the flight line of each flight simulator 11 to each ground station 12. The cluster algorithm and the algorithm adopted by the detection server 13 are selected according to the flight mission requirements of the flight cluster, and the algorithm at least includes an algorithm for avoiding collision of each flight simulator 11 in the flight cluster, or may also include other algorithms for optimizing the performance of the flight cluster.

In step S204, each ground station 12 sends the flight control information to the connected flight simulator 11 after receiving the flight route sent by the clustering algorithm and the detection server 13.

In step S205, after confirming that the preparation of each flight simulator 11 is completed, the cluster algorithm and detection server 13 sends a takeoff instruction to each flight simulator 11 through each ground station 12.

Step S206, each flight simulator 11 takes off and flies according to the preset flight line according to the flight control information sent by the ground station 12, and the flight simulator 11 sends real-time flight attitude information and position information to the cluster algorithm and detection server 13 through the connected ground station 12 in the flying process.

Step S207, the clustering algorithm and detection server 13 determines whether there is a collision possibility according to the information of the size, the real-time speed, the real-time heading, the real-time position, and the like of each flight simulator 11. The specific method can be as follows:

a) because the algorithm already considers the collision detection between the flight lines during the flight line planning, the flight simulator 11 will not generate collision during the normal flight line flight, and will only generate the intersection of the lines during the entering flight line and the return flight, and at this time, will generate the possibility of collision. Connecting the starting point of the flight line distributed for the flight simulator 11 to the flying point to generate a line segment A; and connecting the flight line end point to the drop point to generate a line segment B.

b) Calculating the intersection condition of the line segment A and the line segment B of each flight simulator 11, and if the line segments A and the line segments B are intersected, indicating that collision is possible; if they do not intersect, the minimum distance of the two line segments is calculated, and if the distance is smaller than the sum of the radii of the two flight simulators 11 (and a margin, such as 2m, may be left), a collision is also considered possible.

c) The possibly colliding flight simulators 11 are sequenced and the takeoff altitude and the return altitude are assigned to different flight simulators 11, so that the possibly colliding flight simulators 11 adopt different altitudes when entering a flight line and returning, and thus, the possibility of collision is avoided.

d) For the flight simulator 11 with the temporary fault return flight, the flight simulator 11 is firstly made to fly to the terminal of the flight line in the flight line (the planning range of the flight line is a convex hull, so that any two points of connection in the flight line can be in the convex hull), and then the flight simulator is made to return flight according to the original return flight height, so that the complex collision detection condition caused by the fact that the flight simulator is directly returned from the return flight point to pass through other flight lines is avoided.

In step S208, the flight simulator 11 may simulate a fault, for example, the electric quantity is insufficient, and after the fault is generated, the flight simulator 11 may fall or crash, and at this time, the ground station 12 sends the relevant information to the clustering algorithm and monitoring server 13.

In step S209, the cluster algorithm and monitoring server 13 determines whether to add a new flight simulator 11 according to the mission target and the algorithm of the flight cluster, and determines whether to modify the flight path of each flight simulator 11. If the flight line needs to be corrected or the flight simulator 11 needs to be newly added, step S203 is executed.

After the algorithm is adjusted according to the result of step S207, step S201 is repeatedly performed to perform a new simulation.

The specific algorithm for calculating the flight paths of the flight simulators 11 in the flight cluster by the cluster algorithm and the monitoring server 13 can be designed at will as long as the task requirements of the flight cluster can be met, and collision among the flight simulators 11 can be avoided or the flight paths of the flight cluster can be optimized. For example, the following two algorithms may be included.

1. Simple overlay algorithm task: the target flight area is a polygon, and the n-frame flight simulator is used for completely covering the area.

The algorithm is as follows:

1) and selecting a proper flight path direction to carry out flight path planning on the polygon, wherein the aim is to maximize the ratio of the effective flight distance to the total flight distance.

2) According to the number of flight simulators, the flight line is cut into n parts, so that the effective flight distance is ensured to be consistent as much as possible.

3) And respectively distributing the flight lines to different flight simulators and starting operation.

2. Multi-parcel algorithm tasks: the target flight area is N polygons, and the area is completely covered by using N flight simulators.

The algorithm is as follows:

1) firstly, cutting all polygons to ensure that each polygon available for planning is a convex polygon.

2) And selecting N possible takeoff/landing points, and finding out the most suitable m takeoff points according to the position and the area of each polygon, so that the weighted sum of the distances from the takeoff points to the centers of the peripheral polygons is minimum.

3) And distributing the flight simulator to a plurality of flying starting points according to the area of the polygon around each flying starting point, wherein the large area of the polygon around the flying starting points needs to be distributed with more flight simulators.

4) The flight lines are distributed to different flight simulators, and the flight simulators at the same take-off and landing point are distributed with the flight lines in different directions as much as possible, for example, one flight line is distributed to the north and the other flight line is distributed to the south, so that the flight lines are basically not crossed, and the possibility of collision is avoided to the maximum extent.

Because the behaviors of each aircraft simulator in the flight cluster simulator are consistent with those of the actual aircraft, the flight cluster simulator can use the actual cluster algorithm, the monitoring server and the unmanned aerial vehicle ground station without redevelopment. Saving a lot of development time and cost overhead of testing.

The Flight controller in the unmanned aerial vehicle mainly comprises various sensors, an Inertial Measurement Unit (Inertial Measurement Unit), a Flight Control Unit (FCU) and a controlled Unit. Wherein all kinds of sensors are used for measuring various data of unmanned aerial vehicle flight in-process, and IMU estimates unmanned aerial vehicle's gesture and position according to the data that all kinds of sensors measured. The FCU is responsible for navigation and application related logic control, and the unmanned aerial vehicle is actually controlled in flight through the controlled unit. The IMU and FCU in the drone generally use real-time embedded systems to ensure real-time response. The current flight simulator is generally a pure software simulator running on a Windows or Linux system, which means that there is a great difference in the running environment between the actual unmanned aerial vehicle and the flight simulator. There are many behavioral inconsistencies between the flight simulator and the actual drone.

Fig. 3 is a schematic diagram of a typical architecture of the unmanned aerial vehicle, and as shown in fig. 3, the typical architecture of the unmanned aerial vehicle is divided into an aerial terminal 31 and a ground station 32, where the aerial terminal 31 includes a structure for the unmanned aerial vehicle to fly, various sensors, control devices, and communication devices, and a mission load carried by the unmanned aerial vehicle. The ground station 32 is configured to communicate with the air terminal 31 via a wireless communication link, and includes various information for flying the air terminal 31 and/or receiving feedback from the air terminal 31.

Wherein a typical drone architecture is shown in fig. 3, the air terminal 31 includes a flight controller 311, an energy source 312, a mission load 313, an aerodynamic device 314, and a data transmission 315. The ground station 32 comprises a ground station or remote control. Energy source 312 provides the required energy for the various devices in air terminal 31. The energy source 312 may be a battery, and supplies required energy to each device in the air terminal 31 by electric energy; alternatively, the energy source 312 may be a gasoline, diesel, or other powered engine, with the energy provided by the engine providing the required energy for the various devices in the air terminal 31.

The aerodynamic device 314 is a combination of various devices and structures required by the unmanned aerial vehicle for realizing flight, and comprises a machine body and a driving device which are designed in a matching way, so that the aerodynamic device 314 meets the aerodynamic requirement. The aerodynamic devices 314 of a typical drone are, for example, of the four-axis rotor type, or the aerodynamic devices 314 are, for example, of the fixed-wing type.

Task load 313 is the combination of devices that unmanned aerial vehicle needs to be configured to accomplish the task, for example, unmanned aerial vehicle is agricultural unmanned aerial vehicle, then task load 313 can be for the medicinal cupping and spray the structure, if unmanned aerial vehicle is for patrolling or keeping watch on unmanned aerial vehicle, then task load 313 can be for camera equipment or infrared detection equipment, if unmanned aerial vehicle is relay communication unmanned aerial vehicle, then task load 313 can be for relay communication equipment etc..

Flight controller 311 is the core control device in the unmanned aerial vehicle, and flight controller 311 distributes the energy that energy 312 provided to aerodynamic device 314 to drive the unmanned aerial vehicle and realize the required flight state, and flight controller 311 control task load 313 realizes the task that unmanned aerial vehicle needs to carry out. The flight controller 311 also communicates with the ground station 32 through a data transmission 315, which includes receiving various control commands sent by the ground station 32 and feeding back the flight status information and mission load information of the unmanned aerial vehicle to the ground station 32. Data transmission 315 is connected to ground station 32 via wireless communication. The flight controller 311 may fly according to a flight control command issued by the ground station 32, or may fly according to a preset flight command program.

Fig. 4 is a schematic diagram of the architecture of a flight controller in a typical drone, as shown in fig. 4, the flight controller in the drone includes a sensor 41, an IMU 42, an FCU 43, and a controlled unit 44. Wherein the number of the sensor 41 and the controlled unit 44 is one or more.

The sensor 41 is used for detecting flight data of the unmanned aerial vehicle during flight, and the different types of sensors 41 are used for detecting different types of flight data of the unmanned aerial vehicle during flight. The sensors 41 include, for example, a navigation chip, an accelerometer, a gyroscope, a magnetometer, a radar, etc., wherein each different type of sensor 41 detects one or more types of flight data. For example, the navigation chip is used to obtain flight data such as the number of navigation satellites, navigation Positioning accuracy, latitude and longitude information, etc. when the unmanned aerial vehicle is flying, and the navigation chip may be a navigation chip of any kind of satellite navigation System, such as a Global Positioning System (GPS), a compass, GLONASS (GLONASS), etc. The accelerometer is used for obtaining the acceleration data of the unmanned aerial vehicle during flight. The gyroscope is used for acquiring the angular speed and angular acceleration data of the unmanned aerial vehicle in flight. The geomagnetic instrument is used for acquiring earth magnetic field data. The radar is used for using the radar to survey near the object of unmanned aerial vehicle when unmanned aerial vehicle flies, obtains radar detection data. In summary, the drone is configured with one or more sensors 41, each sensor 41 comprising one or more, depending on the task that the drone is required to perform.

The flight data detected by each sensor 41 are all sent to the IMU 42, and the IMU 42 is responsible for estimating information such as the attitude and the position of the unmanned aerial vehicle according to the flight data sent by each sensor 41. That is to say, the IMU 42 is responsible for performing data fusion processing, and inputs the flight data sent by the various sensors 41 into a preset unmanned aerial vehicle model, so as to obtain information such as the flight attitude and position of the unmanned aerial vehicle.

The FCU 43 is responsible for navigation and application-related logic control, and the IMU 42 processes and estimates the attitude and position of the drone and sends the information to the FCU 43. The automatic flight program of the unmanned aerial vehicle can be preset in the FCU 43, and then the FCU 43 can combine the data sent by the IMU 42 in the automatic flight program to judge whether the flight states of the flying unmanned aerial vehicle, such as the attitude and the position, meet the flight state preset in the automatic flight program. The FCU 43 may also communicate with the ground station 32 via a data transfer 315 to send the flight status data of the drone to the ground station 32 so that the ground station 32 knows the flight status of the drone. Or when the drone is flying for remote control, the FCU 43 may receive the flight control information sent by the ground station 32 via data transfer 315.

The controlled unit 44 is a unit controllable in the unmanned aerial vehicle and capable of changing the flight state of the unmanned aerial vehicle, and the controlled unit 44 controls the FCU 43 so that the flight state of the unmanned aerial vehicle conforms to the designated flight state of the FCU 43. The number of controlled units 44 is set according to the type of drone and flight requirements, for example when the drone is a quad-rotor drone, the controlled units 44 are 4 motors with propellers connected.

In current drones, the IMU and FCU generally use real-time embedded systems to ensure real-time response. With real-time embedded systems, when external events or data occur, they can be accepted and processed at a sufficiently fast rate that the results of the processing can be used to control the manufacturing process or respond quickly to the processing system within a specified time. Therefore, when the IMU and the FCU adopt embedded systems, the unmanned aerial vehicle can make very sensitive flight response.

In the development process of the unmanned aerial vehicle, the flight state of the unmanned aerial vehicle needs to be tested through test flight, but the physical unmanned aerial vehicle is used for testing time and labor consumption, and when software and hardware of the unmanned aerial vehicle need to be modified, longer time and cost are needed. Consequently often use the flight simulator in unmanned aerial vehicle's development process, the various parameters of unmanned aerial vehicle are simulated to the mode that the flight simulator passes through software to only use software simulation unmanned aerial vehicle's flight, can find the problem that unmanned aerial vehicle exists fast like this and be convenient for adjust unmanned aerial vehicle's parameter. Compared with an actual unmanned aerial vehicle, the flight simulator is low in cost, and time and labor are saved. The flight simulator only simulates various parameters of the unmanned aerial vehicle, and simulates the actual flight state of the unmanned aerial vehicle by analyzing various output data, so that the flight simulator mainly simulates the interaction between a flight controller and a ground station.

However, the current flight simulator is a pure software simulator running on a Windows or Linux system, and the FCU in the actual drone adopts a real-time embedded system. There is a large difference in internal architecture between the flight simulator and the FCU, resulting in a large difference in operational results. There are many behavioral inconsistencies between the flight simulator and the actual drone. In addition, the FCU involves a large amount of logic associated with the traffic scenario, and therefore the FCU needs to be updated frequently, and therefore, the conventional flight simulator also needs to follow the update of the FCU and to migrate the update frequently. Moreover, each time the FCU is updated, the FCU needs to be actually tested, and the traditional test, namely the actual flight test, has a relatively high cost. However, the conventional flight simulator is only used for transplanting and simulating the FCU, and does not introduce a real actual FCU into the simulator, so that the original conventional simulator cannot play a role in actually testing the FCU.

In order to overcome the problem that the difference exists between the conventional flight simulator and the actual unmanned aerial vehicle, the flight simulator in the flight cluster simulator provided by the embodiment of the invention can adopt the structure provided by each embodiment described below.

Fig. 5 is a schematic structural diagram of a flight simulator provided in an embodiment of the present invention, and as shown in fig. 5, the flight simulator provided in this embodiment includes: IMU simulator 51, FCU 52 and pass unit 53.

The IMU simulator 51 is a software module running in an operating system environment, the IMU simulator 51 includes at least one sensor module 511, an IMU module 512 and at least one controlled unit module 513, and the FCU 52 is a hardware module implemented by a real-time embedded system.

Because the traditional flight simulator is a pure software simulator running on a Windows or Linux system, there are many behavioral inconsistencies between the flight simulator and an actual unmanned aerial vehicle due to differences between the flight simulator and the actual unmanned aerial vehicle. After analyzing the architecture of the flight controller shown in fig. 4, it can be seen that the IMU in the flight controller mainly involves estimation of the attitude, and the algorithm thereof is more classical, so that the chance of modification is generally smaller. However, FCUs involve a large amount of logic associated with the traffic scenario and therefore need to be updated frequently. Conventional flight simulators therefore require a migration update that is synchronized with the update of the FCU. After the FCU in the flight simulator is updated each time, the FCU needs to be actually tested, and in the traditional flight simulator, the FCU cannot be actually tested.

Therefore, in this embodiment, a flight simulator is proposed, in which the IMU simulator simulated by software and the FCU in the actual drone jointly constitute the main structure of the flight simulator. The IMU simulator 51 is a software module running in an operating system environment, which may be any kind of operating system, and the IMU simulator 51 is designed according to the architecture of the running operating system, which is, for example, a Windows or Linux operating system. And the FCU 52 is a hardware module implemented by a real-time embedded system.

The IMU simulator 51 comprises at least one sensor module 511, an IMU module 512 and at least one controlled unit module 513. Wherein the at least one sensor module 511 is configured to generate at least one flight data simulating the unmanned aerial vehicle in flight, and input the at least one flight data into the IMU module 512; the IMU module 512 is configured to estimate current attitude information of the simulated drone according to at least one flight data, and input the current attitude information of the simulated drone to the FCU 52; the at least one controlled unit 513 is configured to calculate and update the current attitude of the simulated drone according to the control data sent by the FCU 52.

The IMU simulator 51 is equivalent to a software simulation of at least one sensor, IMU, and controlled unit in the flight controller of the actual drone shown in fig. 4. The IMU module 512 and the controlled unit 513 in the IMU simulator 51 have a transmission interface with the FCU 52.

The at least one sensor module 511 is specifically configured to generate flight data simulating at least one of the following when the drone is in flight: the system comprises the navigation satellite number, navigation positioning precision, longitude and latitude information, altitude information, motion course, simulation unmanned aerial vehicle attitude, acceleration, three-axis speed, three-axis acceleration, three-axis angular speed and radar information. The kinds of the sensor modules 511 are one or more, and each sensor module 511 includes one or more. The sensor module 511 is used for simulating the sensor 51 shown in fig. 4, for example, simulating a sensor such as a navigation chip, an accelerometer, a gyroscope, a geomagnetic instrument, or a radar. The at least one sensor module 511 simulates at least one of a navigation satellite number, a navigation positioning accuracy, latitude and longitude information, altitude information, a moving heading, a simulated unmanned aerial vehicle attitude, an acceleration, a three-axis velocity, a three-axis acceleration, a three-axis angular velocity, and radar information, for example. The sensor module 511 may be configured according to the requirements of the drone simulated by the flight simulator.

Further, the sensor module 511 is further configured to generate at least one fault event simulating the unmanned aerial vehicle in flight, and input at least one flight data and the at least one fault event into the IMU module 512; the IMU module 512 is further configured to estimate current attitude information of the simulated drone based on the at least one flight data and the at least one fault event, and input the current attitude information of the simulated drone to the FCU 52.

Since an actual drone may fail due to mechanical failure, electronic failure, or environmental factors during flight, the sensor module 511 may simulate at least one failure event of the drone while flying, in addition to simulating flight data of the simulated drone. For example, the sensor module 511 may generate failure information of each module or unit in the flight simulator according to a preset program or through an external input, and shut down the module or unit simulated as a failure. Or the sensor module 511 may also simulate external factors simulating the drone, such as external wind simulating different wind directions and wind speeds, rain and snow simulating different amounts of rain and snow, or external electromagnetic interference, and the like.

The sensor module 511 inputs the at least one flight data and the at least one fault event into the IMU module 512. The IMU module 512, after receiving the flight data and the fault event sent by the sensor module 511, may estimate the current attitude information of the simulated drone according to at least one flight data and at least one fault event, and input the current attitude information of the simulated drone into the FCU 52. When the simulated drone has a fault event, the attitude information of the drone may be affected by the fault event and change, so the IMU module 512 needs to estimate the current attitude information of the simulated drone according to at least one flight data and at least one fault event at the same time. Can make flight simulator can simulate actual unmanned aerial vehicle flight state under the comprehensive operating mode like this.

Wherein the at least one fault event generated by the sensor module 511 comprises at least one of: the method comprises the following steps of loss of a navigation satellite, disconnection of the navigation satellite, abnormality of a magnetic compass, abnormality of a radar, insufficient power supply, insufficient load dosage, time of medicine break and abnormality of a remote controller. However, the fault event generated by the sensor module 511 is not limited to the above fault event, and any internal or external factor that can affect the normal flight of the drone may be used as the fault event.

Since most drones are currently multi-axis rotary-wing drones, at least one controlled unit 513 comprises at least one drive motor that simulates a drone, i.e. a drive motor that drives each rotor in a multi-axis rotary-wing drone. Multiaxis rotor unmanned aerial vehicle can adjust unmanned aerial vehicle's flight gesture through changing each driving motor's rotational speed.

When the at least one controlled unit 513 is at least one driving motor simulating the drone, the target attitude information includes a target attitude angle and a target angular velocity information, and the control data of the at least one controlled unit 513 includes Pulse Width Modulation (PWM) data simulating the at least one driving motor of the drone.

The simulated drone in the IMU simulator 51 can be considered as an ideal control module, what the target angle is input by the FCU 52, what the attitude angle of the simulated drone is, and no additional calculation is required. Suppose that the simulated unmanned aerial vehicle is a four-axis rotor unmanned aerial vehicle, and each of the simulated unmanned aerial vehicles hovering is setThe PWM value of the shaft driving motor is 1500, the average value X of PWM output by 4 motors is the hovering throttle value of the unmanned aerial vehicle, wherein the normal output PWM value of each driving motor is 1000-2000. When the maximum throttle output is set to be 2000, the upward acceleration of the unmanned aerial vehicle is 5m/s²When the minimum throttle output is set to be 1000, the downward acceleration of the unmanned aerial vehicle is 5m/s². The acceleration in the direction of the day of the drone (assuming aircraft level) in the simulated drone model of the IMU simulator 51 is then: ACC (days) = (X-1500) × 5/500. And the vertical velocity and altitude are calculated from the integration of the unmanned aerial vehicle's day-wise acceleration with time (the altitude is 0 per takeoff by default).

The resultant force output by the driving motor of the unmanned aerial vehicle has a component F (vertical) for providing lift force, and another component F (horizontal) for providing power for horizontal movement, as shown in fig. 6, and fig. 6 is a schematic control diagram of the driving motor of the simulated unmanned aerial vehicle. If the average output PWM value of 4 driving motors of the simulated unmanned aerial vehicle is X, the vertical acceleration ACC (in the sky direction) and the horizontal acceleration ACC (horizontal Xb) and ACC (horizontal Yb) are respectively:

ACC (antenna) = ((X-1000) × cos (ang _ tilt) -500) × 5/500; upper positive and lower negative

ACC (level Xb) = (X-1000) sin (ang _ roll) 5/1000, right positive left negative

ACC (horizontal Yb) = - (X-1000) sin (ang _ pitch) 5/1000, positive front and negative back

Where ang _ tilt = arcos (cos (ang _ roll) × cos (ang _ pitch)), ang _ tilt is a pitch angle, ang _ roll is a roll angle, and ang _ pitch is a course angle. Since three angles (heading, roll, pitch) are involved, the final shape is a three-dimensional matrix, but the principle is shown in fig. 4.

From the above-mentioned day and horizontal accelerations, the three-dimensional velocity and position can be calculated with the time integral, where the calculation integral is directly integrated with the time slice in the simulator at a minimum of 100 Hz.

The FCU 52 is configured to calculate target attitude information of the simulated unmanned aerial vehicle according to current attitude information of the simulated unmanned aerial vehicle and flight control information of the simulated unmanned aerial vehicle, calculate control data of at least one controlled unit according to the target attitude information of the simulated unmanned aerial vehicle, and send corresponding control data to at least one controlled unit 513. The FCU 52 is the same as the FCU in the flight controller of the actual drone shown in fig. 4, with appropriate modifications between the transmission interfaces with the IMU and the controlled unit to accommodate the connection of the software-simulated IMU simulator 51. The FCU 52 may specifically calculate a target attitude angle and target angular velocity information of the simulated unmanned aerial vehicle by simulating current attitude information of the unmanned aerial vehicle and flight control information of the simulated unmanned aerial vehicle, and finally determine an output quantity corresponding to each controlled unit 513.

The FCU 52 may obtain flight control information of the simulated unmanned aerial vehicle according to a preset automatic flight program, and may also receive flight control information sent by the ground station. Where the ground station may be the ground station shown in figure 3 controlling the actual drone. Because the flight control of actual unmanned aerial vehicle divide into two kinds of modes, one is that the flight controller flies according to the automatic flight procedure that predetermines, and the other is that the flight controller receives the flight control instruction flight that the ground station sent. It is also possible for the flight simulator to simulate these two different flight control modes separately. When the automatic flight mode is simulated, the FCU 52 may obtain flight control information of the simulated unmanned aerial vehicle from a preset flight control program; in simulating the manual flight mode, the FCU 52 may receive flight control information transmitted by the ground station.

Further, the IMU module 512 is further configured to provide an initial state setting interface for simulating the drone, and obtain initial attitude information of the simulated drone through the initial state setting interface. That is to say, can provide the interface and make the user directly set up the initial attitude information of simulation unmanned aerial vehicle, just so need not all start the simulation from unmanned aerial vehicle's take-off state at every turn when using the flight simulator to can improve the simulation efficiency who uses the flight simulator. The IMU module 512 may provide a map interface, and a user directly clicks and selects initial location information of the simulated drone in the map, or the IMU module 512 may receive latitude and longitude information input by the user, thereby determining the initial location information of the simulated drone.

Further, the IMU module 512 is specifically configured to establish a current attitude model of the simulated unmanned aerial vehicle according to the current attitude information of the simulated unmanned aerial vehicle, and input the current attitude model of the simulated unmanned aerial vehicle into the FCU 52; the at least one controlled unit 513 is specifically configured to calculate and update the current attitude model of the simulated drone according to the control data sent by the FCU 52. In the IMU module 512, the current attitude model of the simulated unmanned aerial vehicle can be established according to the current attitude information of the simulated unmanned aerial vehicle, that is, various flight data of the simulated unmanned aerial vehicle are integrated to obtain the current attitude model of the simulated unmanned aerial vehicle. Then, the FCU 52 obtains the current attitude model of the simulated unmanned aerial vehicle, then the FCU 52 sends control data to each controlled unit 513, and after at least one controlled unit 513 performs calculation according to the control data, the current attitude model of the simulated unmanned aerial vehicle is updated, so as to obtain the updated current attitude model of the simulated unmanned aerial vehicle. Establishing the current model of the simulated drone in the IMU module 512 enables the flight simulator to be closer to the actual drone's state, and the FCU 52 can complete the data processing more quickly.

A data transmission unit 53, configured to receive, through a wireless communication connection, flight control information sent by a ground station or a remote controller, and send the flight control information to the FCU 52; and receiving the current attitude information of the simulated unmanned aerial vehicle sent by the FCU 52, and sending the current attitude information of the simulated unmanned aerial vehicle to the ground station or the remote controller through wireless communication connection.

The data transmission unit 53 is the same as the data transmission unit in the real unmanned aerial vehicle shown in fig. 3, and is connected to a ground station through a wireless communication connection, and is responsible for data transfer between the FCU 52 and the ground station, where the ground station may be a ground station or a remote controller. When the flight simulator simulates the automatic flight control mode of the unmanned aerial vehicle, the data transmission unit 53 may receive the current attitude information of the simulated unmanned aerial vehicle sent by the FCU 52, and send the current attitude information of the simulated unmanned aerial vehicle to the ground station or the remote controller. When the flight simulator simulates a manual flight control mode of the unmanned aerial vehicle, the data transmission unit 53 may receive the flight control information sent by the ground station or the remote controller through wireless communication connection and send the flight control information to the FCU 52 on the basis of receiving the current attitude information of the simulated unmanned aerial vehicle sent by the FCU 52 and sending the current attitude information of the simulated unmanned aerial vehicle to the ground station or the remote controller.

The flight simulator that this embodiment provided, constitute by the IMU simulator through software simulation and the FCU of hardware form, sensor data in the flight controller to unmanned aerial vehicle through the software mode on the one hand, IMU and controlled unit have simulated, greatly reduced unmanned aerial vehicle's test expense, on the other hand adopts the FCU of hardware form to eliminate the difference that exists between true FCU and the software simulation FCU, make the flight simulator more be close to true unmanned aerial vehicle, and reduced the work of a large amount of FCU codes transplantation to the software simulator, therefore the flight simulator that this embodiment provided can press close to true unmanned aerial vehicle's flight state, and practice thrift the cost and the time of simulating to unmanned aerial vehicle.

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A flight cluster simulator, comprising: the system comprises a plurality of flight simulators, a plurality of ground stations and a cluster algorithm and monitoring server;

the plurality of flight simulators are in one-to-one correspondence with the plurality of ground stations, each flight simulator is connected with one ground station through wireless communication, and each ground station is used for receiving current attitude information sent by the connected flight simulator and sending flight control information to the connected flight simulator;

the cluster algorithm and the monitoring server are connected with the plurality of ground stations and used for calculating and determining flight lines of the flight simulators according to task targets of the flight clusters, sending the flight lines of the flight simulators to the ground stations, enabling the ground stations to send flight control information to the connected flight simulators according to flight plans, receiving current attitude information of the flight simulators forwarded by the ground stations, judging collision probability among the flight simulators according to the current attitude information of the flight simulators, adjusting the flight lines of the flight simulators with the collision probability larger than a preset threshold value, sending the flight lines to the corresponding ground stations, and enabling the ground stations to send the adjusted flight control information to the connected flight simulators according to the adjusted flight lines.

2. The flight constellation simulator of claim 1, wherein the flight lines of the plurality of flight simulators and the adjusted flight lines of the plurality of flight simulators satisfy at least one of the following conditions:

the sum of the flight distances of all the flight simulators is minimum;

the flight distances of all the flight simulators are the same;

3. The flight constellation simulator of claim 1, wherein adjusting the flight path of the flight simulator for which the probability of collision is greater than a preset threshold comprises:

4. The flight cluster simulator of any one of claims 1 to 3, wherein the cluster algorithm and monitoring server is further configured to, when fault information of the flight simulators is received, modify flight lines of the flight simulators or add a new flight simulator to replace the fault flight simulator and determine the flight lines of the flight simulators.

5. A flight cluster simulator as claimed in any one of claims 1 to 3, wherein each flight simulator comprises: the system comprises an inertial measurement unit IMU simulator, a flight control unit FCU and a data transmission unit;

the IMU simulator is a software module running in an operating system environment, the IMU simulator comprises at least one sensor module, an IMU module and at least one controlled unit module, and the FCU is a hardware module realized by a real-time embedded system;

the at least one sensor module is used for generating at least one flight data simulating the unmanned aerial vehicle in flight, and inputting the at least one flight data into the IMU module;

the IMU module is used for estimating the current attitude information of the simulated unmanned aerial vehicle according to the at least one flight data and inputting the current attitude information of the simulated unmanned aerial vehicle into the FCU;

the FCU is used for calculating target attitude information of the simulated unmanned aerial vehicle according to current attitude information of the simulated unmanned aerial vehicle and flight control information of the simulated unmanned aerial vehicle, calculating control data of the at least one controlled unit according to the target attitude information of the simulated unmanned aerial vehicle, and sending corresponding control data to the at least one controlled unit;

the data transmission unit is used for receiving flight control information sent by a ground station through wireless communication connection and sending the flight control information to the FCU; and receiving the current attitude information of the simulated unmanned aerial vehicle sent by the FCU, and sending the current attitude information of the simulated unmanned aerial vehicle to the ground station through wireless communication connection.

6. The flight cluster simulator of claim 5, wherein the FCU is further configured to obtain flight control information for the simulated drones from a preset flight control program.

7. The flight constellation simulator of claim 5, wherein the at least one sensor module is specifically configured to generate at least one of the following flight data of the simulated drone in flight:

8. The flight cluster simulator of claim 5, wherein the sensor module is further configured to generate at least one fault event for the simulated drone while in flight, input the at least one flight data and the at least one fault event to the IMU module;

the IMU module is further configured to estimate current attitude information of the simulated drone according to the at least one flight data and the at least one fault event, and input the current attitude information of the simulated drone to the FCU.

9. The flight cluster simulator of claim 8, wherein the at least one fault event comprises at least one of:

10. The flight cluster simulator of claim 5, wherein the IMU module is specifically configured to establish a current attitude model of the simulated drone according to current attitude information of the simulated drone, and input the current attitude model of the simulated drone into the FCU;