Unmanned aerial vehicle cluster collaborative task search platform and method based on connectivity

Abstract

The invention discloses an unmanned aerial vehicle cluster collaborative task search platform and method based on connectivity, and belongs to the technical field of unmanned aerial vehicles, and the method comprises the steps: S1, constructing an unmanned aerial vehicle target search model lt; u, O, Fgt; ; u represents an unmanned aerial vehicle model; o represents a scene model; f represents a search algorithm; s2, calculating information gains of the unmanned aerial vehicle group at the t moment under different scenes and different search algorithms, and binding the corresponding scene and the search algorithm with the maximum information gain as a'pair ', and recording the'pair' as a scene-search algorithm pair; and S3, judging the current scene of the unmanned aerial vehicle group, searching the scene-search algorithm pair library, matching the current scene with the scene in the scene-search algorithm pair library, after matching is completed, selecting the corresponding scene-search algorithm pair, and executing the search algorithm in the scene-search algorithm pair by the unmanned aerial vehicle group. According to the platform and the method, different search algorithms can be selected according to different scenes.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) search methods, and in particular relates to a connectivity-based UAV cluster collaborative task search platform and method. Background Art

[0002] Since the collaborative search of multiple UAVs for targets in a designated mission area has very important significance and value in theory and engineering practice, studying the collaborative search mechanism of multiple UAVs plays an important role in improving the efficiency and effectiveness of UAV collaborative search.

[0003] Application No. 202211130698.X, entitled "Multi-UAV Collaborative Target Search Method Based on Longicorn Whiskers Search Algorithm" in the prior art mentions that by constructing a fitness function that simultaneously considers environmental benefits, target gain benefits, avoiding repeated search benefits and obstacle avoidance benefits, during the collaborative target search process of the UAV, the UAV is made to move toward the grid unit with a larger fitness function value.

[0004] In this method, the target gain is used to determine which drone is more suitable for attacking the target. This technology does not show how to find the target by adapting different algorithms according to different scenarios. Summary of the invention

[0005] The present invention discloses a connectivity-based UAV cluster collaborative task search platform and method, which aims to solve the problem of how to allocate different search algorithms to UAVs according to actual scenarios.

[0006] A method for searching for collaborative tasks of a drone cluster based on connectivity of the present invention comprises the following steps:

[0007] S1: Building a drone target search model<U,O,F> ; U represents the drone model; O represents the scene model; F represents the search algorithm;

[0008] S2: Calculate the information gain of the drone swarm at time t under different scenarios and different search algorithms, and bind the corresponding scenario and the search algorithm with the largest information gain as a "pair", which is recorded as a scenario-search algorithm pair;

[0009] S3: Determine the current scene of the drone swarm, search the "scene-search algorithm pair" library, match the current scene with the scene in the "scene-search algorithm pair" library, and after the matching is completed, select the corresponding "scene-search algorithm pair", and the drone swarm executes the search algorithm in the "scene-search algorithm pair";

[0010] Furthermore, step S2 includes the following steps:

[0011] S2.1: Assume there are m scenarios and z search algorithms; then the set of scenarios O is represented by: O = {O1, O2, ..., Om}; the set of search algorithms F is represented by: F = {F1, F2, ..., Fz};

[0012] S2.2: Select scenario O1 and search algorithm F1, and calculate the search probability of the drone swarm under this scenario and search algorithm at time t. The formula is:

[0013]

[0014] Pt represents the search probability of the drone group at time t under this scenario and search algorithm; n is the number of targets set in this scenario; si,t min is the minimum distance between all drones in the drone group and the i-th target at time t under this scenario and search algorithm; s (i) is the distance between the ith target and the initial position of the drone group; the initial position of the drone group means: the only birth point of the drone group in this scenario, all drones in the drone group start from this unique birth point, the birth point is set manually, and its coordinate position is determined;

[0015] S2.3: Calculate the information entropy at time t for this scenario and search algorithm. The formula is:

[0016] H(t)=-P(t)*lnP(t) (5);

[0017] S2.4: Calculate the information gain at time t for this scenario and search algorithm. The formula is:

[0018] IG(t)=H(t-1)-H(t) (6)

[0019] S2.5: Replace another search algorithm F2, without changing the scene O1, and calculate the information gain of another search algorithm F2 under the scene O1 at time t according to equations (4), (5), and (6);

[0020] S2.6: And so on... Calculate the information gain of different search algorithms in the scenario O1;

[0021] S2.7: Select the search algorithm with the largest information gain in scenario O1; and combine the search algorithm and scenario O1 to form a scenario-search algorithm pair;

[0022] S2.8: Select scenario O2 and search algorithm F1, and calculate the information gain of search algorithm F1 under scenario O2 at time t according to equations (4), (5), and (6);

[0023] S2.9: Change different search algorithms without changing scene O2; according to equations (4), (5), (6), select the information gain of different search algorithms under scene O2;

[0024] S2.10: Select the search algorithm with the largest information gain in scenario O2, and form a scenario-search algorithm pair with the search algorithm and scenario O2;

[0025] S2.11: And so on...all search algorithms form scene-search algorithm pairs with scenes and are stored in the "scene-search algorithm pair" library.

[0026] Furthermore, if the beetle whisker algorithm is executed in step S3, the following steps are performed:

[0027] Step 1: Let each drone in the drone group contact other drones within its maximum communication distance to build a neighbor drone array set L;

[0028] Step 2: Calculate the distance between the target and each drone in the drone group, select the drone with the smallest distance, and determine whether to assign the target to the drone;

[0029] Step 3: If the i-th target is assigned to the j-th drone, update the neighbor drone array set of other drones except the j-th drone; in the current scene, delete the i-th target and update the number of drones that can be assigned;

[0030] Step 4: By repeating the above steps 1 to 3, the drone cluster can use the beetle whisker algorithm to search for multiple targets.

[0031] Furthermore, in step S1, labels D, M and P are used to divide the scene into 8 typical scenes;

[0032] Where D is the distance label;

[0033] The formula is:

[0034]

[0035] R0 represents the distance judgment threshold, which is set manually; R represents the maximum search radius of the drone; D has values ​​of 0 and 1; 0 represents a smaller search radius of the drone; 1 represents a larger search radius of the drone; M represents the number density label;

[0036] The formula is:

[0037]

[0038] N is the number of targets in the scene; N Uis the number of drones in the drone group; V0 is the number density judgment threshold, which is set manually; V represents the ratio of the number of drones to the number of search targets. If the ratio is greater than V0, the label M value is 1, which means that the resources in the current scene are sufficient; if the ratio is less than or equal to V0, the label M value is 0, which means that the resources in the current scene are scarce;

[0039] P is the quantity label;

[0040] The formula is:

[0041]

[0042] N is the number of targets in the scene; R is the maximum search radius of the drone; A0 is the number judgment threshold, which is set manually; A is the ratio of the search range of the unit drone to the number of targets in the scene. If the ratio is greater than A0, the label P value is 0, which reflects that the number of targets in the current scene is small, the drone search range is large, and the search difficulty is small. If the ratio is less than or equal to A0, the label P value is 1, which reflects that the number of targets in the current scene is large, the drone search range is small, and the search difficulty is large.

[0043] A connectivity-based UAV swarm collaborative task search platform, including:

[0044] The construction module can launch drones in the drone group from a fixed position at the birth point; can customize the number of targets; can randomly distribute all targets on the map; can customize the target movement mode; can generate marker points and paths;

[0045] Planning module, which can select the search algorithm;

[0046] The result display module can display the drone’s starting point, target point, search time, drone status, drone images and drone’s historical path; it can generate search reports for the start and end of the drone search.

[0047] Beneficial Effects

[0048] This platform and method can calculate information gain according to different algorithms in the corresponding scene, and combine the information gain maximum search algorithm and the corresponding scene into a "scene-search algorithm pair". The subsequent drone swarm only needs to judge the current scene and execute the corresponding search algorithm after matching the "scene-search algorithm pair", which can improve the adaptability of the search algorithm to the scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of the location of drone group A. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1: A method for searching for collaborative tasks of a drone cluster based on connectivity, specifically comprising the following steps:

[0052] S1: Building a drone target search model<U,O,F> ;

[0053] Among them, U represents the drone model, which includes the drone's own parameters, namely: drone coordinates, drone running speed and the drone's maximum communication distance;

[0054] O represents the scenario model, hereinafter referred to as the scenario, including the number of drones, the number of targets, target coordinates, and target movement speed;

[0055] F represents the search mode. F is the search algorithm used by the drone to find the target, which is provided by the search mode library.

[0056] In this embodiment, the scene model is divided into 8 typical scenes according to labels D, M and P.

[0057] Specific:

[0058] Where D is the distance label.

[0059] The formula is:

[0060]

[0061] R0 represents the distance judgment threshold, which is set manually. R represents the maximum search radius of the drone. The value of D is 0 and 1; 0 means the search radius of the drone is small; 1 means the search radius of the drone is large.

[0062] M is the number density label.

[0063] The formula is:

[0064]

[0065] N is the number of targets in the scene; N Uis the number of drones in the drone group; V0 is the number density judgment threshold, which is set manually; V represents the ratio of the number of drones to the number of search targets. If the ratio is greater than V0, the label M value is 1, which means that the resources in the current scene are sufficient; if the ratio is less than or equal to V0, the label M value is 0, which means that the resources in the current scene are scarce.

[0066] P is the quantity label.

[0067] The formula is:

[0068]

[0069] N is the number of targets in the scene; R is the maximum search radius of the drone. A0 is the number judgment threshold, which is set manually; A is the ratio of the search range of the unit drone to the number of targets in the scene. If the ratio is greater than A0, the label P value is 0, which reflects that the number of targets in the current scene is small, the drone search range is large, and the search difficulty is small. If the ratio is less than or equal to A0, the label P value is 1, which reflects that the number of targets in the current scene is large, the drone search range is small, and the search difficulty is large.

[0070] In other embodiments, the labels may be other than D, M and P, and the number of typical scenarios may be other than eight.

[0071] S2: Calculate the information gain of the drone swarm at time t under different scenarios and different search algorithms, and bind the corresponding scenario and the search algorithm with the largest information gain as a "pair", which is recorded as a scene-search algorithm pair.

[0072] The specific steps include:

[0073] S2.1: Assume there are m scenarios and z search algorithms; then the set of scenarios O is represented by: O = {O1, O2, ..., Om}; the set of search algorithms F is represented by: F = {F1, F2, ..., Fz};

[0074] S2.2: Select scenario O1 and search algorithm F1, and calculate the search probability of the drone swarm under this scenario and search algorithm at time t. The formula is:

[0075]

[0076] P(t) represents the search probability of the drone swarm at time t under this scenario and search algorithm; n is the number of targets set in this scenario; s(i,t) min is the minimum distance between all drones in the drone group and the i-th target at time t under this scenario and search algorithm; s (i)is the distance between the ith target and the initial position of the drone swarm; the initial position of the drone swarm means: the only birth point of the drone swarm in this scenario. All drones in the drone swarm start from this unique birth point. The birth point is set manually and its coordinate position is determined.

[0077] S2.3: Calculate the information entropy at time t for this scenario and search algorithm. The formula is:

[0078] H(t)=-P(t)*lnP(t) (5); S2.4: Calculate the information gain at time t for this scenario and search algorithm. The formula is:

[0079] IG(t)=H(t-1)-H(t) (6)

[0080] S2.5: Replace another search algorithm F2, without changing the scene O1, and calculate the information gain of another search algorithm F2 under the scene O1 at time t according to equations (4), (5), and (6);

[0081] S2.6: And so on... Calculate the information gain of different search algorithms in the scenario O1;

[0082] S2.7: Select the search algorithm with the largest information gain in scenario O1; and combine the search algorithm and scenario O1 to form a scenario-search algorithm pair;

[0083] S2.8: Select scenario O2 and search algorithm F1, and calculate the information gain of search algorithm F1 under scenario O2 at time t according to equations (4), (5), and (6);

[0084] S2.9: Change different search algorithms without changing scene O2; according to equations (4), (5), (6), select the information gain of different search algorithms under scene O2;

[0085] S2.10: Select the search algorithm with the largest information gain in scenario O2, and form a scenario-search algorithm pair with the search algorithm and scenario O2;

[0086] S2.11: And so on...all search algorithms form scene-search algorithm pairs with scenes and are stored in the "scene-search algorithm pair" library.

[0087] S3: Determine the current scene of the drone swarm, search the "scene-search algorithm pair" library, match the current scene with the scene in the "scene-search algorithm pair" library, and after the matching is completed, select the corresponding "scene-search algorithm pair", and the drone swarm executes the search algorithm in the "scene-search algorithm pair";

[0088] Specifically,

[0089] From step S1, we can see that there are 8 typical scenes, which are labeled as follows: {(0,0,0,(0,0,1),(0,1,0),(0,1,1),(1,0,0),(1,0,1),(1,1,0),(1,1,1))}.

[0090] Assume that the drone swarm relies on equations (1), (2), and (3) in S1 to calculate the current scene as (0, 0, 1), and matches it in the "scene-search algorithm pair" library. After the matching is completed, the corresponding search algorithm is obtained. Assume that the search algorithm at this time is: particle swarm algorithm, then the drone swarm searches for the target with the particle swarm algorithm. Assume that the search algorithm at this time is: simulated annealing algorithm, then the drone swarm searches for the target with the simulated annealing algorithm. Assume that the search algorithm at this time is genetic algorithm, then the drone swarm searches for the target with genetic algorithm.

[0091] It should be noted that the algorithms in the existing library include particle swarm algorithm, simulated annealing algorithm, genetic algorithm, and beetle beard algorithm.

[0092] Among them, particle swarm optimization and genetic algorithm are both intelligent group optimization algorithms. In a particle swarm, multiple particles move in space and share information with each other, thereby improving search efficiency, and each particle adjusts its position based on its own experience and the best experience of the group to adapt to the changing environment. In the genetic algorithm, a group composed of multiple individuals is preset, and a new generation of individuals is generated through operations such as selection, crossover and mutation. The performance of each individual is evaluated by fitness, and individuals with high fitness are selected for reproduction, thereby promoting the evolution of the group to a better solution.

[0093] Among them, although the simulated annealing algorithm is not an intelligent swarm optimization algorithm and does not operate between multiple individuals, it is a global optimization algorithm that uses the fitness function of the sum of the weighted distances between the drone and the target. It starts from an initial solution and finds the global optimal solution by randomly perturbing and improving the solution.

[0094] Among them, the beetle whisker algorithm is a single-body search algorithm that focuses on the behavior of a single individual and lacks the connection between individuals. If it is directly applied to a drone group, it may cause multiple drones to lock on the same target. Therefore, if the beetle whisker algorithm is matched in the "scenario-search algorithm pair" library, perform the following operations:

[0095] Step 1: Let each drone in the drone group contact other drones within its maximum communication distance to build a neighbor drone array set L;

[0096] For example, there are 4 drones in drone group A, which are recorded as l1, l2, l3 and l4 respectively; the diamond position diagram formed by drone group A is shown in Figure 1 .

[0097] For drone l1, it can contact drones l2 and l4 within its maximum communication distance, so the neighbor drone array set of drone l1 is L1 = {l2, l4};

[0098] For drone l2, it can contact drones l1, l3, and l4 within its maximum communication distance, so the neighbor drone array set of drone l2 is L2 = {l1, l3, l4};

[0099] For drone l3, it can contact drones l2 and l4 within its maximum communication distance, so the neighbor drone array set of drone l3 is L3 = {l2, l4};

[0100] For drone l4, its neighbor drone array set L4 = {l1, l2, l3};

[0101] Step 2: Calculate the distance between the target and each drone in the drone group, select the drone with the smallest distance, and determine whether to assign the target to the drone.

[0102] Specifically include:

[0103] (1) Calculate the distance between each drone in the drone group and a target, and transmit it to neighboring drones; select the one with the shortest distance to the target:

[0104] The formula is:

[0105] In the formula, y i represents the coordinates of the i-th target; x j represents the coordinates of the jth UAV; dis_min represents the minimum distance between the jth UAV and the i-th target.

[0106] For example, taking drone group A as an example, suppose the distance between drone l1 and the target is r1; the distance between drone l2 and the target is r2; the distance between drone l3 and the target is r3; the distance between drone l4 and the target is r4;

[0107] According to the relationship of the neighbor drone array set, because drone l1, drone l2 and drone l4 are neighbors, the data of drone l1, drone l2 and drone l4 are interoperable, so drone l1, drone l2 and drone l4 all store r1, r2 and r4; and because drone l3, drone l2 and drone l4 are neighbors, the data of drone l3, drone l2 and drone l4 are interoperable, so drone l2, drone l3 and drone l4 all store r2, r3 and r4; in summary, any drone in drone group A stores distance data r1, r2, r3 and r4.

[0108] Each drone in drone group A determines the size of r1, r2, r3 and r4, and finds that r1 is the smallest, which means that drone l1 is closest to the target.

[0109] (2) The jth UAV determines whether dis_min is less than the maximum communication distance of the jth UAV. If so, the i-th target is assigned to the j-th UAV; otherwise, the i-th target is not assigned to the j-th UAV.

[0110] The judgment formula is:

[0111] Lmax is the maximum communication distance of the jth UAV.

[0112] In (1), it has been determined that UAV l1 is closest to the target. According to the above formula, it is determined whether the distance between UAV l1 and the target is less than the maximum communication distance of UAV l1. If it is less, it indicates that UAV group A can allocate l1 to search for the target.

[0113] Step 3: If the i-th target is assigned to the j-th UAV, update the neighbor UAV array set of other UAVs except the j-th UAV; in the current scene, delete the i-th target and update the number of UAVs that can be assigned targets.

[0114] Specifically include:

[0115] (1): After the i-th target is assigned to the j-th UAV, the j-th UAV performs the beetle whisker algorithm to search for the target.

[0116] (2): For all drones except the jth drone, delete the jth drone in the neighbor drone array set and update the neighbor drone array set; in the corresponding scenario, delete the i-th target and update the number of drones that can be assigned targets.

[0117] For example: after UAV l1 is assigned a target, UAV l1 executes the Longicorn Whiskers algorithm to search for the target;

[0118] The drone l1 that has been assigned a target is deleted from the neighbor drone array sets of l2 and l4, and its neighbor drone array sets are updated. The updated neighbor drone array set L2 of drone l2 is L2 = {l3, l4}; the updated neighbor drone array set L3 of drone l3 is L3 = {l2, l4}; the updated neighbor drone array set L4 of drone l4 is L4 = {l2, l3}. In the corresponding scenario, the i-th target is also deleted, and the number of drones is updated to avoid other drones from repeatedly searching for the i-th target.

[0119] Step 4: By repeating the above steps 1 to 3, the drone cluster can use the beetle whisker algorithm to search for multiple targets.

[0120] Embodiment 2: A connectivity-based UAV cluster collaborative task search platform, comprising:

[0121] The construction module can launch drones in the drone group from a fixed position at the birth point; can customize the number of targets; can randomly distribute all targets on the map; can customize the target movement mode; can generate marker points and paths;

[0122] Planning module, which can select the search algorithm;

[0123] The result display module can display the drone’s starting point, target point, search time, drone status, drone images and drone’s historical path; it can generate search reports for the start and end of the drone search.

[0124] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for searching collaborative tasks of UAV clusters based on connectivity, characterized in that: The following steps are involved: S1: Building a drone target search model<U,O,F> ; U represents the drone model; O represents the scene model; F represents the search algorithm; S2: Calculate the information gain of the drone swarm at time t under different scenarios and different search algorithms, and bind the corresponding scenario and the search algorithm with the largest information gain as a "pair", which is recorded as a scenario-search algorithm pair; S3: Determine the current scene of the drone swarm, search the "scene-search algorithm pair" library, match the current scene with the scene in the "scene-search algorithm pair" library, and after the matching is completed, select the corresponding "scene-search algorithm pair", and the drone swarm executes the search algorithm in the "scene-search algorithm pair".

2. The method for searching for collaborative tasks of UAV clusters based on connectivity according to claim 1, characterized in that: Step S2 includes the following steps: S2.1: Assume there are m scenarios and z search algorithms; then the set of scenarios O is represented by: O = {O1, O2, ..., Om}; the set of search algorithms F is represented by: F = {F1, F2, ..., Fz}; S2.2: Select scenario O1 and search algorithm F1, and calculate the search probability of the drone swarm under this scenario and search algorithm at time t. The formula is: P(t) represents the search probability of the drone swarm at time t under this scenario and search algorithm; n is the number of targets set in this scenario; s(i,t) min is the minimum distance between all drones in the drone group and the i-th target at time t under this scenario and search algorithm; s (i) is the distance between the ith target and the initial position of the drone group; the initial position of the drone group means: the only birth point of the drone group in this scenario, all drones in the drone group start from this unique birth point, the birth point is set manually, and its coordinate position is determined; S2.3: Calculate the information entropy at time t for this scenario and search algorithm. The formula is: H(t)=-P(t)*lnP(t) (5); S2.4: Calculate the information gain at time t for this scenario and search algorithm. The formula is: IG(t)=H(t-1)-H(t) (6) S2.5: Replace another search algorithm F2, without changing the scene O1, and calculate the information gain of another search algorithm F2 under the scene O1 at time t according to equations (4), (5), and (6); S2.6: And so on... Calculate the information gain of different search algorithms in the scenario O1; S2.7: Select the search algorithm with the largest information gain in scenario O1; and combine the search algorithm and scenario O1 to form a scenario-search algorithm pair; S2.8: Select scenario O2 and search algorithm F1, and calculate the information gain of search algorithm F1 under scenario O2 at time t according to equations (4), (5), and (6); S2.9: Change different search algorithms without changing scene O2; according to equations (4), (5), (6), select the information gain of different search algorithms under scene O2; S2.10: Select the search algorithm with the largest information gain in scenario O2, and form a scenario-search algorithm pair with the search algorithm and scenario O2; S2.11: And so on...all search algorithms form scene-search algorithm pairs with scenes and are stored in the "scene-search algorithm pair" library.

3. The method for searching for collaborative tasks of a UAV cluster based on connectivity according to claim 1, characterized in that: If the algorithm executed in step S3 is the beetle whisker algorithm, the following steps are performed: Step 1: Let each drone in the drone group contact other drones within its maximum communication distance to build a neighbor drone array set L; Step 2: Calculate the distance between the target and each drone in the drone group, select the drone with the smallest distance, and determine whether to assign the target to the drone; Step 3: If the i-th target is assigned to the j-th drone, the drone starts to execute the beetle whisker algorithm and updates the neighbor drone array set of other drones except the j-th drone; in the current scene, delete the i-th target and update the number of drones that can be assigned; Step 4: By repeating the above steps 1 to 3, the drone cluster can use the beetle whisker algorithm to search for multiple targets.

4. The method for searching for collaborative tasks of UAV clusters based on connectivity according to claim 1, characterized in that: In step S1, labels D, M and P are used to divide the scene into 8 typical scenes; Where D is the distance label; The formula is: R0 represents the distance judgment threshold, which is set manually; R represents the maximum search radius of the drone; D has values ​​of 0 and 1; 0 represents a smaller search radius of the drone; 1 represents a larger search radius of the drone; M represents the number density label; The formula is: N is the number of targets in the scene; N U is the number of drones in the drone group; V0 is the number density judgment threshold, which is set manually; V represents the ratio of the number of drones to the number of search targets. If the ratio is greater than V0, the label M value is 1, which means that the resources in the current scene are sufficient; if the ratio is less than or equal to V0, the label M value is 0, which means that the resources in the current scene are scarce; P is the quantity label; The formula is: N is the number of targets in the scene; R is the maximum search radius of the drone; A0 is the quantity judgment threshold, which is set manually; A is the ratio of the search range of the unit drone to the number of targets in the scene. If the ratio is greater than A0, the label P value is 0, which reflects that the number of targets in the current scene is small, the drone search range is large, and the search difficulty is small. If the ratio is less than or equal to A0, the label P value is 1, which reflects that the number of targets in the current scene is large, the drone search range is small, and the search difficulty is large.

5. A UAV cluster collaborative task search platform based on connectivity, based on the UAV cluster collaborative task search method based on connectivity described in claim 1, characterized in that: include: A construction module that can launch drones in a swarm from a fixed location at the spawn point; Ability to customize the number of targets; All targets can be randomly distributed on the map; the target movement mode can be customized; marker points and paths can be generated; Planning module, which can select the search algorithm; The result display module can display the drone’s starting point, target point, search time, drone status, drone images and drone’s historical path; it can generate search reports for the start and end of the drone search.

Images