Method, system, device and storage medium for close proximity collision avoidance of an aircraft

Abstract

The application provides a kind of aircraft close-range collision avoidance method, system, equipment and storage medium.The method comprises: obtaining the original data of target aircraft adjacent to the current aircraft;Coordinate conversion is carried out on the original data to obtain the standardized position data of the target aircraft relative to the current aircraft;Based on the standardized position data, the optimal state estimation and collision risk parameter of the target aircraft are obtained;Collision risk index is obtained based on the collision risk parameter;When the collision risk index is greater than the safety threshold, a candidate collision avoidance strategy is obtained;The optimal collision avoidance strategy is selected from the candidate collision avoidance strategy;Collision trajectory parameters are obtained based on the optimal collision avoidance strategy;Flight control instructions are obtained for the current aircraft based on the collision trajectory parameters;The current aircraft performs collision avoidance flight based on the flight control instructions.Implementation of the technical solution provided by the present application overcomes the response delay problem of traditional centralized traffic control system in low-altitude environment, and provides reliable safety guarantee for high-density low-altitude flight.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle flight control, and particularly relates to an aircraft close-range collision avoidance method, system, device and storage medium. BACKGROUND

[0002] In the field of low-altitude flight, close-range collision avoidance between aircrafts is a key safety issue. With the rapid development of unmanned aerial vehicles and urban air traffic, low-altitude airspace is becoming increasingly crowded. Traditional air traffic control systems mainly rely on radar and radio communication, but these technologies have limitations in low-altitude environments. Radar systems are easily disturbed by ground clutter at low altitudes, and radio communication is prone to signal conflicts in dense environments. In addition, low-altitude aircrafts are small in size and highly maneuverable, requiring faster and more accurate collision avoidance mechanisms. There is currently a lack of a low-altitude collision avoidance solution that can simultaneously meet the requirements of high-precision detection and rapid information exchange. The most prominent technical challenge is how to achieve real-time and reliable position perception and collision avoidance decision-making between aircrafts in a low-altitude complex environment, while overcoming the technical limitations of traditional radar and radio communication. SUMMARY

[0003] The present application provides an aircraft close-range collision avoidance method, system, device and storage medium, which can realize collaborative collision avoidance decision-making between aircrafts.

[0004] In a first aspect of the present application, an aircraft close-range collision avoidance method is provided, specifically comprising:

[0005] Obtaining original data of a target aircraft adjacent to a current aircraft;

[0006] Performing coordinate conversion on the original data to map the coordinates of the original data into the coordinate system of the current aircraft to obtain standardized position data of the target aircraft relative to the current aircraft;

[0007] Obtaining optimal state estimation and collision risk parameters of the target aircraft based on the standardized position data;

[0008] Obtaining a collision risk index based on the collision risk parameters;

[0009] When the collision risk index is greater than a safety threshold, obtaining a candidate collision avoidance strategy based on the optimal state estimation, constraint conditions defined by mixed integer linear programming, and airspace rules;

[0010] Selecting an optimal collision avoidance strategy from the candidate collision avoidance strategies;

[0011] Obtaining collision avoidance trajectory parameters based on the optimal collision avoidance strategy;

[0012] Obtaining flight control instructions for the current aircraft based on the collision avoidance trajectory parameters;

[0013] The current aircraft performs collision avoidance flight based on the flight control instruction.

[0014] By adopting the technical scheme, the application can detect the positions and motion states of other aircrafts, realize the cooperative collision avoidance decision among the aircrafts, and is especially suitable for the low-altitude dense flight scenes such as urban air traffic and unmanned aerial vehicle group operation, and overcomes the response delay problem of the traditional centralized traffic control system in the low-altitude environment, thereby providing reliable safety guarantee for high-density low-altitude flight.

[0015] Optionally, the collision risk parameter comprises a time closest point and a distance closest point.

[0016] Optionally, a collision risk index is obtained based on the collision risk parameter comprises:

[0017]

[0018] wherein, DCPA is the distance closest point; is a safety distance threshold value; is a time warning threshold value; k is a sensitivity coefficient; TCPA is the time closest point.

[0019] Optionally, the constraint condition defined by the mixed integer linear programming comprises:

[0020]

[0021]

[0022] wherein, u is a decision variable set; N is the number of target aircrafts adjacent to the current aircraft; is the heading angle change amount of the current aircraft i; is the height change amount of the current aircraft i; is the speed change amount of the current aircraft i; is a heading change weight; is a height change weight; is a speed change weight; has the meaning of “subject to”; is the maximum heading angle adjustment limit of the current aircraft; is the maximum height adjustment limit of the current aircraft; is the maximum speed adjustment limit of the current aircraft; is the distance between the current aircraft i and the adjacent target aircraft j; is a safety distance threshold value.

[0023] Optionally, the optimal collision avoidance strategy is selected from the candidate collision avoidance strategies, comprising:

[0024] The total cost of each candidate collision avoidance strategy is obtained based on a mixed integer linear programming;

[0025] The candidate collision avoidance strategy with the minimum total cost is deduced based on a Monte Carlo tree search to obtain the optimal collision avoidance strategy.

[0026] Optionally, the collision avoidance trajectory parameters are obtained based on the optimal collision avoidance strategy, comprising:

[0027] The smooth collision avoidance trajectory is obtained based on the optimal collision avoidance strategy;

[0028] The collision avoidance trajectory parameters are obtained based on the smooth collision avoidance trajectory.

[0029] Optionally, after the current aircraft performs collision avoidance flight based on the flight control instructions, the method further comprises:

[0030] The current aircraft performing collision avoidance flight is tracked based on a quaternion attitude error model;

[0031] The trajectory deviation index is obtained based on the trajectory tracking result;

[0032] When the trajectory deviation index is greater than a preset index, the collision avoidance flight of the current aircraft is optimized and adjusted.

[0033] In a second aspect of the present application, a near-collision avoidance system for an aircraft is provided, specifically comprising:

[0034] A data acquisition module is configured to acquire original data of a target aircraft adjacent to a current aircraft;

[0035] A coordinate conversion module is configured to perform coordinate conversion on the original data, and map the coordinates of the original data into a coordinate system of the current aircraft to obtain standardized position data of the target aircraft relative to the current aircraft;

[0036] An optimal state estimation and collision risk parameter acquisition module is configured to obtain optimal state estimation and collision risk parameters of the target aircraft based on the standardized position data;

[0037] A collision risk index acquisition module is configured to obtain a collision risk index based on the collision risk parameters;

[0038] A candidate collision avoidance strategy acquisition module is configured to obtain candidate collision avoidance strategies based on the optimal state estimation, constraint conditions defined by a mixed integer linear programming, and airspace rules when the collision risk index is greater than a safety threshold;

[0039] An optimal collision avoidance strategy acquisition module is configured to select an optimal collision avoidance strategy from the candidate collision avoidance strategies.

[0040] A collision avoidance trajectory parameter acquisition module is configured to obtain collision avoidance trajectory parameters based on the optimal collision avoidance strategy.

[0041] A flight control instruction acquisition module is configured to obtain flight control instructions of the current aircraft based on the collision avoidance trajectory parameters; and the current aircraft performs collision avoidance flight based on the flight control instructions.

[0042] By using the above technical solutions, the position and motion state of other aircrafts can be detected, and the collaborative collision avoidance decision between aircrafts can be realized. The solutions are particularly suitable for low-altitude dense flight scenarios such as urban air traffic and unmanned aerial vehicle group operation, and overcome the response delay problem of traditional centralized traffic control systems in low-altitude environments, thereby providing reliable safety guarantee for high-density low-altitude flight.

[0043] In a third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface, and a network interface. The memory is configured to store instructions, and the user interface and the network interface are configured to communicate with other devices. The processor is configured to execute the instructions stored in the memory, so that the electronic device performs the method according to any one of the above aspects.

[0044] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores instructions. When the instructions are executed, the method according to any one of the above aspects is performed. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 is a flowchart of a method for aircraft close-range collision avoidance provided by an embodiment of the present application;

[0046] Figure 2 is a structural schematic diagram of a system for aircraft close-range collision avoidance provided by an embodiment of the present application;

[0047] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0048] REFERENCE SIGNS: 100, data acquisition module; 101, coordinate conversion module; 102, optimal state evaluation and collision risk parameter acquisition module; 103, collision risk index acquisition module; 104, candidate collision avoidance strategy acquisition module; 105, optimal collision avoidance strategy acquisition module; 106, collision avoidance trajectory parameter acquisition module; 107, flight control instruction acquisition module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0049] In order for the person skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in combination with the drawings in the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all.

[0050] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, illustration or description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "for example" or "for instance" are intended to present the relevant concept in a specific manner.

[0051] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the indicated technical features. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more features. The terms "include", "contain", "have" and their variants mean "include but are not limited to", unless otherwise specifically emphasized.

[0052] Referring to Figure 1 , the present application provides a near collision avoidance method for an aircraft, which can include the following steps: S10-S90.

[0053] Step S10, referring to Figure 1 S10 step in the foregoing, obtaining original data of a target aircraft adjacent to a current aircraft.

[0054] Step S20, referring to Figure 1 S20 step in the foregoing, performing coordinate conversion on the original data, mapping the coordinates of the original data into a coordinate system of the current aircraft, to obtain standardized position data of the target aircraft relative to the current aircraft.

[0055] Step S30, referring to Figure 1 S30 step in the foregoing, obtaining optimal state estimation and collision risk parameters of the target aircraft based on the standardized position data.

[0056] As an example, the collision risk parameters can include a time-to-closest point (TCPA) and a distance-to-closest point (TCPA).

[0057] Step S40, see S40 in Figure 1 , obtains a collision risk index based on the collision risk parameter.

[0058] As an example, the collision risk index can be obtained based on the collision risk parameter includes:

[0059]

[0060] wherein, DCPA is a distance closest point; is a safety distance threshold; is a time warning threshold; k is a sensitivity coefficient; TCPA is a time closest point.

[0061] Step S50, see S50 in Figure 1 , when the collision risk index is greater than a safety threshold, obtains a candidate collision avoidance strategy based on the optimal state estimation, constraint conditions defined by a mixed integer linear programming (MILP) and airspace rules.

[0062] As an example, the constraint conditions defined by the mixed integer linear programming can include:

[0063]

[0064]

[0065] wherein, u is a decision variable set; N is a number of target aircrafts adjacent to the current aircraft; is a heading angle change amount of the current aircraft i; is a height change amount of the current aircraft i; is a speed change amount of the current aircraft i; is a heading change weight; is a height change weight; is a speed change weight; has the meaning of “subject to”; is a maximum heading angle adjustment limit of the current aircraft; is a maximum height adjustment limit of the current aircraft; is a maximum speed adjustment limit of the current aircraft; is a distance between the current aircraft i and the adjacent target aircraft j; is a safety distance threshold.

[0066] Step S60, see S60 in Figure 1S60, selecting an optimal collision avoidance strategy from the candidate collision avoidance strategies.

[0067] As an example, in step S60, selecting an optimal collision avoidance strategy from the candidate collision avoidance strategies can include the following steps:

[0068] S601: obtaining the total cost of each candidate collision avoidance strategy based on mixed integer linear programming;

[0069] S602: inferring the candidate collision avoidance strategy with the minimum total cost based on Monte Carlo tree search (MCTS) to obtain the optimal collision avoidance strategy.

[0070] Step S70, see Figure 1 S70, obtaining collision trajectory parameters based on the optimal collision avoidance strategy.

[0071] As an example, in step S70, obtaining collision trajectory parameters based on the optimal collision avoidance strategy can include the following steps:

[0072] S701: obtaining a smooth collision trajectory based on the optimal collision avoidance strategy;

[0073] S702: obtaining collision trajectory parameters based on the smooth collision trajectory.

[0074] Step S80, see Figure 1 S80, obtaining flight control instructions for the current aircraft based on the collision trajectory parameters.

[0075] Step S90, see Figure 1 S90, the current aircraft performs collision avoidance flight based on the flight control instructions.

[0076] As an example, after step S90, i.e. after the current aircraft performs collision avoidance flight based on the flight control instructions, the following content can also be included:

[0077] Trajectory tracking of the current aircraft performing collision avoidance flight based on a quaternion attitude error model;

[0078] Obtaining a trajectory deviation index based on the trajectory tracking result;

[0079] When the trajectory deviation index is greater than a preset index, optimizing and adjusting the collision avoidance flight of the current aircraft.

[0080] As an example, in step S10, flight data (may include but not limited to distance, azimuth and elevation) of the target aircraft adjacent to the current aircraft can be obtained by the on-board millimeter wave radar; meanwhile, the contour features (such as the shape and size related features of the target aircraft) of the target aircraft adjacent to the current aircraft can be identified by the visual sensor; the contour features and the flight data together constitute the original data.

[0081] As an example, in step S20, the coordinate system of the current aircraft is a three-dimensional rectangular coordinate system with the current aircraft as the origin, and x / y / z correspond to horizontal front / back, horizontal left / right and vertical height respectively.

[0082] As an example, in step S30, the motion state of the target aircraft can be optimally estimated based on the Kalman filtering algorithm through the state equation and the observation equation to obtain the optimal state estimation of the target aircraft. The state vector of the optimal state estimation is a 9-dimensional vector containing the position, velocity and acceleration of the target aircraft.

[0083] Specifically, for the following parameters of the target aircraft: maximum horizontal acceleration 2m / s², maximum vertical acceleration 1m / s², minimum turning radius 50 meters. These parameters are encoded as the process noise matrix of the Kalman filter to ensure that the prediction result conforms to the actual maneuvering ability of the target aircraft.

[0084] As an example, in step S30, the collision risk assessment can adopt the dual indicators of time-to-closest-point and distance-to-closest-point, and the minimum distance between the trajectories of the current aircraft and the target aircraft within a preset future time (for example, within 10 seconds) and the time to reach the distance can be calculated.

[0085] In one specific example, assuming that the current aircraft is flying horizontally at a speed of 10m / s and at a height of 100 meters; a drone (i.e. the target aircraft adjacent to the current aircraft) is detected to be located in the 45-degree direction to the right front of the current aircraft at a distance of 150 meters and flying to the left side of the current aircraft at a speed of 8m / s. The sensor measurement data is converted to obtain the relative position of the target aircraft as (106m, 106m, 0m). After the Kalman filter is initialized, the speed of the target aircraft is estimated as (-5.66m / s, 5.66m / s, 0m / s) after 5 iterations of updating. The prediction model based on the dynamics constraint shows that the two aircrafts will reach a minimum distance of 28 meters after 6.2 seconds, at which time the TCPA is 6.2 seconds and the DCPA is 28 meters. According to the preset safety threshold (horizontal interval of 30 meters and vertical interval of 10 meters), it is determined that there is a collision risk, triggering the collision avoidance decision-making process. During the prediction process, the target state estimation is updated every 0.1 seconds, and the TCPA and DCPA values are recalculated to ensure continuous and accurate tracking of the fast maneuvering target aircraft.

[0086] As an example, in step S40, the improved probability conflict detection algorithm can be used to obtain the collision risk index , the collision risk index can take a risk value in the interval [0, 1].

[0087] As an example, in step S50, the safety threshold can be set according to actual needs, and the safety threshold can be, but is not limited to, 0.7.

[0088] As an example, in step S50, when the collision risk index is greater than the safety threshold, the collision avoidance decision is triggered.

[0089] As an example, in step S50, by using the constraint condition defined by the mixed integer linear programming, it can be ensured that the maneuver does not exceed the performance limit of the aircraft.

[0090] In one specific example, when the system detects a conflict risk of = 0.8 (DCPA = 25 m, TCPA = 8 s), the decision module is started, and the following is executed:

[0091] 1. Three candidate collision avoidance strategies are generated, as follows:

[0092] Strategy A: Turn right by 15° + Climb by 5 m;

[0093] Strategy B: Decelerate by 20% + Maintain altitude;

[0094] Strategy C: Turn left by 10° + Descend by 3 m.

[0095] 2. The total cost of each candidate collision avoidance strategy is calculated by MILP, and the calculation results are shown in Table 1 below:

[0096] Table 1

[0097]

[0098] 3. Based on the MTCS, the conflict probability of the candidate collision avoidance strategy C with the minimum total cost is calculated to be 2%, and the candidate collision avoidance strategy is selected as the optimal collision avoidance strategy.

[0099] As an example, in step S70, the improved B-spline curve interpolation method can be used to generate a smooth collision avoidance trajectory, which can include the following contents:

[0100] Define the control point set , where is the current position;

[0101] Use the cubic B-spline basis function The interpolation is performed, and the corresponding formula can be as follows:

[0102]

[0103]

[0104]

[0105] wherein, is a point (which can be a 2D coordinate or a 3D coordinate) on the B-spline curve when the parameter is t; n is the number of control points; is a 3rd-order B-spline basis function; P i is the ith control point; is the ith 0th-order B-spline basis function, with the parameter being t; is the ith kth-order B-spline basis function, with the parameter being t; is the ith k-1th-order B-spline basis function; is the (i+1)th k-1th-order B-spline basis function; t i , t i+1 , …, t i+k+1 is a consecutive node in the node vector;

[0106] By constraining the radius of curvature , the trajectory is ensured to comply with the aircraft dynamics limit; wherein, is the minimum radius of curvature; v is the flight speed of the current aircraft; is the maximum allowed centripetal acceleration of the current aircraft.

[0107] As an example, in step S80, the obtained flight control instruction can include a heading control amount , an altitude control amount , and a speed control amount .

[0108] The formula of the heading control amount can be as follows:

[0109]

[0110] wherein, is a proportional coefficient; is a differential coefficient; is a desired heading angle; is an actual heading angle.

[0111] The formula of the altitude control amount can be as follows:

[0112]

[0113] in, For the desired height; The expected rate of change; This refers to the actual height. This represents the actual rate of change in height.

[0114] Speed ​​control quantity The formula can be expressed as follows:

[0115]

[0116] in, For the desired speed; This refers to the actual speed.

[0117] As an example, the formula for trajectory tracking of a collision-avoiding aircraft based on the quaternion attitude error model can be as follows:

[0118]

[0119]

[0120] in, For attitude deviation quaternions; The desired quaternion; The actual attitude quaternion; This is a four-element multiplication; The desired angular velocity command; This is the proportionality coefficient.

[0121] As an example, a trajectory deviation index is obtained based on the trajectory tracking results. The formula can be expressed as follows:

[0122]

[0123] Where N is the number of samples; The attitude deviation quaternion is the four elements at time i. The calculation formula can be found in the attitude deviation quaternion. The calculation formula; The desired angular velocity command at time i is given; the calculation formula can be found in the desired angular velocity command section. The calculation formula is as follows: , where is the weighting coefficient.

[0124] In a specific example, taking the collision avoidance strategy of "turning 15° right + climbing 5m" as an example, it can specifically include the following:

[0125] Trajectory planning stage:

[0126] Set control points P0(0,0,0), P1(50,15,2), and P2(100,30,5);

[0127] The B-spline parameter t = [0, 0.33, 0.66, 1.0] is calculated;

[0128] The maximum curvature R of the trajectory is generated min = 120 m, satisfying a max = 3.3 m / s 2 Limit.

[0129] Control command conversion:

[0130] The trajectory point (35, 10.5, 3.5) is obtained at a sampling period of 100 ms;

[0131] The heading error = 12°, and the rudder command = 8°;

[0132] The height error Dh = 3.2 m, and the elevator command = -4°.

[0133] Execution monitoring:

[0134] The actual attitude quaternion = [0.98, 0, 0, 0.2];

[0135] The expected attitude = [0.96, 0, 0.17, 0.21];

[0136] The error = [0.99, 0, -0.08, 0.01], and the angular velocity command = [0, -0.16, 0] rad / s;

[0137] The deviation degree = 0.12 < 0.2 threshold, and it is determined that the tracking is normal.

[0138] Please refer to Figure 2The application also provides a near-collision avoidance system for an aircraft, which can specifically include: a data acquisition module 100, a coordinate conversion module 101, an optimal state estimation and collision risk parameter acquisition module 102, a collision risk index acquisition module 103, a candidate collision avoidance strategy acquisition module 104, an optimal collision avoidance strategy acquisition module 105, a collision avoidance trajectory parameter acquisition module 106, and a flight control instruction acquisition module 107. The data acquisition module 100 is configured to acquire original data of a target aircraft adjacent to a current aircraft. The coordinate conversion module 101 is configured to perform coordinate conversion on the original data, map the coordinates of the original data to a coordinate system of the current aircraft, and obtain standardized position data of the target aircraft relative to the current aircraft. The optimal state estimation and collision risk parameter acquisition module 102 is configured to obtain optimal state estimation and collision risk parameters of the target aircraft based on the standardized position data. The collision risk index acquisition module 103 is configured to obtain a collision risk index based on the collision risk parameters. The candidate collision avoidance strategy acquisition module 104 is configured to obtain a candidate collision avoidance strategy based on the optimal state estimation, constraint conditions defined by a mixed integer linear programming, and airspace rules when the collision risk index is greater than a safety threshold. The optimal collision avoidance strategy acquisition module 105 is configured to select an optimal collision avoidance strategy from the candidate collision avoidance strategies. The collision avoidance trajectory parameter acquisition module 106 is configured to obtain collision avoidance trajectory parameters based on the optimal collision avoidance strategy. The flight control instruction acquisition module 107 is configured to obtain flight control instructions of the current aircraft based on the collision avoidance trajectory parameters. The current aircraft performs collision avoidance flight based on the flight control instructions.

[0139] By adopting the technical solution, the application can detect the positions and motion states of other aircrafts, and realize cooperative collision avoidance decision between aircrafts. The application is particularly suitable for low-altitude dense flight scenarios such as urban air traffic and unmanned aerial vehicle group operation, and overcomes the response delay problem of traditional centralized traffic control systems in low-altitude environments, thereby providing reliable safety guarantee for high-density low-altitude flight.

[0140] It should be noted that the system provided in the above embodiments is only used as an example to divide the above functional modules when realizing the functions, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be repeated here.

[0141] The embodiment of the present application further provides a computer storage medium, which can store a plurality of instructions, the instructions being suitable for being loaded by a processor and performing the aircraft close-range collision avoidance method of the above-described embodiment, and the specific execution process can be referred to the specific description of the above-described embodiment, which is not described herein.

[0142] Please refer to Figure 3 The present application further discloses an electronic device. Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiment of the present application. The electronic device 300 can include at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0143] The communication bus 302 is configured to realize the connection and communication between the components.

[0144] The user interface 303 can include a display screen (Display) and a camera (Camera), and the optional user interface 303 can further include a standard wired interface and a wireless interface.

[0145] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0146] The processor 301 can include one or more processing cores. The processor 301 connects various parts of the server through various interfaces and lines, executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be realized in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU is mainly used to process an operating system, a user interface, and an application program; the GPU is used to render and draw the content to be displayed on the display screen; and the modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0147] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a close-range collision avoidance method for aircraft.

[0148] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a close-range collision avoidance method for aircraft. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0149] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0150] In several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other manners. For example, the division of the system embodiments described above is merely an example, and the division of the units can be different, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0151] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0152] In addition, the functional units in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0153] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable memory. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: U disk, mobile hard disk, magnetic disk or optical disk and various program code storage media.

[0154] The above is only an exemplary embodiment of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of other embodiments of the present disclosure after considering the specification and the true disclosure.

[0155] The present application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure.

Claims

1. A method for close-range collision avoidance of an aircraft, characterized in that, The method includes: Acquire raw data of target aircraft that are near the current aircraft; The original data is transformed to map the coordinates of the original data to the coordinate system of the current aircraft to obtain the standardized position data of the target aircraft relative to the current aircraft. Based on the standardized position data, the optimal state estimate and collision risk parameters of the target aircraft are obtained; The collision risk index is obtained based on the aforementioned collision risk parameters; When the collision risk index is greater than the safety threshold, candidate collision avoidance strategies are obtained based on the optimal state estimation, the constraints defined by mixed integer linear programming, and the spatial rules. Select the optimal collision avoidance strategy from the candidate collision avoidance strategies; The collision avoidance trajectory parameters are obtained based on the optimal collision avoidance strategy. The flight control command for the current aircraft is obtained based on the collision avoidance trajectory parameters; The current aircraft performs collision avoidance flight based on the flight control commands; After the current aircraft performs collision avoidance flight based on the flight control commands, it also includes: The trajectory tracking of the current aircraft in collision avoidance flight is performed based on the quaternion attitude error model, and the corresponding formula is: ， ， in, For attitude deviation quaternions; The desired quaternion; The actual attitude quaternion; This is a four-element multiplication; The desired angular velocity command; This is the proportionality coefficient; The trajectory deviation index is obtained based on the trajectory tracking results. The corresponding formula is: ， Where N is the number of samples; The attitude deviation quaternion is the four elements at time i, and the calculation formula is the attitude deviation quaternion. The calculation formula; The desired angular velocity command at time i is calculated using the formula: [Desired angular velocity command] The calculation formula; These are the weighting coefficients; When the trajectory deviation index is greater than the preset index, the collision avoidance flight of the current aircraft is optimized and adjusted.

2. The aircraft close-range collision avoidance method according to claim 1, characterized in that, The collision risk parameters include the closest point in time and the closest point in distance.

3. The aircraft close-range collision avoidance method according to claim 2, characterized in that, The collision risk index is obtained based on the collision risk parameters. include: Among them, DCPA is the closest point; This is the safe distance threshold; is the time-based early warning threshold; k is the sensitivity coefficient; TCPA is the closest point in time.

4. The aircraft close-range collision avoidance method according to claim 1, characterized in that, The constraints defined for mixed-integer linear programming include: Where u is the set of decision variables; N is the number of target aircraft near the current aircraft; This represents the change in the heading angle of aircraft i at present; This represents the change in altitude of aircraft i. This represents the change in velocity of aircraft i at present; Weighting for changes in heading; Weights for high variability; Weights for velocity changes; The meaning is "to obey"; Adjust the current maximum heading angle limit for the aircraft. This represents the current maximum altitude adjustment limit for the aircraft. Adjust the limits to the current maximum speed of the aircraft; The distance between the current aircraft i and the neighboring target aircraft j; This is the safe distance threshold.

5. The aircraft close-range collision avoidance method according to claim 1, characterized in that, Selecting the optimal collision avoidance strategy from the candidate collision avoidance strategies includes: The total cost of each candidate collision avoidance strategy is obtained based on mixed-integer linear programming. The optimal collision avoidance strategy is obtained by deriving the candidate collision avoidance strategy with the minimum total cost based on Monte Carlo tree search.

6. The close-range collision avoidance method for aircraft according to claim 1, characterized in that, The collision avoidance trajectory parameters are obtained based on the optimal collision avoidance strategy, including: A smooth collision avoidance trajectory is obtained based on the optimal collision avoidance strategy; The collision avoidance trajectory parameters are obtained based on the smooth collision avoidance trajectory.

7. A close-range collision avoidance system for aircraft, characterized in that, The system includes: The data acquisition module is used to acquire raw data from target aircraft that are near the current aircraft. The coordinate transformation module is used to perform coordinate transformation on the original data, mapping the coordinates of the original data to the coordinate system of the current aircraft, so as to obtain the standardized position data of the target aircraft relative to the current aircraft; The optimal state estimation and collision risk parameter acquisition module is used to obtain the optimal state estimation and collision risk parameters of the target aircraft based on the standardized position data. The collision risk index acquisition module is used to obtain the collision risk index based on the collision risk parameters. The candidate collision avoidance strategy acquisition module is used to obtain candidate collision avoidance strategies based on the optimal state estimation, the constraints defined by the mixed integer linear programming, and the spatial rules when the collision risk index is greater than the safety threshold. The optimal collision avoidance strategy acquisition module is used to select the optimal collision avoidance strategy from the candidate collision avoidance strategies; The collision avoidance trajectory parameter acquisition module is used to obtain collision avoidance trajectory parameters based on the optimal collision avoidance strategy. A flight control command acquisition module is used to obtain flight control commands for the current aircraft based on the collision avoidance trajectory parameters; the current aircraft performs collision avoidance flight based on the flight control commands. Furthermore, after the current aircraft performs collision avoidance flight based on the flight control commands, the system executes: The trajectory tracking of the current aircraft in collision avoidance flight is performed based on the quaternion attitude error model, and the corresponding formula is: , , in, For attitude deviation quaternions; The desired quaternion; The actual attitude quaternion; This is a four-element multiplication; The desired angular velocity command; This is the proportionality coefficient; The trajectory deviation index is obtained based on the trajectory tracking results. The corresponding formula is: ， Where N is the number of samples; The attitude deviation quaternion is the four elements at time i, and the calculation formula is the attitude deviation quaternion. The calculation formula; The desired angular velocity command at time i is calculated using the formula: [Desired angular velocity command] The calculation formula; These are the weighting coefficients; When the trajectory deviation index is greater than the preset index, the system optimizes and adjusts the collision avoidance flight of the current aircraft.

8. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-6.

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