Unmanned aerial vehicle formation path convergence and change method based on reachable set

Abstract

The invention provides an unmanned aerial vehicle formation path convergence and change method based on a reachable set, and the method comprises the steps: obtaining the flight state information, control information and disturbance quantity information of an interferer unmanned aerial vehicle and an escaper unmanned aerial vehicle when the unmanned aerial vehicle formation path needs to be converged or changed; constructing a system dynamic data set according to the information; determining a safety area according to a game strategy of the escaper unmanned aerial vehicle and the jammer unmanned aerial vehicle, and taking a zero sub-level set of an implicit curved surface function of the safety area as a target set; and constructing a Hamiltonian-Jacobian partial differential equation according to a predefined cost value function for reaching the target set in the current state and a system dynamic data set, and solving the Hamiltonian-Jacobian partial differential equation by taking the implicit curved surface function of the safety region as a termination condition to obtain an optimal control strategy. By implementing the method, the optimal control strategy is calculated in real time, the method can quickly adapt to environment change and obstacles, and the safety of unmanned aerial vehicle formation path convergence and change is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle formations, and in particular relates to a method for merging and changing paths of unmanned aerial vehicle formations based on a reachable set. 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] The leader-follower method has advantages in UAV formation control, such as being easy to implement, highly adaptable and scalable, but it also has disadvantages such as information transmission delay and dependence on the leader. In the field of UAV formation flight, UAVs need to safely merge and change paths under a complex air traffic management system. Each UAV is both an independent flying body and part of the entire formation, and needs to work with other UAVs to complete complex tasks. The relevant technology has not analyzed the process of UAV formation path merging and changing under the leader-follower framework, and cannot guarantee the safety of UAV formation path merging and changing. Summary of the invention

[0007] In view of this, the object of the present invention is to provide a method for UAV formation path merging and changing based on a reachable set to meet the demand for improving the safety of UAV formation path merging and changing.

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

[0009] The present invention provides a method for merging and changing the path of a UAV formation based on a reachable set, comprising: when the path of the UAV formation needs to be merged or changed, obtaining the flight state information, control information and disturbance amount information of a jammer UAV and an evader UAV; constructing a system dynamic data group according to the state information, control information and disturbance amount information of the jammer UAV and the evader UAV; determining a safe area according to the game strategy of the evader UAV and the jammer UAV, and taking the zero sub-level set of the implicit surface function of the safe area as a target set; constructing a Hamilton-Jacobi partial differential equation according to a predefined cost value function of reaching the target set in the current state and the system dynamic data group; taking the implicit surface function of the safe area as a termination condition, solving the Hamilton-Jacobi partial differential equation, and obtaining an optimal control strategy; and sending the optimal control strategy to the UAV whose formation path needs to be merged or changed.

[0010] Optionally, the target set L p The expression is as follows:

[0011]

[0012] in, are respectively a certain position relative to the reference drone, is a specified speed relative to the reference drone, p x,r is the horizontal coordinate relative position variable of the jammer UAV and the evader UAV, p x,r =p x,i -p x,j , p x,i is the abscissa position of the evader drone, p x,j is the horizontal coordinate position of the jammer drone, p y,r is the relative position variable of the vertical coordinates of the jammer UAV and the evader UAV, p y,r =p y,i -p y,j , p y,i is the ordinate position of the evader drone, p y,j is the vertical coordinate position of the jammer UAV, v x,r represents the horizontal axis relative speed variable of the jammer drone and the evader drone, v x,r =v x,i -v x,j , v x,irepresents the abscissa velocity of the evader drone, v x, j represents the horizontal coordinate speed of the jammer UAV, v y,r represents the relative velocity variable of the ordinate of the jammer UAV and the evader UAV, v y,r =v y,i -v y,j , v y,i represents the ordinate velocity of the evader drone, v y,j represents the vertical coordinate speed of the jammer UAV, r px 、r vx 、r py , They respectively represent the preset minimum safe distance difference of the relative position variables of the horizontal coordinates of the jammer UAV and the evader UAV, the minimum safe speed difference of the horizontal coordinate speed variables of the jammer UAV and the evader UAV, the minimum safe distance difference of the relative position variables of the vertical coordinates of the jammer UAV and the evader UAV, and the minimum safe speed difference of the vertical coordinate speed variables of the jammer UAV and the evader UAV.

[0013] Optionally, the predefined cost value function of the current state to reach the target set is:

[0014]

[0015] 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 evader UAV, γ represents the unexpected control strategy, Γ represents the conflict set, U1 represents the control quantity set of the evader UAV, s represents a specific time in [t,0], γ[u1](·) represents the unexpected control strategy of the evader, t represents time, x represents the system state, ξ f The system trajectory ξ that satisfies the initial conditions f (t;x,t,u1(·),γ[u2](·))=x,L p The target collection.

[0016] Optionally, a Hamilton-Jacobi partial differential equation is constructed according to a predefined cost-value function of reaching a target set from a current state and a system dynamic data set, including:

[0017]

[0018] V(0,x)=l(x)

[0019] Among them, D tV(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 evader UAV, u2 is the control function of the jammer UAV, V(0,x) represents the cost-value function at t=0, and l(x) represents the implicit surface function value.

[0020] Optionally, the optimal control strategy includes:

[0021]

[0022] in, represents the optimal control strategy of the evader UAV, represents the optimal control strategy of the jammer 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 evader UAV, u2 is the control function of the jammer UAV, U1 represents the control quantity set of the evader UAV, and U2 represents the control quantity set of the jammer UAV.

[0023] Optionally, the safe area is determined according to the game strategies of the evader UAV and the jammer UAV, and the zero sub-level set of the implicit surface function of the safe area is used as the target set, including:

[0024] Obtain static environment information when the UAV formation path needs to merge or change;

[0025] Determine obstacle factors for drones when merging or changing based on static environment information;

[0026] Transform the perceived obstacle feature GIS data into a mathematical model;

[0027] The obstacle elements converted into mathematical models are used as obstacle avoidance constraints. The safe area is determined according to the game strategies of the evader UAV and the jammer UAV and the obstacle avoidance constraints. The zero sub-level set of the implicit surface function of the safe area is used as the target set.

[0028] Optionally, a method for merging and changing the path of a drone formation based on a reachable set further includes:

[0029] When any UAV merges or changes its formation path, the position and radius of obstacles in its motion plane in the static environment information are measured in real time by the onboard sensors of the detection and avoidance system;

[0030] Determine whether the obstacle has entered the conflict area based on the position of the obstacle and the position of the drone. If the obstacle has entered the conflict area but is not in the backward reachable set, the drone will fly at the first acceleration. The conflict area is an area with the drone position as the center, a straight line connecting the drone position to the obstacle position, and a radius including the distance of the obstacle.

[0031] When the obstacle enters the backward reachable set of the UAV, the flight of the UAV is controlled according to the second acceleration, and the second acceleration is greater than the first acceleration.

[0032] Optionally, a predefined cost-value function for reaching the target set from the current state includes:

[0033] Obtain the predefined UAV energy consumption cost function and flight time cost function;

[0034] The energy consumption cost function and the flight time cost function are added to the predefined cost value function of reaching the target set from the current state to obtain the optimized total cost value function.

[0035] Optionally, a predefined UAV energy cost function C enegy (x,u2) is:

[0036] C enegy (x,u2)=α||u2|| 2 ;

[0037] Among them, α is the energy consumption weight coefficient, u2 is the control function of the jammer UAV, and x represents the current given state;

[0038] Flight time cost function C time (x) is:

[0039] C time (x) = β(Tt);

[0040] Among them, β is the weight coefficient of time cost, and T is the expected arrival time.

[0041] The present invention provides a method for merging and changing the path of a UAV formation based on a reachable set. The method based on a reachable set allows the UAV to calculate a backward reachable set and an optimal control strategy in real time, thereby quickly adapting to environmental changes and obstacles, improving the real-time and adaptability of the system, and improving the safety of the UAV formation path merging and changing.

[0042] 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

[0043] 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:

[0044] Figure 1 A specific example flow chart of a method for merging and changing the path of a UAV formation based on a reachable set in the present invention;

[0045] Figure 2 This is a specific schematic diagram of the path intersection scenario of the present invention. DETAILED DESCRIPTION

[0046] 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 them. 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.

[0047] 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.

[0048] 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.

[0049] This embodiment provides a method for merging and changing the paths of UAV formations based on reachable sets, such as Figure 1 As shown, including:

[0050] S101, when the UAV formation path needs to merge or change, obtain the flight status information, control information and disturbance information of the interferer UAV and the evader UAV;

[0051] S102, constructing a system dynamic data group according to the status information, control information and disturbance information of the jammer UAV and the evader UAV;

[0052] S103, determining a safe area according to the game strategies of the evader UAV and the jammer UAV, and taking the zero sub-level set of the implicit surface function of the safe area as a target set;

[0053] S104, constructing a Hamilton-Jacobi partial differential equation according to a predefined cost-value function of reaching a target set from a current state and a system dynamic data group;

[0054] S105, taking the implicit surface function of the safety area as the termination condition, solving the Hamilton-Jacobi partial differential equation to obtain the optimal control strategy;

[0055] S106, sending the optimal control strategy to the UAVs whose formation paths need to be merged or changed.

[0056] For example, the application scenario of this embodiment is as follows Figure 2 As shown, a scene of path intersection includes a jammer drone and an evader drone. A jammer drone is a drone that interferes during the intersection of drone formation paths. An evader drone is a drone that wants to avoid collision, such as a drone in the original formation. In this scene, some drones can change queues during the path convergence process, and some drones can change from a master to a slave. This embodiment does not limit the transformation method, but for the above two transformation methods, a method for drone formation path convergence and change based on a reachable set proposed in this embodiment is followed.

[0057] First, in order to calculate the backward reachable set, the system model is constructed using ordinary differential equations to obtain the system dynamic data set. Specifically,

[0058]

[0059] Where x∈R n is the system state variable; u(t)∈U is the control variable; d(t)∈D is the disturbance variable; τ is a certain moment; t f is the terminal time. f = {x∈R n |T(x)≤0}, T f represents the target set of the system, T(x) can be an implicit surface function that describes certain properties and constraints of the system state x. For fixed u and d, it is assumed that f(·) is uniformly continuous, bounded, and Lipschitz continuous on x; the control functions u1(·)∈U1, u2(·)∈U2 are drawn from the set of measurable functions.

[0060] The jammer drone is allowed to use an unintended strategy γ, defined as:

[0061] For all For all r∈[t,s]}; (2)

[0062] 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.

[0063] In the differential game, the jammer drone’s goal is to make the system enter the target set T f , the goal of the evader drone is to keep the system away from the target set T f . Set T f Represented as a bounded Lipschitz continuous function L p : R n →R’s zero sublevel set. We call l(·) the set T f Implicit surface function of: L p = {x∈R n |l(x)≤0}.

[0064] For a given dynamic, and the target set L p , calculate BRSV(t,x), the calculation formula is as follows:

[0065]

[0066] 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 evader UAV, γ represents the unexpected control strategy, Γ represents the conflict set, U1 represents the control quantity set of the evader UAV, s represents a specific time in [t,0], γ[u1](·) represents the unexpected control strategy of the evader, t represents time, x represents the system state, and L p is the target 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]:

[0067]

[0068] γ[u2](·) represents the unexpected control strategy of the interferer, u1(s) represents the control amount of the evader at a specific time s, and u2(s) represents the control amount of the interferer at a specific time s.

[0069] It should be noted that the cost-value function V(t,x) defined here is a theoretical construction that represents the expected value of the minimum cumulative cost (or maximum cumulative reward) starting from the current state x at time t, following the optimal control policy, until the end of the termination time T. The specific V(t,x) function is found by solving the Hamilton-Jacobi equation, which is a partial differential equation that describes how the value function changes over time and how it is affected by the system state and control strategy. Solving this equation can obtain a specific function that gives the value at any given time and state.

[0070] Many methods involving solving Hamilton-Jacobi partial differential equations (HJ PDEs) and Hamilton-Jacobi variational inequalities (HJ variational inequalities) have been developed for computing backward reachable sets (BRS). These HJ PDEs and HJ variational inequalities can be solved using mature numerical methods and will not be described in detail here.

[0071] 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 backward reachable set is 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) 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.

[0072] According to the predefined cost-value function of reaching the target set from the current state and the system dynamic data set, the Hamilton-Jacobi partial differential equation is constructed, including:

[0073]

[0074] 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 evader UAV, u2 is the control function of the jammer UAV, V(0,x) represents the cost-value function at t=0, and l(x) represents the implicit surface function value.

[0075] 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 Lp And the implicit surface function l(x) to solve V(t,x).

[0076] In this embodiment, the relative dynamics Q between the two quadrotors will also be considered i ;Q j , where Q i stands for Evader Drone, Q j represents a jammer drone. These dynamics can be captured by defining the relevant variables:

[0077] p x,r =p x,i -p x,j ;

[0078] p y,r =p y,i -p y,j ; (6)

[0079] v x,r =v x,i -v x,j ;

[0080] v y,r =v y,i -v y,j ;

[0081] Among them, p x,r is the horizontal coordinate relative position variable of the jammer UAV and the evader UAV, p x,i is the abscissa position of the evader drone, p x, j is the horizontal coordinate position of the jammer drone, p y,r is the relative position variable of the vertical coordinates of the jammer UAV and the evader UAV, p y,i is the ordinate position of the evader drone, p y, j is the ordinate position of the jammer UAV, v x,r represents the horizontal axis relative speed variable of the jammer drone and the evader drone, v x,i represents the abscissa velocity of the evader drone, v x, j represents the horizontal coordinate speed of the jammer UAV, v y,r represents the relative velocity variable of the ordinate of the jammer UAV and the evader UAV, v y,i represents the ordinate velocity of the evader drone, v y,j Indicates the vertical velocity of the jammer drone.

[0082] According to the relative variables given in formula (6), we can get:

[0083]

[0084] Among them, ux,i is the control value of the evader UAV on the horizontal axis, u x, j is the control value of the jammer UAV on the horizontal axis, u y,i is the control value of the evader UAV in the ordinate, u y, j is the control amount of the jammer UAV in the vertical coordinate.

[0085] Increase Q i Speed ​​of the evader drone to impose a speed limit on the quadcopter:

[0086]

[0087]

[0088] In a teaming environment, being able to enter a state relative to another mobile drone is important for teaming and joining. For example, a free drone can join an existing fleet of drones on a highway and change mode to become a follower.

[0089] Additionally, a leader or follower may join another platoon and then enter follower mode.

[0090] To construct a controller to achieve a state relative to another UAV, the relative dynamics of the two UAVs are used, as shown in Equation (7). In general, the target state is specified as a position and speed Relative to a reference drone. In the case of drones joining a platoon maintaining a single column, the reference drone will be the platoon leader, i.e. the leader in the drone formation, with the desired relative position being some distance behind the platoon leader, depending on how many other drones are already in the platoon, and the desired relative speed being (0,0) in order to maintain formation.

[0091] Therefore, the safe area is determined according to the game strategies of the evader drone and the jammer drone, and the zero sub-level set of the implicit surface function of the safe area is used as the target set. p The expression is as follows:

[0092]

[0093] in, are respectively a certain position relative to the reference drone, is a specified speed relative to the reference drone, p x,r is the horizontal coordinate relative position variable of the jammer UAV and the evader UAV, p x,r =p x,i -p x,j , p x,iis the abscissa position of the evader drone, p x, j is the horizontal coordinate position of the jammer drone, p y,r is the relative position variable of the vertical coordinates of the jammer UAV and the evader UAV, p y,r =p y,i -p y,j , p y,i is the ordinate position of the evader drone, p y,j is the vertical coordinate position of the jammer UAV, v x,r represents the horizontal axis relative speed variable of the jammer drone and the evader drone, v x,r =v x,i -v x,j , v x,i represents the abscissa velocity of the evader drone, v x, j represents the horizontal coordinate speed of the jammer UAV, v y,r represents the relative velocity variable of the ordinate of the jammer UAV and the evader UAV, v y,r =v y,i -v y,j , v y,i represents the ordinate velocity of the evader drone, v y,j represents the vertical coordinate speed of the jammer UAV, r px 、r vx 、r py 、r vy They respectively represent the preset minimum safe distance difference of the relative position variable of the horizontal coordinate of the jammer UAV and the evader UAV, the minimum safe speed difference of the horizontal coordinate speed variable of the jammer UAV and the evader UAV, the minimum safe distance difference of the relative position variable of the vertical coordinate of the jammer UAV and the evader UAV, and the minimum safe speed difference of the vertical coordinate speed variable of the jammer UAV and the evader UAV. The setting of the above minimum safe distance and minimum safe speed is determined according to the actual situation, and this embodiment does not limit this.

[0094] Target set L p By the implicit surface function l p The zero sublevel set of (x) specifies the terminal condition (5) of HJPDE. p (-T,x) gives the relative state set of the quadrotor drone to reach the relative coordinate target within the duration of T.

[0095] Optimal control of evader and jammer drones is achieved by:

[0096]

[0097] In the BRS calculation, assuming that the reference UAV (platoon leader) is moving at a constant speed, the control input u j (t) = 0. The following is a suitable algorithm for the drone to join the platoon leader and follow the platoon leader:

[0098] 1) Move in a straight line towards the target point at a certain speed Move until the value function V p (-T,x)≤0, when the value function V p Stopping movement when (-T,x)≤0 means that the drone can safely reach the target set within the remaining time T from the current state x.

[0099] 2) According to formula (10), apply V p (-T,x) extracts the optimal control until the target set L is reached p .

[0100] Furthermore, in the special case of only one participant, we obtain the optimal control problem for a system with the dynamics In addition, a leader or a follower may join another platoon and then enter the follower mode.

[0101]

[0102] In this case, the backward reachable set (BRS) is given by the Hamilton-Jacobi partial differential equation:

[0103]

[0104] The optimal control is:

[0105]

[0106] This embodiment provides a method for UAV formation path merging and changing based on reachable sets. The method based on reachable sets allows the UAV to calculate the backward reachable sets and the optimal control strategy in real time, so as to quickly adapt to environmental changes and obstacles, thereby improving the real-time and adaptability of the system and improving the safety of UAV formation path merging and changing.

[0107] As an optional implementation, a safe area is determined according to the game strategies of the evader UAV and the jammer UAV, and the zero sub-level set of the implicit surface function of the safe area is used as the target set, including: obtaining static environmental information when the UAV formation path needs to converge or change; determining the obstacle elements of the UAV when converging or changing according to the static environmental information; converting the perceived obstacle element geographic information system data into a mathematical model; using the obstacle elements converted into the mathematical model as obstacle avoidance constraints, determining the safe area according to the game strategies of the evader UAV and the jammer UAV and the obstacle avoidance constraints, and using the zero sub-level set of the implicit surface function of the safe area as the target set.

[0108] Exemplarily, use geographic information system (GIS) data and sensors carried by drones (such as lidar, cameras) to obtain static information about the surrounding environment, including the location and shape of obstacles such as buildings, trees, and no-fly zones. Then, extract important features in the environment, such as the height, width, and relative position of obstacles, and identify all static obstacles that may affect drone convergence and path changes. Convert the characteristics of obstacles (such as position, shape, and size) into a mathematical model, which can be represented by geometric shapes (such as circles, rectangles, polygons, etc.). Define an implicit surface function for each obstacle. For example, for a circular obstacle, it can be defined as:

[0109] l(x,y)=(x c ) 2 +(yy c ) 2 -R 2 ;

[0110] Among them, (x c ,y c ) represents the center coordinate of the obstacle, and R represents the radius of the obstacle.

[0111] Then, a safe distance is defined for each obstacle to ensure a safe interval between the drone and the obstacle. The implicit surface function of the obstacle is combined with the safe distance to form an obstacle avoidance constraint:

[0112]

[0113] This means that the drone must stay within the safe region, which can be represented as the zero sublevel set of the implicit surface function: S safe ={(x,y)|l(x,y)≤0}, which contains all points in the safe area.

[0114] Adjust the definition of the safe zone based on the game strategy of the evader drone and the jammer drone. For example, the evader drone may need a larger safe zone to avoid the approach of the jammer drone. During real-time flight, monitor the status of the jammer drone and dynamically adjust the boundaries of the safe zone.

[0115] This embodiment provides a method for merging and changing the path of a drone formation based on a reachable set. When determining the static environmental information when the drone formation path needs to be merged or changed, this embodiment takes the static environmental information into consideration, thereby avoiding conflicts or collisions caused by obstacles such as static environment when the drones merge and change the formation path, thereby improving the safety of the drone formation path merging and changing.

[0116] As an optional implementation, a method for merging and changing the path of a UAV formation based on a reachable set also includes: when any UAV merges and changes the formation path, the position and radius of the obstacle in the static environment information in its motion plane are measured in real time by the onboard sensor of the detection and avoidance system; whether the obstacle enters the conflict area is determined according to the position of the obstacle and the position of the UAV; if it enters the conflict area but is not in the backward reachable set, the UAV flies according to a first acceleration, and the conflict area is an area with the UAV position as the center, a straight line connecting the UAV position to the obstacle position, and a radius including the distance of the obstacle; when the obstacle enters the backward reachable set of the UAV, the flight of the UAV is controlled according to a second acceleration, and the second acceleration is greater than the first acceleration.

[0117] For example, the obstacle avoidance strategy of drones during formation flight when merging and changing paths usually involves real-time detection of static environmental information by onboard sensors, including the location and radius of obstacles. When an obstacle enters a specific area of ​​the drone, i.e., the conflict area, the drone will avoid it according to a preset algorithm.

[0118] Specifically, the drone uses onboard sensors to monitor obstacles in its motion plane in real time and obtain the location and radius information of the obstacles. Then, it is determined whether the obstacle enters the conflict area related to its position. The conflict area is centered on the drone, connecting the positions of the drone and the obstacle with a straight line, and includes the radius of the obstacle. If the obstacle enters the conflict area but does not enter the backward reachable set of the drone, the drone will fly according to the first acceleration, where the backward reachable set refers to the set of all positions that the drone can reach within a given time. When the obstacle enters the backward reachable set of the drone, the drone will fly according to the second acceleration, which is usually greater than the first acceleration to achieve faster obstacle avoidance.

[0119] The magnitude of the first acceleration and the second acceleration of the drone can be determined using a real-time collision avoidance algorithm based on reachable set analysis. For example, the reachable set of the drone is analyzed and calculated using the level set method and optimal control theory. The magnitude of the first acceleration and the second acceleration can be determined using a target detection and obstacle avoidance algorithm based on a dynamic visual sensor. This type of algorithm designs filtering methods and motion compensation algorithms to filter out noise in the event stream, and designs a dynamic target fusion detection algorithm that fuses event images and RGB images to ensure the reliability of detection. In addition to the above methods, the magnitude of the first acceleration and the second acceleration can also be determined using an obstacle avoidance method based on reinforcement learning, such as the Greedy-DDPG algorithm, which improves the exploration strategy of DDPG through greedy selection, shortens the training time and improves the training effect.

[0120] This embodiment provides a method for UAV formation path merging and changing based on a reachable set. During the process of formation path merging and changing, the UAVs detect and avoid obstacles in static environment information in real time through the onboard sensors of the system, and judge the relationship between the detected obstacles and the position of the UAV itself, adjust the speed of the UAV, avoid collision, and improve the safety of UAV formation path merging and changing.

[0121] As an optional implementation, a method for UAV formation path merging and changing based on a reachable set, a predefined cost-value function for reaching a target set from a current state, including: obtaining a predefined UAV energy consumption cost function and a flight time cost function; adding the energy consumption cost function and the flight time cost function to the predefined cost-value function for reaching a target set from the current state to obtain an optimized total cost-value function.

[0122] For example, the predefined UAV energy consumption cost function C enegy (x,u2) is: C enegy (x,u2)=α||u2|| 2 ; Among them, α is the energy consumption weight coefficient, u2 is the control function of the jammer drone, and x represents the current given state; the flight time cost function C time (x) is: C time (x) = β(Tt); where β is the weight coefficient of the time cost and T is the expected arrival time. By adding the energy consumption cost function and the flight time cost function to the predefined cost value function of reaching the target set from the current state, the optimized total cost value function can be obtained. By adding considerations of energy efficiency and flight time, the purpose of multi-objective optimization can be achieved.

[0123] 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 merging and changing the paths of UAV formations based on reachable sets, characterized in that: include: When the UAV formation path needs to merge or change, obtain the flight status information, control information and disturbance information of the jammer UAV and the evader UAV; Constructing a system dynamic data group based on the status information, control information and disturbance information of the jammer UAV and the evader UAV; The safe area is determined according to the game strategies of the evader UAV and the jammer UAV, and the zero sub-level set of the implicit surface function of the safe area is used as the target set; Constructing Hamilton-Jacobi partial differential equations according to the predefined cost-value function of reaching the target set from the current state and the system dynamic data set; Taking the implicit surface function of the safety region as the termination condition, the Hamilton-Jacobi partial differential equation is solved to obtain the optimal control strategy; The optimal control strategy is sent to the UAVs whose formation paths need to be merged or changed.

2. The method for merging and changing the path of a UAV formation based on a reachable set according to claim 1, characterized in that: Target set L p The expression is as follows: in, are respectively a certain position relative to the reference drone, is a specified speed relative to the reference drone, p x,r is the horizontal coordinate relative position variable of the jammer UAV and the evader UAV, p x,r =p x,i -p x,j , p x,i is the abscissa position of the evader drone, p x, j is the horizontal coordinate position of the jammer drone, p y,r is the relative position variable of the vertical coordinates of the jammer UAV and the evader UAV, p y,r =p y,i -p y,j , p y,i is the ordinate position of the evader drone, p y, j is the ordinate position of the jammer UAV, v x,r represents the horizontal axis relative speed variable of the jammer drone and the evader drone, v x,r =v x,i -v x,j , v x,i represents the abscissa velocity of the evader drone, v x,j represents the horizontal axis speed of the jammer UAV, v y,r represents the relative velocity variable of the ordinate of the jammer UAV and the evader UAV, v y,r =v y,i -v y,j , v y,i represents the ordinate velocity of the evader drone, v y,j represents the vertical coordinate speed of the jammer drone, They respectively represent the preset minimum safe distance difference of the relative position variables of the horizontal coordinates of the jammer UAV and the evader UAV, the minimum safe speed difference of the horizontal coordinate speed variables of the jammer UAV and the evader UAV, the minimum safe distance difference of the relative position variables of the vertical coordinates of the jammer UAV and the evader UAV, and the minimum safe speed difference of the vertical coordinate speed variables of the jammer UAV and the evader UAV.

3. The method for merging and changing the path of a UAV formation based on a reachable set according to claim 2, characterized in that: The predefined cost value function of the current state to reach the target set is: 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 evader UAV, γ represents the unexpected control strategy, Γ represents the conflict set, U1 represents the control quantity set of the evader UAV, s represents a specific time in [t,0], γ[u1](·) represents the unexpected control strategy of the evader, t represents time, x represents the system state, ξ f The system trajectory ξ that satisfies the initial conditions f (t;x,t,u1(·),γ[u2](·))=x,L p The target collection.

4. The method for merging and changing the path of a UAV formation based on a reachable set according to claim 3 is characterized in that: in, According to the predefined cost-value function of reaching the target set from the current state and the system dynamic data set, 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 evader UAV, u2 is the control function of the jammer UAV, V(0,x) represents the cost value function at t=0, l(x) represents the implicit surface function value, and U2 represents the control quantity set of the jammer UAV.

5. The method for merging and changing the path of a UAV formation based on a reachable set according to claim 4, characterized in that: Optimal control strategies, including: in, represents the optimal control strategy of the evader UAV, represents the optimal control strategy of the jammer 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 evader UAV, u2 is the control function of the jammer UAV, U1 represents the control quantity set of the evader UAV, and U2 represents the control quantity set of the jammer UAV.

6. The method for merging and changing the path of a UAV formation based on a reachable set according to claim 1, characterized in that: The safe area is determined according to the game strategies of the evader drone and the jammer drone, and the zero sub-level set of the implicit surface function of the safe area is used as the target set, including: Obtain static environment information when the UAV formation path needs to merge or change; Determine obstacle factors for drones when merging or changing based on static environment information; Transform the perceived obstacle feature GIS data into a mathematical model; The obstacle elements converted into mathematical models are used as obstacle avoidance constraints. The safe area is determined according to the game strategies of the evader UAV and the jammer UAV and the obstacle avoidance constraints. The zero sub-level set of the implicit surface function of the safe area is used as the target set.

7. The method for merging and changing the path of a UAV formation based on a reachable set according to claim 1, characterized in that: Also includes: When any UAV merges or changes its formation path, the position and radius of obstacles in its motion plane in the static environment information are measured in real time by the onboard sensors of the detection and avoidance system; Determine whether the obstacle has entered the conflict area based on the position of the obstacle and the position of the drone. If the obstacle has entered the conflict area but is not in the backward reachable set, the drone will fly at the first acceleration. The conflict area is an area with the drone position as the center, a straight line connecting the drone position to the obstacle position, and a radius including the distance of the obstacle. When the obstacle enters the backward reachable set of the UAV, the flight of the UAV is controlled according to the second acceleration, and the second acceleration is greater than the first acceleration.

8. The method for merging and changing the path of a UAV formation based on a reachable set according to claim 1, characterized in that: The predefined cost-value function of the current state to reach the target set includes: Obtain the predefined UAV energy consumption cost function and flight time cost function; The energy consumption cost function and the flight time cost function are added to the predefined cost value function of reaching the target set from the current state to obtain the optimized total cost value function.

9. The method for merging and changing the path of a UAV formation based on a reachable set according to claim 8, characterized in that: Predefined UAV energy consumption cost function C enegy (x,u2) is: C enegy (x,u2)=α||u2|| 2 ; Among them, α is the energy consumption weight coefficient, u2 is the control function of the jammer UAV, and x represents the current given state; Flight time cost function C time (x) is: C time (x)=β(T-t); Among them, β is the weight coefficient of time cost, and T is the expected arrival time.

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