A control method and system for a multi-machine collaborative security patrol vehicle based on autonomous navigation

Abstract

The present invention relates to a control method and system for a multi-machine collaborative security patrol car based on autonomous navigation. The method comprises: U1. multiple security patrol cars perform patrol tasks, collect data information on the historical driving trajectory of each patrol car, and obtain data information on the position and status of each patrol car in real time, and predict the trajectory points of each patrol car based on an improved BP neural network regression prediction algorithm with a Spearman correlation coefficient to obtain data information on the predicted trajectory points of each patrol car, and construct a multi-machine collaborative path planning model for the security patrol car, perform synchronous dynamic planning on the driving path of each patrol car, and obtain data information on the planned driving path of each patrol car. The present invention not only allows the inspection and security tasks of different vehicles to be shared on a cloud terminal, and changes the task arrangement according to the execution status of the task to expand the group operation efficiency of the security patrol car, but also improves the practical application value of the security patrol car.

Description

Technical Field

[0001] The present invention relates to the technical field of security patrol vehicles, and in particular to a control method and system for a security patrol vehicle based on autonomous navigation and multi-machine collaboration. Background Art

[0002] As cities continue to expand, security plays an increasingly important role in today's society. In densely populated areas such as residential communities, commercial districts, and business offices, various incidents frequently occur, often necessitating the deployment of police officers and patrol cars to facilitate patrol inspections and rapid response to emergencies.

[0003] However, judging by the functions currently demonstrated in the industry, the service focus of security patrol cars (robots) will be on monitoring and patrolling. Through fully autonomous patrolling and monitoring in the scene, they will collect data from the real world, analyze and identify it through AI algorithms, check for risk factors that affect social security, and provide feedback to the background and remind manual personnel or contact the police for processing.

[0004] For automated patrols, which rely on autonomous navigation systems, the industry primarily uses laser SLAM technology. While SLAM technology is constantly evolving, and single-vehicle (robot) mapping and navigation technology currently possesses a certain degree of robustness, when faced with mapping requirements in large-scale environments, global errors accumulate, significantly impacting the mapping accuracy of a single vehicle (robot). Furthermore, in some harsh environments, unexpected failures of a single vehicle can render it unable to complete security missions.

[0005] Compared with single-vehicle intelligence, multi-vehicle intelligent collaborative technology has stronger perception and action capabilities in large and complex scenarios, and is the key to improving efficiency and robustness. It includes complex scene matching, path planning and cruising, obstacle avoidance, and intelligent target monitoring in military and civilian activities. Summary of the Invention

[0006] In view of the above problems, the present invention provides a control method and system for a multi-machine collaborative security patrol vehicle based on autonomous navigation. Not only can the inspection and security tasks of different vehicles be shared on the cloud terminal, and the task arrangement can be changed according to the execution status of the task to expand the group operation efficiency of the security patrol vehicle, but also the practical application value of the security patrol vehicle can be improved.

[0007] In order to achieve the above-mentioned and other related purposes, the present invention provides the following technical solutions: a control method for a multi-machine collaborative security patrol vehicle based on autonomous navigation, the method comprising:

[0008] U1. Multiple security patrol cars conduct patrol missions, collect data on the historical driving trajectory of each patrol car, and obtain data on the location and status of each patrol car in real time;

[0009] U2. Based on the historical driving trajectory data information of each patrol car, the trajectory points of each patrol car are predicted using an improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient to obtain the predicted trajectory point data information of each patrol car;

[0010] U3. Based on the predicted trajectory data of each patrol car, the position data of each patrol car, and the status data of each patrol car, a multi-machine collaborative path planning model for security patrol cars is constructed. The driving path of each patrol car is synchronously and dynamically planned to obtain the planned driving path data of each patrol car.

[0011] U4. Based on the data information of the planned driving path of each patrol car, the improved multi-subgroup symbiotic non-uniform Gaussian variation salp intima group optimization algorithm is used to optimize the patrol car's driving path to obtain the optimized data information of the driving path of each patrol car.

[0012] Furthermore, in step U2, the prediction of the trajectory points of each patrol car using the improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient includes:

[0013] U21. Based on the data information of the historical driving trajectory of each patrol car, establish the Spearman correlation function Q of the patrol car,

[0014]

[0015] Among them, x i is the data information of the historical driving trajectory of the patrol car, f is the ranking function of the patrol car's historical driving trajectory, n is the sample size, λ i is the weight factor of the patrol car's historical driving trajectory, characterizes the correlation of the patrol car's historical driving trajectory, and obtains data information on the correlation of the patrol car's historical driving trajectory;

[0016] U22. The data information related to the patrol car's historical driving trajectory is input into the BP neural network for training and learning to determine the regression prediction function W of the patrol car's trajectory point.

[0017] Among them, y is the data information of the correlation of the historical driving trajectory of the patrol car, α1, α2 and α3 are the regression prediction parameters of the trajectory points of the patrol car, and the trained neural network model is obtained;

[0018] U23. Based on the trained neural network model, the data information of the historical driving trajectory of each patrol car is input, the trajectory points of each patrol car are predicted, and the data information of the predicted trajectory points of each patrol car is obtained.

[0019] Furthermore, the regression prediction parameters α1, α2 and α3 of the patrol car's trajectory points are,

[0020]

[0021]

[0022] Among them, y is the data information of the correlation of the historical driving trajectory of the patrol car.

[0023] Furthermore, the ranking function f of the patrol car's historical driving trajectory is,

[0024]

[0025] Among them, x i is the historical driving trajectory data of the i-th patrol car.

[0026] Furthermore, in step U3, the multi-machine collaborative path planning model for the security patrol car is constructed, and the synchronous dynamic planning of the driving path of each patrol car includes:

[0027] U31. Based on the predicted trajectory point data information of each patrol car, the position data information of each patrol car and the state data information of each patrol car, a patrol car trajectory point distribution function R is established by Fourier series fitting.

[0028]

[0029] , where r1 is the data information of the trajectory point of each patrol car after prediction, r2 is the data information of the position of each patrol car, r3 is the data information of the state of each patrol car, δ1, δ2 and δ3 are the penalty weight factors of the patrol car trajectory point distribution, and g is the Dirac function of the patrol car trajectory point distribution. The distribution of the patrol car trajectory points is characterized to obtain the data information of the patrol car trajectory point distribution;

[0030] U32. Based on the data information of the distribution of the patrol car's trajectory points, construct a synchronous dynamic programming function P of the patrol car's driving path,

[0031]

[0032] Among them, z is the data information of the distribution of the patrol car's trajectory points, γ1, γ2 and γ3 are the dynamic programming factors of the patrol car's trajectory;

[0033] U33. Based on the synchronous dynamic programming function P of the patrol car's driving path, perform synchronous dynamic programming on the driving path of each patrol car to obtain data information of the planned driving path of each patrol car.

[0034] Furthermore, the Dirac function g of the patrol car trajectory point distribution is,

[0035]

[0036] Among them, r1 is the data information of the predicted trajectory point of each patrol car, r2 is the data information of the position of each patrol car, and r3 is the data information of the status of each patrol car.

[0037] Furthermore, the constraints of the penalty weight factors δ1, δ2, and δ3 of the patrol car trajectory point distribution are:

[0038]

[0039] Furthermore, in step U4, the optimization of the patrol car's driving path using the improved multi-subgroup symbiotic non-uniform Gaussian variation salp swarm optimization algorithm includes:

[0040] U41. Based on the data information of the travel path of each patrol car after the planning, the salp population is initialized, the maximum number of iterations of the population parameters is determined, and the data information of the initialized salp population is obtained;

[0041] U42. Based on the data information of the initialized salp population, establish the fitness function S of the population individuals,

[0042]

[0043] Among them, h is the data information of the initialized salp population, η1, η2 and η3 are the fitness determining factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals;

[0044] U43. Based on the data information of the fitness values ​​of the individuals in the population, establish the target optimization function G of the population,

[0045]

[0046] Among them, a is the data information of the fitness value of the individual in the population, q is the non-uniform Gaussian variation function of the multi-subgroup, μ1, μ2 and μ3 are the variation factors of the adaptive adjustment step size, and the driving path of the patrol car is optimized to obtain the data information of the optimized driving path of each patrol car.

[0047] Furthermore, the non-uniform Gaussian variation function q of the multi-subgroup is,

[0048]

[0049] Among them, a is the data information of the fitness value of the individual in the population.

[0050] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a control system based on autonomous navigation multi-machine collaborative security patrol car, including a computer device, which is programmed or configured to execute any one of the steps of the control method based on autonomous navigation multi-machine collaborative security patrol car.

[0051] The present invention has the following positive effects:

[0052] 1. The present invention predicts the trajectory points of each patrol car by adopting an improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient, and combines it with the construction of a multi-machine collaborative path planning model for security patrol cars to perform synchronous dynamic planning of the driving path of each patrol car. This not only enables accurate prediction of the driving trajectory points of each patrol car, thereby performing dynamic adjustments to improve the operating efficiency of the patrol car, but also solves the problem of difficulty in overall or local deployment and scheduling.

[0053] 2. The present invention optimizes the driving path of patrol cars by adopting an improved multi-subgroup symbiotic non-uniform Gaussian variation salp swarm optimization algorithm. Not only can the inspection and security tasks of different vehicles be shared on the cloud terminal, and the task arrangement can be changed according to the execution of the task to expand the group operation efficiency of the security patrol car, but also the practical application value of the security patrol car is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the method flow of the present invention;

[0055] Figure 2 Schematic diagram of the process of the improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient of the present invention;

[0056] Figure 3 This is a flow chart of the multi-machine collaborative path planning model for building a security patrol car according to the present invention;

[0057] Figure 4 This is a flow chart of the improved multi-subgroup symbiotic non-uniform Gaussian mutation salp group optimization algorithm of the present invention. DETAILED DESCRIPTION

[0058] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0059] Example 1: Figure 1 As shown, a control method for a multi-machine collaborative security patrol vehicle based on autonomous navigation includes:

[0060] U1. Multiple security patrol cars conduct patrol missions, collect data on the historical driving trajectory of each patrol car, and obtain data on the location and status of each patrol car in real time;

[0061] U2. Based on the historical driving trajectory data information of each patrol car, the trajectory points of each patrol car are predicted using an improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient to obtain the predicted trajectory point data information of each patrol car;

[0062] U3. Based on the predicted trajectory data of each patrol car, the position data of each patrol car, and the status data of each patrol car, a multi-machine collaborative path planning model for security patrol cars is constructed. The driving path of each patrol car is synchronously and dynamically planned to obtain the planned driving path data of each patrol car.

[0063] U4. Based on the data information of the planned driving path of each patrol car, the improved multi-subgroup symbiotic non-uniform Gaussian variation salp intima group optimization algorithm is used to optimize the patrol car's driving path to obtain the optimized data information of the driving path of each patrol car.

[0064] In this embodiment, if Figure 2 As shown, in step U2, the improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient is used to predict the trajectory points of each patrol car, including:

[0065] U21. Based on the data information of the historical driving trajectory of each patrol car, establish the Spearman correlation function Q of the patrol car,

[0066]

[0067] Among them, x i is the data information of the historical driving trajectory of the patrol car, f is the ranking function of the patrol car's historical driving trajectory, n is the sample size, λ iis the weight factor of the patrol car's historical driving trajectory, characterizes the correlation of the patrol car's historical driving trajectory, and obtains data information on the correlation of the patrol car's historical driving trajectory;

[0068] U22. The data information related to the patrol car's historical driving trajectory is input into the BP neural network for training and learning to determine the regression prediction function W of the patrol car's trajectory point.

[0069] Among them, y is the data information of the correlation of the historical driving trajectory of the patrol car, α1, α2 and α3 are the regression prediction parameters of the trajectory points of the patrol car, and the trained BP neural network model is obtained;

[0070] U23. Based on the trained BP neural network model, the data information of the historical driving trajectory of each patrol car is input, the trajectory points of each patrol car are predicted, and the predicted data information of the trajectory points of each patrol car is obtained.

[0071] In this embodiment, the regression prediction parameters α1, α2 and α3 of the patrol car's trajectory points are:

[0072]

[0073]

[0074] Among them, y is the data information of the correlation of the historical driving trajectory of the patrol car.

[0075] In this embodiment, the ranking function f of the patrol car's historical driving trajectory is:

[0076]

[0077] Among them, x i is the historical driving trajectory data of the i-th patrol car.

[0078] In this embodiment, if Figure 3 As shown, in step U3, the multi-machine collaborative path planning model for security patrol cars is constructed, and the synchronous dynamic planning of the driving path of each patrol car includes:

[0079] U31. Based on the predicted trajectory point data information of each patrol car, the position data information of each patrol car and the state data information of each patrol car, a patrol car trajectory point distribution function R is established by Fourier series fitting.

[0080]

[0081] , where r1 is the data information of the trajectory point of each patrol car after prediction, r2 is the data information of the position of each patrol car, r3 is the data information of the state of each patrol car, δ1, δ2 and δ3 are the penalty weight factors of the patrol car trajectory point distribution, and g is the Dirac function of the patrol car trajectory point distribution. The distribution of the patrol car trajectory points is characterized to obtain the data information of the patrol car trajectory point distribution;

[0082] U32. Based on the data information of the distribution of the patrol car's trajectory points, construct a synchronous dynamic programming function P of the patrol car's driving path,

[0083]

[0084] Among them, z is the data information of the distribution of the patrol car's trajectory points, γ1, γ2 and γ3 are the dynamic programming factors of the patrol car's trajectory;

[0085] U33. Based on the synchronous dynamic programming function P of the patrol car's driving path, perform synchronous dynamic programming on the driving path of each patrol car to obtain data information of the planned driving path of each patrol car.

[0086] In this embodiment, the Dirac function g of the patrol car trajectory point distribution is,

[0087]

[0088] Among them, r1 is the data information of the predicted trajectory point of each patrol car, r2 is the data information of the position of each patrol car, and r3 is the data information of the status of each patrol car.

[0089] In this embodiment, the constraints of the penalty weight factors δ1, δ2, and δ3 of the patrol car trajectory point distribution are:

[0090]

[0091] Example 2: Based on the control method of the autonomous navigation multi-machine collaborative security patrol car in Example 1, the present invention is further illustrated and described below.

[0092] like Figure 1 As shown, a control method for a multi-machine collaborative security patrol vehicle based on autonomous navigation includes:

[0093] U1. Multiple security patrol cars conduct patrol missions, collect data on the historical driving trajectory of each patrol car, and obtain data on the location and status of each patrol car in real time;

[0094] U2. Based on the historical driving trajectory data information of each patrol car, the trajectory points of each patrol car are predicted using an improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient to obtain the predicted trajectory point data information of each patrol car;

[0095] U3. Based on the predicted trajectory data of each patrol car, the position data of each patrol car, and the status data of each patrol car, a multi-machine collaborative path planning model for security patrol cars is constructed. The driving path of each patrol car is synchronously and dynamically planned to obtain the planned driving path data of each patrol car.

[0096] U4. Based on the data information of the planned driving path of each patrol car, the improved multi-subgroup symbiotic non-uniform Gaussian variation salp intima group optimization algorithm is used to optimize the patrol car's driving path to obtain the optimized data information of the driving path of each patrol car.

[0097] In this embodiment, if Figure 4 As shown, in step U4, the optimization of the patrol car's driving path using the improved multi-subgroup symbiotic non-uniform Gaussian variation salp swarm optimization algorithm includes:

[0098] U41. Based on the data information of the travel path of each patrol car after the planning, the salp population is initialized, the maximum number of iterations of the population parameters is determined, and the data information of the initialized salp population is obtained;

[0099] U42. Based on the data information of the initialized salp population, establish the fitness function S of the population individuals,

[0100]

[0101] Among them, h is the data information of the initialized salp population, η1, η2 and η3 are the fitness determining factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals;

[0102] U43. Based on the data information of the fitness values ​​of the individuals in the population, establish the target optimization function G of the population,

[0103]

[0104] Among them, a is the data information of the fitness value of the individual in the population, q is the non-uniform Gaussian variation function of the multi-subgroup, μ1, μ2 and μ3 are the variation factors of the adaptive adjustment step size, and the driving path of the patrol car is optimized to obtain the data information of the optimized driving path of each patrol car.

[0105] In this embodiment, the non-uniform Gaussian variance function q of the multi-subgroup is:

[0106]

[0107] Among them, a is the data information of the fitness value of the individual in the population.

[0108] In this embodiment, the present invention provides a control system based on autonomous navigation multi-machine collaborative security patrol car, including a computer device that is programmed or configured to execute any one of the steps of the control method based on autonomous navigation multi-machine collaborative security patrol car.

[0109] In this embodiment, the present invention provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the control methods for a multi-machine collaborative security patrol vehicle based on autonomous navigation.

[0110] Any reference to memory, storage, database or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0111] In summary, the present invention not only allows the inspection and security tasks of different vehicles to be shared on the cloud terminal, and changes the task arrangement according to the execution status of the task to expand the group operation efficiency of the security patrol car, but also improves the practical application value of the security patrol car.

[0112] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A control method for a multi-machine collaborative security patrol vehicle based on autonomous navigation, characterized in that: The method comprises: U1. Multiple security patrol cars conduct patrol missions, collect data on the historical driving trajectory of each patrol car, and obtain data on the location and status of each patrol car in real time; U2. Based on the historical driving trajectory data information of each patrol car, the trajectory points of each patrol car are predicted using an improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient to obtain the predicted trajectory point data information of each patrol car; U3. Based on the predicted trajectory data of each patrol car, the position data of each patrol car, and the status data of each patrol car, a multi-machine collaborative path planning model for security patrol cars is constructed. The driving path of each patrol car is synchronously and dynamically planned to obtain the planned driving path data of each patrol car. U4. Based on the data information of the planned driving path of each patrol car, the patrol car's driving path is optimized using an improved multi-subgroup symbiotic non-uniform Gaussian mutation salp swarm optimization algorithm to obtain the optimized driving path data information of each patrol car; In step U2, the improved BP neural network regression prediction algorithm based on the Spearman correlation coefficient is used to predict the trajectory points of each patrol car, including: U21. Based on the data information of the historical driving trajectory of each patrol car, establish the Spearman correlation function Q of the patrol car, ， Among them, x i is the data information of the historical driving trajectory of the patrol car i, f is the ranking function of the patrol car's historical driving trajectory, n is the sample size, λ i is the weight factor of the patrol car's historical driving trajectory, characterizes the correlation of the patrol car's historical driving trajectory, and obtains data information on the correlation of the patrol car's historical driving trajectory; U22. The data information related to the patrol car's historical driving trajectory is input into the BP neural network for training and learning to determine the regression prediction function W of the patrol car's trajectory point. ， Among them, y is the data information of the correlation of the historical driving trajectory of the patrol car, α1, α2 and α3 are the regression prediction parameters of the trajectory points of the patrol car, and the trained neural network model is obtained; U23. Based on the trained neural network model, the data information of the historical driving trajectory of each patrol car is input, the trajectory points of each patrol car are predicted, and the data information of the predicted trajectory points of each patrol car is obtained.

2. The control method of a multi-machine coordinated security patrol vehicle based on autonomous navigation according to claim 1 is characterized in that: The regression prediction parameters α1, α2 and α3 of the patrol car's trajectory points are, ， ， ， Among them, y is the data information of the correlation of the historical driving trajectory of the patrol car.

3. The control method of a multi-machine coordinated security patrol vehicle based on autonomous navigation according to claim 1 is characterized in that: The ranking function f of the patrol car's historical driving trajectory is: ， Among them, x i is the historical driving trajectory data of the i-th patrol car.

4. The control method of the multi-machine coordinated security patrol vehicle based on autonomous navigation according to claim 1 is characterized in that: In step U3, the multi-machine collaborative path planning model for security patrol vehicles is constructed, and the synchronous dynamic planning of the driving path of each patrol vehicle includes: U31. Based on the predicted trajectory point data information of each patrol car, the position data information of each patrol car and the state data information of each patrol car, a patrol car trajectory point distribution function R is established by Fourier series fitting. ， Among them, r1 is the data information of the trajectory point of each patrol car after prediction, r2 is the data information of the position of each patrol car, r3 is the data information of the state of each patrol car, δ1, δ2 and δ3 are the penalty weight factors of the patrol car trajectory point distribution, and g is the Dirac function of the patrol car trajectory point distribution. The distribution of the patrol car trajectory points is characterized to obtain the data information of the patrol car trajectory point distribution; U32. Based on the data information of the distribution of the patrol car's trajectory points, construct a synchronous dynamic programming function P of the patrol car's driving path, ， Among them, z is the data information of the distribution of the patrol car's trajectory points, γ1, γ2 and γ3 are the dynamic programming factors of the patrol car's trajectory; U33. Based on the synchronous dynamic programming function P of the patrol car's driving path, perform synchronous dynamic programming on the driving path of each patrol car to obtain data information of the planned driving path of each patrol car.

5. The control method of a multi-machine coordinated security patrol vehicle based on autonomous navigation according to claim 4 is characterized in that: The Dirac function g of the patrol car trajectory distribution is, ， Among them, r1 is the data information of the predicted trajectory point of each patrol car, r2 is the data information of the position of each patrol car, and r3 is the data information of the status of each patrol car.

6. The control method of a multi-machine coordinated security patrol vehicle based on autonomous navigation according to claim 4 is characterized in that: The constraints of the penalty weight factors δ1, δ2 and δ3 of the patrol car trajectory point distribution are: 。 7. The control method of the multi-machine coordinated security patrol vehicle based on autonomous navigation according to claim 1 is characterized in that: In step U4, the optimization of the patrol car's driving path using the improved multi-subgroup symbiotic non-uniform Gaussian variation salp swarm optimization algorithm includes: U41. Based on the data information of the travel path of each patrol car after the planning, the salp population is initialized, the maximum number of iterations of the population parameters is determined, and the data information of the initialized salp population is obtained; U42. Based on the data information of the initialized salp population, establish the fitness function S of the population individuals, ， Among them, h is the data information of the initialized salp population, η1, η2 and η3 are the fitness determining factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals; U43. Based on the data information of the fitness values ​​of the individuals in the population, establish the target optimization function G of the population, ， Among them, a is the data information of the fitness value of the individual in the population, q is the non-uniform Gaussian variation function of the multi-subgroup, µ1, µ2 and µ3 are the variation factors of the adaptive adjustment step size, and the driving path of the patrol car is optimized to obtain the data information of the optimized driving path of each patrol car.

8. The control method of a multi-machine coordinated security patrol vehicle based on autonomous navigation according to claim 7 is characterized in that: The non-uniform Gaussian variation function q of the multi-subgroup is, ， Among them, a is the data information of the fitness value of the individual in the population.

9. A control system based on autonomous navigation and multi-machine collaborative security patrol vehicle, including computer equipment, characterized in that: The computer device is programmed or configured to execute the steps of the control method of the autonomous navigation multi-machine collaborative security patrol car according to any one of claims 1 to 8.

Images