Multi-unmanned aerial vehicle cooperative search task allocation and path planning method

Abstract

The invention provides a multi-unmanned aerial vehicle cooperative search task allocation and path planning method, and belongs to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining a search task point set, an unmanned aerial vehicle take-off and landing site set, the number of unmanned aerial vehicles in each take-off and landing site, and the maximum voyage and speed of the unmanned aerial vehicles; based on the acquired data, constructing an optimization model, defining an objective function to minimize task completion time or minimize the number of dispatched unmanned aerial vehicles, and setting constraint conditions; and solving the optimization model based on a genetic algorithm, including initializing a population, defining a fitness function, performing iterative optimization by applying a combined operator of genetic operators, and outputting an optimal unmanned aerial vehicle task allocation and path planning result. An optimization model is established by comprehensively considering constraint conditions such as the number of unmanned aerial vehicles in each take-off and landing site, the voyage and the minimum task load, the optimization model is solved based on a double-segment coding strategy and an improved genetic algorithm of multi-operator hybrid optimization, and the problem of task allocation and path planning of multi-unmanned aerial vehicle collaborative search is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles (UAVs), and in particular relates to a method for allocating search tasks and planning paths among multiple UAVs in a collaborative manner. Background Art

[0002] Multi-UAV collaborative search is an important type of UAV application task. Task allocation and path planning are two key issues, especially when faced with a large number of search points. Their quality directly impacts the overall system performance. The multi-UAV collaborative search problem studies how to efficiently utilize multiple UAVs to complete a series of search tasks. Specifically, given a set of search points and a set of available UAVs, the challenge lies in rationally assigning search points to each UAV and planning a flight path to optimize the overall system's search task completion efficiency, while satisfying various constraints such as UAV range, number of UAVs, and takeoff and landing points.

[0003] Domestic and foreign scholars have conducted extensive research on the problem of multi-UAV collaborative search, mainly focusing on the application of intelligent algorithms in multi-UAV trajectory planning. However, how to achieve the optimal allocation of multi-UAV search tasks and trajectory planning when the number of UAVs is uncertain and the mission time limit is given needs further research.

[0004] To this end, the present invention provides a multi-UAV collaborative search task allocation and path planning method. Summary of the Invention

[0005] The present invention provides a multi-UAV collaborative search task allocation and path planning method to solve the multi-UAV collaborative search track planning problem of a given number of UAVs and the UAV number optimization and path planning problem within a given task completion time limit.

[0006] The present invention provides a method for allocating and planning multi-UAV collaborative search tasks and paths, the method comprising: Obtain the search mission point set, the drone take-off and landing site set, the number of drones at each take-off and landing site, and the maximum range and speed of drones; Based on the acquired data, an optimization model is constructed, the objective function is defined as minimizing the task completion time or minimizing the number of dispatched drones, and constraints are set; Solving the optimization model based on a genetic algorithm includes initializing a population, defining a fitness function, applying a combination operator of genetic operators for iterative optimization, and outputting optimal UAV task allocation and path planning results.

[0007] Furthermore, the search task point set is ,in, Indicates the number of search task points, search task points The location is ; The drone take-off and landing site collection is ,in, Indicates the number of drone take-off and landing sites, drone take-off and landing sites The location is ; Each take-off and landing site The number of drones in ; The maximum range of the drone is ; The drone's speed is .

[0008] Furthermore, based on the acquired data, an optimization model is constructed. The construction process is as follows: take-off and landing sites The collection of dispatched drones:

[0009] in, Indicates take-off and landing site The drones dispatched are gathered, Indicates take-off and landing site Number of drones dispatched, Indicates drone, ; drones The search task point set:

[0010] in, Indicates drone The search task point set, Indicates the allocation to the drone The number of search task points; drones Task path:

[0011] in, Indicates drone From the take-off and landing site Click to start and pass through the search task points in sequence , search for mission points 、 , search for mission points Then return to the take-off and landing site ; Path Length:

[0012] in, Indicates drone From the take-off and landing site Click to start and pass through the search task points in sequence , search for mission points 、 , search for mission points Then return to the take-off and landing site The path length, is the distance function between two points; drones Task time:

[0013] in, Indicates drone Task time; Multi-UAV collaborative search mission completion time:

[0014] in, Indicates the time it takes for multiple UAVs to complete a collaborative search mission.

[0015] Furthermore, the objective function of the optimization model is: At a given take-off and landing site Number of drones dispatched Minimize the time to complete the task , whose expression is:

[0016] Complete the task within a given time limit Minimize the total number of dispatched drones under the condition of , and its expression is: .

[0017] Furthermore, the constraints include: Drone path length Not exceeding the maximum range ,Right now:

[0018] Drone take-off and landing sites Number of drones dispatched No more than take-off and landing area Total number of drones ,Right now:

[0019] Each dispatched drone completes at least one search mission point, that is, the search mission point set Not empty, that is: .

[0020] Furthermore, solving the optimization model based on a genetic algorithm includes initializing a population, defining a fitness function, applying a combination operator of genetic operators for iterative optimization, and outputting the optimal UAV task allocation and path planning results, specifically including: A random initialization process is used to generate the initial population; Calculate the fitness value of all individuals in the current population; Divide the entire population into several subgroups; Each subgroup individual randomly selects a combination operator to perform operations to obtain a newly generated offspring individual; Determine whether the newly generated offspring individuals meet the constraints; If satisfied: retain the newly generated individual; If not satisfied: retain the original individual; Determine whether the maximum number of iterations has been reached; If it reaches: the process ends and the currently found optimal solution is output; If not reached: return to calculate the fitness values ​​of all individuals in the current population and start a new cycle.

[0021] Furthermore, the genetic algorithm adopts a two-segment chromosome encoding scheme; The first segment is a random arrangement of the search task point sequence; The second section is the segmentation position code, which is used to divide the UAV's search mission point sequence.

[0022] Furthermore, the fitness function is defined as the maximum time required for multiple UAVs to complete a collaborative search task, namely:

[0023] in, For population individuals.

[0024] Furthermore, the genetic algorithm further includes dividing the population into multiple subgroups for group optimization, and the population size and the subgroup size satisfy the following relationship:

[0025] in, is the population size, is the subgroup size, The number of subgroups is an integer. The population is divided into multiple subgroups, each of which evolves independently, improving population diversity and avoiding premature convergence.

[0026] Furthermore, the genetic operators include a flip operator, a swap operator, a shift operator, and a random split operator; The flipping operator randomly selects a gene of the path encoding based on the best individual in the subgroup and flips its order; The exchange operator is based on the best individual in the subgroup, randomly selecting two genes in the path encoding to exchange; The shift operator is based on the optimal individual in the subgroup, randomly selecting a gene segment of the path encoding for shift operation; The random splitting operator is based on the optimal individual in the subgroup and performs a random regeneration operation on the split position encoding; The combination operators include a combination of a flip operator and a random segmentation operator, a combination of a swap operator and a random segmentation operator, and a combination of a shift operator and a random segmentation operator.

[0027] It can be seen from the above technical solutions that the present invention has the following advantages: In the multi-UAV collaborative search task allocation and path planning method provided in this application, an optimization model is established by comprehensively considering constraints such as the number of UAVs at each take-off and landing site, the range, the minimum task volume, etc., and the optimization model is solved based on an improved genetic algorithm with a two-segment coding strategy and multi-operator hybrid optimization, thereby solving the multi-UAV collaborative search task allocation and path planning problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0029] Figure 1 This is a flowchart of an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0030] Figure 2 Schematic diagram of a coding scheme for an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0031] Figure 3 Schematic diagram of a flip operator in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0032] Figure 4 Schematic diagram of the exchange operator in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0033] Figure 5 Schematic diagram of a shift operator in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0034] Figure 6Schematic diagram of a random segmentation operator in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0035] Figure 7 Schematic diagram of the combination of a flip operator and a random segmentation operator in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0036] Figure 8 Schematic diagram of the combination of an exchange operator and a random segmentation operator in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0037] Figure 9 Schematic diagram of the combination of a shift operator and a random segmentation operator in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0038] Figure 10 This is a flow chart of a genetic algorithm solution model in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0039] Figure 11 This is a task situation diagram of an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0040] Figure 12 Schematic diagram of the shortest time task allocation and path planning of an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0041] Figure 13 Schematic diagram of the relationship between the shortest time and the number of iterations in an embodiment of the multi-UAV collaborative search task allocation and path planning method of the present invention.

[0042] Figure 14 This is a schematic diagram of the relationship between the length of a drone path and the number of iterations in an embodiment of the multi-drone collaborative search task allocation and path planning method of the present invention.

[0043] Figure 15 Schematic diagram of the relationship between the number of drones and the minimum task time in an embodiment of the multi-drone collaborative search task allocation and path planning method of the present invention. DETAILED DESCRIPTION

[0044] In order to make the application objectives, features, and advantages of this application more obvious and easy to understand, the technical solutions protected by this application will be clearly and completely described below using specific embodiments and drawings. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0045] The embodiments of the present application provide a multi-UAV collaborative search task allocation and path planning method to solve the currently urgently needed technical problems of realizing multi-UAV collaborative search trajectory planning for a given number of UAVs and optimizing the number of UAVs and their path planning within a given task completion time limit.

[0046] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0047] Figure 1 This is a flowchart of a multi-UAV collaborative search task allocation and path planning method provided in an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a multi-UAV collaborative search task allocation and path planning method, the method specifically comprising the following steps: Step S1: Obtain the search mission point set, the drone take-off and landing site set, the number of drones at each take-off and landing site, and the maximum range and speed of the drones.

[0048] Step S2: Based on the acquired data, an optimization model is constructed, the objective function is defined as minimizing the task completion time or minimizing the number of dispatched drones, and constraints are set.

[0049] Step S3: Solving the optimization model based on a genetic algorithm, including initializing the population, defining a fitness function, applying a combination operator of genetic operators for iterative optimization, and outputting the optimal UAV task allocation and path planning results.

[0050] The present invention establishes an optimization model by comprehensively considering the constraints such as the number of drones taking off and landing at each site, the range, the minimum mission volume, etc., solves the optimization model based on the genetic algorithm, and solves the problem of multi-drone collaborative search task allocation and path planning.

[0051] It supports optimization goals such as minimizing task completion time or minimizing the number of dispatched drones, adapting to the needs of different mission scenarios. Based on a genetic algorithm, a closed loop from task input to model construction, iterative optimization, and finally result output is achieved, avoiding the inefficiency of manual planning. Furthermore, as a refinement and expansion of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another multi-drone collaborative search task allocation and path planning method is provided, which includes: Obtain the set of search mission points, the set of drone take-off and landing sites, the number of drones at each take-off and landing site, and the maximum range and speed of drones.

[0052] Among them, the search task point set is ,in, Indicates the number of search task points, search task points The location is ; The drone take-off and landing site collection is ,in, Indicates the number of drone take-off and landing sites, drone take-off and landing sites The location is ; Various take-off and landing sites The number of drones in The maximum range of the drone is ; UAV speed is .

[0053] According to another embodiment of the present invention, an optimization model is constructed based on the acquired data, and the construction process is as follows: take-off and landing sites The collection of dispatched drones:

[0054] in, Indicates take-off and landing site The drones dispatched are gathered, Indicates take-off and landing site Number of drones dispatched, Indicates drone, ; drones The search task point set:

[0055] in, Indicates drone The search task point set, Indicates the allocation to the drone The number of search task points; drones Task path:

[0056] in, Indicates drone From the take-off and landing site Click to start and pass through the search task points in sequence , search for mission points 、 , search for mission points Then return to the take-off and landing site ; Path Length:

[0057] in, Indicates drone From the take-off and landing site Click to start and pass through the search task points in sequence , search for mission points 、 , search for mission points Then return to the take-off and landing site The path length, is the distance function between two points; drones Task time:

[0058] in, Indicates drone Task time; Multi-UAV collaborative search mission completion time:

[0059] in, Indicates the time it takes for multiple UAVs to complete a collaborative search mission.

[0060] Define the objective function as minimizing the task completion time or minimizing the number of dispatched drones, and set constraints.

[0061] In an exemplary embodiment, the objective function of the optimization model is: At a given take-off and landing site Number of drones dispatched Minimize the time to complete the task , whose expression is:

[0062] Complete the task within a given time limit Minimize the total number of dispatched drones under the condition of , and its expression is: .

[0063] The present invention addresses the two key issues of task allocation and path planning in multi-UAV collaborative search, takes the shortest task time and the minimum number of dispatched UAVs as the objective functions, and establishes an optimization model by comprehensively considering constraints such as the number of UAVs at each take-off and landing site, range, and minimum task volume.

[0064] According to yet another embodiment of the present invention, the constraint conditions include: Drone path length Not exceeding the maximum range ,Right now:

[0065] Drone take-off and landing sites Number of drones dispatched No more than take-off and landing area Total number of drones ,Right now:

[0066] Each dispatched drone completes at least one search mission point, that is, the search mission point set Not empty, that is: .

[0067] Solving the optimization model based on a genetic algorithm includes initializing the population, defining a fitness function, applying a combination of genetic operators for iterative optimization, and outputting the optimal UAV task allocation and path planning results, specifically including: Step S31: Generate an initial population by random initialization process; Step S32: Calculate the fitness value of all individuals in the current population; the fitness function is defined as the maximum time required for multiple UAVs to complete the collaborative search task, that is:

[0068] in, For individuals in the population, the smaller the fitness function value is, the shorter the task time is and the better the quality of the individual is; Step S33: Divide the entire population into several subgroups; divide the population into multiple subgroups for group optimization, and the population size and subgroup size satisfy the following relationship:

[0069] in, is the population size, is the subgroup size, Represents the number of subgroups, which is an integer.

[0070] Divide the population into multiple subgroups, each of which evolves independently, to improve population diversity and avoid premature convergence.

[0071] Step S34: Each subgroup individual randomly selects a combination operator to perform operations to obtain a newly generated offspring individual; Step S35: Determine whether the newly generated offspring individual meets the constraint conditions; the constraint conditions include: Drone path length Not exceeding the maximum range ,Right now:

[0072] Drone take-off and landing sites Number of drones dispatched No more than take-off and landing area Total number of drones ,Right now:

[0073] Each dispatched drone completes at least one search mission point, that is, the search mission point set Not empty, that is: ; If satisfied: retain the newly generated individual; If not satisfied: retain the original individual; Step S36: Determine whether the maximum number of iterations has been reached; If it reaches: the process ends and the currently found optimal solution is output; If not reached: return to step S32 to calculate the fitness values ​​of all individuals in the current population and start a new cycle.

[0074] The genetic algorithm adopts a two-segment chromosome encoding scheme; The first segment is a random arrangement of the search task point sequence; The second section is the segmentation position code, which is used to divide the UAV's search mission point sequence.

[0075] The chromosome encoding should reflect the search mission points for each drone and the order in which they are searched. To improve the efficiency of the solution, a two-part chromosome encoding scheme is adopted. The first part is the path encoding, ensuring that each mission point is searched only once; the second part is the path segment position encoding, which clearly defines the search mission allocation for each drone.

[0076] like Figure 2 As shown in the figure, for a search problem with n task points and m drones, the length of the chromosome is n+m-1. The first n bits are the random arrangement of the n search task points, and the last m-1 bits are the arrangement of the segmentation positions of the m drones on the n search task point sequence. The m-1 segmentation positions divide the n search task points into m segments, where each segment represents the search track of a drone.

[0077] Following the above encoding scheme and rules, in order to ensure the diversity of the population and a wide search space, a random initialization process is adopted to generate the initial population.

[0078] The fitness function is defined as the maximum time required for multiple UAVs to complete a collaborative search task, namely:

[0079] in, For individuals in the population, the smaller the fitness function value is, the shorter the task time is and the better the quality of the individual is.

[0080] The genetic algorithm further includes dividing the population into multiple subgroups for group optimization, and the population size and subgroup size satisfy the following relationship:

[0081] in, is the population size, is the subgroup size, Represents the number of subgroups, which is an integer.

[0082] Divide the population into multiple subgroups, each of which evolves independently, to improve population diversity and avoid premature convergence.

[0083] The genetic operators include a flip operator, an exchange operator, a shift operator, a random segmentation operator and a combination operator; like Figure 3 As shown, the flipping operator is based on the best individual in the subgroup, randomly selecting a gene of the path encoding and flipping its order; like Figure 4 As shown, the exchange operator is based on the best individual in the subgroup, randomly selecting two genes in the path encoding to exchange; like Figure 5 As shown, the shift operator is based on the optimal individual in the subgroup, randomly selecting a gene segment of the path encoding for shift operation; As shown in 6, the random segmentation operator is based on the optimal individual in the subgroup and performs a random regeneration operation on the segmentation position encoding; like Figure 7 、 Figure 8 、 Figure 9 As shown, the combination operator includes a combination of a flip operator and a random segmentation operator, a combination of a swap operator and a random segmentation operator, and a combination of a shift operator and a random segmentation operator.

[0084] The present invention is based on an improved genetic algorithm solution model with a dual-segment coding strategy (task point sequence coding + segmentation position coding) and multi-operator hybrid optimization (flip, exchange, shift, random segmentation and their combination operators), which effectively solves the task allocation and path planning problems of multi-UAV multi-target collaborative search.

[0085] For example, the algorithm parameters are set as follows: population size 80, subgroup size 8, and maximum number of iterations 5000.

[0086] Scenario 1: Given a given number of drones, the shortest time trajectory planning simulation: like Figure 11 As shown, it is assumed that there are 3 drone take-off and landing sites, namely ,in, The position is (0,0), The position is (50000,0), The position is (0,50000) and the unit is , at 100km 30 drone search mission points are randomly generated within a 100km range.

[0087] take-off and landing sites 、 、 Each drone is dispatched to complete the search mission of 30 mission points. The maximum range of each drone is 500km, and the speed is 100m / s, 80m / s, and 60m / s respectively. The algorithm solves the problem and the shortest time is 54.4 minutes. The search mission allocation and path planning of each drone are as follows: Figure 12 shown.

[0088] During the algorithm iteration process, the shortest time required for the drone to complete the search task changes as follows: Figure 13 As shown; the search path changes of each UAV are as follows Figure 14 shown.

[0089] Scenario 2: Mission time limit, number of drones and trajectory planning simulation: If the take-off and landing site 、 、 There are 5 drones in each scene, and other parameters are the same as those in scene 1. The relationship between the search mission time and the number of dispatched drones is solved by the algorithm. Figure 15 shown.

[0090] Figure 15 In the figure, the different mission times corresponding to the same number of drones represent different combinations of drones dispatched from different take-off and landing sites under the same number of drones.

[0091] like Figure 15 As shown in Figure 1, if the given mission completion time limit is 3000s, the minimum number of UAVs to be dispatched should be 3. The relationship between the different number of UAV combinations dispatched from different take-off and landing sites and the search mission time is shown in Table 1. Among them, the combinations [0, 3, 0], [1, 2, 0], [2, 0, 1], [2, 1, 0], and [3, 0, 0] all meet the conditions, and the combination [3, 0, 0] takes the least time.

[0092] Table 1 Relationship between different combinations and search task time

[0093] This paper addresses the two key issues of task allocation and path planning in collaborative search among multiple UAVs. Taking minimizing task time and minimizing the number of deployed UAVs as the objective functions, an optimization model is established by comprehensively considering constraints such as the number of UAVs at each take-off and landing site, the flight range, and the minimum task load. An improved genetic algorithm solution model is designed based on a two-segment coding strategy (task point sequence coding + segmentation position coding) and multi-operator hybrid optimization (flipping, swapping, shifting, random segmentation, and their combination operators). Simulation verification using typical scenarios demonstrates that the proposed model and algorithm can effectively solve the task allocation and path planning problems in collaborative search among multiple UAVs and multiple targets.

[0094] The above detailed description of the multi-UAV collaborative search task allocation and path planning method will further describe various embodiments of the present disclosure. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, and that the present disclosure should be construed to encompass all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of the present disclosure.

[0095] In the above, the terms "include" or "may include" used in various embodiments of the present disclosure indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have" and their cognates are intended only to indicate specific features, numbers, steps, operations, or combinations of the foregoing items, and should not be understood as excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing items.

[0096] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0097] Obviously, the embodiments described above are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0098] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0099] These changes, modifications, substitutions and variations to the embodiments without departing from the principles and spirit of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-UAV collaborative search task allocation and path planning method, characterized in that: The method comprises: Obtain the search mission point set, the drone take-off and landing site set, the number of drones at each take-off and landing site, and the maximum range and speed of drones; Based on the acquired data, an optimization model is constructed, the objective function is defined as minimizing the task completion time or minimizing the number of dispatched drones, and constraints are set; Solving the optimization model based on a genetic algorithm includes initializing a population, defining a fitness function, applying a combination operator of genetic operators for iterative optimization, and outputting optimal UAV task allocation and path planning results.

2. The multi-UAV collaborative search task allocation and path planning method according to claim 1, characterized in that: The search task point set is ,in, Indicates the number of search task points, search task points The location is ; The drone take-off and landing site collection is ,in, Indicates the number of drone take-off and landing sites, drone take-off and landing sites The location is ; Each take-off and landing site The number of drones in ; The maximum range of the drone is ; The drone's speed is .

3. The multi-UAV collaborative search task allocation and path planning method according to claim 2, characterized in that: Based on the acquired data, an optimization model is constructed. The construction process is as follows: take-off and landing sites The collection of dispatched drones: in, Indicates take-off and landing site The drones dispatched are gathered, Indicates take-off and landing site Number of drones dispatched, Indicates drone, ; drones The search task point set: in, Indicates drone The search task point set, Indicates the allocation to the drone The number of search task points; drones Task path: in, Indicates drone From the take-off and landing site Click to start and pass through the search task points in sequence , search for mission points 、 , search for mission points Then return to the take-off and landing site ; Path Length: in, Indicates drone From the take-off and landing site Click to start and pass through the search task points in sequence , search for mission points 、 , search for mission points Then return to the take-off and landing site The path length, is the distance function between two points; drones Task time: in, Indicates drone Task time; Multi-UAV collaborative search mission completion time: in, Indicates the time it takes for multiple UAVs to complete a collaborative search mission.

4. The multi-UAV collaborative search task allocation and path planning method according to claim 3, characterized in that: The objective function of the optimization model is: At a given take-off and landing site Number of drones dispatched Minimize the time to complete the task , whose expression is: Complete the task within a given time limit Minimize the total number of dispatched drones under the condition of , and its expression is: 。 5. The multi-UAV collaborative search task allocation and path planning method according to claim 4, characterized in that: The constraints include: Drone path length Not exceeding the maximum range ,Right now: Drone take-off and landing sites Number of drones dispatched No more than take-off and landing area Total number of drones ,Right now: Each dispatched drone completes at least one search mission point, that is, the search mission point set Not empty, that is: 。 6. The multi-UAV collaborative search task allocation and path planning method according to claim 5, characterized in that: Solving the optimization model based on a genetic algorithm includes initializing the population, defining a fitness function, applying a combination of genetic operators for iterative optimization, and outputting the optimal UAV task allocation and path planning results, specifically including: A random initialization process is used to generate the initial population; Calculate the fitness value of all individuals in the current population; Divide the entire population into several subgroups; Each subgroup individual randomly selects a combination operator to perform operations to obtain a newly generated offspring individual; Determine whether the newly generated offspring individuals meet the constraints; If satisfied: retain the newly generated individual; If not satisfied: retain the original individual; Determine whether the maximum number of iterations has been reached; If it reaches: the process ends and the currently found optimal solution is output; If not reached: return to calculate the fitness values ​​of all individuals in the current population and start a new cycle.

7. The multi-UAV collaborative search task allocation and path planning method according to claim 6, characterized in that: The genetic algorithm adopts a two-segment chromosome encoding scheme; The first segment is a random arrangement of the search task point sequence; The second section is the segmentation position code, which is used to divide the UAV's search mission point sequence.

8. The multi-UAV collaborative search task allocation and path planning method according to claim 7, characterized in that: The fitness function is defined as the maximum time required for multiple UAVs to complete a collaborative search task, namely: in, For population individuals.

9. The multi-UAV collaborative search task allocation and path planning method according to claim 8, characterized in that: The genetic algorithm further includes dividing the population into multiple subgroups for group optimization, and the population size and subgroup size satisfy the following relationship: in, is the population size, is the subgroup size, Represents the number of subgroups, which is an integer.

10. The multi-UAV collaborative search task allocation and path planning method according to claim 9, characterized in that: The genetic operators include a flip operator, an exchange operator, a shift operator, and a random segmentation operator; The flipping operator randomly selects a gene of the path encoding based on the best individual in the subgroup and flips its order; The exchange operator is based on the best individual in the subgroup, randomly selecting two genes in the path encoding to exchange; The shift operator is based on the optimal individual in the subgroup, randomly selecting a gene segment of the path encoding for shift operation; The random splitting operator is based on the optimal individual in the subgroup and performs a random regeneration operation on the split position encoding; The combination operators include a combination of a flip operator and a random segmentation operator, a combination of a swap operator and a random segmentation operator, and a combination of a shift operator and a random segmentation operator.

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