CN115167502A

CN115167502A - Unmanned aerial vehicle collaborative flight path planning method and device based on immune clone algorithm

Info

Publication number: CN115167502A
Application number: CN202210654309.7A
Authority: CN
Inventors: 朱先强; 朱承; 赖远坤; 郭园园; 张橹; 年爱欣; 赵润豪; 刘斌; 刘毅
Original assignee: National University of Defense Technology
Current assignee: National University of Defense Technology
Priority date: 2022-06-10
Filing date: 2022-06-10
Publication date: 2022-10-11

Abstract

The application relates to an unmanned aerial vehicle collaborative flight path planning method and device based on an immune clone algorithm. The method comprises the following steps: calculating the environmental threat cost of an area to be planned, and constructing an objective function of unmanned aerial vehicle flight path planning according to the environmental threat cost and the fuel cost of unmanned aerial vehicle flight; dividing a region to be planned into grids by adopting a grid method, wherein all nodes in the grids form a node set; calculating and updating an antibody group of the node set according to an immune clone algorithm to obtain a final antibody group, setting track cost according to a target function of unmanned aerial vehicle track planning and a preset weight, and constructing a target function of unmanned aerial vehicle collaborative planning by utilizing the track cost and coordination time; performing unmanned aerial vehicle track selection on the final antibody group according to a target function of unmanned aerial vehicle collaborative planning to obtain a coordination scheme; and carrying out unmanned aerial vehicle collaborative track planning on the area to be planned according to the coordination scheme. By adopting the method, the collaborative planning efficiency of the unmanned aerial vehicle can be improved.

Description

Unmanned aerial vehicle collaborative flight path planning method and device based on immune clone algorithm

Technical Field

The application relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle collaborative flight path planning method and device based on an immune clone algorithm, computer equipment and a storage medium.

Background

With the development of unmanned aerial vehicle technology, unmanned Aerial Vehicles (UAVs) are widely used in life due to safety, flexibility and reusability, and have an important role in conveying rescue goods and materials in disaster scenes. The unmanned aerial vehicle flight path planning is to design a flight path from an initial position to a target position under the condition of meeting unmanned aerial vehicle performance constraints and surrounding environment constraints, so that the damage cost is the lowest. Generally, there are dangerous terrain and bad weather in rescue complex environments. To ensure the safety of the drone, the planned path should help to reduce the probability of being destroyed as much as possible. In this case, many unmanned aerial vehicles have certain advantage in improving rescue goods and materials transport and disaster exploration, because two kinds of antibodies can provide mutual protection and share information to reduce the probability of being destroyed. Different from single unmanned aerial vehicle flight path planning, the multi-unmanned aerial vehicle collaborative flight path planning not only considers the performance constraint and the environment constraint of a single unmanned aerial vehicle, but also considers the time constraint of the multi-unmanned aerial vehicle collaborative flight.

However, at present, a great deal of research is already carried out at home and abroad on static track planning in a known environment, and the existing method is easy to deviate from an optimal solution when unmanned aerial vehicle cooperation is carried out, and has high calculation complexity, limited convergence speed and low efficiency.

Disclosure of Invention

Based on this, it is necessary to provide an unmanned aerial vehicle collaborative flight path planning method and apparatus based on an immune clone algorithm, a computer device and a storage medium, which can improve the efficiency of unmanned aerial vehicle collaborative planning, in order to solve the above technical problems.

An unmanned aerial vehicle collaborative flight path planning method based on an immune clone algorithm comprises the following steps:

acquiring a region to be planned;

calculating the environmental threat cost of an area to be planned, and constructing an objective function of unmanned aerial vehicle flight path planning according to the environmental threat cost and the fuel cost of unmanned aerial vehicle flight;

dividing a region to be planned into grids by adopting a grid method, wherein all nodes in the grids form a node set; the nodes are concentrated into a plurality of adjacent nodes to form an unmanned aerial vehicle track; the nodes represent flight point positions of the unmanned aerial vehicle in the area to be planned;

calculating the transition probability of each node in the node set according to the guide factors and the heuristic factors, and taking the node with the maximum transition probability as the next transition node to obtain an initial antibody group; the antibody is an unmanned aerial vehicle track;

selecting an initial antibody population according to a clone selection algorithm selected by the championship to obtain a first candidate antibody population;

performing immune clone operation and immune gene operation on the first candidate antibody group to obtain a second candidate antibody group and a third candidate antibody group;

combining the second candidate antibody group and the third candidate antibody group to perform antibody modification to obtain a fourth candidate antibody group;

performing rapid non-dominated sorting on the fourth candidate antibody group, calculating a crowded distance, and updating the fourth candidate antibody group according to the non-dominated sorting and the crowded distance to obtain a final antibody group; the final antibody population includes a plurality of candidate trajectories for a plurality of drones;

setting track cost according to a target function of unmanned aerial vehicle track planning and preset weight, and constructing a target function of unmanned aerial vehicle collaborative planning by using the track cost and coordination time;

performing unmanned aerial vehicle track selection on the final antibody group according to an objective function of unmanned aerial vehicle collaborative planning to obtain a coordination scheme;

and carrying out unmanned aerial vehicle collaborative track planning on the area to be planned according to the coordination scheme.

In one embodiment, setting an objective function for drone trajectory planning based on environmental threat costs and fuel costs of drone flight includes:

setting an objective function of the unmanned aerial vehicle flight path planning as

Wherein, J _length Represents the fuel cost, w _L Representing coefficient, L track length, J _threat Represents a threat cost, w _R ，w _M ，w _A ，w _C ，w _H And w _T Threat weight coefficients of radar, missile, gun, climate, altitude and mountain range respectively; j. the design is a square _R ，J _M ，J _A ，J _C ，J _H And J _T Respectively, radar, missile, artillery, weather, altitude, and mountain threat costs.

In one embodiment, calculating the transition probability of each node in the node set according to the guidance factor and the heuristic factor includes:

calculating the transition probability of each node in the node set according to the guide factors and the heuristic factors to obtain the transition probability of the node

Wherein i, j and s respectively represent any node in the node set, eta _j Denotes a heuristic factor, λ _j Denotes a lead factor, alpha denotes the importance of a elicitor, beta denotes the importance of a lead factor, B _k,i Representing a set of nodes.

In one embodiment, selecting the initial population of antibodies according to a tournament-selected clone selection algorithm to obtain a first candidate population of antibodies comprises:

setting the scale of the championship as n, setting the scale of the initial antibody group as m, randomly selecting n antibodies from the initial antibody group for comparison, retaining the antibodies with the lowest non-dominance grade and the highest crowding distance, and repeating the process for m times to obtain m antibodies; m antibodies are the first candidate antibody population.

In one embodiment, the step of subjecting the first candidate antibody population to an immunocloning procedure and an immunogenetic procedure to obtain a second candidate antibody population and a third candidate antibody population comprises:

performing an immunocoloration operation on the first candidate antibody to obtain a second candidate antibody population

P ⁽²⁾ (t)＝P ⁽¹⁾¹ (t)+P ⁽¹⁾² (t)+…+P ^(1)mc (t)＝{p ₁ ⁽¹⁾¹ (t)，…，p _m ⁽¹⁾¹ (t)}+…+{p ₁ ^(1)mc (t),…，p _m ^(1)mc (t)}＝{p ₁ ⁽²⁾ (t)，p ₂ ⁽²⁾ (t)，…，p _N(m) ⁽²⁾ (t)}

Wherein N (m) = m × m _c M is the number of antibodies retained by the selection procedure, m _c Is the cloning ratio, P ⁽¹⁾ (t)＝{p ₁ ⁽¹⁾ (t)，p ₂ ⁽¹⁾ (t)，…，p _m ⁽¹⁾ (t) } is the first candidate antibody population.

In one embodiment, a first candidate antibody group is subjected to gene recombination and gene mutation, in the gene recombination, two antibodies are randomly selected from the first candidate antibody group, the starting points and the end points of the two antibodies are fixed, the node pairs closest to each other in the two antibodies are calculated, if there is only one pair, the two nodes are selected as an intersection, the two antibodies before and after the intersection are exchanged according to recombination probability, if there are multiple pairs, one of the two antibodies is randomly selected as the intersection, and the two antibodies before and after the two intersections are exchanged according to recombination probability to obtain an antibody group after the gene recombination;

fixing the starting point and the ending node of each antibody in the antibody group after gene recombination, randomly selecting one node in the antibodies to mutate in a mutation range, and obtaining a third candidate antibody group; the mutation range is the intersection of the set of neighboring nodes of the ith node and the set of neighboring nodes of the (i + 1) th node.

In one embodiment, combining the second candidate antibody population with the third candidate antibody population for antibody modification to yield a fourth candidate antibody population comprises:

combining the second candidate antibody group and the third candidate antibody group, traversing all nodes of each antibody in the combined antibody group, if the ith node and the jth node are continuous on a flight path, and the jth node is not in the neighbor set Ne of the ith node _i Then, the jth node is corrected, and the neighbor node set Ne of the ith node is selected _i And a set Ne of neighbor nodes of the jth node _j As the insert node set B _insert If B is _insert Null indicates more than two lattices between the ith node and the jth node, the antibody is deleted if B _insert And if not, randomly selecting a node as an insertion node to obtain a modified fourth candidate antibody group.

In one embodiment, updating the fourth candidate antibody population according to the non-dominated sorting and the crowding distance, resulting in a final antibody population, comprises:

the method comprises the following steps: setting a =1, generating a renewed antibody population G;

step two: from the fourth candidate antibody group P ⁽⁴⁾ (t) selecting an antibody group F with non-dominant ordering a, combining the antibody groups F and G to generate a combined group F + G = Fu G, carrying out non-dominant ordering on the combined group, and marking the non-dominant ordering value of the combined group F + G as length (F + G);

step three: if length (F + G) < N ', N' indicates the size of the final antibody population, F is added to G, a = a +1, go to step two; if length (F + G) > N', let NM = N-length (F + G), sort the antibodies in F according to the crowding distance, add the first NM antibodies to G; if length (F + G) = N', add F to G; g is the final antibody population.

In one embodiment, the method for constructing the unmanned aerial vehicle collaborative planning objective function by using the track cost and the coordination time comprises the following steps:

an objective function of unmanned aerial vehicle collaborative planning is constructed by using track cost and coordination time

Wherein, W _i,si S representing the ith drone plan _i The track cost of each candidate, lambda represents the synergy coefficient, M represents the total number of drones, T _d Indicating the coordination time.

An unmanned aerial vehicle collaborative flight path planning device based on an immune clone algorithm, the device comprising:

the target function construction module for unmanned aerial vehicle flight path planning is used for acquiring an area to be planned; calculating the environmental threat cost of an area to be planned, and constructing an objective function of unmanned aerial vehicle flight path planning according to the environmental threat cost and the fuel cost of unmanned aerial vehicle flight;

the area dividing module is used for dividing the area to be planned into grids by adopting a grid method, and all nodes in the grids form a node set; the nodes are concentrated into a plurality of adjacent nodes to form an unmanned aerial vehicle track; the nodes represent flight point positions of the unmanned aerial vehicle in the area to be planned;

the initial antibody cluster building module is used for calculating the transition probability of each node in the node set according to the guide factors and the heuristic factors, and taking the node with the maximum transition probability as the next transition node to obtain an initial antibody cluster; the antibody is an unmanned aerial vehicle track;

the antibody population optimization module is used for selecting an initial antibody population according to a clone selection algorithm selected by the championship to obtain a first candidate antibody population; performing immune clone operation and immune gene operation on the first candidate antibody group to obtain a second candidate antibody group and a third candidate antibody group; combining the second candidate antibody group and the third candidate antibody group to perform antibody modification to obtain a fourth candidate antibody group; performing rapid non-dominated sorting on the fourth candidate antibody group, calculating a crowded distance, and updating the fourth candidate antibody group according to the non-dominated sorting and the crowded distance to obtain a final antibody group; the final antibody population includes a plurality of candidate trajectories for a plurality of drones;

the unmanned aerial vehicle collaborative planning objective function construction module is used for setting track cost according to an objective function of unmanned aerial vehicle track planning and preset weight, and constructing the objective function of unmanned aerial vehicle collaborative planning by utilizing the track cost and the coordination time;

the unmanned aerial vehicle collaborative planning module is used for carrying out unmanned aerial vehicle flight path selection on the final antibody group according to a target function of unmanned aerial vehicle collaborative planning to obtain a coordination scheme; and carrying out unmanned aerial vehicle collaborative track planning on the area to be planned according to a coordination scheme.

A computer device comprising a memory storing a computer program and a processor implementing the following steps when the computer program is executed:

acquiring a region to be planned;

selecting an initial antibody population according to a clone selection algorithm selected by a championship to obtain a first candidate antibody population;

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

acquiring a region to be planned;

combining the second candidate antibody group and the third candidate antibody group to carry out antibody modification to obtain a fourth candidate antibody group;

performing unmanned aerial vehicle track selection on the final antibody group according to a target function of unmanned aerial vehicle collaborative planning to obtain a coordination scheme;

According to the unmanned aerial vehicle collaborative flight path planning method, the device, the computer equipment and the storage medium based on the immune clone algorithm, threat cost and fuel cost in a to-be-planned area are set as target functions of unmanned aerial vehicle flight path planning, the threat cost is favorably reduced when the flight path is selected subsequently, flight safety is improved, the performance stability of the unmanned aerial vehicle is guaranteed, the to-be-planned area is divided into grids by adopting a grid method, a plurality of adjacent nodes in the grids form the unmanned aerial vehicle flight path, elicitation factors and guide factors are introduced in the initialization process to generate high-quality antibodies, node transfer is more effective, the quality of the initial antibodies is improved, the convergence speed is accelerated, antibody groups are continuously optimized through immune clone operation and immune gene operation, some infeasible antibodies are corrected, the feasibility of the antibodies is improved, then some infeasible antibodies are modified to enhance the diversity of the antibody groups, a plurality of optimized candidate flight paths of the unmanned aerial vehicle are finally obtained, the flight path planning target functions of the unmanned aerial vehicle collaborative flight path planning are set according to the target functions of the unmanned aerial vehicle flight path planning and the preset weight, and the unmanned aerial vehicle collaborative flight path planning target functions are constructed by utilizing the coordination time; and performing unmanned aerial vehicle flight path selection on the final antibody group according to a target function of unmanned aerial vehicle collaborative planning, selecting a flight path with the minimum coordination cost as a coordination flight path, forming an optimal coordination scheme according to the obtained coordination flight path of each unmanned aerial vehicle, and performing unmanned aerial vehicle collaborative flight path planning on an area to be planned by using the optimal coordination scheme so as to improve the unmanned aerial vehicle collaborative flight path planning efficiency.

Drawings

Fig. 1 is a schematic flow chart of a method for collaborative trajectory planning of an unmanned aerial vehicle based on an immune clone algorithm in an embodiment;

FIG. 2 is a diagram of a reorganization operator in one embodiment;

FIG. 3 is a diagram of a mutation operator in one embodiment;

FIG. 4 is a schematic diagram of an area to be planned in another embodiment;

FIG. 5 is a schematic diagram illustrating a comparison of the effects of the ICA and GFACO algorithms in collaborative track planning according to the present application in one embodiment;

FIG. 6 is a schematic diagram of an apparatus for unmanned aerial vehicle collaborative flight path planning based on an immune clone algorithm according to an embodiment;

FIG. 7 is a diagram of the internal structure of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.

In one embodiment, as shown in fig. 1, there is provided an unmanned aerial vehicle collaborative flight path planning method based on an immune clone algorithm, including the following steps:

step 102, acquiring a region to be planned; and calculating the environmental threat cost of the area to be planned, and constructing an objective function of unmanned aerial vehicle flight path planning according to the environmental threat cost and the fuel cost of unmanned aerial vehicle flight.

Unmanned aerial vehicles are mainly limited by environmental threats and performance constraints during flight. Therefore, the establishment of the objective function mainly takes into account the threat cost and the fuel cost. Threat sources in a battlefield environment comprise radars, missiles, artillery, climate, altitude or mountains, environment threat cost calculation is carried out on an area to be planned, and the threat cost is given by the following formula:

J _threat ＝w _R ·J _R +w _M ·J _M +w _A ·J _A +w _C ·J _C +w _H ·J _H +w _T ·J _T

wherein, w _R ，w _M ，w _A ，w _C ，w _H And w _T Respectively, threat weight coefficients for radar, missile, artillery, climate, altitude, and mountains. J is a unit of _R ，J _M ，J _A ，J _C ，J _H And J _T Respectively, radar, missile, artillery, weather, altitude, and mountain threat costs.

In the above formula, N _L Number of track nodes, N _R For the number of radar threats,

the Euclidean distance between the ith node and the kth radar is obtained, and the radar detection probability is as follows:

is the minimum detection distance that the drone will be detected after entering this range,

the maximum detection distance that the unmanned aerial vehicle will not be threatened after being far away from the range.

In the above formula, N _M Is the number of threat to the missile,

is the Euclidean distance between the ith node and the kth missile, and the damage probability of the missiles is as follows:

is the minimum injury range that the unmanned aerial vehicle will be destroyed after entering the range,

the maximum injury range that unmanned aerial vehicle can not receive the threat after keeping away from this scope.

In the above formula, N _A Is the number of artillery threats,

is the Euclidean distance between the ith node and the kth air defense weapon, and the damage probability of the artillery is as follows:

the unmanned aerial vehicle can not be threatened after being far away from the rangeThe maximum injury range of (1).

In the above formula, N _C Is the number of weather threats and is,

is the euclidean distance between the ith node and the kth weather threat, and the influence probability of the weather is as follows:

is the minimum injury range that the drone will be destroyed once it enters this range,

is the maximum injury range that the drone will not be threatened once it is away from this range.

In the above formula, h _i Is the height of node i.

Between the ith node and the kth mountain central axisThe distance of (a) to (b),

is the radius of the cross section of the mountain with the height h,

is the minimum damage range of the unmanned plane impacting the ground after entering the range,

the maximum damage range that the unmanned aerial vehicle is not threatened by the terrain after leaving the range.

The fuel cost associated with the flight path length is given by the following equation:

J _length ＝w _L ·L

wherein w _L Indicating the coefficient, L the track length,

N _L representing the number of track nodes.

An objective function of unmanned aerial vehicle flight path planning is constructed according to the environment threat cost and the fuel cost of unmanned aerial vehicle flight, so that the threat cost is reduced, the flight safety is improved, and the stable performance is ensured.

Step 104, dividing the area to be planned into grids by adopting a grid method, wherein all nodes in the grids form a node set; the nodes are concentrated into a plurality of adjacent nodes to form an unmanned aerial vehicle track; the nodes represent flight point positions of the unmanned aerial vehicle in the area to be planned.

The mesh method is adopted to divide the area to be planned into meshes, nodes in the meshes represent the flying point positions of the unmanned aerial vehicles in the area to be planned, namely the positions of the unmanned aerial vehicles passing through the flying tracks, the positions and the tracks of the unmanned aerial vehicles can be calculated and obtained more clearly and accurately, and the relative positions and the tracks of the unmanned aerial vehicles and the unmanned aerial vehicles in the area to be planned, so that collaborative planning is facilitated.

106, calculating the transition probability of each node in the node set according to the guide factors and the heuristic factors, and taking the node with the maximum transition probability as the next transition node to obtain an initial antibody group; the antibody is unmanned aerial vehicle track.

The key point of generating the initial antibody set is how the nodes are transmitted, the transfer rule is formed by introducing a guide factor and a heuristic factor, the heuristic factor is helpful for node transfer to avoid threats to the greatest extent, the guide factor can provide direction information for the node transfer, the transfer probability of each node in the node set is calculated, the node with the maximum transfer probability is used as the next transfer node, the size of the initial antibody group is set, the starting point is continuously changed, and a plurality of tracks, namely candidate tracks, can be generated in the node transfer process, so that the initial antibody group is generated.

108, selecting an initial antibody population according to a clone selection algorithm selected by the championship to obtain a first candidate antibody population; performing immune clone operation and immune gene operation on the first candidate antibody group to obtain a second candidate antibody group and a third candidate antibody group; combining the second candidate antibody group and the third candidate antibody group to carry out antibody modification to obtain a fourth candidate antibody group; performing rapid non-dominated sorting on the fourth candidate antibody group, calculating a crowded distance, and updating the fourth candidate antibody group according to the non-dominated sorting and the crowded distance to obtain a final antibody group; the final antibody population includes a plurality of candidate trajectories for a plurality of drones.

Clonal selection may select for antibodies with higher affinity. The tournament selection method selects antibodies with higher affinity to ensure the quality and diversity of offspring population, the immune cloning operation is to generate more antibodies by copying a certain proportion of excellent antibodies, and further more genetic operations can be performed to expand the search space, the immune genetic operations include genetic recombination and genetic mutation, the diversity of population can be enhanced, but two infeasible antibodies can be generated in the recombination and mutation processes, and infeasible antibodies containing repeated nodes can be directly deleted because they are likely to be closed loops; other feasible antibodies containing two continuous nodes crossing multiple grids are possibly corrected, node pairs with small span can be supplemented by the nodes to enable the antibodies to be feasible, the antibodies are modified subsequently and some infeasible antibodies are corrected, the feasibility of the antibodies can be improved, the antibody population is optimized in multiple rounds, finally, redundant antibodies exceeding the size of the initial antibody population are removed according to non-dominated sorting and crowding distances, the size of the antibody population is unchanged, the optimized final antibody population is obtained, the antibodies in the final antibody population represent the candidate flight path of the unmanned aerial vehicle, and the candidate flight path of the unmanned aerial vehicle is optimized by optimizing the antibody population.

And step 110, setting track cost according to the target function of the unmanned aerial vehicle track planning and preset weight, and constructing the target function of the unmanned aerial vehicle collaborative planning by using the track cost and the coordination time.

When the unmanned aerial vehicles carry out collaborative planning, a plurality of candidate tracks are required to be planned for each unmanned aerial vehicle in advance so as to meet different task requirements, a track cost and coordination time are utilized to construct an objective function of the collaborative planning of the unmanned aerial vehicles, so that when the collaborative planning is carried out subsequently, each unmanned aerial vehicle can meet the time coordination requirement, and when the coordination cost is minimum, one candidate track is selected for each unmanned aerial vehicle, and each unmanned aerial vehicle can arrive at a target area at the same time so as to complete the collaborative task, so that the collaborative efficiency is improved.

112, performing unmanned aerial vehicle track selection on the final antibody group according to a target function of unmanned aerial vehicle collaborative planning to obtain a coordination scheme; and planning the unmanned aerial vehicle collaborative flight path of the area to be planned according to a coordination scheme.

And performing unmanned aerial vehicle flight path selection on the final antibody group according to a target function of unmanned aerial vehicle collaborative planning, selecting a flight path with the minimum coordination cost as a final flight path, thereby obtaining a coordination scheme, and performing unmanned aerial vehicle collaborative flight path planning on an area to be planned by using the coordination scheme so as to improve unmanned aerial vehicle collaborative flight path planning efficiency.

In the unmanned aerial vehicle collaborative flight path planning method based on the immune clone algorithm, threat cost and fuel cost in a to-be-planned area are set as an objective function of unmanned aerial vehicle flight path planning, the threat cost is favorably reduced when subsequent flight path selection is carried out, flight safety is improved, the unmanned aerial vehicle performance is ensured to be stable, the to-be-planned area is divided into grids by adopting a grid method, a plurality of adjacent nodes in the grids form unmanned aerial vehicle flight paths, heuristic factors and guide factors are introduced in an initialization process to generate high-quality antibodies, node transfer is more effective, the quality of initial antibodies is improved, convergence speed is accelerated, antibody groups are continuously optimized and infeasible antibodies are corrected through immune clone operation and immune gene operation, the feasibility of the antibodies is improved, then infeasible antibodies are modified to enhance the diversity of the antibody groups, a plurality of candidate flight paths after optimization of the unmanned aerial vehicle are finally obtained, flight path cost is set according to the objective function of unmanned aerial vehicle flight path planning and preset weight, and an unmanned aerial vehicle collaborative objective function is constructed by utilizing flight path cost and coordination time; and performing unmanned aerial vehicle flight path selection on the final antibody group according to a target function of unmanned aerial vehicle collaborative planning, selecting a flight path with the minimum coordination cost as a coordination flight path, forming an optimal coordination scheme according to the obtained coordination flight path of each unmanned aerial vehicle, and performing unmanned aerial vehicle collaborative flight path planning on an area to be planned by using the optimal coordination scheme so as to improve the unmanned aerial vehicle collaborative flight path planning efficiency.

setting an objective function of the unmanned aerial vehicle flight path planning to be according to the environmental threat cost and the fuel cost of the unmanned aerial vehicle flight

In one embodiment, calculating the transition probability of each node in the node set according to the leading factor and the heuristic factor includes:

calculating the transition probability of each node in the node set according to the guide factor and the heuristic factor to obtain the transition probability of the node

Wherein i, j and s respectively represent any node in the node set, η _j Denotes a heuristic factor, λ _j Denotes a lead factor, alpha denotes the importance of a elicitor, beta denotes the importance of a lead factor, B _k,i A set of nodes is represented.

In particular embodiments, the key to generating the initial set of antibodies is how the nodes transmit. When selecting a node set

When one node i in the node set is used as the next node, introducing a heuristic factor and a leading factor to form a transfer rule, calculating the transfer probability of each node i in the node set, and then selecting the node with the maximum transmission probability as the next node. The heuristic factors are calculated as follows:

wherein w _R ，w _M ，w _A ，w _C ，w _H And w _T Respectively, threat weight coefficients for radar, missile, artillery, climate, altitude, and mountains. N is a radical of _R ，N _M ，N _A ，N _C And N _T Respectively the number of radars, missiles, artillery, weather and mountains.

Representing the distance between the jth node and the nth radar threat,

representing the distance between the jth node and the nth missile threat,

representing the distance between the jth node and the nth gun.

Representing the distance between the jth node and the nth weather threat. As for the height of the jth node,

is the distance between the jth node and the nth mountain. The larger the heuristic factor, the further the distance between the node and the threat source.

The guidance factors are calculated as follows:

wherein d is _j,end Representing the jth node and the end node p _k,end The distance between them. The larger the guidance factor, the smaller the distance between the node and the end node.

Heuristic factors help node migration to avoid threats as much as possible, and guidance factors can provide direction information for node migration. By introducing heuristic factors and guide factors, the node transfer is more effective, so that the quality of the initial antibody is improved, and the convergence speed of the method is accelerated.

With the determination of the transition rule, the initial antibody can be obtained by searching for a node from the start point to the end node. After performing the above operation N times, N antibodies were obtained to form an initial antibody population.

P(t)＝{p ₁ (t)，p ₂ (t)，…，p _N (t)}

Wherein t represents the current generation, p _i (t) represents an antibody, p _i (t)＝{Start，p _i1 ，p _i2 ，…，p _ik ，End}，p _ik To representNumber of nodes, i _k ∈{1，2，…，m×n}。

setting the scale of the championship as n, setting the scale of the initial antibody group as m, randomly selecting n antibodies from the initial antibody group for comparison, reserving the antibodies with the lowest non-dominance grade and the highest crowding distance, and repeating the process for m times to obtain m antibodies; the m antibodies are the first candidate antibody population.

In a specific embodiment, the tournament selected clone selection algorithm is shown in Table 1:

first, the race scale was set to n and the size of the selected antibody population was set to m. N antibodies were then randomly selected from the antibody population for comparison, leaving antibodies with smaller non-dominant rank and larger crowding distance for the next generation. Repeating the above process m times, retaining m kinds of antibodies in the next generation, and obtaining P as the first candidate antibody group ⁽¹⁾ (t)＝{p ₁ ⁽¹⁾ (t)，p ₂ ⁽¹⁾ (t)，…，p _m ⁽¹⁾ (t)}。

performing an immunocontraceal operation on the first candidate antibody to obtain a second candidate antibody population

fixing the starting point and the end node of each antibody in the antibody group after gene recombination, randomly selecting one node in the antibodies to mutate in a mutation range, and obtaining a third candidate antibody group; the mutation range is the intersection of the set of neighboring nodes of the ith node and the set of neighboring nodes of the (i + 1) th node.

In particular embodiments, immunogenetic manipulation, including gene recombination and gene mutation, can enhance population diversity. Random recombination and mutation readily produce antibodies that are not feasible. Antibodies that are not feasible include: an antibody comprising a repeating node or an antibody comprising two adjacent nodes spanning multiple grids. Therefore, not only is it desirable to produce a population of antibodies with good diversity, but consideration is also given to improving the feasibility of antibodies during gene recombination and gene mutation.

In genetic recombination, two antibodies are randomly selected from the progeny population and their start and end points are fixed. Selecting the closest two nodes as the crossover point may reduce the impracticality of antibody production. The pair of nodes that are closest to each other in the two antibodies is then calculated. The number of pairs may be one or more. If a pair of the two nodes exists, selecting the two nodes as cross points, and exchanging two antibodies before and after the cross points according to recombination probability; if there are multiple pairs, one of them is randomly selected as a crossover point, and the two antibodies before and after the two crossover points are exchanged according to the recombination probability, as shown in FIG. 2.

For each antibody, when the starting and ending nodes are fixed, one node is randomly selected for mutation, and mutation operators are shown in fig. 3. Randomly selecting the ith node as a mutation point, and searching the mutated range B _mutation Is the intersection of the set of neighboring nodes of the ith node and the set of neighboring nodes of the (i + 1) th node, B _insert ＝Ne _i-1 ∩Ne ₊₁ 。

The antibody population obtained by the immune gene operator is as follows:

P ⁽³⁾ (t)＝{p ₁ ⁽³⁾ (t)，p ₂ ⁽³⁾ (t)，…，p _N(m) ⁽³⁾ (t)}

the process of genetic manipulation of the antibody population is described in table 2:

combining the second candidate antibody group and the third candidate antibody group, traversing all nodes of each antibody in the combined antibody group, if the ith node and the jth node are continuous on a flight path, and the jth node is not in the neighbor set Ne of the ith node _i Then, the jth node is corrected, and the neighbor node set Ne of the ith node is selected _i And neighbor node set Ne of jth node _j As the insertion node set B _insert If B is _insert Null indicates more than two cells between the ith node and the jth nodeIf B is not present, the antibody is deleted _insert And if not, randomly selecting a node as an insertion node to obtain a modified fourth candidate antibody group.

step two: from the fourth candidate antibody group P ⁽⁴⁾ Selecting an antibody group F with non-dominant ordering a in (t), combining the antibody groups F and G to generate a combined group F + G = F ≧ G, performing non-dominant ordering on the combined group, and recording the non-dominant ordering value of the combined group F + G as length (F + G);

step three: if length (F + G) < N ', N' indicates the size of the final antibody population, F is added to G, a = a +1, go to step two; if length (F + G) > N', let NM = N-length (F + G), sort the antibodies in F according to the crowding distance, add the top NM antibodies to G; if length (F + G) = N', add F to G; g is the final antibody population.

In a specific embodiment, the initial population of antibodies is N ' in size, and if the number of antibodies is greater than N ', the antibodies are removed according to rank and crowding distance until the antibody size is N '. The final antibody population is P ⁽⁵⁾ (t)＝{p ₁ ⁽⁵⁾ (t)，p ₂ ⁽⁵⁾ (t)，…，p _N ⁽⁵⁾ (t) }. Excess antibody that exceeds the size of the initial antibody population is removed according to the non-dominated sorting and crowding distance, leaving the size of the antibody population unchanged, optimizing antibody quality without changing the size of the antibody population.

Wherein the content of the first and second substances,

s representing the ith drone plan _i The track cost of each candidate, lambda represents a cooperative coefficient, M represents the total number of drones, and T _d Indicating the coordination time.

In a specific embodiment, as shown in fig. 4, a pool of 4 drones is planned. In order to guarantee the maximum attack range, the unmanned aerial vehicles respectively enter the target area from different directions, three adjacent nodes exist before each unmanned aerial vehicle enters the target area, and the nodes serve as end nodes of the UAV candidate flight path. And obtaining three optimal candidate tracks of each unmanned aerial vehicle through track planning, and selecting the candidate tracks according to a coordination function.

The coordination function is a relationship between coordination cost, track cost, and coordination time. The coordination cost for the jth candidate flight path for the ith drone plan may be calculated as:

C _i,j ＝λW _i,j +(1-λ)t _i,j

wherein, W _i,j Track cost, t, of the jth candidate track representing the ith UAV plan _i,j Indicates the cooperative time variation, and λ indicates the cooperative coefficient.

Track cost of W _i,j ＝0.9*J _threat +0.1*J _length

Assuming that M drones are provided, each drone plans num candidate tracks, the candidate track set for the ith drone may be represented as { P } _i1 ,P _i2 ,,…,P _i,j ,…,P _i,num }，P _i,j Representing the jth candidate track planned by the ith unmanned aerial vehicle, and realizing the collaborative planning by the following steps:

(1) The time range of the unmanned aerial vehicle reaching the target area is calculated according to the speed range of the unmanned aerial vehicle and the length of the candidate flight path as follows:

S _i,j track length ith unmanned aerial vehicle of jth candidate track representing unmanned aerial vehicle planningThe time range of the ith unmanned aerial vehicle reaching the target is

(2) Determining the intersection of the time ranges D = D ₁ ∩D ₂ ...∩D _M If D is not null, there is a point in time T _d E D, so that each drone has a candidate flight path with a range of arrival times at least containing T _d Time T of _d Can be as coordinating arrival time, can guarantee that all unmanned aerial vehicles arrive the target area simultaneously.

(3) To minimize the reconciliation cost, the minimum value of D is generally selected as the reconciliation arrival time T _d I.e. T _d = minD, mixing T with _d Substitution into C _i,j ＝λW _i,j +(1-λ)t _i,j Calculating a coordination cost C of candidates _i,j The track meeting the coordination time range, if the candidate track coordinates the cost C _i ,s _i And if the minimum value is obtained, selecting the s th planned by the ith unmanned aerial vehicle _i The candidate flight path is used as a coordination flight path, s _i Belongs to {1,2, \8230;, num }, and the final coordination scheme is

The total coordination cost is as follows:

wherein the content of the first and second substances,

s representing the ith drone plan _i A candidate track cost.

In one embodiment, to validate the present application, the application ICA and GFACO are used for collaborative track planning respectively, and the evolution of the costs including the coordination cost, path cost, fuel cost and threat cost is shown in fig. 5. Fig. 5 (a) shows the evolution of the coordination cost, fig. 5 (b) shows the evolution of the track cost, and fig. 5 (c) shows the evolution of the fuel cost. Fig. 5 (d) shows the evolution of the threat cost. As can be seen from fig. 5 (a) and 5 (b), the convergence rate of ICA is similar to that of the ant colony algorithm. As the number of iterations increases, the coordination cost and the tracking cost obtained by ICA are less than GFACO. As can be seen from fig. 5 (c), the ICA underperforms on the track length. As can be seen from fig. 5 (d), ICA has faster convergence speed and lower threat cost. This means that ICA is better than GFACO in avoiding threats.

It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

In one embodiment, as shown in fig. 6, there is provided an unmanned aerial vehicle collaborative flight path planning apparatus based on an immune clone algorithm, including: an objective function construction module 602 for unmanned aerial vehicle track planning, a region division module 604, an initial antibody group construction module 606, an antibody group optimization module 608, an objective function construction module 610 for unmanned aerial vehicle collaborative planning, and an unmanned aerial vehicle collaborative planning module 612, wherein:

an objective function construction module 602 for unmanned aerial vehicle track planning, configured to obtain an area to be planned; calculating the environmental threat cost of an area to be planned, and constructing an objective function of unmanned aerial vehicle flight path planning according to the environmental threat cost and the fuel cost of unmanned aerial vehicle flight;

the area dividing module 604 is configured to divide an area to be planned into grids by using a grid method, where all nodes in the grids form a node set; the nodes are concentrated into a plurality of adjacent nodes to form an unmanned aerial vehicle track; the nodes represent flight point positions of the unmanned aerial vehicle in the area to be planned;

an initial antibody cluster construction module 606, configured to calculate a transition probability of each node in the node set according to the guidance factor and the heuristic factor, and use the node with the largest transition probability as a next transition node to obtain an initial antibody cluster; the antibody is an unmanned aerial vehicle track;

an antibody population optimization module 608, configured to select an initial antibody population according to a tournament-selected clonal selection algorithm to obtain a first candidate antibody population; performing immune clone operation and immune gene operation on the first candidate antibody group to obtain a second candidate antibody group and a third candidate antibody group; combining the second candidate antibody group and the third candidate antibody group to carry out antibody modification to obtain a fourth candidate antibody group; performing rapid non-dominated sorting on the fourth candidate antibody group, calculating a crowded distance, and updating the fourth candidate antibody group according to the non-dominated sorting and the crowded distance to obtain a final antibody group; the final antibody population includes a plurality of candidate trajectories for a plurality of drones;

an objective function construction module 610 for unmanned aerial vehicle collaborative planning, configured to set a track cost according to an objective function for unmanned aerial vehicle track planning and a preset weight, and construct an objective function for unmanned aerial vehicle collaborative planning by using the track cost and coordination time;

an unmanned aerial vehicle collaborative planning module 612, configured to perform unmanned aerial vehicle track selection on the final antibody group according to a target function of unmanned aerial vehicle collaborative planning to obtain a coordination scheme; and planning the unmanned aerial vehicle collaborative flight path of the area to be planned according to a coordination scheme.

In one embodiment, the objective function construction module 602 for unmanned aerial vehicle flight path planning is further configured to set an objective function for unmanned aerial vehicle flight path planning according to the environmental threat cost and the fuel cost for unmanned aerial vehicle flight, including:

Wherein, J _length Represents the fuel cost, w _L Denotes coefficient, L denotes track length, J _threat Represents a threat cost, w _R ，w _M ，w _A ，w _C ，w _H And w _T Threat weight coefficients of radar, missile, artillery, climate, altitude and mountain, respectively; j. the design is a square _R ，J _M ，J _A ，J _C ，J _H And J _T Respectively, threat costs for radar, missile, artillery, weather, altitude, and mountain.

In one embodiment, the initial antibody population building block 606 is further configured to calculate a transition probability for each node in the node set according to the guidance factor and the heuristic factor, including:

Wherein i, j and s respectively represent any node in the node set, eta _j Denotes a heuristic factor, λ _j Denotes a lead factor, alpha denotes the importance of a elicitor, beta denotes the importance of a lead factor, B _k,i A set of nodes is represented.

In one embodiment, the antibody population optimization module 608 is further configured to select an initial antibody population according to a tournament selected clone selection algorithm to obtain a first candidate antibody population, comprising:

In one embodiment, the antibody population optimization module 608 is further configured to perform an immune cloning operation and an immune gene operation on the first candidate antibody population to obtain a second candidate antibody population and a third candidate antibody population, including:

In one embodiment, the antibody population optimizing module 608 is further configured to perform gene recombination and gene mutation on the first candidate antibody population, in which two antibodies are randomly selected from the first candidate antibody population, start points and end points of the two antibodies are fixed, node pairs closest to each other in the two antibodies are calculated, if there is only one pair, the two nodes are selected as intersections, two antibodies before and after the intersection are exchanged according to recombination probabilities, if there are multiple pairs, one of the two antibodies is randomly selected as an intersection, and the two antibodies before and after the two intersections are exchanged according to the recombination probabilities, so as to obtain an antibody population after the gene recombination;

In one embodiment, the antibody population optimization module 608 is further configured to combine the second candidate antibody population and the third candidate antibody population for antibody modification to obtain a fourth candidate antibody population, including:

combining the second candidate antibody group and the third candidate antibody group, traversing all nodes of each antibody in the combined antibody group, if the ith node and the jth node are continuous on a flight path, and the jth node is not in the neighbor set Ne of the ith node _i In (3), the jth node is corrected, and the neighbor node set Ne of the ith node is selected _i And neighbor node set Ne of jth node _j As the insertion node set B _insert If B is _insert Null indicates more than two grids between the ith node and the jth node, the antibody is deleted if B _insert And if not, randomly selecting a node as an insertion node to obtain a modified fourth candidate antibody group.

In one embodiment, the antibody population optimization module 608 is further configured to update the fourth candidate antibody population according to the non-dominated ranking and the crowding distance to obtain a final antibody population, including:

In one embodiment, the objective function construction module 610 of collaborative planning for unmanned aerial vehicles is further configured to construct an objective function of collaborative planning for unmanned aerial vehicles by using the track cost and the coordinated time, including:

an objective function of unmanned aerial vehicle collaborative planning is constructed by utilizing the track cost and the coordination time as

For specific limitations of the unmanned aerial vehicle collaborative flight path planning apparatus based on the immune clone algorithm, reference may be made to the above limitations of the unmanned aerial vehicle collaborative flight path planning method based on the immune clone algorithm, which are not described herein again. All modules in the unmanned aerial vehicle collaborative flight path planning device based on the immune clone algorithm can be wholly or partially realized through software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize the unmanned aerial vehicle collaborative flight path planning method based on the immune clone algorithm. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the configuration shown in fig. 7 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.

In an embodiment, a computer storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, is adapted to carry out the steps of the method of the above embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.

Claims

1. An unmanned aerial vehicle collaborative flight path planning method based on an immune clone algorithm is characterized by comprising the following steps:

acquiring a region to be planned;

calculating the environmental threat cost of the area to be planned, and constructing an objective function of unmanned aerial vehicle flight path planning according to the environmental threat cost and the fuel cost of unmanned aerial vehicle flight;

dividing the area to be planned into grids by adopting a grid method, wherein all nodes in the grids form a node set; the nodes are concentrated into a plurality of adjacent nodes to form an unmanned aerial vehicle track; the nodes represent flight point positions of the unmanned aerial vehicle in the area to be planned;

selecting the initial antibody population according to a clone selection algorithm selected by the championship to obtain a first candidate antibody population;

performing rapid non-dominant sorting on the fourth candidate antibody group, calculating a crowded distance, and updating the fourth candidate antibody group according to the non-dominant sorting and the crowded distance to obtain a final antibody group; the final antibody population includes a plurality of candidate trajectories for a plurality of drones;

setting track cost according to the target function of the unmanned aerial vehicle track planning and preset weight, and constructing the target function of the unmanned aerial vehicle collaborative planning by using the track cost and the coordination time;

performing unmanned aerial vehicle track selection on the final antibody group according to the objective function of the unmanned aerial vehicle collaborative planning to obtain a coordination scheme;

and planning the unmanned aerial vehicle collaborative flight path of the area to be planned according to the coordination scheme.

2. The method of claim 1, wherein setting an objective function for drone trajectory planning as a function of the environmental threat cost and a fuel cost for drone flight comprises:

setting an objective function of unmanned aerial vehicle flight path planning as

Wherein, J _length Represents the fuel cost, w _L Representing coefficient, L track length, J _threat Represents a threat cost, w _R ，w _M ，w _A ，w _C ，w _H And w _T Threat weight coefficients of radar, missile, artillery, climate, altitude and mountain, respectively; j. the design is a square _R ，J _M ，J _A ，J _C ，J _H And J _T Respectively, threat costs for radar, missile, artillery, weather, altitude, and mountain.

3. The method of claim 1, wherein calculating the transition probability for each node in the set of nodes based on the bootstrap factor and the heuristic factor comprises:

Wherein i, j and s respectively represent any node in the node set, η _j Denotes a heuristic factor, λ _j Denotes a lead factor, alpha denotes the importance of a elicitor, beta denotes the importance of a lead factor, B _k,i Representing a set of nodes.

4. A method as claimed in any one of claims 1 to 3 wherein the initial population of antibodies is selected according to a tournament selected clone selection algorithm to obtain a first candidate population of antibodies comprising:

setting the scale of the championship as n, setting the scale of the initial antibody group as m, randomly selecting n antibodies from the initial antibody group for comparison, retaining the antibodies with the lowest non-dominance grade and the highest crowding distance, and repeating the process for m times to obtain m antibodies; the m antibodies are a first candidate antibody population.

5. The method of claim 4, wherein performing an immunocontoloration and immunogenetic manipulation of the first population of candidate antibodies to obtain a second population of candidate antibodies and a third population of candidate antibodies comprises:

performing an immunocoloclone operation on the first candidate antibody to obtain a second candidate antibody population

P ⁽²⁾ (t)＝P ⁽¹⁾¹ (t)+P ⁽¹⁾² (t)+…+P ^(1)mc (t)＝{p ₁ ⁽¹⁾¹ (t)，…，

p _m ⁽¹⁾¹ (t)}+…+{p ₁ ^(1)mc (t),…，p _m ^(1)mc (t)}＝{p ₁ ⁽²⁾ (t)，p ₂ ⁽²⁾ (t)，…，p _N(m) ⁽²⁾ (t)}

6. The method of claim 5, further comprising:

performing gene recombination and gene mutation on the first candidate antibody group, in the gene recombination, randomly selecting two antibodies from the first candidate antibody group, fixing the starting point and the end point of the two antibodies, calculating the node pair which is closest to each other in the two antibodies, if only one pair exists, selecting the two nodes as an intersection, exchanging the two antibodies before and after the intersection according to recombination probability, if multiple pairs exist, randomly selecting one of the two antibodies as the intersection, and exchanging the two antibodies before and after the two intersections according to the recombination probability to obtain an antibody group after the gene recombination;

fixing the starting point and the ending node of each antibody in the antibody population after the gene recombination, and randomly selecting one node in the antibodies to mutate in a mutation range to obtain a third candidate antibody population; the mutation range is the intersection of the set of neighbor nodes of the ith node and the set of neighbor nodes of the (i + 1) th node.

7. The method of claim 6, wherein combining the second candidate antibody population and the third candidate antibody population to perform antibody modification to obtain a fourth candidate antibody population comprises:

combining the second candidate antibody group and the third candidate antibody group, traversing all nodes of each antibody in the combined antibody group, if the ith node and the jth node are continuous on a flight path, and the jth node is not in the neighbor set Ne of the ith node _i In (1), then to the jth nodeMaking a correction to select the neighbor node set Ne of the ith node _i And a set Ne of neighbor nodes of the jth node _j As the insertion node set B _insert If B is _insert Null indicates more than two grids between the ith node and the jth node, the antibody is deleted if B _insert And if not, randomly selecting a node as an insertion node to obtain a modified fourth candidate antibody group.

8. The method of claim 7, wherein updating the fourth candidate antibody population according to the non-dominated ranking and crowding distance to obtain a final antibody population comprises:

9. The method of claim 6, wherein constructing an objective function of unmanned aerial vehicle collaborative planning using the track cost and the coordinated time comprises:

Wherein, the first and the second end of the pipe are connected with each other,

10. An unmanned aerial vehicle collaborative flight path planning device based on an immune clone algorithm is characterized by comprising:

the unmanned aerial vehicle flight path planning target function building module is used for obtaining an area to be planned; calculating the environmental threat cost of the area to be planned, and constructing an objective function of unmanned aerial vehicle flight path planning according to the environmental threat cost and the fuel cost of unmanned aerial vehicle flight;

the initial antibody cluster construction module is used for calculating the transition probability of each node in the node set according to the guide factor and the heuristic factor, and taking the node with the maximum transition probability as the next transition node to obtain an initial antibody cluster; the antibody is an unmanned aerial vehicle track;

the antibody population optimization module is used for selecting the initial antibody population according to a clone selection algorithm selected by the championship to obtain a first candidate antibody population; performing immune clone operation and immune gene operation on the first candidate antibody group to obtain a second candidate antibody group and a third candidate antibody group; combining the second candidate antibody group and the third candidate antibody group to perform antibody modification to obtain a fourth candidate antibody group; performing rapid non-dominant sorting on the fourth candidate antibody group, calculating a crowded distance, and updating the fourth candidate antibody group according to the non-dominant sorting and the crowded distance to obtain a final antibody group; the final antibody population comprises a plurality of candidate trajectories for a plurality of drones;

the unmanned aerial vehicle collaborative planning objective function construction module is used for setting track cost according to an objective function of the unmanned aerial vehicle track planning and preset weight, and constructing the objective function of the unmanned aerial vehicle collaborative planning by using the track cost and the coordination time;

the unmanned aerial vehicle collaborative planning module is used for carrying out unmanned aerial vehicle flight path selection on the final antibody group according to a target function of the unmanned aerial vehicle collaborative planning to obtain a coordination scheme; and carrying out unmanned aerial vehicle collaborative track planning on the area to be planned according to the coordination scheme.