Unmanned aerial vehicle three-dimensional space dynamic conflict risk characterization method

Abstract

The invention relates to an unmanned aerial vehicle three-dimensional space dynamic conflict risk characterization method, and the method comprises the following steps: carrying out the space-time discretization of an unmanned aerial vehicle three-dimensional coordinate space, and building an unmanned aerial vehicle space-time dynamic model; establishing an unmanned aerial vehicle flight dynamic constraint space based on an unmanned aerial vehicle space-time dynamic model; and aiming at the flight dynamic constraint space of the unmanned aerial vehicle, establishing an unmanned aerial vehicle three-dimensional space dynamic conflict risk representation. Compared with the prior art, the method has the advantages that quantitative characterization of unmanned aerial vehicle conflicts in a three-dimensional space is achieved, and quantitative guidance is provided for unmanned aerial vehicle intelligent conflict avoidance research.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) conflict technology, and in particular to a method for characterizing the dynamic conflict risk of unmanned aerial vehicles in three-dimensional space. Background Art

[0002] As a flexible and efficient flying platform, drones are widely used in many fields such as monitoring, logistics, and inspection. However, with the increase in the number of drones and the density of their activities in the airspace, especially in low-altitude and complex urban environments, the risk of collision between drones and between drones and other flying objects has increased significantly. Therefore, how to effectively identify and prevent dynamic conflicts in drone operations and ensure flight safety has become an urgent problem to be solved. Traditional collision avoidance methods are mostly based on static obstacle avoidance or local dynamic detection. However, in the face of multi-dimensional dynamic conflict scenarios, these methods are difficult to adapt to the real-time obstacle avoidance requirements in complex spatial environments. To this end, a three-dimensional dynamic conflict risk characterization method is proposed to dynamically monitor multiple parameters such as the position, speed, and acceleration of drones, and to evaluate the possibility of conflict in real time in combination with spatial position and motion trend. The method establishes a spatial dynamic risk field and defines areas of different risk levels on the drone's motion trajectory, so that the drone can predict and avoid according to risk warning signals. The three-dimensional dynamic conflict risk characterization can improve the flight safety of drones in complex environments, provide technical support for the management of low-altitude airspace and the collaboration of drone groups, and provide a theoretical basis for automatic obstacle avoidance and autonomous flight of drones.

[0003] How to achieve dynamic conflict risk characterization of UAVs in three-dimensional space has become a technical problem that needs to be solved. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for characterizing the risk of dynamic conflict of unmanned aerial vehicles in three-dimensional space.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] According to one aspect of the present invention, a method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles is provided, and the method comprises the following steps:

[0007] Step 1: Discretize the UAV's three-dimensional coordinate space in time and space, and establish a UAV's space-time dynamics model;

[0008] Step 2: Based on the UAV space-time dynamics model, establish the UAV flight dynamic constraint space;

[0009] Step 3: Establish a three-dimensional dynamic conflict risk representation of UAVs based on the dynamic constraint space of UAV flight.

[0010] Preferably, in step 1, the spatial and temporal discretization of the three-dimensional coordinate space of the drone is specifically performed as follows:

[0011] The UAV time dimension is discretized into positive integer multiples of the UAV control time according to the discrete time interval Δt, Δt = n·ΔT, where Δt is the discrete time interval, n is a positive integer, and f is the UAV control frequency.

[0012] Preferably, the space-time dynamics model of the drone in step 1 is specifically:

[0013] The space-time dynamics model of the UAV after discretization is:

[0014]

[0015] Among them, x t ,y t and z t They are respectively represented as the flight distance of the UAV along the X, Y and Z axes within the time interval Δt at time t; and They represent the speed of the human-machine along the X, Y and Z axes at time t respectively; and They represent the acceleration of the drone along the X, Y and Z axes at time t respectively.

[0016] Preferably, the UAV flight dynamic constraint space is expressed as:

[0017]

[0018] in It represents the minimum flight distance of the drone at time t in the time interval Δt. represents the maximum distance that the drone can fly at time t in the interval Δt, v t represents the flight speed of the drone at time t, a max It represents the maximum acceleration that the UAV can reach, and s represents the dynamic constraint space of the UAV flight.

[0019] Preferably, the process of characterizing the risk of dynamic conflict in three-dimensional space of UAVs in step 3 includes:

[0020] Based on the dynamic constraint space s, the conflict risk probability density function is designed;

[0021] Calculate the conflict risk of the drone in the non-truncated space and truncate the conflict risk into the dynamic constraint space;

[0022] Determine the drone conflict risk characterization function;

[0023] And the UAV conflict risk characterization function is mapped to the three-dimensional coordinate space to obtain the UAV conflict risk scalar value.

[0024] More preferably, the conflict risk probability density function is based on Gaussian distribution, specifically:

[0025]

[0026] Among them, p t (l t ) is the probability density function of UAV conflict risk, μ t is the mean flight distance of the UAV at time t, σ t is the standard deviation of the UAV’s flight distance at time t, l t is the distance between the drone and the obstacle at time t, π is a constant, l i is the flight distance of the UAV at the i-th Δt time within time t, v t represents the flight speed of the UAV at time t.

[0027] More preferably, the UAV non-truncated space conflict risk is specifically:

[0028]

[0029] Among them, l t is the distance between the UAV and the obstacle at time t, P t ′(l t ) is the quantitative value of the conflict risk of drones in non-truncated space, p t (l t ) is the conflict risk probability density function.

[0030] More preferably, the conflict risk is truncated into the dynamic constraint space, expressed as:

[0031]

[0032] Among them, P t (l t ) is the quantitative value of the conflict risk of UAVs in cutoff space, The flight distance of the drone is less than the minimum flight distance The probability of The flight distance of the drone is less than the maximum flight distance probability.

[0033] Preferably, the UAV three-dimensional space dynamic conflict risk characterization function is specifically:

[0034]

[0035] Among them, Qt is the conflict risk characterization function, l t It represents the flight distance of the drone at time t in the time interval Δt. and They represent the minimum and maximum distances that the UAV can fly at time t and time interval Δt, respectively. t (l t ) represents the UAV conflict risk in the dynamic constraint space.

[0036] Preferably, the drone conflict risk scalar value is specifically:

[0037]

[0038]

[0039] Among them, Q t is the obstacle collision risk scalar with coordinates (X, Y, Z) in the UAV body coordinate system, and Respectively represent the quantitative value of drone conflict risk in the X, Y, and Z axis directions, and They represent the UAV conflict risk characterization functions in the X, Y, and Z axis directions respectively; and They represent the speed of the human-machine along the X, Y and Z axes at time t respectively; and They represent the acceleration of the drone along the X, Y and Z axes at time t respectively.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1) The present invention establishes a space-time dynamic model of UAVs, and establishes a UAV motion constraint space based on the space-time dynamic model. A new three-dimensional dynamic conflict risk representation is designed for UAV conflict risks, which realizes the quantitative representation of UAV conflicts in three-dimensional space and provides quantitative guidance for the research on UAV intelligent conflict avoidance. Therefore, it is of great significance in UAV conflict resolution and avoidance.

[0042] 2) The present invention designs a method for characterizing the conflict risk in the dynamic constraint space of UAV motion. First, according to the different flight characteristics of the UAV in three dimensions, a three-dimensional dynamic conflict constraint space of the UAV is established; then, based on the dynamic characteristics of the UAV, a UAV conflict risk distribution model is established, and combined with the UAV dynamic conflict constraint space, the UAV conflict risk is truncated to the dynamic constraint space; finally, the conflict risk is mapped to the three-dimensional spatial coordinates of the UAV, providing an accurate guidance method for the characterization of UAV conflict risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the three-dimensional constraint space of dynamic flight conflicts under the coordinates of the UAV in the present invention;

[0044] Figure 2 Schematic diagram of the constraint space generated when the motion characteristics of the drone in the XY axis direction are different in the present invention;

[0045] Figure 3 This is a schematic diagram of the constraint space generated when the motion characteristics of the drone in the XY axis direction are the same;

[0046] Figure 4 It is a schematic diagram of different conflict risk probability density curves generated when the XYZ axis motion characteristics of the drone in the present invention are different;

[0047] Figure 5 It is a flow chart of the method for characterizing the risk of dynamic conflict in three-dimensional space of a machine in the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0049] This embodiment relates to a method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles. Figure 5 , including the following steps:

[0050] Step S1, performing space-time discretization processing on the three-dimensional coordinate space of the UAV, and establishing a space-time dynamic model of the UAV;

[0051] Step S2: establish a UAV flight dynamic constraint space based on the UAV space-time dynamics model, and design a three-dimensional spatial dynamic conflict risk representation of the UAV so that the UAV can perform three-dimensional dynamic conflict risk representation within the perception range.

[0052] Step S1 includes the following steps:

[0053] Step S11, deriving the kinematic trajectory of the UAV in three-dimensional coordinate space;

[0054] The drone can achieve flight and movement by adjusting the speed of the drone blades to generate forces in the XYZ direction of the body coordinate space. The drone's position information, obstacle distance information, flight speed information, and its own acceleration information can be obtained through the sensors carried by the drone.

[0055] The UAV motion information is represented as:

[0056]

[0057] in, represents the speed of the drone along the X axis at time t, represents the speed of the drone along the Y axis at time t, Indicates the speed of the drone along the Z axis at time t; represents the acceleration of the drone along the X-axis at time t, represents the acceleration of the drone along the Y axis at time t, Indicates the acceleration of the drone along the Z axis at time t.

[0058] Step S12: performing space-time discretization processing on the three-dimensional coordinate space of the UAV and establishing a space-time dynamic model of the UAV after discretization processing, specifically including:

[0059] Step S121: discretize the drone time dimension into positive integer multiples of the drone control time, where Δt is the discrete time interval, n is a positive integer, and f is the UAV control frequency per second;

[0060] Step S122: After discretization, the UAV space-time dynamics model is:

[0061]

[0062] Among them, x t It is expressed as the flight distance of the drone along the X axis in the Δt time interval at time t, y t It is expressed as the flight distance of the UAV along the Y axis in the Δt time interval at time t, z t It is expressed as the flight distance of the UAV along the Z axis within the time interval Δt at time t; represents the speed of the human-machine along the X-axis at time t, It represents the speed of the human-machine along the Y axis at time t. Indicates the speed of the human-machine along the Z axis at time t; represents the acceleration of the drone along the X-axis at time t, represents the acceleration of the drone along the Y axis at time t, It represents the acceleration of the UAV along the Z axis at time t. This spatiotemporal dynamics model only considers the risk of UAV conflict in an ideal environment, and does not consider the interference in the UAV operating environment.

[0063] Step S2 includes the following steps:

[0064] Step S21, designing a dynamic constraint space s for the UAV space-time dynamics model established in step S122;

[0065] The dynamic constraint space s is expressed as:

[0066]

[0067] in It represents the minimum flight distance of the drone at time t in the Δt time interval. represents the maximum distance that the drone can fly in the Δt time interval at time t, v t represents the flight speed of the drone at time t, a max Indicates the maximum acceleration that the drone can reach.

[0068] Figure 2 It is the constraint space generated when the motion characteristics of the drone in the XY axis direction are different; Figure 3 It is the constraint space generated when the motion characteristics of the drone in the XY axis direction are the same.

[0069] Step S22: for the dynamic constraint space s of step S21, a conflict risk characterization function of the UAV in the dynamic constraint space is designed, including:

[0070] Step S221, designing a conflict risk probability density function based on the dynamic constraint space s;

[0071] According to the UAV space-time dynamic equation and motion characteristics, it can be concluded that:

[0072]

[0073] Among them, l i is the flight distance of the UAV in each Δt time;

[0074] The mean and variance of the UAV movement distance at time t Substitute the probability density function of drone conflict risk into the probability density function of drone conflict risk and establish the probability density function of drone conflict risk based on Gaussian distribution p t (l);

[0075]

[0076] Among them, p t (l t ) is the probability density function of UAV conflict risk, μ t is the mean flight distance of the UAV at time t, σ t is the standard deviation of the UAV’s flight distance at time t, l t is the distance between the drone and the obstacle at time t, π is a constant, l i is the flight distance of the UAV at the i-th Δt time within time t, v t represents the flight speed of the UAV at time t.

[0077] like Figure 4 These are the probability density curves of different conflict risks generated by different XYZ-axis motion characteristics of the UAV.

[0078] Step S222: For the unmanned aircraft constraint space in S21, when the unmanned aircraft is flying, the flight distance cannot exceed the maximum flight distance within the Δt time interval, and the flight distance cannot be less than the minimum flight distance. Therefore, it is necessary to truncate the unmanned aircraft conflict risk representation to the dynamic constraint space, which is as follows:

[0079] Calculate the probability that the drone's flight distance is less than the minimum flight distance in the non-truncated case:

[0080]

[0081] in, is the probability that the UAV’s flight distance is less than the minimum flight distance.

[0082] Calculate the probability that the drone's flight distance is less than the maximum flight distance in the non-truncated case:

[0083]

[0084] in, is the probability that the UAV’s flight distance is less than the maximum flight distance.

[0085] Calculate the risk of UAV non-truncated space conflict:

[0086]

[0087] Among them, l t is the distance between the drone and the obstacle at time t, P t ′(l t ) is the quantitative value of the conflict risk of drones in non-truncated space.

[0088] Truncating the drone conflict risk representation into a dynamic constraint space:

[0089]

[0090] Among them, P t (l t ) is the quantitative value of the conflict risk of UAVs in cutoff space.

[0091] The three-dimensional constraint space of UAV dynamic flight conflict is as follows Figure 1 shown.

[0092] Step S223: Process the conflict risk representation outside and inside the dynamic constraint space of the UAV.

[0093] When the distance between the drone and the obstacle is less than the minimum flight distance of the drone, that is, even if the drone flies at the maximum acceleration and deceleration, it will still collide with the obstacle, so the drone conflict risk characterization function is:

[0094]

[0095] Among them, Q t is the conflict risk value of the UAV at time t;

[0096] When the distance between the drone and the obstacle is greater than the maximum flight distance of the drone, that is, even if the drone flies at the maximum acceleration, it will not collide with the obstacle. Therefore, the drone conflict risk characterization function is:

[0097]

[0098] When the distance between the UAV and the obstacle is within the dynamic constraint space of the UAV, as the distance between the UAV and the obstacle increases, the UAV conflict risk gradually decreases, so the UAV conflict risk characterization function is:

[0099]

[0100] Based on the above, the UAV conflict risk characterization function is:

[0101]

[0102] S224. Map the drone conflict risk characterization function to the three-dimensional coordinate space:

[0103]

[0104] in Indicates the quantitative value of the UAV conflict risk in the X-axis direction, Indicates the quantitative value of drone conflict risk in the Y-axis direction, Indicates the quantitative value of the UAV conflict risk in the Z-axis direction, It represents the characterization function of the UAV conflict risk in the X-axis direction; the quantitative value of the UAV conflict risk is a real number in the range of 0 to 1.

[0105] The input value of the drone conflict risk characterization function is the motion state value of the drone at time t, that is, the acceleration and speed in each axis direction; the drone conflict risk quantification value 0 is the minimum value, indicating that the drone has no conflict, and 1 is the maximum value, indicating that the drone will definitely conflict.

[0106] In the three-dimensional coordinate space, the scalar value of the UAV conflict risk is:

[0107]

[0108] Among them, Q t is the obstacle conflict risk scalar with coordinates (X, Y, Z) in the UAV body coordinate system.

[0109] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles, characterized in that: The method comprises the following steps: Step 1: Discretize the UAV's three-dimensional coordinate space in time and space, and establish a UAV's space-time dynamics model; Step 2: Based on the UAV space-time dynamics model, establish the UAV flight dynamic constraint space; Step 3: Establish a three-dimensional dynamic conflict risk representation of UAVs based on the dynamic constraint space of UAV flight.

2. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 1, characterized in that: In step 1, the spatial and temporal discretization of the three-dimensional coordinate space of the drone is specifically performed as follows: The UAV time dimension is discretized into positive integer multiples of the UAV control time according to the discrete time interval Δt, Δt = n·ΔT, where Δt is the discrete time interval, n is a positive integer, and f is the UAV control frequency.

3. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 1 is characterized in that: The specific spatiotemporal dynamic model of the UAV in step 1 is: The space-time dynamics model of the UAV after discretization is: Among them, x t ,y t and z t They are respectively represented as the flight distance of the UAV along the X, Y and Z axes within the time interval Δt at time t; and They represent the speed of the human-machine along the X, Y and Z axes at time t respectively; and They represent the acceleration of the drone along the X, Y and Z axes at time t respectively.

4. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 1, characterized in that: The UAV flight dynamic constraint space is expressed as: in It represents the minimum flight distance of the drone at time t in the time interval Δt. represents the maximum distance that the drone can fly at time t in the interval Δt, v t represents the flight speed of the drone at time t, a max It represents the maximum acceleration that the UAV can reach, and s represents the dynamic constraint space of the UAV flight.

5. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 1, characterized in that: The process of characterizing the risk of dynamic conflict in three-dimensional space of UAVs in step 3 includes: Based on the dynamic constraint space s, the conflict risk probability density function is designed; Calculate the conflict risk of the drone in the non-truncated space and truncate the conflict risk into the dynamic constraint space; Determine the drone conflict risk characterization function; And the UAV conflict risk characterization function is mapped to the three-dimensional coordinate space to obtain the UAV conflict risk scalar value.

6. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 5, characterized in that: The conflict risk probability density function is based on Gaussian distribution, specifically: Among them, p t (l t ) is the probability density function of UAV conflict risk, μ t is the mean flight distance of the UAV at time t, σ t is the standard deviation of the UAV’s flight distance at time t, l t is the distance between the drone and the obstacle at time t, π is a constant, l i is the flight distance of the UAV at the i-th Δt time within time t, v t represents the flying speed of the UAV at time t.

7. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 5, characterized in that: The above-mentioned UAV non-truncated space conflict risks are specifically: Among them, l t is the distance between the UAV and the obstacle at time t, P t ′(l t ) is the quantitative value of the conflict risk of drones in non-truncated space, p t (l t ) is the conflict risk probability density function.

8. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 7, characterized in that: The conflict risk is truncated into the dynamic constraint space and expressed as: Among them, P t (l t ) is the quantitative value of the conflict risk of UAVs in cutoff space, The flight distance of the drone is less than the minimum flight distance The probability of The flight distance of the drone is less than the maximum flight distance probability.

9. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 1, characterized in that: The three-dimensional dynamic conflict risk characterization function of the UAV is specifically: Among them, Q t is the conflict risk characterization function, l t It represents the flight distance of the drone at time t in the time interval Δt. and They represent the minimum and maximum distances that the UAV can fly at time t and time interval Δt, respectively. t (l t ) represents the UAV conflict risk in the dynamic constraint space.

10. The method for characterizing the risk of dynamic conflict in three-dimensional space of unmanned aerial vehicles according to claim 1, characterized in that: The UAV conflict risk scalar value is specifically: Among them, Q t is the obstacle collision risk scalar with coordinates (X, Y, Z) in the UAV body coordinate system, and Respectively represent the quantitative value of drone conflict risk in the X, Y, and Z axis directions, and They represent the UAV conflict risk characterization functions in the X, Y, and Z axis directions respectively; and They represent the speed of the human-machine along the X, Y and Z axes at time t respectively; and They represent the acceleration of the drone along the X, Y and Z axes at time t respectively.

Images