A method for coping with non-cooperative targets by a UAV formation based on reachable set

Abstract

The application provides a method for coping with non-cooperative targets by a UAV formation based on a reachable set, comprising: obtaining flight state information and control information of target UAVs and non-cooperative target UAVs, and detecting whether potential conflicts exist between the target UAVs and the non-cooperative target UAVs; when potential conflicts exist, determining a dangerous set according to the flight state information of the target UAVs and the non-cooperative target UAVs, and constructing a system dynamic data group; constructing a Hamilton-Jacobi partial differential equation with the system dynamic data group and a pre-defined cost value function; taking an implicit surface function corresponding to the dangerous set as a termination condition, solving the Hamilton-Jacobi partial differential equation, and obtaining an optimal control strategy; determining a boundary reachable set according to the optimal control strategy; and when the target UAVs reach a critical range of the boundary reachable set, waking up a safety controller to adjust the control strategy of the target UAVs. By implementing the method, the calculation is reduced, potential collisions are avoided, and flight safety is ensured.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of unmanned aerial vehicles, and particularly relates to a method for coping with non-cooperative targets by an unmanned aerial vehicle formation based on a reachable set. BACKGROUND

[0002] Unmanned aerial vehicle formation flight has a wide range of applications in military, commercial, scientific research and entertainment fields. The leader-follower method is a common method for controlling unmanned aerial vehicle formation, which sets one unmanned aerial vehicle as a leader, and other unmanned aerial vehicles follow the leader to move. The leader is responsible for guiding the direction and speed of the entire formation, and the follower adjusts its position and speed according to the state of the leader to maintain the shape and formation of the formation. This method mainly includes the following key steps:

[0003] 1) Selection of the leader. Select one unmanned aerial vehicle as the leader in the formation. The principle of selecting the leader can be based on its position, speed, ability, etc. The leader unmanned aerial vehicle can be set by artificial or selected by autonomous algorithm.

[0004] 2) The method of the follower following the leader. The follower unmanned aerial vehicle follows the position and speed information of the leader. The communication system between unmanned aerial vehicles can be used to realize the information exchange between the leader and the follower. The follower moves according to the instructions or predetermined trajectory of the leader. The follower unmanned aerial vehicle needs to adjust in real time according to the movement of the leader, which can obtain the information of the surrounding environment such as relative position, speed, etc. through sensors, and then adjust itself through formation control algorithm.

[0005] 3) Formation maintenance strategy. The leader and the follower need to maintain a certain relative position and distance to ensure the overall stability of the unmanned aerial vehicle formation. In the movement process of the formation, tracking algorithms can be used to adjust the position and speed of the unmanned aerial vehicle to ensure the shape and structure of the formation.

[0006] In the field of unmanned aerial vehicle formation flight, unmanned aerial vehicles need to fly safely under the complex air traffic management system, and need to avoid obstacles of sudden non-cooperative targets, wherein the non-cooperative target can be another unmanned aerial vehicle. In related technologies, the obstacle avoidance method for sudden non-cooperative targets includes obstacle recognition and obstacle avoidance trajectory control based on neural networks. The obstacle recognition and obstacle avoidance trajectory control based on neural networks depend on a large amount of training data and require more computing resources. SUMMARY

[0007] Therefore, the purpose of the present application is to provide a method and device for coping with non-cooperative targets by an unmanned aerial vehicle formation based on a reachable set, to meet the needs of reducing the amount of calculation while realizing obstacle avoidance processing for sudden non-cooperative targets.

[0008] To achieve the above object, the present application provides the following technical solutions:

[0009] According to the first aspect, the present application provides a method for coping with non-cooperative targets by a UAV formation based on reachable set, applied to a target UAV, comprising: obtaining flight state information and control information of the target UAV and a non-cooperative target UAV, 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 dangerous set according to the flight state information of the target UAV and the non-cooperative target UAV; constructing a system dynamic data group according to the state information and control information of the target UAV and the non-cooperative target UAV; constructing a Hamilton-Jacobi partial differential equation with the system dynamic data group and a pre-defined cost value function; taking an implicit surface function corresponding to the dangerous 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; and when the target UAV reaches a critical range of the boundary reachable set, waking up a safety controller to adjust the control strategy of the target UAV.

[0010] Optionally, the flight state information of the target UAV and the non-cooperative target UAV includes a flight speed of the target UAV and a distance between the target UAV and the non-cooperative target UAV, and the determination of the dangerous set according to the flight state information of the target UAV and the non-cooperative target UAV comprises:

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

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

[0013] Optionally, the Hamilton-Jacobi partial differential equation is constructed with the system dynamic data group and the pre-defined cost value function, comprising:

[0014]

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

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

[0017] Optionally, the optimal control strategy comprises:

[0018]

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

[0020] Optionally, the safety controller is woken up to adjust the control strategy of the target unmanned aerial vehicle, comprising: when the safety controller receives a wake-up signal, obtaining flight state information of the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle, and adjusting the acceleration of the target unmanned aerial vehicle according to a pre-designed control law; determining the avoidance direction of the unmanned aerial vehicle according to the flight task of the target unmanned aerial vehicle; and determining the expected flight attitude of the target unmanned aerial vehicle at each moment during flight according to the boundary position of the target unmanned aerial vehicle and the boundary reachable set, the acceleration, and the avoidance direction.

[0021] Optionally, the acceleration of the target unmanned aerial vehicle is adjusted according to the pre-designed control law as follows:

[0022]

[0023] wherein p is the center position of the target unmanned aerial vehicle, p1 is the center position of the non-cooperative target unmanned aerial vehicle, p2 is the intersection of the line connecting the center position of the target unmanned aerial vehicle and the center position of the non-cooperative target unmanned aerial vehicle on the boundary reachable set, r p1a is a radius of the non-cooperative target UAV n a is a current acceleration of the target UAV max a is a maximum acceleration of the target UAV.

[0024] Optionally, the method for coping with the non-cooperative target based on the reachable set of the UAV formation further comprises: acquiring sensing data of a plurality of sensors carried on the target UAV at any time; performing data fusion on the plurality of sensing data, and performing attitude calculation to obtain an actual flight attitude of the target UAV at the time; solving an attitude error according to the actual flight attitude and an expected flight attitude; inputting the attitude error into a pre-set attitude control algorithm to determine an attitude adjustment parameter; and inputting the attitude adjustment parameter into a flight controller to adjust the flight attitude of the target UAV.

[0025] Optionally, the method for adjusting the control strategy of the target UAV by waking up the safety controller comprises: when the safety controller receives a wake-up signal, acquiring a current position of the target UAV and a range of the boundary reachable set; taking the current position of the target UAV as an initial control point, and determining a final control point according to a flight task of the target UAV and the range of the boundary reachable set; making an intersection point of the boundary reachable set in a flight direction of the target UAV; making a normal line of the boundary reachable set at the intersection point, and extending the normal line to a center of the boundary reachable set to obtain a second control point at a first target distance from the intersection point on the extended line; making an extended line of the final control point and an extended line of the second control point, and making the two extended lines perpendicular to each other, so that an intersection point of the two extended lines is a 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, taking the Bezier curve as an obstacle avoidance route, and controlling the target UAV to fly.

[0026] According to a second aspect, an electronic device is provided, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the steps of the method for coping with the non-cooperative target based on the reachable set of the UAV formation according to the first aspect or any of the embodiments of the first aspect.

[0027] According to a third aspect, a computer storage medium is provided, which stores computer instructions, and the instructions are executed by a processor to implement the steps of the method for coping with the non-cooperative target based on the reachable set of the UAV formation according to the first aspect or any of the embodiments of the first aspect.

[0028] The embodiment provides a method for coping with non-cooperative targets by a UAV formation based on a reachable set.

[0029] Other advantages, objects, and features of the application will be set forth in the descriptions below and will be apparent to those skilled in the art from the descriptions below and the appended claims. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the description below. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to make the objectives, technical solutions and beneficial effects of the present application clearer, the present application provides the following drawings for description:

[0031] Figure 1 A specific example flow chart of the method for coping with non-cooperative targets by a UAV formation based on a reachable set of the present application;

[0032] Figure 2 A principle block diagram of a specific example of the electronic device in the embodiment of the present application. DETAILED DESCRIPTION

[0033] The technical solutions of the present application will be described below in detail with reference to the drawings, obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0034] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integral connection, it can be mechanical connection, or electrical connection, it can be direct connection, or indirect connection through an intermediate medium, it can be the internal communication of two elements, it can be wireless connection, or wired connection. For those skilled in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

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

[0036] The embodiment provides a method for coping with a non-cooperative target by a UAV formation based on a reachable set, as shown in the method. Figure 1 As shown in the application to the target UAV, the method comprises the following steps.

[0037] In S101, flight state information and control information of the target UAV and the non-cooperative target UAV are acquired, and it is continuously detected whether there is a potential conflict between the target UAV and the non-cooperative target UAV.

[0038] In S102, when there is a potential conflict, a dangerous set is determined according to the flight state information of the target UAV and the non-cooperative target UAV.

[0039] In S103, a system dynamic data group is constructed according to the state information and the control information of the target UAV and the non-cooperative target UAV.

[0040] In S104, a Hamilton-Jacobi partial differential equation is constructed by using the system dynamic data group and a pre-defined cost value function.

[0041] In S105, an implicit surface function corresponding to the dangerous set is taken as a termination condition, the Hamilton-Jacobi partial differential equation is solved, and an optimal control strategy is obtained.

[0042] In S106, a boundary reachable set is determined according to the optimal control strategy.

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

[0044] Exemplarily, the flight state information includes position, speed, acceleration, attitude and the like of the UAV, and the flight state information is acquired in real time by using sensors such as a GPS, an IMU (inertial measurement unit) and a barometer carried by the UAV. The control information includes instruction input of the UAV, such as a position of a joystick of a remote controller, and a response of the UAV to the instruction, and the control information is acquired through a corresponding control interface. The embodiment does not limit the manner of acquiring the flight state information and the control information, and a person skilled in the art can determine the manner according to needs.

[0045] With respect to the target UAV, other UAVs can be divided into cooperative target UAVs and non-cooperative target UAVs according to flight targets. The cooperative target UAV represents that the UAV has a cooperative relationship with the target UAV, for example, the cooperative target UAV and the target UAV need to exchange queues. The non-cooperative target UAV represents that the UAV does not have a cooperative relationship with the target UAV.

[0046] The manner of detecting whether the target UAV and the non-cooperative target UAV exist potential conflict can be judging whether the target UAV satisfies 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 travels at a speed higher than the speed limit v max

[0047] When there is a conflict, the dangerous set is determined according to the flight state 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] Wherein, 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 vertical coordinate relative position variable 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 vertical coordinate speed of the non-cooperative target UAV, d represents the minimum separation distance of the target UAV from the non-cooperative target UAV in the x and y directions, and v max represents the maximum speed limit.

[0050] The relative dynamics of the two UAVs are considered in the above process, which are as follows: p 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 vertical coordinate position of the target UAV, and p y,j is the vertical coordinate position of the non-cooperative target UAV.

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

[0052] For almost all t∈[-T,0]. (2)

[0053] Wherein, x∈R n ​is the system state variable; u1(t) e U1is the control of the target UAV; u2(t) e U2is the control of the non-cooperative target UAV. Assume that f: R n × U1x U2→ R n is uniformly continuous, bounded, and Lipschitz continuous in x for fixed u1(t), u2(t); the control functions u1(·) e U1, u2(·) e U2are drawn from a measurable function set. The non-target cooperative UAV allows the use of an unintended strategy γ, defined as:

[0054] for all for all r e [t, s];

[0055] where r represents any time in [t, s], s represents a specific time, N[u1](r) represents the actual state set, represents the estimated state set.

[0056] for a given dynamic |u x,y |≤u max and the dangerous set L s , the BRSV(t, x) is calculated as follows:

[0057]

[0058] where V(t, x) is the cost value function, x e 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 unintended control strategy, Γ represents the conflict set, U1represents the control set of the target UAV, s represents a specific time within [t, 0], γ[u1](·) represents the unintended control strategy of the target UAV, t represents time, x represents the system state, L s is the dangerous set, ξ f is the system trajectory satisfying the initial condition ξ f (t; x, t, u1(·), γ[u2](·)) = x and all the following differential equations within the interval [-t, 0]:

[0059]

[0060] γ[u2](·) represents the unintended control strategy of the non-cooperative target UAV, u1(s) represents the control of the target UAV at a specific time s, u2(s) represents the control 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 VI) have been developed to compute the boundary reachable set, which can be solved by well-established numerical methods and are not described here.

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

[0063] With the system dynamic data set and the pre-defined cost value function, the Hamilton-Jacobi partial differential equation is constructed, including:

[0064]

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

[0066] where 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 influence of the disturbance quantity is small and can be ignored, where x ∈ R n is the state variable of the system, R n represents the state of the unmanned aerial vehicle in n-dimensional space, u1 is the control function of the non-cooperative target unmanned aerial vehicle, u2 is the control function of the target unmanned aerial vehicle, V(0, x) represents the cost value function at t = 0, and l(x) represents the value of the implicit surface function.

[0067] From which V(t) = {x ∈ R n |V(t, x) ≤ 0} is obtained, where V(t, x) ≤ 0 means that from the state x, there exists at least one control strategy that can make the system reach the target set within a given time with a cost of not more than zero. In many cases, zero cost represents collision-free, safe arrival or satisfaction of other constraint conditions. According to the target set L p and the implicit surface function l(x), V(t, x) is solved.

[0068] In this embodiment, the relative dynamics Q i ; Q j between the two quadrotors are also considered, where Q i represents the target unmanned aerial vehicle, and Q j represents the non-cooperative target unmanned aerial vehicle. These dynamics can be obtained by defining 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] wherein p x,r is a horizontal relative position variable of the target UAV and the non-cooperative target UAV, p x,i is a horizontal position of the non-cooperative target UAV, p x, j is a horizontal position of the target UAV, p y,r is a vertical relative position variable of the target UAV and the non-cooperative target UAV, p y,i is a vertical position of the non-cooperative target UAV, p y, j is a vertical position of the target UAV, v x,r represents a horizontal relative velocity variable of the target UAV and the non-cooperative target UAV, v x,i represents a horizontal velocity of the non-cooperative target UAV, v x, j represents a horizontal velocity of the target UAV, v y,r represents a vertical relative velocity variable of the target UAV and the non-cooperative target UAV, v y,i represents a vertical velocity of the non-cooperative target UAV, v y, j represents a vertical velocity of the target UAV.

[0074] According to the relative variables given in formula (6), it can be obtained that:

[0075]

[0076] wherein u x,i is a control variable of the target UAV in the horizontal direction, u x,j is a control variable of the non-cooperative target UAV in the horizontal direction, u y,i is a control variable of the target UAV in the vertical direction, u y, j is a control variable of the non-cooperative target UAV in the vertical direction.

[0077] The speed of the target UAV Q i is increased to impose a speed limit on the quadrotor UAV:

[0078]

[0079] The target UAV can collide with the non-cooperative target UAV in the near future, it must switch to the safety controller. The safety controller is available in every mode, and executing the safety controller does not change the mode of the aircraft.

[0080] In the formation concept of this embodiment, the unsafe configuration is defined as follows: the aircraft is within the minimum separation distance d from the reference aircraft in the x and y directions, or travels 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 dangerous set as formula (1).

[0081] Thus, we can define the L s The corresponding implicit surface function l s (x) and solve HJPDE (5) with l s (x) as the termination condition to obtain the optimal control strategy of 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, which is the set of all positions that the UAV can reach under the optimal control strategy.

[0084] As mentioned earlier, the zero sub-level set of V s (t,x) specifies the BRS V s (t), which represents the state in the augmented relative coordinates, as defined by equation (8), that Qi cannot avoid LS within the time period t using the worst-case control. To avoid collision, Qi must apply the safety controller on the BRS boundary according to equation (9) to avoid entering the BRS. The following algorithm will wrap the safety controller around the target satisfaction controller, which is the controller used by the target UAV when there is no collision possibility between the target UAV and the non-cooperative target UAV:

[0085] 1) For a given time horizon t, solve V s (-t,x i -x j ), j∈Q(i). Q(i) is a set of quadrotors that Qi checks for safety.

[0086] 2) According to V s (-t,x i -x j) using a safety or goal satisfaction controller, j e Q(i): if then Qi and Qj have potential conflict and must use a safety controller; otherwise, Qi will use a goal satisfaction controller.

[0087] Safety controllers usually involve control barrier functions (CBF) or safe barrier certificates (SBC) that can ensure the system state will not enter the dangerous set at any time. Control barrier functions (CBF) or safe barrier certificates (SBC) are both prior art and will not be described here.

[0088] The embodiment provides a method for coping with non-cooperative targets by a UAV formation based on a reachable set, compared with a neural network, the method does not depend on a large amount of training data, reduces computing resources, and the method can continuously detect whether there is potential conflict between a target UAV and a non-cooperative target UAV by acquiring flight state information and control information of the target UAV and the non-cooperative target UAV, so that preventive measures are taken in advance, and a boundary reachable set is determined according to an optimal control strategy, so that the UAV can dynamically adjust a flight path while maintaining a safe distance to cope with a changing flight environment. Finally, when the UAV reaches a critical range of the boundary reachable set, a safety controller is woken up to adjust a control strategy of the UAV in real time, so that potential collision is avoided and flight safety is ensured.

[0089] As an optional implementation, the safety controller is woken up to adjust the control strategy of the target UAV, and the method comprises the following steps: when the safety controller receives a wake-up signal, acquiring flight state information of the target UAV and the non-cooperative target UAV, and adjusting an acceleration of the target UAV flight according to a pre-designed control law; determining an avoidance direction of the UAV according to a flight task of the target UAV; and determining an expected flight attitude of the target UAV at each moment in the flight process according to a boundary position of the boundary reachable set, the acceleration and the avoidance direction of the target UAV.

[0090] Exemplarily, the safety controller first acquires real-time flight state information of the target UAV and the non-cooperative target UAV, including position, velocity, acceleration, attitude and other parameters. These information can be acquired by sensors on the UAV (such as GPS, IMU, radar, etc.) and sent to the safety controller through the 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 according to the deviation between the current state and the desired state of the UAV, so as to adjust the engine thrust, rudder deflection or other actuators of the UAV to change the acceleration of the UAV. According to the flight mission and the current flight path of the target UAV, the safety controller will determine the avoidance direction of the UAV to avoid the potential conflict area. Specifically, a path planning algorithm such as artificial potential field method is used to calculate a new path that avoids obstacles. The safety controller will determine the desired flight attitude of the UAV at each time during flight according to the boundary position of the target UAV and the boundary reachable set, the calculated acceleration and the determined avoidance direction, including the adjustment of 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] where 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 on the boundary of the boundary reachable set, r p1 is the radius of the non-cooperative target UAV, a n is the current acceleration of the target UAV, 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 position of the boundary reachable set, the greater the acceleration, further reducing the possibility of the UAV reaching the boundary of the boundary reachable set and improving the flight safety of the UAV.

[0094] As an optional implementation, a method for dealing with non-cooperative targets based on the reachable set of UAV formation, further comprising: acquiring sensing data of multiple sensors carried on the target UAV at any time; performing data fusion on the multiple sensing data and performing attitude calculation to obtain the actual flight attitude of the target UAV at that time; solving the attitude error according to the actual flight attitude and the desired flight attitude; inputting the attitude error into the pre-designed attitude control algorithm to determine the attitude adjustment parameter; inputting the attitude adjustment parameter into the flight controller to adjust the flight attitude of the target UAV.

[0095] For example, after determining the expected flight attitude of the target UAV at each moment during flight, it is necessary to determine in real time whether there is an attitude error during the actual flight of the UAV. If there is an attitude error, the attitude error needs to be adjusted. In this embodiment, first, data from multiple sensors carried by the UAV is obtained, including accelerometers, gyroscopes, magnetometers, etc. Then, data fusion is performed on the sensor data. The purpose of data fusion is to utilize the advantages of each sensor and integrate the data through algorithms 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 fusing sensor data with the same or slightly different data update frequencies, 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 UAV's actuators, 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, the safety controller is woken up to adjust the control strategy of the target UAV, including: 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 an initial control point, determining a final control point according to the flight task of the target UAV and the range of the boundary reachable set; making an intersection point of the boundary reachable set in the flight direction of the target UAV; at the intersection point, making a normal line of the boundary reachable set, extending to the center of the boundary reachable set, and taking a second control point at a first target distance from the intersection point on the extended line; making an extension line of the final control point and an extension line of the second control point, and making the two extension lines perpendicular to each other, and taking an intersection point of the two extension lines as a 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 taking the Bezier curve as an obstacle avoidance route to control the flight of the target UAV.

[0102] Illustratively, according to the flight task of the target UAV and the range of the boundary reachable set, the final control point is determined by determining the proposed flight route of the target UAV through the target location in the flight task, and in order to avoid collision, it is necessary to avoid entering the edge of the boundary reachable set. First, the way to determine the final control point according to the flight task of the target UAV and the range of the boundary reachable set can be to extend outward in the current flight direction of the UAV to perform the current flight task, select any point on the outward extension line as an intersection point, and select a target point on the line segment within the boundary reachable set range as the final control point. The target point can be a point within the boundary reachable set range that is a target distance from the boundary 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 point is a predetermined angle, which can be 45 degrees. The size of the angle and the position of the target point in this embodiment are not limited, and those skilled in the art can determine them as needed.

[0103] Making an intersection point of the boundary reachable set in the flight direction of the target UAV; at the intersection point, making a normal line of the boundary reachable set, extending to the center of the boundary reachable set, and taking a second control point at a first target distance from the intersection point on the extended line; making an extension line of the final control point and an extension line of the second control point, and making the two extension lines perpendicular to each other, and taking an intersection point of the two extension lines as a third control point. This embodiment constructs a Bezier curve according to the initial control point, the second control point, the third control point and the final control point, which can generate a smooth curve path, and the curve path is taken as an obstacle avoidance route.

[0104] Wherein, 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] Wherein, P0 is an initial control point, P3 is a last control point, P1 is a second control point, P2 is a third control point, t is a parameter varying from 0 to 1, which determines the position of the point on the curve.

[0107] The embodiment provides a method for a non-cooperative target by a UAV formation based on a reachable set. When the UAV reaches a critical range of the boundary reachable set, the Bezier curve is changed by adjusting the control point, dynamic path planning is realized, and the changing flight environment is responded. By accurately determining the control point of the Bezier curve, a smooth path meeting the flight task requirements of the UAV can be generated, the accuracy and feasibility of the path are ensured, the control point is determined by using the method of the boundary reachable set and the normal line extension, the UAV can effectively avoid obstacles in a complex environment, and the flight safety is improved.

[0108] The embodiment of the application further provides an electronic device, such as Figure 2 As shown in the figure, the processor 501 and the memory 502 can be connected through a bus or other means.

[0109] The processor 501 can be a central processing unit (CPU). The processor 501 can 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.

[0110] The memory 502 is a non-transitory computer readable storage medium, which can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as the program instructions / modules of the method for a non-cooperative target by a UAV formation based on a reachable set. The processor executes various functions of the processor and data processing by running the non-transitory software programs, instructions and modules stored in the memory.

[0111] The memory 502 can include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, and the data storage area can store data created by the processor and the like. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 502 can optionally include a memory disposed remotely from the processor, which can be connected to the processor through a network. Examples of the above 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, perform the method of responding to a non-cooperative target based on a reachable set as Figure 1 The method of responding to a non-cooperative target based on a reachable set in the embodiment shown.

[0113] The specific details of the above electronic device can be referred to in Figure 1 The corresponding related description and effects in the embodiment shown are not repeated here.

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

[0115] Finally, it should be explained that the above preferred embodiments are only used to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail through the above preferred embodiments, those skilled in the art should understand that various modifications can be made in form and details without departing from the scope defined by the claims of the present application.

Claims

1. A method for dealing with non-cooperative targets by a UAV formation based on reachable set, characterized in that, The application is applied to a target unmanned aerial vehicle, and comprises: acquiring flight state information and control information of the target unmanned aerial vehicle and a non-cooperative target unmanned aerial vehicle, continuously detecting whether there is a potential conflict between the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle; when the potential conflict exists, determining a dangerous set according to the flight state information of the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle; constructing a system dynamic data group according to the state information and the control information of the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle; constructing a Hamilton-Jacobian partial differential equation with the system dynamic data group and a pre-defined cost value function; taking an implicit surface function corresponding to the dangerous set as a termination condition, solving the Hamilton-Jacobian partial differential equation to obtain an optimal control strategy; determining a boundary reachable set according to the optimal control strategy; when the target unmanned aerial vehicle reaches a critical range of the boundary reachable set, waking up a safety controller to adjust the control strategy of the target unmanned aerial vehicle; wherein the flight state information of the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle comprises a flight speed of the target unmanned aerial vehicle and a distance between the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle, and the dangerous set is determined according to the flight state information of the target unmanned aerial vehicle and the non-cooperative target unmanned aerial vehicle, comprising: ； wherein, represents a dangerous set, is a lateral relative position variable of the target UAV and the non-cooperative target UAV, is a longitudinal relative position variable of the target UAV and the non-cooperative target UAV, represents a lateral velocity of the target UAV, represents a longitudinal velocity of the non-cooperative target UAV, represents a minimum separation distance of the target UAV from the non-cooperative target UAV in x and y directions, represents a maximum speed limit; waking up the safety controller to adjust the control strategy of the target unmanned aerial vehicle, comprising: when the safety controller receives a wake-up signal, acquiring a current position of the target unmanned aerial vehicle and a range of the boundary reachable set; taking the current position of the target unmanned aerial vehicle as an initial control point, determining a final control point according to a flight task of the target unmanned aerial vehicle and the range of the boundary reachable set; taking a flight direction of the target unmanned aerial vehicle as an intersection of the boundary reachable set; at the intersection, extending a normal line of the boundary reachable set to a center of the boundary reachable set, and taking a first target distance away from the intersection on the extended line as a second control point; extending a line from the final control point and a line from the second control point, and making the two extended lines perpendicular to each other, and taking an intersection of the two lines as a 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, taking the Bezier curve as an obstacle avoidance route, and controlling the target unmanned aerial vehicle to fly.

2. The method of claim 1, wherein, constructing the Hamilton-Jacobian partial differential equation with the system dynamic data group and the pre-defined cost value function, comprising: wherein is the partial differential equation of the cost value function with respect to t, is the partial differential equation of the cost value function with respect to x is the partial differential equation of the cost value function with respect to denotes a system dynamic data set, wherein is a system state variable, denotes the state of the UAV in n-dimensional space, is a control function of the target UAV, is a control function of the non-cooperative target UAV, denotes the cost value function at t = 0, denotes the value of an implicit surface function, denotes a set of control quantities of the target UAV, denotes a set of control quantities of the non-cooperative target UAV.

3. The method of claim 1, wherein, the optimal control strategy, comprising: wherein, represents an optimal control strategy of the target UAV, represents an optimal control strategy of the non-cooperative target UAV.

4. The method of claim 1, wherein, waking up the safety controller to adjust the control strategy of the target unmanned aerial vehicle, comprising: when the safety controller receives a wake-up signal, acquiring flight state information of the target unmanned aerial vehicle and a non-cooperative target unmanned aerial vehicle, and adjusting an acceleration of the target unmanned aerial vehicle to fly according to a pre-designed control law; determining an avoidance direction of the target unmanned aerial vehicle according to a flight task of the target unmanned aerial vehicle; determining an expected flight attitude of the target unmanned aerial vehicle at each time during flight according to a boundary position of the target unmanned aerial vehicle and the boundary reachable set, an acceleration and the avoidance direction.

5. The method of claim 4, wherein, adjusting the acceleration of the target unmanned aerial vehicle to fly according to the pre-designed control law is: ; wherein, is a center position of the target UAV, is a center position of the non-cooperative target UAV, is an intersection of a line connecting the center position of the target UAV and the center position of the non-cooperative target UAV at the boundary reachable set, is a radius of the non-cooperative target UAV, is a current acceleration of the target UAV, is a maximum acceleration of the target UAV.

6. The method of claim 4 or 5, wherein, further comprising: acquiring sensing data of a plurality of sensors carried on the target unmanned aerial vehicle at any time; performing data fusion on the plurality of sensing data, and performing attitude calculation to obtain an actual flight attitude of the target unmanned aerial vehicle at the time; solving an attitude error according to the actual flight attitude and the expected flight attitude; The attitude error is input into a preset attitude control algorithm to determine an attitude adjustment parameter; The attitude adjustment parameter is input into a flight controller to adjust the flight attitude of the target UAV.

7. An electronic device, the device comprising: The memory, the processor and the computer program stored in the memory and executable on the processor, characterized in that the processor executes the steps of the method for dealing with non-cooperative targets by a UAV formation based on reachable set according to any one of claims 1-6.

8. A computer storage medium having stored thereon computer instructions, wherein the computer instructions comprise the steps of: The instructions are executed by the processor to implement the steps of the method for dealing with non-cooperative targets by a UAV formation based on reachable set according to any one of claims 1-6.

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