An unmanned ship obstacle removal task allocation method based on an improved genetic algorithm

Abstract

The present application relates to the technical field of unmanned ship autonomous control, and particularly relates to an unmanned ship obstacle removal task allocation method based on an improved genetic algorithm. The present application reasonably determines a task target and accurately analyzes task constraints by comprehensively considering factors such as obstacle target position distribution, navigation speed of each ship and number of carried explosives, constructs a multi-ship cooperative task allocation mathematical model that fits actual use scenarios, reasonably allocates an obstacle removal target sequence to each unmanned ship, and ensures smooth execution of the obstacle removal task. The present application uses an improved genetic algorithm to solve the multi-ship cooperative obstacle removal task allocation problem, ensures comprehensiveness of individual information by adding a breeding selection strategy, enhances local search capability of the algorithm through local mutation operation, balances time for each ship to execute a task while ensuring that the total navigation route of the unmanned ship obstacle removal is the shortest, realizes cooperative allocation of targets of each ship, and greatly improves overall execution efficiency of the task.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous control of unmanned boats, and in particular to a method for allocating obstacle clearance tasks of unmanned boats based on an improved genetic algorithm. Background Art

[0002] Currently, extensive research has been conducted both domestically and internationally on the problem of task allocation. The primary goal of task allocation is to ensure that tasks are executed with minimal energy consumption, over short distances, and within short timeframes. However, due to the constraints of practical scenarios, task size and complexity increase the difficulty of solving this problem. Key approaches currently include swarm intelligence algorithms, contract net algorithms, and evolutionary algorithms. Swarm intelligence algorithms mimic the behavior of biological populations in nature. Typical swarm algorithms include ant colony algorithms, particle swarm algorithms, and bee colony algorithms. Contract net algorithms, derived from contract mechanisms in business processes, simulate task negotiation by assigning roles to bidders, bidders, and successful bidders, enabling a rational analysis and evaluation of resources to achieve task allocation. Evolutionary algorithms, including genetic algorithms, genetic programming, and evolutionary programming, all mimic the mechanisms of biological evolution and inheritance in nature and are used to solve large-scale, complex engineering problems. Genetic algorithms are a typical example of evolutionary algorithms. These algorithms employ global random search and optimization methods based on natural selection, following the fundamental Darwinian principle of "survival of the fittest." During the evolutionary process, they engage in organized yet random information exchange. As the population evolves, the genes of superior individuals are gradually retained and recombined, continuously producing new and better individuals. This process is similar to biological evolution, where excellent characteristics are continuously inherited, and repeated evolution produces optimal or suboptimal solutions for the population.

[0003] The genetic algorithm, first proposed by J. Holland in 1975, is a method for optimizing by simulating the genetic mechanisms of biological evolution in nature. This method first initializes the population, giving it a set of initial solutions to the problem. It then performs genetic operations such as individual selection, crossover, and mutation to generate new solutions that meet the required fitness. This process is repeated until the optimal solution is achieved. Individual fitness is a key factor in the genetic algorithm's "population selection" process. The algorithm uses this fitness to select those who will survive into the next generation. Individuals with high fitness are more likely to be selected. These selected individuals undergo a series of genetic operations, such as crossover and mutation, to transform and reorganize their genes. Because heredity ensures the continuity and stability of genes during biological evolution, while variability creates genetic diversity, genetic algorithms not only enrich the population but also optimize individual quality, preserving high-quality genes. This, in turn, results in the algorithm continuously optimizing solutions and gradually approaching the optimal solution. As a highly adaptable evolutionary algorithm, the genetic algorithm is virtually unconstrained by the scenario or problem type. It does not require complex derivation operations or specialized rules, but simply performs a random search within the problem's solution space. At the same time, genetic algorithms also have problems such as difficult encoding methods and weak local search capabilities. The application research and improvement of genetic algorithms are currently hot issues in the field of optimization.

[0004] When performing obstacle clearance tasks, unmanned boats in existing technologies travel above obstacles and use delivery equipment to drop explosives to clear the obstacles. However, when multiple unmanned boats jointly perform obstacle clearance tasks, existing technologies lack an efficient task allocation method, which can easily cause some unmanned boats to be idle, affecting the efficiency of obstacle clearance tasks. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to propose a method for allocating obstacle clearance tasks for unmanned boats based on an improved genetic algorithm, which can comprehensively consider factors such as the location distribution of obstacle targets, the navigation speed of each boat, and the number of explosives carried, and reasonably allocate obstacle clearance target sequences for each unmanned boat, ensuring that the workload of each boat is balanced under the premise of smooth execution of the task, thereby improving the overall execution efficiency of the task.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for allocating obstacle clearance tasks for unmanned boats based on an improved genetic algorithm comprises the following steps:

[0008] S1, building a multi-boat collaborative obstacle clearance task allocation model;

[0009] S2, determine the initial position of the UAV cluster, the distribution of obstacle targets, the speed of the UAVs, and the number of explosives they carry;

[0010] S3, using the double chromosome encoding method to encode the correspondence between the unmanned boat and the target task sequence, and initialize the population;

[0011] S4, evaluate the fitness of the population through the fitness function, calculate the fitness value of the individual, and determine whether the evolution termination condition is met. If so, output the best individual and end the allocation process. If not, continue to step S5;

[0012] S5, select individuals from the population to be retained to the next generation, and calculate the number of offspring produced by the individuals, so that individuals with high fitness generate multiple offspring, while retaining individuals with poor fitness to produce a small number of offspring;

[0013] S6, cross-match the individuals according to the crossover probability, exchange the gene fragments of the two parent individuals, and perform corresponding mutation operations on the individuals according to the mutation probability to generate new individuals; when the evolution termination condition is met, output the best individual, that is, the best allocation sequence for the unmanned boat obstacle clearance task, otherwise re-execute step S4.

[0014] Furthermore, in step S1, the multi-boat collaborative obstacle removal task allocation model is constructed, specifically comprising the following steps:

[0015] S11, assuming that the unmanned boat cluster performing the obstacle clearance task is U={U1,U2,...,U n}, unmanned boat U i , the starting coordinate of i∈[1,n] is The amount of explosives carried is The navigation speed of the unmanned boat in the obstacle clearance mission is recorded as The obstacle target to be cleared is M={M1,M2,...,M m}, obstacle M j , the coordinates of j∈[1,m] are Obstacle M j With obstacle M k , the Euclidean distance between k∈[1,m] is:

[0016]

[0017] Unmanned Boat U i The assigned obstacle target is recorded as Contains the set M goals;

[0018] S12, determine the objective function of multi-boat collaborative obstacle clearance task allocation:

[0019]

[0020] in, Unmanned Boat U iFrom the initial position to the obstacle M j distance, and is the indicative function of whether the unmanned boat is associated with the obstacle, λ1 and λ2 are the distance cost and time cost coefficients respectively;

[0021] S13, determine the constraints:

[0022] 1) The number of explosives carried by the unmanned boat must be greater than the number of obstacles assigned to it.

[0023]

[0024] 2) The UAV needs to eliminate all targets, and each target can only be assigned to one UAV, satisfying:

[0025]

[0026] 3) Each unmanned boat is assigned at least one target, which satisfies:

[0027]

[0028] Furthermore, in step S3, the dual chromosome encoding method is used, in which one chromosome represents the unmanned boat sequence U and the other chromosome represents the target sequence M; the gene position on the chromosome represents the unmanned boat or target number, and the alleles of the two chromosomes represent the mapping relationship between the unmanned boat and the target.

[0029] The initialization population is specifically:

[0030] For m obstacle targets, they are numbered from 1 to m, and then a sequence of 1 to m chromosome genes is randomly generated. At the same time, any number from 1 to n is generated on the allele of another chromosome, and the access sequence of the unmanned boat to the obstacle targets is obtained.

[0031] Furthermore, in step S4, the fitness function is set to:

[0032]

[0033] Furthermore, in step S5, the number of offspring generated by individual i in the population is:

[0034]

[0035] Among them pop max is the maximum number of offspring that an individual can generate, pop min is the minimum number of offspring that an individual can generate, f i is the fitness of individual i, minf is the best fitness of the individual, and maxf is the worst fitness of the individual.

[0036] Furthermore, in step S6, the mutation operation specifically includes:

[0037] 1) Exchange mutation

[0038] Randomly swap the positions of two targets in the target sequence, i.e.

[0039]

[0040] 2) Reversal of mutation

[0041] Randomly reverse a segment in the target sequence, that is,

[0042]

[0043] 3) Insertion mutation

[0044] Randomly insert a target in the target sequence into another position, that is,

[0045]

[0046] 4) Local variation

[0047] For the target point M that is closer j and M k , if it is assigned to different unmanned boats U in the above genetic operation p and U q , then the target points are randomly exchanged between the above unmanned boats and the fitness value is calculated. If the fitness value decreases, the target sequence is updated; otherwise, no change is made.

[0048] Furthermore, the evolution termination conditions include: the target individual no longer changes or the maximum number of iterations is reached.

[0049] Compared with the prior art, the present invention has the following main advantages:

[0050] 1. This paper proposes a method for assigning obstacle clearance tasks to unmanned boats based on an improved genetic algorithm. By comprehensively considering factors such as the location distribution of obstacle targets, the navigation speed of each boat, and the number of explosives carried, the method rationally determines the task objectives, accurately analyzes the task constraints, and constructs a mathematical model for multi-boat collaborative task allocation that fits the actual usage scenario. This method rationally assigns an obstacle clearance target sequence to each unmanned boat, ensuring the smooth execution of the obstacle clearance task.

[0051] 2. The present invention adopts an improved genetic algorithm to solve the problem of multi-boat collaborative obstacle clearance task allocation. By adding a breeding selection strategy, the comprehensiveness of individual information is guaranteed; through local mutation operations, the algorithm's local search capability is enhanced; while ensuring the shortest overall route for unmanned boat obstacle clearance, the time it takes for each boat to execute the task is balanced, achieving collaborative allocation of targets for each boat and significantly improving the overall efficiency of task execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of an improved genetic algorithm according to an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of a double chromosome encoding method in an embodiment of the present invention;

[0054] Figure 3 Schematic diagram of local variation in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention 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 for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0056] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, or two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0057] Example 1: This embodiment provides a method for allocating unmanned boat obstacle clearance tasks based on an improved genetic algorithm, which mainly includes the following steps:

[0058] The first step is to build a multi-boat collaborative obstacle clearance task allocation model:

[0059] minJ=λ1d U +λ2σ t

[0060]

[0061] Among them, U={U1,U2,...,U n} is a cluster of unmanned boats, Unmanned Boat U i ,i∈[1,n] navigation speed in the obstacle clearance task, is the number of explosives carried by the unmanned boat, M={M1,M2,...,M m} is the obstacle target sequence, Unmanned Boat U i From the initial position to the obstacle M j distance, is the obstacle M j and obstacle M k , the distance between k and j∈[1,m], and is the indicative function of whether the unmanned boat is associated with the obstacle, λ1 and λ2 are the distance cost and time cost coefficients respectively.

[0062] The second step is to set the relevant parameters and the initial position of the unmanned boat cluster. Obstacle target location distribution Unmanned boat speed and the number of explosives

[0063] In the third step, the correspondence between the UAV and the target sequence is encoded using the dual-chromosome encoding method, and the population is initialized. For m obstacle targets, they are numbered from 1 to m, and then a sequence of chromosome genes from 1 to m is randomly generated. At the same time, any number from 1 to n is generated on the allele of the other chromosome, and the access sequence of the UAV to the obstacle target is obtained.

[0064] The fourth step is to evaluate the fitness of the population and calculate the fitness value f of the individual i , determine whether the evolution termination conditions are met. If so, output the best individual and end the process; otherwise, perform genetic operations on the population and go to the fifth step.

[0065] The fifth step is to select individuals from the population to be retained for the next generation using the following formula:

[0066]

[0067] Calculate the number of offspring produced by individuals so that individuals with high fitness can generate multiple offspring to ensure the continuity of good genes. At the same time, in order to avoid losing useful information from individuals with poor fitness, individuals with poor fitness are retained to produce a small number of offspring.

[0068] Step 6: According to the crossover probability P c Cross-match the individuals and exchange the gene fragments of the two parent individuals; according to the mutation probability P m Perform operations such as reversal mutation, swap mutation, insertion mutation, and local mutation on individuals. Through these genetic operations, new individuals are generated and the population evolves.

[0069] Example 2: This example provides a method for allocating unmanned boat obstacle clearance tasks based on an improved genetic algorithm, including:

[0070] (1) Basic assumptions

[0071] Assume that the unmanned boat cluster performing the obstacle removal task is U={U1,U2,...,U n}, unmanned boat U i , the starting coordinate of i∈[1,n] is The amount of explosives carried is The navigation speed of the unmanned boat in the obstacle clearance mission is recorded as The obstacle target to be cleared is M={M1,M2,...,M m}, obstacle M j , the coordinates of j∈[1,m] are Obstacle M j With obstacle M k , the Euclidean distance between k∈[1,m] is:

[0072]

[0073] Unmanned Boat U i The assigned obstacle target is recorded as Contains the set M goals.

[0074] (2) Objective function

[0075] When multiple boats work together to perform obstacle removal tasks, it is necessary to achieve the shortest overall obstacle removal route and a balanced obstacle removal time for each boat. The total obstacle removal route for multiple boats is

[0076]

[0077] The first half is the sum of the distances from each boat to its first target, and the second half is the sum of the distances to all targets. and Is the characteristic function:

[0078]

[0079] Unmanned Boat U i Obstacle removal collection The time resources consumed are

[0080]

[0081] The average time taken by each boat to perform the obstacle clearance task is

[0082]

[0083] In order to ensure that the time taken by each boat to perform the task is relatively balanced, that is, the deviation between the time taken by each boat to clear the obstacle and the average time is small, the standard deviation is used to represent the deviation of the time taken by each boat relative to the average value.

[0084]

[0085] A large standard deviation indicates that the time taken by most boats differs greatly from the average time taken; a small standard deviation indicates that the time taken by each boat is close to the average time taken.

[0086] In summary, in order to ensure that the total route is shortest and the time consumption of each boat is relatively balanced when multiple boats cooperate to perform the obstacle removal task, the multi-objective function is expressed as

[0087]

[0088] Among them, λ1 and λ2 are the distance cost and time cost coefficients respectively.

[0089] (3) Constraints

[0090] 1) The number of explosives carried by the unmanned boat must be greater than the number of obstacles assigned to it.

[0091]

[0092] 2) The UAV needs to eliminate all targets, and each target can only be assigned to one UAV, satisfying:

[0093]

[0094] 3) Each unmanned boat is assigned at least one target, which satisfies:

[0095]

[0096] like Figures 1 to 3 As shown, this application uses an improved genetic algorithm to solve the above-mentioned multi-boat collaborative obstacle clearance task allocation model. Each allocation scheme is regarded as a chromosome. Several chromosomes form a population. Through genetic operations such as crossover and mutation, chromosomes with strong adaptability are generated. The population continues to evolve and individuals with good fitness are retained. To ensure the diversity of population evolution and improve the efficiency of population evolution, an improved genetic algorithm with a local optimization operator is proposed. The improved genetic algorithm includes the following steps:

[0097] S1, gene encoding

[0098] The UAV task allocation scheme is represented by a dual-chromosome encoding method, where one chromosome represents the UAV sequence U and the other represents the target sequence M. The gene positions on the chromosome represent the UAV or target number, and the alleles of the two chromosomes represent the mapping relationship between the UAV and the target.

[0099] S2, population initialization

[0100] Initialize the obstacle target sequence and unmanned boat sequence of each individual in the population, which are generally generated by random generation, that is, generating chromosome genes M1 to M m Randomly arranged individuals, the other chromosome randomly generates alleles numbered from U1 to U n genes.

[0101] S3, calculate the fitness function

[0102] The fitness function is an important indicator for measuring the quality of an individual and an important factor affecting the convergence speed of the algorithm. It is usually set as the objective function of the mathematical model. In the multi-vessel collaborative obstacle removal task allocation model, on the one hand, it is necessary to ensure that the total route of all unmanned boats performing the obstacle removal task is the shortest, and on the other hand, the time spent by each boat in performing the obstacle removal task is relatively balanced. Therefore, the fitness function is set as:

[0103]

[0104] S4, select operation

[0105] Selection refers to the selection of certain individuals from the current population to the next generation population. During the evolutionary process, individuals with high fitness are usually more likely to be selected. In order to ensure genetic diversity, offspring populations are generated through reproduction strategies, that is, individuals with high fitness generate multiple offspring to ensure the continuity of excellent genes. At the same time, in order to avoid losing useful information from individuals with poor fitness, individuals with poor fitness are retained to produce a small number of offspring, thereby more comprehensively representing population information.

[0106] The number of offspring produced by individual i in the population is:

[0107]

[0108] Among them pop max is the maximum number of offspring that an individual can generate, pop min is the minimum number of offspring that an individual can generate, f i is the fitness of individual i, minf is the best fitness of the individual, and maxf is the worst fitness of the individual.

[0109] S5, genetic operator operation

[0110] 1) Cross

[0111] Crossover refers to the exchange of some genes between two individuals in a mating exchange manner to form a new individual, which can improve the diversity of chromosomes and expand the search space of solutions. The crossover operation of chromosomes is carried out by partial matching crossover. In the process of evolution, the crossover probability P cPerform a crossover operation. First, randomly generate two breakpoints and exchange the genes between the breakpoints of the two individuals. For example, the target sequences of individuals I and J are:

[0112] I:M1-M l -M k -M j

[0113] J:M2-M m -M s -M t

[0114] The generated breakpoints are 1 and 3. The two middle gene positions are selected for exchange, and then I' and J' are:

[0115] I′:M1-M m -M s -M j

[0116] J′:M2-M l -M k -M t

[0117] Since each target must be eliminated by an unmanned boat and only needs to be eliminated once, the target sequence of an unmanned boat cannot have the same number or a target with a certain number missing at the same time. For the phenomenon of gene duplication and / or deletion, conflict detection is required. The corresponding gene is modified according to the mapping relationship of the exchanged gene fragments and the original chromosome, and the two parent individuals are tested in turn until the detection is completed and the transformation is completed, then two new individuals are generated.

[0118] 2) Variation:

[0119] Mutation is the operation of generating probabilistic mutations in individual genes, allowing the genes on chromosomes to recombine and form new individuals. The following mutation operations are performed according to the mutation probability:

[0120] i. Exchange mutation

[0121] Randomly swap the positions of two targets in the target sequence, i.e.

[0122]

[0123] ii. Reversal of mutation

[0124] Randomly reverse a segment in the target sequence, that is,

[0125]

[0126] iii. Insertion mutation

[0127] Randomly insert a target in the target sequence into another position, that is,

[0128]

[0129] iv. Local variation

[0130] In order to enhance the local search capability of the algorithm, an individual local mutation strategy is proposed. j and M k , if it is assigned to different unmanned boats U in the above genetic operation p and U q , then the target points are randomly exchanged between the above unmanned boats and the fitness value is calculated. If the fitness value decreases, the target sequence is updated; otherwise, no change is made.

[0131] S6, evolution termination judgment

[0132] The fitness function of the multi-vessel collaborative obstacle clearance task allocation model is to minimize the total route of the unmanned boats and ensure that each boat performs the task in a balanced manner. Therefore, when the target no longer changes during the process, the termination condition of the genetic algorithm is met. At the same time, the maximum number of iterations of the algorithm is set. When the maximum number of iterations is reached and the termination condition is also met, the genetic algorithm is terminated and the optimal individual is output, that is, the optimal allocation sequence for the unmanned boat obstacle clearance task. Otherwise, the fitness of the individuals in the new population is recalculated.

[0133] Furthermore, all parts of this application that are not described in detail are the same as the existing technology or are implemented using the existing technology.

[0134] In summary:

[0135] 1. This paper proposes a method for assigning obstacle clearance tasks to unmanned boats based on an improved genetic algorithm. By comprehensively considering factors such as the location distribution of obstacle targets, the navigation speed of each boat, and the number of explosives carried, the method rationally determines the task objectives, accurately analyzes the task constraints, and constructs a mathematical model for multi-boat collaborative task allocation that fits the actual usage scenario. This method rationally assigns an obstacle clearance target sequence to each unmanned boat, ensuring the smooth execution of the obstacle clearance task.

[0136] 2. The present invention adopts an improved genetic algorithm to solve the problem of multi-boat collaborative obstacle clearance task allocation. By adding a breeding selection strategy, the comprehensiveness of individual information is guaranteed; through local mutation operations, the algorithm's local search capability is enhanced; while ensuring the shortest overall route for unmanned boat obstacle clearance, the time it takes for each boat to execute the task is balanced, achieving collaborative allocation of targets for each boat and significantly improving the overall efficiency of task execution.

[0137] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for allocating unmanned boat obstacle clearance tasks based on an improved genetic algorithm, characterized in that: The steps include: S1, building a multi-boat collaborative obstacle clearance task allocation model; S2, determine the initial position of the UAV cluster, the distribution of obstacle targets, the speed of the UAVs, and the number of explosives they carry; S3, using the double chromosome encoding method to encode the correspondence between the unmanned boat and the target task sequence, and initialize the population; S4, evaluate the fitness of the population through the fitness function, calculate the fitness value of the individual, and determine whether the evolution termination condition is met. If so, output the best individual and end the allocation process. If not, continue to step S5; S5, select individuals from the population to be retained to the next generation, and calculate the number of offspring produced by the individuals, so that individuals with high fitness generate multiple offspring, while retaining individuals with poor fitness to produce a small number of offspring; S6, cross-matching the individuals according to the crossover probability, exchanging the gene segments of the two parent individuals, and performing corresponding mutation operations on the individuals according to the mutation probability to generate new individuals; when the evolution termination condition is met, the best individual is output, that is, the best allocation sequence for the unmanned boat obstacle clearance task, otherwise step S4 is repeated; In step S1, the multi-boat collaborative obstacle removal task allocation model is constructed, which specifically includes the following steps: S11, assuming that the unmanned boat cluster performing the obstacle clearance task is U={U1,U2,...,U n }, unmanned boat U i , the starting coordinate of i∈[1,n] is The amount of explosives carried is The navigation speed of the unmanned boat in the obstacle clearance mission is recorded as The obstacle target to be cleared is M={M1,M2,...,M m }, obstacle M j , the coordinates of j∈[1,m] are Obstacle M j With obstacle M k , the Euclidean distance between k∈[1,m] is: Unmanned Boat U i The assigned obstacle target is recorded as Contains the set M goals; S12, determine the objective function of multi-boat collaborative obstacle clearance task allocation: in, Unmanned Boat U i From the initial position to the obstacle M j distance, and is the indicative function of whether the unmanned boat is associated with the obstacle, λ1 and λ2 are the distance cost and time cost coefficients respectively; S13, determine the constraints: 1) The number of explosives carried by the unmanned boat must be greater than the number of obstacles assigned to it. 2) The UAV needs to eliminate all targets, and each target can only be assigned to one UAV, satisfying: 3) Each unmanned boat is assigned at least one target, which satisfies:

2. The unmanned boat obstacle removal task allocation method based on improved genetic algorithm according to claim 1 is characterized in that In step S3, the dual chromosome encoding method is used, in which one chromosome represents the unmanned boat sequence U and the other chromosome represents the target sequence M; the gene position on the chromosome represents the unmanned boat or target number, and the alleles of the two chromosomes represent the mapping relationship between the unmanned boat and the target.

3. The unmanned boat obstacle removal task allocation method based on improved genetic algorithm according to claim 2 is characterized in that: The initialization population is specifically: For m obstacle targets, they are numbered from 1 to m, and then a sequence of 1 to m chromosome genes is randomly generated. At the same time, any number from 1 to n is generated on the allele of another chromosome, and the access sequence of the unmanned boat to the obstacle targets is obtained.

4. The unmanned boat obstacle removal task allocation method based on improved genetic algorithm according to claim 1 is characterized in that In step S4, the fitness function is set to:

5. The unmanned boat obstacle removal task allocation method based on improved genetic algorithm according to claim 4 is characterized in that In step S5, the number of offspring produced by individual i in the population is: Among them pop max is the maximum number of offspring that an individual can generate, pop min is the minimum number of offspring that an individual can generate, f i is the fitness of individual i, minf is the best fitness of the individual, and maxf is the worst fitness of the individual.

6. The unmanned boat obstacle removal task allocation method based on improved genetic algorithm according to claim 1 is characterized in that In step S6, the mutation operation specifically includes: 1) Exchange mutation Randomly swap the positions of two targets in the target sequence, i.e. 2) Reversal of mutation Randomly reverse a segment in the target sequence, that is, 3) Insertion mutation Randomly insert a target in the target sequence into another position, that is, 4) Local variation For the target point M that is closer j and M k , if it is assigned to different unmanned boats U in the above genetic operation p and U q , then the target points are randomly exchanged between the above unmanned boats and the fitness value is calculated. If the fitness value decreases, the target sequence is updated; otherwise, no change is made.

7. The unmanned boat obstacle removal task allocation method based on improved genetic algorithm according to claim 6, characterized in that: The evolution termination conditions include: the target individual no longer changes or the maximum number of iterations is reached.

Images