Detection area coverage planning method based on remote gliding aircraft cooperation, storage medium and equipment

Abstract

The invention discloses a detection area coverage planning method based on remote gliding aircraft cooperation, a storage medium and equipment, and belongs to the technical field of aircraft cooperation coverage planning. The method solves the problems that an existing method cannot carry out online task planning of the remote gliding aircraft, the existing method is prone to falling into local optimum, and the planning effect is limited. According to the method, the maneuverability, the coverage range and the launching condition of a single remote gliding aircraft are combined, and the maximum longitudinal range index, the minimum longitudinal range index, the maximum transverse range index and the ground coverage constraint are considered to establish a multi-target optimization model for cooperative ground coverage of multiple remote gliding aircrafts. A multi-objective optimization problem is divided into a plurality of sub-populations to be solved, and the strategy of each particle is subjected to game parallel evolution by means of the Nash equilibrium theory, so that the calculation efficiency is improved. The Pareto set updating of the multi-objective optimization problem is fused into the population evolution process, so that the Pareto leading edge can be pushed to move, and local optimum is avoided. The method can be applied to the field of collaborative coverage of aircrafts.

Description

Technical Field

[0001] The present invention belongs to the technical field of cooperative coverage planning of aircraft, and particularly relates to a method, a storage medium and a device for covering and planning a detection area based on cooperation of remotely gliding aircraft. Background Art

[0002] A remotely gliding aircraft is a small and medium-sized unmanned aircraft. After being launched by a carrier aircraft, it can fly hundreds of kilometers and can be used to observe and collect data such as the environment, landform and human interaction in a specific area. The application scenarios include disaster area rescue, resource exploration, environmental monitoring, etc.

[0003] Although the remotely gliding aircraft has the ability of airdrop and long-distance flight, the future aviation field will develop towards high efficiency and fast response. Therefore, the aircraft must obtain the maximum use efficiency at the fastest speed and the lowest cost. In terms of the ground detection area coverage planning based on the cooperation of multiple remotely gliding aircraft, the existing cooperation planning methods of remotely gliding aircraft mainly consider time or space cooperation, and have not considered the problem of the maximum coverage of ground targets by simultaneously launching multiple remotely gliding aircraft from one aircraft, and have not maximally exerted the cooperative detection ability and airborne launch ability of the remotely gliding aircraft, resulting in limited planning effects. Moreover, the existing algorithms for the cooperation planning of remotely gliding aircraft are prone to falling into local optima, and the calculation speed is slow, which is not suitable for the online mission planning of remotely gliding aircraft. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems that the existing methods cannot perform the online mission planning of remotely gliding aircraft, and the existing methods are prone to falling into local optima and have limited planning effects, and a method, a storage medium and a device for covering and planning a detection area based on cooperation of remotely gliding aircraft are proposed.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a method for covering and planning a detection area based on cooperation of remotely gliding aircraft, the method specifically includes the following steps:

[0006] Step 1, establish a dynamic model of the remotely gliding aircraft;

[0007] Step 2, establish a multi-objective optimization model for the cooperative ground coverage of N remotely gliding aircraft;

[0008] Step 3, based on the established dynamic model and multi-objective optimization model, adopt a multi-objective parallel game evolution algorithm based on Nash equilibrium to optimize the maneuver overload time series of each remotely gliding aircraft.

[0009] Further, the dynamic model of the remotely gliding aircraft is:

[0010] V′(t) = aP -a D -gsinγ(t)

[0011]

[0012] h′(t) = V(t)sinγ(t)

[0013]

[0014] where V(t) represents the speed of the remotely gliding aircraft at time t, and V′(t) represents the first derivative of V(t); a P represents the thrust acceleration of the remotely gliding aircraft;

[0015] a D represents the air resistance acceleration;

[0016] g represents the gravitational acceleration;

[0017] γ(t) represents the ballistic inclination angle at time t, and γ′(t) represents the first derivative of γ(t);

[0018] a v (t) represents the normal aerodynamic acceleration at time t in the vertical plane;

[0019] a h (t) represents the normal aerodynamic acceleration at time t in the horizontal plane;

[0020] h(t) represents the altitude of the remotely gliding aircraft at time t, h′(t) represents the first derivative of h(t); ψ(t) represents the heading angle of the remotely gliding aircraft at time t, ψ′(t) represents the first derivative of ψ(t);

[0021] represents the latitude of the remotely gliding aircraft at time t, represents the first derivative of;

[0022] θ(t) represents the longitude of the remotely gliding aircraft at time t, θ′(t) represents the first derivative of θ(t);

[0023] R represents the radius of the Earth.

[0024] Furthermore, the multi-objective optimization model is:

[0025]

[0026] s.t. u = {a v,i (t), a h,i (t)|i = 1, 2... N}

[0027]

[0028] Among them, L per represents the longitudinal range, represents the reciprocal of L per ;

[0029] L para represents the transverse range, represents the reciprocal of L para ;

[0030] a v,i (t) represents the normal aerodynamic acceleration of the i-th long-range gliding aircraft in the vertical plane at time t;

[0031] a h,i (t) represents the normal aerodynamic acceleration of the i-th long-range gliding aircraft in the horizontal plane at time t;

[0032] a max represents the maximum allowable maneuvering acceleration of the gliding aircraft;

[0033] s i ′ represents the coverage area of the i-th long-range gliding aircraft;

[0034] S omit represents the non-overlapping coverage area of all long-range gliding aircraft around the ground target;

[0035] S target represents the area of the area to be covered centered on the ground target;

[0036] u represents the variable to be optimized, that is, the maneuvering overload time series of each long-range gliding aircraft;

[0037] J represents the target value.

[0038] Furthermore, the specific process of the third step is as follows:

[0039] Step 3-1: Split the variable to be optimized u into N subsets, and the N subsets formed form multiple particles;

[0040] u = {u1, u2..., u N}

[0041] Among them, u1, u2..., u N represents the N subsets formed by splitting;

[0042] And define u -i = {u1, u2, u i-1 , u i+1 ..., u N};

[0043] Step 32: Each particle initializes its own strategy. Denote the strategy initialized by the \(i\)-th particle as Regard \(N\) subsets as \(N\) players; initialize the Pareto set as empty;

[0044] Step 33: Initialize the iteration number \(l = 1\);

[0045] Step 34: Convert the multi-objective optimization model into the payoffs of players:

[0046]

[0047] where \(J_1(u_1, u -1 ) represents the objective value of the first particle;

[0048] J_2(u_1, u -1 ) represents the objective value of the second particle;

[0049] represents the payoff of player 1, which is the reciprocal of \(J_1(u_1, u -1 ) ;

[0050] represents the payoff of player 2;

[0051]

[0052] The \(i\)-th player selects its own strategy with the aim of maximizing the payoff \(U according to the strategies selected by other players in the \((l - 1)\)-th iteration i i.e.,

[0053] Step 35: Determine the non-dominated particles obtained in the \(l\)-th iteration according to the strategies selected by \(N\) players in the \(l\)-th iteration, add the strategies of the non-dominated particles to the Pareto set, and update the Pareto front according to the payoffs of the non-dominated particles;

[0054] Step 36: Judge whether the set maximum iteration number is reached and whether the Pareto set reaches the capacity limit;

[0055] If the set maximum iteration number is reached or the Pareto set reaches the capacity limit, then execute Step 37;

[0056] If the set maximum iteration number is not reached and the Pareto set does not reach the capacity limit, then let \(l = l + 1\), and return to execute Step 34;

[0057] Step 37: Select the maximum payoff in the Pareto front, and obtain the optimization results of the maneuver overload time series of each remote gliding aircraft according to the strategy combination corresponding to the maximum payoff.

[0058] Furthermore, the N subsets satisfy

[0059] A computer storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the method for detecting area coverage planning based on cooperation of remote gliding aircraft.

[0060] A device for detecting area coverage planning based on cooperation of remote gliding aircraft, the device includes a processor and a memory, and the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the method for detecting area coverage planning based on cooperation of remote gliding aircraft.

[0061] The beneficial effects of the present invention are:

[0062] The present invention combines the maneuverability, coverage range and delivery conditions of a single remote gliding aircraft, and considers the maximum longitudinal range, minimum longitudinal range and maximum lateral range indexes as well as the ground coverage constraint to establish a multi-objective optimization model for multi-remote gliding aircraft cooperative ground coverage. And the multi-objective optimization problem is divided into multiple sub-populations for solution, and the strategies of each particle are evolved through game using the Nash equilibrium theory, so as to improve the calculation efficiency and meet the online mission planning requirements of the remote gliding aircraft. The present invention adopts a multi-objective optimization algorithm, updates the Pareto set of the multi-objective optimization problem into the population evolution process, promotes the movement of the Pareto front while improving the particle fitness, and avoids falling into local optimum, so as to improve the planning effect, and finally realizes the maximum area coverage of ground targets by simultaneously launching multiple remote gliding aircraft from one aircraft. Description of the Drawings

[0063] Figure 1 is a schematic diagram of the maximum coverage multi-objective optimization model;

[0064] Figure 2 is a flowchart of the Nash equilibrium multi-objective parallel game evolution algorithm. Detailed Embodiments

[0065] Detailed Embodiment 1: The method for detecting area coverage planning based on cooperation of remote gliding aircraft described in this embodiment specifically includes the following steps:

[0066] Step 1, establish a dynamic model of the remote gliding aircraft;

[0067] Step 2, establish a multi-objective optimization model for N remote gliding aircraft cooperative ground coverage;

[0068] Step 3: Based on the established dynamic model and multi-objective optimization model, use the multi-objective parallel game evolution algorithm based on Nash equilibrium to optimize the maneuver overload time series of each remotely gliding aircraft.

[0069] Specific Embodiment 2: Different from Specific Embodiment 1, the dynamic model of the remotely gliding aircraft is:

[0070] V′(t) = a P -a D -gsinγ(t)

[0071]

[0072] h′(t) = V(t)sinγ(t)

[0073]

[0074] Where, V(t) represents the speed of the remotely gliding aircraft at time t, and V′(t) represents the first derivative of V(t);

[0075] a P represents the thrust acceleration of the remotely gliding aircraft;

[0076] a D represents the air resistance acceleration;

[0077] g represents the gravitational acceleration;

[0078] γ(t) represents the ballistic inclination angle at time t, and γ′(t) represents the first derivative of γ(t);

[0079] a v (t) represents the normal aerodynamic acceleration in the vertical plane at time t;

[0080] a h (t) represents the normal aerodynamic acceleration in the horizontal plane at time t;

[0081] h(t) represents the altitude of the remotely gliding aircraft at time t, and h′(t) represents the first derivative of h(t);

[0082] ψ(t) represents the heading angle of the remotely gliding aircraft at time t, and ψ′(t) represents the first derivative of ψ(t);

[0083] represents the latitude of the remotely gliding aircraft at time t, represents the first derivative of;

[0084] θ(t) represents the longitude of the remotely gliding aircraft at time t, and θ′(t) represents the first derivative of θ(t);

[0085] R represents the radius of the earth.

[0086] Other steps and parameters are the same as those in the first specific implementation manner.

[0087] Specific implementation manner three: The difference between this implementation manner and the first or second specific implementation manner is that the multi-objective optimization model is:

[0088]

[0089] s.t. u = {a v,i (t), a h,i (t)|i = 1, 2... N}

[0090]

[0091] Among them, L per represents the longitudinal range, represents the reciprocal of L per ;

[0092] L para represents the lateral range, represents the reciprocal of L para ;

[0093] a v,i (t) represents the normal aerodynamic acceleration of the i-th long-range gliding aircraft in the vertical plane at time t;

[0094] a h,i (t) represents the normal aerodynamic acceleration of the i-th long-range gliding aircraft in the horizontal plane at time t;

[0095] a max represents the maximum allowable maneuvering acceleration of the gliding aircraft;

[0096] s i ′ represents the coverage area of the i-th long-range gliding aircraft;

[0097] S omit represents the non-overlapping coverage area of all long-range gliding aircraft around the ground target;

[0098] S target represents the area of the area to be covered centered on the ground target;

[0099] u represents the variable to be optimized, that is, the maneuvering overload time series of each long-range gliding aircraft;

[0100] J represents the target value.

[0101] Other steps and parameters are the same as those in the first or second specific implementation manner.

[0102] The present invention defines the longitudinal range and the lateral range as follows:

[0103] The connection line from the ground projection of the current position of the gliding aircraft to the ground target point is denoted as the planned baseline. Around this ground target point, the longitudinal range is the vertical component of the actual landing point of the gliding aircraft relative to this baseline, and the lateral range is the horizontal component of the actual landing point of the gliding aircraft relative to this baseline.

[0104] In order to complete the large - area cooperative coverage of ground targets by multiple long - range gliding aircraft, it is necessary to consider the maneuverability of a single long - range gliding aircraft, and combine airborne release conditions such as altitude and speed, so that each long - range gliding aircraft flies to the farthest point b, the nearest point d, the left - most point a, and the right - most point c centered on the ground target, as Figure 1 shown. At the same time, it is also necessary to judge the total coverage situation based on the coverage range of a single long - range gliding aircraft to avoid omissions around the ground target when expanding the coverage range.

[0105] Specific Embodiment Four: Combined with Figure 2 This embodiment is described. The difference between this embodiment and one of Embodiments One to Three is that the specific process of Step Three is as follows:

[0106] Step Three One: Split the variable u to be optimized into N subsets, and the N subsets formed split into multiple particles;

[0107] u = {u1, u2..., u N}

[0108] where u1, u2..., u N represent the N subsets formed by splitting;

[0109] And define u -i = {u1, u2, u i-1 , u i+1 ..., u N};

[0110] Step Three Two: Each particle initializes its own strategy. Denote the strategy initialized by the i - th particle as Strategy is equivalent to u i , regard the N subsets as N players; initialize the Pareto set as empty;

[0111] Step Three Three: Initialize the iteration number l = 1;

[0112] Step Three Four: Convert the multi - objective optimization model into player benefits:

[0113]

[0114] where J1(u1, u-1 ) represents the target value of the first particle;

[0115] J2(u1,u -1 ) represents the target value of the second particle;

[0116] represents the payoff of player 1, which is the reciprocal of J1(u1,u -1 );

[0117] represents the payoff of player 2;

[0118]

[0119] The i-th player determines its own strategy according to the strategies selected by other players in the (l - 1)-th iteration to maximize the payoff U i as the goal to select its own strategy That is

[0120] Step 35: Determine the non-dominated particles obtained in the l-th iteration according to the strategies selected by N players in the l-th iteration, add the strategies of the non-dominated particles to the Pareto set (if the i-th particle is a non-dominated particle, then the strategy combination ) is added to the Pareto set), and update the Pareto front according to the payoffs of the non-dominated particles;

[0121] Step 36: Determine whether the set maximum number of iterations is reached and whether the Pareto set reaches the capacity limit;

[0122] If the set maximum number of iterations is reached or the Pareto set reaches the capacity limit, then execute Step 37;

[0123] If the set maximum number of iterations is not reached and the Pareto set does not reach the capacity limit, then set l = l + 1, and return to execute Step 34;

[0124] Step 37: Select the maximum payoff in the Pareto front, and obtain the optimization results of the maneuver overload time series of each remotely gliding aircraft according to the strategy combination corresponding to the maximum payoff.

[0125] Other steps and parameters are the same as those in any one of the specific embodiments 1 to 3.

[0126] The maximum coverage multi-objective optimization problem involves optimization metrics such as the farthest point, the nearest point, the leftmost side, and the rightmost side. The terminal states of gliding aircraft are different for different metrics, and separate optimization solutions are required. The present invention uses a multi-objective optimization algorithm to incorporate the update of the Pareto set of the multi-objective optimization problem into the population evolution process, promoting the movement of the Pareto front while improving the population fitness. The multi-objective particle swarm optimization algorithm does not rely on initial values and precise gradient information and supports parallel computing.

[0127] For the maximum coverage multi-objective optimization problem, a population game collaborative evolution Pareto set update strategy is proposed. The design variable u is split into N mutually exclusive subsets to form multiple sub-populations, facilitating parallel computing. However, this will shield the data flow, disrupt the internal connection of the original problem space, and affect the optimality of the solution. Therefore, referring to the Nash equilibrium player game idea, a sub-problem information interaction strategy is designed. The strategies of other players are all derived from the previous round of the game and remain unchanged in this round. After each round of the game, the players exchange strategies until each player obtains the optimal strategy when reaching the Nash equilibrium. It covers the Pareto set of the original problem, from which the extreme landing points such as the farthest point, the nearest point, the leftmost side, and the rightmost side can be found.

[0128] Specific implementation manner five: Different from one of the first to fourth specific implementation manners, the N subsets satisfy

[0129] Other steps and parameters are the same as those in one of the first to fourth specific implementation manners.

[0130] Specific implementation manner six: A computer storage medium in this implementation manner, where at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method for detecting area coverage planning based on the cooperation of remote gliding aircraft.

[0131] It should be understood that the instruction includes a computer program product, software, or computerized method corresponding to any method described in the present invention; the instruction can be used to program a computer system or other electronic devices. The computer storage medium can include a readable medium on which the instruction is stored, which can include but is not limited to a magnetic storage medium, an optical storage medium; the magneto-optical storage medium includes a read-only memory, a random access memory, an erasable programmable memory such as, and as well as a flash memory layer, or other types of media suitable for storing electronic instructions.

[0132] Specific implementation manner seven: This implementation manner is a device for detecting area coverage planning based on the cooperation of remote gliding aircraft. The device includes a processor and a memory. It should be understood that it includes any device including a processor and a memory described in the present invention. The device may further include other units and modules for displaying, interacting, processing, controlling, etc. through signals or instructions, as well as other functions;

[0133] At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by a processor to implement the method for detecting area coverage planning based on cooperation of remote gliding aircraft as described above.

[0134] The above numerical examples of the present invention are only for explaining in detail the calculation model and calculation process of the present invention, rather than limiting the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or variations can be made on the basis of the above description. It is impossible to list all the implementation manners here. Any obvious changes or variations derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. A detection area coverage planning method based on long-range gliding aircraft cooperation, characterized in that: The method specifically comprises the following steps: Step 1: Establish a dynamic model of long-range glider aircraft; Step 2: Establish a multi-objective optimization model for coordinated ground coverage of N long-range gliding aircraft; Step 3: Based on the established dynamic model and multi-objective optimization model, a multi-objective parallel game evolutionary algorithm based on Nash equilibrium is used to optimize the maneuvering overload time series of each long-range gliding aircraft.

2. The detection area coverage planning method based on long-range gliding aircraft cooperation according to claim 1 is characterized in that: The dynamic model of the long-range glider aircraft is: V′(t) is P -am D -gsinγ(t) h′(t)=V(t)sinγ(t) Where V(t) represents the speed of the long-range glider at time t, and V′(t) represents the first-order derivative of V(t); a P Expresses the thrust acceleration of a long-range gliding aircraft; a D represents the air resistance acceleration; g represents gravitational acceleration; γ(t) represents the ballistic inclination angle at time t, γ′(t) represents the first-order derivative of γ(t); a v (t) represents the normal aerodynamic acceleration at time t in the vertical plane; a h (t) represents the normal aerodynamic acceleration at time t in the horizontal plane; h(t) represents the altitude of the long-range glider at time t, and h′(t) represents the first-order derivative of h(t); ψ(t) represents the heading angle of the long-range glider aircraft at time t, and ψ′(t) represents the first-order derivative of ψ(t); represents the latitude of the long-range glider aircraft at time t, express The first derivative of ; θ(t) represents the longitude of the long-range glider aircraft at time t, θ′(t) represents the first-order derivative of θ(t); R represents the radius of the Earth.

3. The detection area coverage planning method based on long-range gliding aircraft cooperation according to claim 2 is characterized in that: The multi-objective optimization model is: Among them, L per Indicates vertical range, Indicates L per The reciprocal of L para Indicates horizontal distance, Indicates L para The reciprocal of a v,i (t) represents the normal aerodynamic acceleration of the i-th long-range glider aircraft in the vertical plane at time t; a h,i (t) represents the normal aerodynamic acceleration of the i-th long-range glider aircraft in the horizontal plane at time t; a max It represents the maximum permissible maneuvering acceleration of a gliding aircraft; s i ′ represents the coverage area of ​​the i-th long-range glider aircraft; S omit represents the non-missing coverage area around ground targets by all long-range gliding aircraft; S target Indicates the area that needs to be covered centered on the ground target; u represents the variable to be optimized, i.e., the maneuvering overload time series of each long-range glider aircraft; J represents the target value.

4. The detection area coverage planning method based on long-range gliding aircraft cooperation according to claim 3 is characterized in that: The specific process of step three is: Step 31: Split the variable u to be optimized into N subsets, and the N subsets formed into multiple particles; u={u1,u2...,u N } Among them, u1,u2...,u N Represents the N subsets split into; And define u -i ={u1, u2, u i-1 , u i+1 ..., u N}; Step 3.2: Each particle initializes its own strategy. The strategy initialized by the i-th particle is recorded as Treat N subsets as N players; initialize the Pareto set to be empty; Step 33, initialize the number of iterations l = 1; Step 3 and 4: Convert the multi-objective optimization model into player benefits: Among them, J1(u1,u -1 ) represents the target value of the first particle; J2(u1,u -1 ) represents the target value of the second particle; represents the payoff of player 1, is J1(u1,u -1 ) represents the payoff of player 2; The i-th player chooses the strategy based on the strategies chosen by other players at the l-1th iteration. To maximize the profit U i Choose your own strategy for the target Right now Step 35: Determine the non-dominated particles obtained in the lth iteration according to the strategies selected by the N players in the lth iteration, add the strategies of the non-dominated particles to the Pareto set, and update the Pareto frontier according to the benefits of the non-dominated particles; Step 36: Determine whether the maximum number of iterations has been reached and whether the Pareto set has reached the upper capacity limit; If the maximum number of iterations is reached or the Pareto set reaches the upper capacity limit, execute step 37; If the maximum number of iterations is not reached and the Pareto set has not reached the upper capacity limit, set l = l + 1 and return to step 3 and 4; Step 37: Select the maximum benefit in the Pareto frontier, and obtain the optimization results of the maneuvering overload time series of each long-range gliding aircraft based on the strategy combination corresponding to the maximum benefit.

5. The detection area coverage planning method based on long-range gliding aircraft cooperation according to claim 4 is characterized in that: The N subsets satisfy 6. A computer storage medium, characterized in that: The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the detection area coverage planning method based on long-range gliding aircraft cooperation as described in any one of claims 1 to 5.

7. A detection area coverage planning device based on long-range gliding aircraft cooperation, characterized in that: The device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement a detection area coverage planning method based on long-range gliding aircraft cooperation as described in any one of claims 1 to 5.

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