Method for unmanned aerial vehicle formation to cope with non-cooperative target based on reachable set

Abstract

The invention provides a method for an unmanned aerial vehicle formation to cope with a non-cooperative target based on a reachable set, and the method comprises the steps: obtaining the flight state information and control information of a target unmanned aerial vehicle and a non-cooperative target unmanned aerial vehicle, and detecting whether the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle have potential conflicts or not; when the potential conflict exists, determining a danger set according to the flight state information of the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle, and constructing a system dynamic data set; constructing a Hamiltonian-Jacobian partial differential equation according to the system dynamic data set and a predefined cost value function; solving a Hamiltonian-Jacobian partial differential equation by taking an implicit curved surface function corresponding to the danger set as a termination condition to obtain an optimal control strategy; determining a boundary reachable set according to the optimal control strategy; and when the target unmanned aerial vehicle reaches the boundary reachable set critical range, waking up the safety controller to adjust the control strategy of the target unmanned aerial vehicle. By implementing the method, calculation is reduced, potential collision is avoided, and flight safety is ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a method for unmanned aerial vehicle formations to cope with non-cooperative targets based on reachable sets. Background Art

[0002] UAV formation flight has a wide range of applications in military, commercial, scientific research and entertainment. The leader-follower method is a common UAV formation control method. It sets one UAV as the leader UAV, and the other UAVs follow the leader. The leader UAV is responsible for guiding the direction and speed of the entire formation, while the follower UAVs adjust their positions and speeds according to the status of the leader to maintain the shape and formation of the formation. This method mainly includes the following key steps:

[0003] 1) Leader selection. Select a UAV as the leader in the formation. The principle of selecting a leader can usually be based on factors such as its position, speed, and capabilities. The leader UAV can be set manually or selected by an autonomous algorithm.

[0004] 2) Follower follows the leader. The follower drone follows the leader based on its position and speed information. The communication system between drones can be used to achieve information exchange between the leader and the follower, and the follower moves according to the leader's instructions or predetermined trajectory. And the follower drone needs to make corresponding adjustments in real time according to the leader's movement. This can be achieved by obtaining information about the surrounding environment through sensors, such as relative position, speed, etc., and then adjusting itself through the formation control algorithm.

[0005] 3) Formation maintenance strategy. The leader and followers need to maintain a certain relative position and distance to ensure the overall stability of the UAV formation. During the movement of the formation, the tracking algorithm can be used to adjust the position and speed of the UAV to ensure the shape and structure of the formation.

[0006] In the field of UAV formation flight, UAVs need to fly safely under complex air traffic management systems, which requires UAVs to avoid unexpected non-cooperative targets, where non-cooperative targets can be other UAVs. In related technologies, obstacle avoidance methods for unexpected non-cooperative targets include obstacle recognition based on neural networks and obstacle avoidance trajectory control. Obstacle recognition and obstacle avoidance trajectory control based on neural networks rely on a large amount of training data and require more computing resources. Summary of the invention

[0007] In view of this, the purpose of the present invention is to provide a method and device for UAV formation to deal with non-cooperative targets based on reachable sets, so as to meet the demand of reducing the amount of calculation while achieving obstacle avoidance for sudden non-cooperative targets.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] According to a first aspect, the present invention provides a method for a UAV formation to deal with non-cooperative targets based on a reachable set, which is applied to a target UAV, including: obtaining flight status information and control information of the target UAV and the non-cooperative target UAV, and continuously detecting whether there is a potential conflict between the target UAV and the non-cooperative target UAV; when there is a potential conflict, determining a danger set according to the flight status information of the target UAV and the non-cooperative target UAV; constructing a system dynamic data group according to the status information and control information of the target UAV and the non-cooperative target UAV; constructing a Hamilton-Jacobi partial differential equation using the system dynamic data group and a predefined cost-value function; using the implicit surface function corresponding to the danger set as a termination condition, solving the Hamilton-Jacobi partial differential equation to obtain an optimal control strategy; determining a boundary reachable set according to the optimal control strategy; when the target UAV reaches the critical range of the boundary reachable set, waking up the safety controller to adjust the control strategy of the target UAV.

[0010] Optionally, the flight status information of the target UAV and the non-cooperative target UAV includes the flight speed of the target UAV and the distance between the target UAV and the non-cooperative target UAV. According to the flight status information of the target UAV and the non-cooperative target UAV, the danger set is determined, including:

[0011] L s ={x:|p x,r |,|p y,r |≤d∨|v x,i |≥v max ∨|v y,j |≥v max};

[0012] Among them, L s represents the dangerous set, p x,r is the horizontal coordinate relative position variable of the target UAV and the non-cooperative target UAV, p y,r is the relative position variable of the ordinate of the target UAV and the non-cooperative target UAV, v x,i represents the horizontal coordinate speed of the target UAV, v y,j represents the ordinate velocity of the non-cooperative target UAV, d represents the minimum separation distance between the target UAV and the non-cooperative target UAV in the x and y directions, and v max Indicates the maximum speed limit.

[0013] Optionally, a Hamilton-Jacobi partial differential equation is constructed with a system dynamics data set and a predefined cost-value function, including:

[0014]

[0015] V(0,x)=l(x)

[0016] Among them, D t V(t,x) is the partial differential equation of the cost-value function, f(x,u1,u2) represents the system dynamic data set, where x∈R n is the system state variable, R n represents the state of the UAV in the n-dimensional space, u1 is the control function of the target UAV, u2 is the control function of the non-cooperative target UAV, V(0,x) represents the cost value function at t=0, l(x) represents the implicit surface function value, U1 represents the control quantity set of the target UAV, and U2 represents the control quantity set of the non-cooperative target UAV.

[0017] Optionally, the optimal control strategy includes:

[0018]

[0019] in, represents the optimal control strategy of the target UAV, represents the optimal control strategy of the non-cooperative target UAV, D t V(t,x) is the partial differential equation of the cost value function, f(x,u1,u2) represents the system dynamic data set, where is the system state variable, R n Represents the state of the UAV in the n-dimensional space, u1 is the control function of the target UAV, u2 is the control function of the non-cooperative target UAV, U1 represents the control quantity set of the target UAV, and U2 represents the control quantity set of the non-cooperative target UAV.

[0020] Optionally, the safety controller is awakened to adjust the control strategy of the target UAV, including: when the safety controller receives the awakening signal, the flight status information of the target UAV and the non-cooperative target UAV is obtained, and the acceleration of the target UAV's flight is adjusted according to a pre-designed control law; the avoidance direction of the UAV is determined according to the flight mission of the target UAV; according to the boundary position, acceleration and avoidance direction of the target UAV and the boundary reachable set, the expected flight posture of the target UAV at each moment during the flight process is determined.

[0021] Optionally, the acceleration of the target UAV flight is adjusted according to a pre-designed control law as follows:

[0022]

[0023] Among them, p is the center position of the target UAV, p1 is the center position of the non-cooperative target UAV, p2 is the intersection of the line connecting the center position of the target UAV and the center position of the non-cooperative target UAV in the boundary reachable set, and r p1is the radius of the non-cooperative target drone, a n is the current acceleration of the target drone, a max is the maximum acceleration of the target UAV.

[0024] Optionally, a method for a UAV formation to deal with non-cooperative targets based on a reachable set also includes: acquiring sensor data from multiple sensors mounted on the target UAV at any moment; fusing the multiple sensor data and performing attitude calculation to obtain the actual flight attitude of the target UAV at that moment; solving the attitude error based on the actual flight attitude and the expected flight attitude; inputting the attitude error into a pre-set attitude control algorithm to determine attitude adjustment parameters; and inputting the attitude adjustment parameters into the flight controller to adjust the flight attitude of the target UAV.

[0025] Optionally, the safety controller is awakened to adjust the control strategy of the target UAV, including: when the safety controller receives the wake-up signal, the current position of the target UAV and the range of the boundary reachable set are obtained; the current position of the target UAV is used as the initial control point, and the final control point is determined according to the flight mission of the target UAV and the range of the boundary reachable set; the intersection of the boundary reachable set is made with the flight direction of the target UAV; at the intersection, the normal of the boundary reachable set is made, extending toward the center of the boundary reachable set, and the second control point is taken as the point on the extension line that is separated from the intersection by a first target distance; an extension line of the final control point and an extension line of the second control point are made, so that the two extension lines intersect vertically, and the intersection point is taken as the third control point; a Bezier curve is constructed according to the initial control point, the second control point, the third control point and the final control point, and the Bezier curve is used as an obstacle avoidance route to control the flight of the target UAV.

[0026] According to the second aspect, an embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the method for a drone formation to deal with non-cooperative targets based on a reachable set as described in the first aspect or any implementation scheme of the first aspect.

[0027] According to the third aspect, an embodiment of the present invention provides a computer storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the method for a drone formation to deal with non-cooperative targets based on a reachable set as described in the first aspect or any embodiment of the first aspect.

[0028] This embodiment provides a method for a UAV formation to deal with non-cooperative targets based on a reachable set. Compared with a neural network, this method does not rely on a large amount of training data, reduces computing resources, and by acquiring flight status information and control information of a target UAV and a non-cooperative target UAV, this method can continuously detect whether there is a potential conflict between the two, so as to take preventive measures in advance, and determine a boundary reachable set according to an optimal control strategy, allowing the UAV to dynamically adjust the flight path while maintaining a safe distance to cope with a changing flight environment. Finally, in this method, when a UAV reaches the critical range of the boundary reachable set, the safety controller will be awakened and the control strategy of the UAV will be adjusted in real time to avoid potential collisions and ensure flight safety.

[0029] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0031] Figure 1 A specific example flow chart of a method for a UAV formation to deal with a non-cooperative target based on a reachable set according to the present invention;

[0032] Figure 2 It is a principle block diagram of a specific example of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 are within the scope of protection of the present invention.

[0034] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0035] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0036] This embodiment provides a method for UAV formation to deal with non-cooperative targets based on reachable sets, such as Figure 1 As shown, it is applied to target drones, including:

[0037] S101, obtaining flight status information and control information of the target UAV and the non-cooperative target UAV, and continuously detecting whether there is a potential conflict between the target UAV and the non-cooperative target UAV;

[0038] S102, when there is a potential conflict, determining a danger set based on the flight status information of the target UAV and the non-cooperative target UAV;

[0039] S103, constructing a system dynamic data group according to the state information and control information of the target UAV and the non-cooperative target UAV;

[0040] S104, constructing a Hamilton-Jacobi partial differential equation based on the system dynamic data set and a predefined cost-value function;

[0041] S105, taking the implicit surface function corresponding to the danger set as the termination condition, solving the Hamilton-Jacobi partial differential equation to obtain the optimal control strategy;

[0042] S106, determining a boundary reachable set according to an optimal control strategy;

[0043] S107, when the target UAV reaches the critical range of the boundary reachable set, the safety controller is awakened to adjust the control strategy of the target UAV.

[0044] Exemplarily, the flight status information includes the position, speed, acceleration, attitude and other information of the drone, which is obtained in real time using sensors carried by the drone, such as GPS, IMU (inertial measurement unit), barometer, etc. The control information includes the command input of the drone, such as the position of the joystick of the remote controller, and the response of the drone to these commands, which is obtained through the corresponding control interface. This embodiment does not limit the method of obtaining the flight status information and control information, and those skilled in the art can determine it as needed.

[0045] Compared with the target UAV, other UAVs can be divided into cooperative target UAVs and non-cooperative target UAVs according to their flight targets. Cooperative target UAVs indicate that there is a cooperative relationship between the UAV and the target UAV, for example, the cooperative target UAV and the target UAV need to exchange queues. Non-cooperative target UAVs indicate that there is no cooperative relationship between the UAV and the target UAV.

[0046] The method of detecting whether there is a potential conflict between the target UAV and the non-cooperative target UAV can be to determine whether the target UAV meets the following unsafe configuration: the target UAV is within the minimum separation distance d of the non-cooperative target UAV in the x and y directions, or is traveling at a speed higher than the speed limit v in the x and y directions. max speed of travel.

[0047] When there is a conflict, the danger set is determined based on the flight status information of the target UAV and the non-cooperative target UAV, including:

[0048] L s ={x:|p x,r |,|p y,r |≤d∨|v x,i |≥v max ∨|v y,j |≥v max}; (1)

[0049] Among them, L s represents the danger set, p x,r is the horizontal coordinate relative position variable of the target UAV and the non-cooperative target UAV, p y,r is the relative position variable of the ordinate of the target UAV and the non-cooperative target UAV, v x,i represents the horizontal velocity of the target UAV, v y,j represents the ordinate velocity of the non-cooperative target UAV, d represents the minimum separation distance between the target UAV and the non-cooperative target UAV in the x and y directions, and v max Indicates the maximum speed limit.

[0050] The relative dynamics of the two drones are considered in the above process, as follows: x,r =p x,i -p x,j , p y,r =p y,i -p y,j , p x,i is the horizontal coordinate position of the target UAV, p x,j is the horizontal coordinate position of the non-cooperative target UAV, p y,i is the ordinate position of the target UAV, p y,j is the ordinate position of the non-cooperative target UAV.

[0051] In order to solve the boundary reachable set, the system model is constructed using ordinary differential equations to obtain the system dynamic data set, specifically:

[0052] For almost all t∈[-T,0]. (2)

[0053] Where x∈R nis the system state variable; u1(t)∈U1 is the control variable of the target UAV; u2(t)∈U2 is the control variable of the non-cooperative target UAV. Assume f:R n ×U1×U2→R n is uniformly continuous, bounded, and Lipschitz continuous on x for fixed u1(t), u2(t); the control functions u1(·)∈U1, u2(·)∈U2 are drawn from a set of measurable functions. Non-target cooperative drones are allowed to use unexpected strategies γ, defined as:

[0054] For all for all r∈[t,s]};

[0055] Where r represents any time between [t, s], s represents a specific time, and N[u1](r) represents the actual state set. Represents a set of estimated states.

[0056] For a given dynamic, |u x,y |≤u max and the danger set L s , calculate BRSV(t,x), the calculation formula is as follows:

[0057]

[0058] Among them, V(t,x) is the cost value function, x∈R n is the system state variable, R n represents the state of the UAV in n-dimensional space, μ1(·) is the control function of the target UAV, γ represents the unexpected control strategy, Γ represents the conflict set, U1 represents the control quantity set of the target UAV, s represents a specific time in [t,0], γ[u1](·) represents the unexpected control strategy of the target UAV, t represents time, x represents the system state, and L s is the dangerous set, ξ f The system trajectory ξ that satisfies the initial conditions f (t; x, t, u1(·), γ[u2](·)) = x and the following differential equations in all intervals [-t, 0]:

[0059]

[0060] γ[u2](·) represents the unexpected control strategy of the non-cooperative target UAV, u1(s) represents the control amount of the target UAV at a specific time s, and u2(s) represents the control amount of the non-cooperative target UAV at a specific time s.

[0061] Many methods involving solving Hamilton-Jacobi partial differential equations (HJ PDEs) and Hamilton-Jacobi variational inequalities (HJ variational inequalities) have been developed for computing bounded reachable sets. These HJ PDEs and HJ variational inequalities can be solved using mature numerical methods and will not be elaborated here.

[0062] For the method described in the embodiment of the present invention, using the formula in (1), it can be obtained that the boundary of the boundary reachable set is the zero sub-level set of the value function. In the terminal value problem of the Hamilton-Jacobi partial differential equation, the value function V(x,T) at a given terminal time T is usually called the terminal condition. This condition is the key part of the problem because it defines the state that the system needs to reach at the terminal time.

[0063] Based on the system dynamics data set and the predefined cost-value function, the Hamilton-Jacobi partial differential equation is constructed, including:

[0064]

[0065] V(0,x)=l(x) (5)

[0066] Among them, D t V(t,x) is the partial differential equation of the cost value function, f(x,u1,u2) represents the system dynamic data set, where the disturbance has a small impact and can be ignored, where x∈R n is the system state variable, R n represents the state of the UAV in the n-dimensional space, u1 is the control function of the non-cooperative target UAV, u2 is the control function of the target UAV, V(0,x) represents the cost value function at t=0, and l(x) represents the implicit surface function value.

[0067] From this we can get V(t) = {x∈R n |V(t,x)≤0}, V(t,x)≤0 means that starting from state x, there is at least one control strategy that allows the system to reach the target set within a given time, and the cost does not exceed zero. In many cases, zero cost means no collision, safe arrival, or meeting other constraints. According to the target set L p And the implicit surface function l(x) to solve V(t,x).

[0068] In this embodiment, the relative dynamics Q between the two quadrotors will also be considered i ;Q j , where Q i represents the target drone, Q j represents a non-cooperative target drone. These dynamics can be captured by defining the relevant variables:

[0069] px,r =p x,i -p x,j ;

[0070] p y,r =p y,i -p y,j ; (6)

[0071] v x,r =v x,i -v x,j ;

[0072] v y,r =v y,i -v y,j ;

[0073] Among them, p x,r is the horizontal coordinate relative position variable of the target UAV and the non-cooperative target UAV, p x,i is the horizontal coordinate position of the non-cooperative target UAV, p x, j is the horizontal coordinate position of the target UAV, p y,r is the relative position variable of the ordinate of the target UAV and the non-cooperative target UAV, p y,i is the ordinate position of the non-cooperative target UAV, p y, j is the ordinate position of the target UAV, v x,r represents the horizontal axis relative speed variable of the target UAV and the non-cooperative target UAV, v x,i represents the horizontal coordinate speed of the non-cooperative target UAV, v x, j represents the horizontal coordinate speed of the target UAV, v y,r represents the relative velocity variable of the target UAV and the non-cooperative target UAV, v y,i represents the ordinate velocity of the non-cooperative target UAV, v y, j represents the ordinate velocity of the target UAV.

[0074] According to the relative variables given in formula (6), we can get:

[0075]

[0076] Among them, u x,i is the control amount of the target UAV in the horizontal coordinate, u x,j is the control amount of the non-cooperative target UAV in the horizontal coordinate, u y,i is the control amount of the target UAV in the ordinate, u y, j is the control amount of the non-cooperative target UAV in the vertical coordinate.

[0077] Added target drone Q i to impose a speed limit on the quadrotor:

[0078]

[0079] The target drone may collide with the non-cooperative target drone in the next short time, and it must switch to the safety controller. The safety controller is available in every mode, and executing the safety controller to perform an evasive maneuver will not change the mode of the aircraft.

[0080] In the platoon concept of this embodiment, the following unsafe configuration is defined: the aircraft is within the minimum separation distance d from the reference aircraft in the x and y directions, or is traveling at a speed higher than the speed limit vmax in the x and y directions. Considering this specification, we use the augmented relative dynamics given by Equation (8) for the reachability problem and define the danger set as Equation (1).

[0081] Therefore, we can define L determined by formula (1): s The corresponding implicit surface function l s (x), and l s (x) is used as the termination condition to solve HJPDE (5) and obtain the optimal control strategies for the target UAV and the non-cooperative target UAV:

[0082]

[0083] After obtaining the optimal control strategy, the boundary reachable set can be determined by analyzing the optimal trajectory and system dynamics, that is, the set of all locations that the drone can reach under the optimal control strategy.

[0084] As mentioned above, solving V s The zero sublevel set of (t,x) specifies BRSV s (t), which represents the state in the augmented relative coordinates, as defined in Equation (8). If Qj uses the worst-case control, Qi cannot avoid LS within time period t. To avoid collision, Qi must apply a safety controller on the BRS boundary according to Equation (9) to avoid entering the BRS. The following algorithm wraps the safety controller around the goal satisfaction controller, which is the controller used by the target UAV when there is no possibility of collision between the target UAV and the non-cooperative target UAV:

[0085] 1) For a specified time range t, find V s (-t,x i -x j ), j∈Q(i). Q(i) is a set of quadrotors that the target drone Qi checks for safety.

[0086] 2) According to V s (-t,x i -x j) using a safety or goal satisfaction controller, j∈Q(i): If there is a potential conflict between Qi and Qj, the safety controller must be used; otherwise, Qi will use the target satisfaction controller.

[0087] The safety controller usually involves a control barrier function (CBF) or a safety barrier certificate (SBC), which can ensure that the system state will not enter the danger set at any time. The control barrier function (CBF) or the safety barrier certificate (SBC) are both existing technologies and will not be described in detail here.

[0088] This embodiment provides a method for a UAV formation to deal with non-cooperative targets based on a reachable set. Compared with a neural network, this method does not rely on a large amount of training data, reduces computing resources, and by acquiring flight status information and control information of a target UAV and a non-cooperative target UAV, this method can continuously detect whether there is a potential conflict between the two, so as to take preventive measures in advance, and determine a boundary reachable set according to an optimal control strategy, allowing the UAV to dynamically adjust the flight path while maintaining a safe distance to cope with a changing flight environment. Finally, in this method, when a UAV reaches the critical range of the boundary reachable set, the safety controller will be awakened and the control strategy of the UAV will be adjusted in real time to avoid potential collisions and ensure flight safety.

[0089] As an optional implementation, waking up the safety controller to adjust the control strategy of the target UAV includes: when the safety controller receives the wake-up signal, obtaining the flight status information of the target UAV and the non-cooperative target UAV, and adjusting the acceleration of the target UAV's flight according to a pre-designed control law; determining the UAV's avoidance direction according to the target UAV's flight mission; determining the expected flight attitude of the target UAV at each moment during the flight process according to the boundary position, acceleration and avoidance direction of the target UAV and the boundary reachable set.

[0090] Exemplarily, the safety controller first obtains the real-time flight status information of the target UAV and the non-cooperative target UAV, including parameters such as position, speed, acceleration, attitude, etc. This information can be obtained through sensors on the UAV (such as GPS, IMU, radar, etc.) and sent to the safety controller through a wireless communication system. According to the pre-designed control law (such as PID control law), the safety controller will calculate the required control input to adjust the flight acceleration of the target UAV. The control law will calculate the control input based on the deviation between the current state of the UAV and the desired state, thereby adjusting the engine thrust, rudder deflection or other actuators of the UAV to change the acceleration of the UAV. According to the flight mission of the target UAV and the current flight path, the safety controller will determine the avoidance direction of the UAV to avoid potential conflict areas. Specifically, it involves a path planning algorithm, such as an artificial potential field method, to calculate a new path to avoid obstacles. The safety controller will determine the expected flight attitude of the UAV at each moment during the flight process based on the boundary position of the target UAV and the boundary reachable set, the calculated acceleration, and the determined avoidance direction, including the adjustment of the pitch angle, roll angle, and yaw angle to ensure that the UAV flies along a safe path and maintains the required flight performance.

[0091] The pre-designed control law can be:

[0092]

[0093] Among them, p is the center position of the target UAV, p1 is the center position of the non-cooperative target UAV, p2 is the intersection of the line connecting the center position of the target UAV and the center position of the non-cooperative target UAV in the boundary reachable set, and r p1 is the radius of the non-cooperative target drone, a n is the current acceleration of the target drone, a max is the maximum acceleration of the target UAV. The control law adjusts the acceleration in such a way that the closer the target UAV is to the boundary of the boundary reachable set, the greater its acceleration, which further reduces the possibility of the UAV reaching the boundary of the boundary reachable set and improves the flight safety of the UAV.

[0094] As an optional implementation, a method for a UAV formation to deal with non-cooperative targets based on a reachable set also includes: acquiring sensor data from multiple sensors mounted on the target UAV at any moment; fusing the multiple sensor data and performing attitude calculation to obtain the actual flight attitude of the target UAV at that moment; solving the attitude error based on the actual flight attitude and the expected flight attitude; inputting the attitude error into a pre-set attitude control algorithm to determine the attitude adjustment parameters; and inputting the attitude adjustment parameters into the flight controller to adjust the flight attitude of the target UAV.

[0095] Exemplarily, after determining the expected flight attitude at each moment during the flight of the target UAV, it is necessary to determine in real time whether there is an attitude error during the actual flight of the UAV. When there is an attitude error, the attitude error needs to be adjusted. In this embodiment, first, a variety of sensor data carried by the UAV is obtained, including accelerometers, gyroscopes, magnetometers, etc. Then, the sensor data is fused. The purpose of data fusion is to utilize the advantages of each sensor and integrate the data through an algorithm to improve the accuracy and robustness of the UAV attitude estimation. The specific data fusion method can be based on complementary filtering and Kalman filtering. Complementary filtering is suitable for sensor data fusion with the same or little difference in data update frequency, while Kalman filtering is suitable for processing sensor data with different noise characteristics and time update frequencies. This embodiment does not limit the method of data fusion. After obtaining the fused data, attitude solution is performed.

[0096] Attitude solution mainly calculates the pitch angle, roll angle and yaw angle of the drone. These angles describe the rotation of the drone around the x-axis, y-axis and z-axis of its body coordinate system respectively. Attitude solution can be achieved through Euler angles, quaternions or rotation matrices. After solving the actual flight attitude, it is necessary to compare it with the desired flight attitude to solve the attitude error. This error is used as input for the attitude control algorithm of the drone. The goal of the attitude control algorithm is to minimize the attitude error, thereby adjusting the flight attitude of the drone to reach or approach the desired attitude. The attitude control algorithm can be:

[0097]

[0098] Among them, μ represents the attitude adjustment parameter, e represents the attitude error, ∫e represents the attitude error integral, represents the attitude error differential, K p , K i , K d Represents attitude error, attitude error integral, and attitude error differential gain.

[0099] Finally, the attitude adjustment parameters are input into the flight controller, which adjusts the actuators of the drone, such as servos or motors, according to these parameters to achieve attitude adjustment.

[0100] This embodiment provides a method for a drone formation to deal with non-cooperative targets based on a reachable set. By real-time monitoring of the drone's flight attitude and adjustment parameters, it can ensure that the drone can adjust its flight attitude in time when encountering potential conflicts to avoid collisions, thereby improving flight safety.

[0101] As an optional implementation, waking up the safety controller to adjust the control strategy of the target UAV includes: when the safety controller receives the wake-up signal, obtaining the current position of the target UAV and the range of the boundary reachable set; taking the current position of the target UAV as the initial control point, and determining the final control point according to the flight mission of the target UAV and the range of the boundary reachable set; making the intersection of the boundary reachable set with the flight direction of the target UAV; at the intersection, making the normal of the boundary reachable set, extending toward the center of the boundary reachable set, and making the second control point at a first target distance from the intersection on the extension line; making an extension line of the final control point and an extension line of the second control point, so that the two extension lines intersect vertically, and the intersection point is used as the third control point; constructing a Bezier curve according to the initial control point, the second control point, the third control point and the final control point, and using the Bezier curve as an obstacle avoidance route to control the flight of the target UAV.

[0102] Exemplarily, the last control point is determined according to the flight mission of the target drone and the range of the boundary reachable set. The proposed flight route of the target drone is determined through the target location in the flight mission. In order to avoid collision, it is necessary to avoid entering the edge of the boundary reachable set. First, according to the flight mission of the target drone and the range of the boundary reachable set, the way to determine the last control point can be that the drone extends outward in order to perform the current flight mission, and any point is selected on the outward extension line, and the point is used as the intersection point. One end of the other line segment of the intersection is within the range of the boundary reachable set, and the target point on the line segment within the range of the boundary reachable set is selected as the last control point. The target point can be a point on the line segment within the range of the boundary reachable set that is preset to be a distance from the boundary target of the boundary reachable set. It should be noted that the angle between the outward extension line and the other line segment of the intersection is a preset angle, which can be 45 degrees. This embodiment does not limit the setting of the size of the angle and the setting of the target point position, and those skilled in the art can determine it as needed.

[0103] The target drone's flight direction is used as the intersection of the boundary reachable set; at the intersection, the normal of the boundary reachable set is made, extending toward the center of the boundary reachable set, and the point on the extension line that is separated from the intersection by the first target distance is used as the second control point; the last control point extension line and the second control point extension line are made, so that the two extension lines intersect vertically, and their intersection is used as the third control point. This embodiment constructs a Bezier curve based on the initial control point, the second control point, the third control point, and the last control point, and can generate a smooth curve path, which is used as an obstacle avoidance route.

[0104] Among them, the Bezier curve can be:

[0105] B(t)=(1-t) 3 P0+3(1-t) 2 P1+3(1-t)t2 P2+t 3 P3 t∈[0,1];

[0106] Among them, P0 is the initial control point, P3 is the last control point, P1 is the second control point, P2 is the third control point, and t is a parameter that varies from 0 to 1, which determines the position of the point on the curve.

[0107] This embodiment provides a method for a drone formation to deal with non-cooperative targets based on a reachable set. When the drone reaches the critical range of the boundary reachable set, the Bezier curve is changed by adjusting the control points to achieve dynamic path planning to cope with the ever-changing flight environment. By accurately determining the control points of the Bezier curve, a smooth path that meets the requirements of the drone flight mission can be generated to ensure the accuracy and feasibility of the path. The control points are determined by using the boundary reachable set and normal extension method, which helps the drone to effectively avoid obstacles in complex environments and improve flight safety.

[0108] The present application also provides an electronic device, such as Figure 2 As shown, a processor 501 and a memory 502, wherein the processor 501 and the memory 502 may be connected via a bus or other means.

[0109] The processor 501 may be a central processing unit (CPU). The processor 501 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0110] The memory 502 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the method for the drone formation to deal with non-cooperative targets based on reachable sets in the embodiment of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory.

[0111] The memory 502 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 502 may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0112] The one or more modules are stored in the memory 502, and when executed by the processor 501, the following is performed: Figure 1 The reachable set-based method for UAV formations to deal with non-cooperative targets in the illustrated embodiment.

[0113] For details of the above electronic equipment, please refer to Figure 1 The corresponding related descriptions and effects in the illustrated embodiments can be understood and will not be repeated here.

[0114] This embodiment also provides a computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the method of using a drone formation to deal with non-cooperative targets based on a reachable set in any of the above method embodiments. The storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0115] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for UAV formation to deal with non-cooperative targets based on reachable sets, characterized in that: Application to target drones, including: Obtain the flight status information and control information of the target UAV and the non-cooperative target UAV, and continuously detect whether there is a potential conflict between the target UAV and the non-cooperative target UAV; When there is a potential conflict, the danger set is determined based on the flight status information of the target UAV and the non-cooperative target UAV; Construct a system dynamic data group based on the status information and control information of the target UAV and the non-cooperative target UAV; The Hamilton-Jacobi partial differential equation is constructed with the system dynamic data set and the predefined cost-value function; Taking the implicit surface function corresponding to the danger set as the termination condition, solving the Hamilton-Jacobi partial differential equation, and obtaining the optimal control strategy; According to the optimal control strategy, determine the boundary reachable set; When the target UAV reaches the critical range of the boundary reachable set, the safety controller is awakened to adjust the control strategy of the target UAV.

2. The method for UAV formation to deal with non-cooperative targets based on reachable sets according to claim 1 is characterized in that: The flight status information of the target UAV and the non-cooperative target UAV includes the flight speed of the target UAV and the distance between the target UAV and the non-cooperative target UAV. According to the flight status information of the target UAV and the non-cooperative target UAV, the danger set is determined, including: L s ={x:|p x,r |,|p y,r |≤d∨|v x,i |≥v max ∨|v y,j |≥v max }; Among them, L s represents the danger set, p x,r is the horizontal coordinate relative position variable of the target UAV and the non-cooperative target UAV, p y,r is the relative position variable of the ordinate of the target UAV and the non-cooperative target UAV, v x,i represents the horizontal velocity of the target UAV, v y, j represents the ordinate velocity of the non-cooperative target UAV, d represents the minimum separation distance between the target UAV and the non-cooperative target UAV in the x and y directions, and v max Indicates the maximum speed limit.

3. The method for UAV formation to deal with non-cooperative targets based on reachable sets according to claim 1 is characterized in that: Based on the system dynamics data set and the predefined cost-value function, the Hamilton-Jacobi partial differential equation is constructed, including: V(0,x)=l(x) Among them, D t V(t,x) is the partial differential equation of the cost-value function, f(x,u1,u2) represents the system dynamic data set, where x∈R n is the system state variable, R n represents the state of the UAV in the n-dimensional space, u1 is the control function of the target UAV, u2 is the control function of the non-cooperative target UAV, V(0,x) represents the cost value function at t=0, l(x) represents the implicit surface function value, U1 represents the control quantity set of the target UAV, and U2 represents the control quantity set of the non-cooperative target UAV.

4. The method for UAV formation to deal with non-cooperative targets based on reachable sets according to claim 1 is characterized in that: Optimal control strategies, including: in, represents the optimal control strategy of the target UAV, represents the optimal control strategy of the non-cooperative target UAV, D t V(t,x) is the partial differential equation of the cost-value function, f(x,u1,u2) represents the system dynamic data set, where x∈R n is the system state variable, R n Represents the state of the UAV in the n-dimensional space, u1 is the control function of the target UAV, u2 is the control function of the non-cooperative target UAV, U1 represents the control quantity set of the target UAV, and U2 represents the control quantity set of the non-cooperative target UAV.

5. The method for UAV formation to deal with non-cooperative targets based on reachable sets according to claim 1 is characterized in that: Wake up the security controller to adjust the control strategy of the target drone, including: When the safety controller receives the wake-up signal, it obtains the flight status information of the target UAV and the non-cooperative target UAV, and adjusts the flight acceleration of the target UAV according to the pre-designed control law; Determine the avoidance direction of the drone according to the flight mission of the target drone; According to the boundary position, acceleration and avoidance direction of the target UAV and the boundary reachable set, the expected flight attitude of the target UAV at each moment during its flight is determined.

6. The method for UAV formation to deal with non-cooperative targets based on reachable sets according to claim 5 is characterized in that: The acceleration of the target UAV flight is adjusted according to the pre-designed control law: Among them, p is the center position of the target UAV, p1 is the center position of the non-cooperative target UAV, p2 is the intersection of the line connecting the center position of the target UAV and the center position of the non-cooperative target UAV in the boundary reachable set, and r p1 is the radius of the non-cooperative target drone, a n is the current acceleration of the target drone, a max is the maximum acceleration of the target UAV.

7. The method for UAV formation to deal with non-cooperative targets based on reachable sets according to claim 5 or 6, characterized in that: Also includes: Obtain sensor data from multiple sensors mounted on the target drone at any time; Perform data fusion on multiple sensor data and perform attitude calculation to obtain the actual flight attitude of the target UAV at that moment; Solve the attitude error based on the actual flight attitude and the expected flight attitude; Input the attitude error into a pre-set attitude control algorithm to determine attitude adjustment parameters; The attitude adjustment parameters are input into the flight controller to adjust the flight attitude of the target UAV.

8. The method for UAV formation to deal with non-cooperative targets based on reachable sets according to claim 1, characterized in that: Wake up the security controller to adjust the control strategy of the target drone, including: When the safety controller receives the wake-up signal, it obtains the current position of the target drone and the boundary reachable set range; The current position of the target UAV is used as the initial control point, and the final control point is determined according to the flight mission of the target UAV and the range of the boundary reachable set; The target drone’s flight direction is used as the intersection of the boundary reachable set; At the intersection, make a normal line of the boundary reachable set, extend it toward the center of the boundary reachable set, and use the point on the extension line that is the first target distance from the intersection as the second control point; Draw an extension line for the last control point and the second control point, so that the two extension lines intersect vertically, and their intersection point is used as the third control point; A Bezier curve is constructed according to the initial control point, the second control point, the third control point and the final control point, and the Bezier curve is used as an obstacle avoidance route to control the flight of the target UAV.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the steps of the method for a drone formation to deal with non-cooperative targets based on a reachable set as described in any one of claims 1 to 8.

10. A computer storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by the processor, the steps of the method for a drone formation to deal with non-cooperative targets based on a reachable set as described in any one of claims 1-8 are implemented.

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