Path planning method and device, equipment and storage medium

Abstract

The invention relates to the technical field of unmanned driving, and provides a path planning method and device, equipment and a storage medium. The method comprises the steps that a plurality of ancestor individuals of a first round of iteration are randomly generated, any one individual represents a driving path of a plurality of stations in a series park, and the starting point and the end point of the driving path are respectively a planning starting point and a planning end point in a path planning task; and cyclically executing the following iterative operations: introducing a population energy index reflecting the current search progress of the algorithm, and adopting different dynamic competition mechanisms for a plurality of candidate individuals of the current iteration to ensure the balance of the algorithm between local development and global search, taking the candidate individuals reserved after screening and new candidate individuals generated based on at least part of the reserved candidate individuals as candidate individuals of next iteration; and taking the driving path obtained by decoding the optimal candidate individual with the highest fitness in the last iteration as the optimal path between the planning starting point and the planning ending point until the maximum iteration number is met.

Description

Background Art

[0002] With the continuous development of unmanned driving technology, unmanned shuttle buses based on this technology also have broad application prospects in logistics transportation, disaster relief, environmental monitoring and other fields.

[0003] Among them, path planning is an important component of the application of unmanned shuttle buses. Harris Eagle Optimization Algorithm, as a commonly used path planning algorithm, plans the route of unmanned shuttle buses based on the information of the park.

[0004] Due to the lack of global search capabilities in the later stages of the algorithm, it is easy to fall into a local optimal solution, resulting in obvious limitations in the driving routes planned for the unmanned shuttle buses. The driving route may not cover all stations in the park, and even if the planned route can pass through all stations, it may not be the most time-efficient option. Summary of the invention

[0005] The embodiments of the present application provide a path planning method, apparatus, device and storage medium to solve the path planning limitation problem caused by insufficient global search capability in the later stage of the algorithm.

[0006] In a first aspect, an embodiment of the present application provides a path planning method, including: In response to the path planning task, a plurality of ancestor individuals of the first round of iterations are randomly generated, wherein any individual is used to represent a driving path connecting a plurality of stations in the park in series, and the starting point and the end point of the driving path are respectively the planning starting point and the planning end point in the path planning task; The following iterative operations are performed cyclically: based on the population energy index of this iteration, multiple candidate individuals of this iteration are screened, and the candidate individuals retained after the screening and new candidate individuals generated based on at least part of the retained candidate individuals are used as candidate individuals for the next iteration; Until the maximum number of iterations is met, the optimal candidate individual with the highest fitness in the last iteration is obtained, and the driving path obtained by decoding the optimal candidate individual is used as the optimal path between the planning starting point and the planning end point.

[0007] Optionally, based on the population energy index of this iteration, multiple candidate individuals of this iteration are screened, and the candidate individuals retained after the screening and new candidate individuals generated based on at least part of the retained candidate individuals are used as candidate individuals for the next iteration, including: When the population energy index of this iteration is within the first value range, the candidate individuals whose fitness ranking in this iteration is lower than the first preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals whose fitness ranking in this iteration is not lower than the second preset ranking are used as the candidate individuals for the next iteration; When the population energy index of this iteration is within the second value range, the candidate individuals whose fitness ranking in this iteration is lower than the third preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals with the highest fitness in this iteration are used as candidate individuals for the next iteration; When the population energy index of this iteration is within the third value range, the individuals except the candidate individual with the highest fitness in this iteration are eliminated, and the candidate individual with the highest fitness in this iteration and the new candidate individual generated based on the candidate individual with the highest fitness in this iteration are used as the candidate individuals for the next iteration.

[0008] Optionally, the new candidate individual is generated by performing the following operations: Based on the fitness of a candidate individual in this iteration, the fitness of all candidate individuals and the update radius of this iteration, a candidate individual is iteratively updated to obtain a candidate individual for the next iteration.

[0009] Optionally, the fitness of any individual is obtained by performing the following operations: Obtaining, from the abstract map of the park, actual site distances between sites corresponding to adjacent vector values ​​in any of the individuals; The sum of the site distances between sites corresponding to all adjacent vector values ​​is taken as the fitness of any individual.

[0010] Optionally, the population energy index of this iteration is obtained by performing the following operations: Based on the number of individuals in this iteration and the current individual energy of each candidate individual in this iteration, the population energy index of this iteration is obtained.

[0011] Optionally, the current individual energy of each candidate individual in this iteration is obtained by performing the following operations: Based on the initial individual energy of the ancestor individual associated with each candidate individual in this iteration, the number of iterations and the total number of iterations, the current individual energy of each candidate individual in this iteration is obtained respectively.

[0012] Optionally, the optimal path is obtained by decoding the optimal candidate individual by performing the following operations: Each vector value in the optimal candidate individual is replaced with each associated site label value, and a path sequence between the site label values ​​is constructed based on the arrangement sequence of the vector values ​​to form an optimal path between the planning starting point and the planning end point.

[0013] In a second aspect, an embodiment of the present application further provides a path planning device, including: An initialization unit, for randomly generating a plurality of ancestor individuals of a first round of iterations in response to a path planning task, wherein any individual is used to represent a driving path connecting a plurality of sites in a park in series, and a starting point and an end point of the driving path are respectively a planning starting point and a planning end point in the path planning task; An iterative updating unit, configured to cyclically perform the following iterative operations: based on the population energy index of this iteration, screening multiple candidate individuals of this iteration, and using the candidate individuals retained after screening and new candidate individuals generated based on at least part of the retained candidate individuals as candidate individuals for the next iteration; The decoding processing unit is used to obtain the optimal candidate individual with the highest fitness in the last iteration until the maximum number of iterations is met, and the driving path obtained by decoding the optimal candidate individual is used as the optimal path between the planned starting point and the planned end point.

[0014] Optionally, the iterative updating unit is used to: When the population energy index of this iteration is within the first value range, the candidate individuals whose fitness ranking in this iteration is lower than the first preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals whose fitness ranking in this iteration is not lower than the second preset ranking are used as the candidate individuals for the next iteration; When the population energy index of this iteration is within the second value range, the candidate individuals whose fitness ranking in this iteration is lower than the third preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals with the highest fitness in this iteration are used as candidate individuals for the next iteration; When the population energy index of this iteration is within the third value range, the individuals except the candidate individual with the highest fitness in this iteration are eliminated, and the candidate individual with the highest fitness in this iteration and the new candidate individual generated based on the candidate individual with the highest fitness in this iteration are used as the candidate individuals for the next iteration.

[0015] Optionally, the iterative updating unit generates a new candidate individual by performing the following operations: Based on the fitness of a candidate individual in this iteration, the fitness of all candidate individuals and the update radius of this iteration, a candidate individual is iteratively updated to obtain a candidate individual for the next iteration.

[0016] Optionally, the iterative updating unit obtains the fitness of any individual by performing the following operations: Obtaining, from the abstract map of the park, actual site distances between sites corresponding to adjacent vector values ​​in any of the individuals; The sum of the site distances between sites corresponding to all adjacent vector values ​​is taken as the fitness of any individual.

[0017] Optionally, the iterative updating unit obtains the population energy index of this iteration by performing the following operations: Based on the number of individuals in this iteration and the current individual energy of each candidate individual in this iteration, the population energy index of this iteration is obtained.

[0018] Optionally, the iterative updating unit obtains the current individual energy of each candidate individual in this iteration by performing the following operations: Based on the initial individual energy of the ancestor individual associated with each candidate individual in this iteration, the number of iterations and the total number of iterations, the current individual energy of each candidate individual in this iteration is obtained respectively.

[0019] Optionally, the decoding processing unit decodes the optimal path by performing the following operations on the optimal candidate individual: Each vector value in the optimal candidate individual is replaced with each associated site label value, and a path sequence between the site label values ​​is constructed based on the arrangement sequence of the vector values ​​to form an optimal path between the planning starting point and the planning end point.

[0020] In a third aspect, an embodiment of the present application further provides a computer device, comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of any one of the above-mentioned path planning methods.

[0021] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which includes a program code. When the program product is run on a computer device, the program code is used to enable the computer device to execute the steps of any one of the above-mentioned path planning methods.

[0022] The beneficial effects of this application are as follows: An embodiment of the present application provides a path planning method, apparatus, device and storage medium, the method comprising: in response to a path planning task, randomly generating multiple ancestral individuals for a first round of iterations, wherein any individual is used to represent a driving path connecting multiple sites in a park in series, and the starting point and the end point of the driving path are the planning starting point and the planning end point in the path planning task, respectively; cyclically performing the following iterative operations: based on the population energy index of this iteration, screening multiple candidate individuals of this iteration, and using the candidate individuals retained after screening and new candidate individuals generated based on at least some of the retained candidate individuals as candidate individuals for the next iteration; until the maximum number of iterations is met, obtaining the optimal candidate individual with the highest fitness in the last iteration, and using the driving path obtained by decoding the optimal candidate individual as the optimal path between the planning starting point and the planning end point.

[0023] This application aims to address the path planning limitations of the Harris Eagle optimization algorithm due to insufficient global search capabilities in the later stages. It introduces a population energy indicator that reflects the current search progress of the algorithm. Different dynamic competition mechanisms are adopted for different stages of the algorithm to ensure the balance between local development and global search, thereby more accurately and effectively executing different mechanisms, improving the convergence speed and accuracy of the algorithm, and improving the path planning limitations caused by falling into local optimal solutions.

[0024] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 Schematic diagram of the existing Harris Hawk optimization algorithm; Figure 2A A schematic diagram of a process for planning a driving route for an unmanned shuttle bus in a park provided in an embodiment of the present application; Figure 2B A logical schematic diagram of planning a driving route for an unmanned shuttle bus in a park provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a path planning device provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present application; Figure 5Schematic diagram of the structure of a computing device in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the technical solution of the present application, rather than all of the embodiments. Based on the embodiments recorded in the application documents, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the technical solution of the present application.

[0027] The following is a brief introduction to the design concept of the embodiment of the present application: With the continuous development of unmanned driving technology, unmanned shuttle buses based on this technology also have broad application prospects in logistics transportation, disaster relief, environmental monitoring and other fields.

[0028] Among them, path planning is an important component of the application of unmanned shuttle buses. Harris Eagle Optimization Algorithm, as a commonly used path planning algorithm, plans the route of unmanned shuttle buses based on the information of the park.

[0029] like Figure 1 As shown in Figure 1, the Harris Eagle optimization algorithm includes an exploration phase and an exploitation phase, and different position update strategies are implemented in each phase. Due to the lack of global search capabilities in the later stage of the algorithm, it is easy to fall into a local optimal solution, resulting in obvious limitations in the driving routes planned for the unmanned shuttle bus. The driving route may not cover all stations in the park, and even if the planned route can pass through all stations, it may not be the most time-efficient option.

[0030] In view of this, the embodiments of the present application provide a path planning method, device, equipment and storage medium. The method includes: in response to the path planning task, randomly generate multiple ancestral individuals of the first round of iterations, wherein any individual is used to represent a driving path connecting multiple sites in the park, and the starting point and the end point of the driving path are respectively the planning starting point and the planning end point in the path planning task; loop the following iterative operations: based on the population energy index of this iteration, screen the multiple candidate individuals of this iteration, and use the candidate individuals retained after screening and the new candidate individuals generated based on at least part of the retained candidate individuals as the candidate individuals for the next iteration; until the maximum number of iterations is met, the optimal candidate individual with the highest fitness in the last iteration is obtained, and the driving path obtained by decoding the optimal candidate individual is used as the optimal path between the planning starting point and the planning end point.

[0031] This application aims to address the path planning limitations of the Harris Eagle optimization algorithm due to insufficient global search capabilities in the later stages. It introduces a population energy indicator that reflects the current search progress of the algorithm. Different dynamic competition mechanisms are adopted for different stages of the algorithm to ensure the balance between local development and global search, thereby more accurately and effectively executing different mechanisms, improving the convergence speed and accuracy of the algorithm, and improving the path planning limitations caused by falling into local optimal solutions.

[0032] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0033] Combination Figure 2A-2B The schematic diagram shown specifically introduces planning a driving route for an unmanned shuttle bus traveling in the park, so that the unmanned shuttle bus passes all stations in the park in the shortest time.

[0034] S201: In response to the path planning task, a plurality of ancestor individuals of the first round of iterations are randomly generated, wherein any individual is used to represent a driving path connecting multiple sites in the park in series, and the starting point and the end point of the driving path are respectively the planning starting point and the planning end point in the path planning task.

[0035] Formula 1 is used to randomly generate multiple ancestor individuals for the first round of iterations. Each individual contains multiple vector values, which are used to represent a driving path that connects multiple sites in the park, and different individuals contain the same planned starting point and planned end point. Among them, is a random function, is the initial individual energy of the ancestor individual corresponding to the i-th individual. The ancestor individual of any individual is the initialization individual that directly or indirectly generates the individual, or when the individual itself is the initialization individual, the ancestor individual of the individual is itself.

[0036] Formula 1; S202: looping and executing the following iterative operations: based on the population energy index of this iteration, screening multiple candidate individuals of this iteration, and taking the candidate individuals retained after screening and new candidate individuals generated based on at least part of the retained candidate individuals as candidate individuals for the next iteration.

[0037] (1) When the population energy index of this iteration is within the first value range, the candidate individuals whose fitness ranking in this iteration is lower than the first preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals whose fitness ranking in this iteration is not lower than the second preset ranking are used as candidate individuals for the next iteration.

[0038] The population energy index in this iteration , indicating that the Harris Hawk optimization algorithm is still in the early search stage, eliminating the candidate individuals whose fitness ranking is lower than the first preset ranking in this iteration, retaining the remaining individuals, and selecting the candidate individuals whose fitness ranking is not lower than the second preset ranking from the other retained individuals to generate new candidate individuals, and taking the other individuals retained in this iteration and the newly generated candidate individuals in this iteration as the candidate individuals for the next iteration.

[0039] (2) When the population energy index of this iteration is within the second value range, the candidate individuals whose fitness ranking in this iteration is lower than the third preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals with the highest fitness in this iteration are used as candidate individuals for the next iteration.

[0040] The population energy index in this iteration , indicating that the Harris Hawk optimization algorithm has just entered the development stage. In order to achieve a balance between global exploration and local development, the candidate individuals whose fitness ranking is lower than the third preset ranking in this iteration are eliminated, the remaining individuals are retained, and the candidate individuals with the highest fitness are selected from the other retained individuals to generate new candidate individuals. The other individuals retained in this iteration and the newly generated candidate individuals in this iteration are used as candidate individuals for the next iteration.

[0041] (3) When the population energy index of this iteration is within the third value range, all individuals except the candidate individual with the highest fitness in this iteration are eliminated, and the candidate individual with the highest fitness in this iteration and the new candidate individual generated based on the candidate individual with the highest fitness in this iteration are used as the candidate individuals for the next iteration.

[0042] The population energy index in this iteration When , it indicates that the Harris Hawk optimization algorithm has entered the final stage of iteration. In order to improve the convergence accuracy and convergence speed of the algorithm in the later stage, a more intense competition mechanism will be adopted in this stage to eliminate individuals other than the candidate with the highest fitness in this iteration, generate new candidate individuals based on the candidate with the highest fitness, and use the individuals retained in this iteration and the newly generated candidate individuals in this iteration as candidate individuals for the next iteration.

[0043] As shown in Formula 2, the initial individual energy of the ancestor individual associated with each candidate individual in this iteration is , the number of iterations t and the total number of iterations T, respectively obtain the current individual energy of each candidate individual in this iteration .

[0044] Formula 2; As shown in Formula 3, based on the number of individuals N in this iteration and the current individual energy of each candidate individual in this iteration , get the population energy index of this iteration .

[0045] Formula 3; As shown in Formula 4, new candidate individuals in the next iteration are generated by performing the following operations: : Based on the fitness of a candidate individual in this iteration , the fitness of all candidate individuals and the update radius of this iteration , for a candidate individual Perform iterative updates to obtain a candidate individual for the next iteration. is a random constant, The fitness of the best individual calculated so far as this iteration (i.e. the distance of the shortest path) is The fitness of the worst individual calculated so far as of this iteration (i.e., the distance of the longest path).

[0046] Formula 4; As shown in Formula 5, the fitness of any individual is obtained by performing the following operations: :From the abstract map of the park, obtain the actual site distance between the sites corresponding to each adjacent vector value in any individual , and then the sum of the site distances between sites corresponding to all adjacent vector values ​​is taken as the fitness of any individual.

[0047] Formula 5; Formula 6 is the calculation formula for the update radius of this iteration, K, is a constant, t is the number of iterations this time, and T is the total number of iterations.

[0048] Formula 6; A macro map of a campus is constructed based on a general overview of a large geographic area, which often ignores specific obstacles between sites, such as buildings, terrain changes, or other physical barriers. This simplification helps provide a clear overall view, but it can cause problems in practical application.

[0049] For example, the driving route planned based on the macro map will also ignore the existence of these obstacles, making it difficult for the unmanned shuttle to reach the designated station according to the pre-planned route during the actual driving process. Due to these unconsidered obstacles, the travel time and complexity are increased, affecting the overall transportation efficiency and service quality.

[0050] Therefore, the embodiment of the present application performs terrain weighting on the initial site distance between sites according to the terrain type of the roads between the sites, obtains the actual distance between the sites, and marks the actual site distance between each site and other sites in the park in the abstract map.

[0051] For example, converting the shortest actual distance between station 1 and station 2 into Euclidean distance, the initial station distance between the two points is 1250m. There are three paths between the two points, and path 1 is a road with a slope. Therefore, the actual station distance of path 1 is 1250m. (1+0.2)=1500m. Path 2 is a straight and flat road, so the actual station distance of Path 2 is 1250m. Path 3 is a road with many bends, so the actual station distance of Path 3 is 1250m. (1+0.1)=1375m. The shortest path 2 is selected as the legal path, so the actual site distance between the two ends is 1375m.

[0052] Although the abstract map is inconsistent with the actual terrain in appearance, each station in the map corresponds one-to-one to a station in the actual scene, and the distances between the stations are the same. A decimal number of 1-M is then used to encode each station in the abstract map as an integer, and each integer represents the index of a station. Using the path planning method provided in this application, the path obtained is the optimal path in the current scene.

[0053] S203: until the maximum number of iterations is met, the best candidate individual with the highest fitness in the last iteration is obtained, and the driving path obtained by decoding the best candidate individual is used as the best path between the planning start point and the planning end point.

[0054] Since the solution in the continuous space generated by the Harris Hawk optimization algorithm is not necessarily applicable to the discrete path of the Traveling Salesman Problem (TSP), the embodiment of the present application adopts a random key encoding scheme to replace each vector value composed of uniformly distributed random real numbers in the optimal candidate individual with the associated site label values, and construct the path order between each site label value based on the arrangement order of each vector value to form the optimal path between the planning starting point and the planning end point. Among them, the first-dimensional vector value of the optimal candidate individual is used to associate with the site label value of the planning starting point, and the last-dimensional vector value is used to associate with the site label value of the planning end point. For example, the vector [0.5, 0.1, 3.4] composed of uniformly distributed random real numbers is converted into an integer sequence [2, 1, 3], where each integer represents the index of a site.

[0055] The iterative stopping condition of the present application is that when the current number of iterations meets the maximum number of iterations, the loop steps are stopped, and the driving path planned in the last iteration is used as the optimal path for the unmanned shuttle bus to ensure that the vehicle passes through all stations in the park in the shortest time, thereby improving the situation where the planned driving route may not cover all stations in the park due to falling into a local optimal solution. Even if the planned route can pass through all stations, it may not be the most time-efficient option.

[0056] Based on the same inventive concept as the above method embodiment, the present application embodiment also provides a path planning device. Figure 3 As shown, the path planning device 300 may include: Initialization unit 301, used for randomly generating a plurality of ancestor individuals of the first round of iteration in response to the path planning task, wherein any individual is used to represent a driving path connecting a plurality of stations in the park in series, and the starting point and the end point of the driving path are respectively the planning starting point and the planning end point in the path planning task; The iterative updating unit 302 is used to cyclically perform the following iterative operations: based on the population energy index of this iteration, multiple candidate individuals of this iteration are screened, and the candidate individuals retained after the screening and new candidate individuals generated based on at least part of the retained candidate individuals are used as candidate individuals for the next iteration; The decoding processing unit 303 is used to obtain the optimal candidate individual with the highest fitness in the last iteration until the maximum number of iterations is met, and use the driving path obtained by decoding the optimal candidate individual as the optimal path between the planned starting point and the planned end point.

[0057] Optionally, the iterative updating unit 302 is used to: When the population energy index of this iteration is within the first value range, the candidate individuals whose fitness ranking in this iteration is lower than the first preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals whose fitness ranking in this iteration is not lower than the second preset ranking are used as the candidate individuals for the next iteration; When the population energy index of this iteration is within the second value range, the candidate individuals whose fitness ranking in this iteration is lower than the third preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals with the highest fitness in this iteration are used as candidate individuals for the next iteration; When the population energy index of this iteration is within the third value range, the individuals except the candidate individual with the highest fitness in this iteration are eliminated, and the candidate individual with the highest fitness in this iteration and the new candidate individual generated based on the candidate individual with the highest fitness in this iteration are used as the candidate individuals for the next iteration.

[0058] Optionally, the iterative updating unit 302 generates new candidate individuals by performing the following operations: Based on the fitness of a candidate individual in this iteration, the fitness of all candidate individuals and the update radius of this iteration, a candidate individual is iteratively updated to obtain a candidate individual for the next iteration.

[0059] Optionally, the iterative updating unit 302 obtains the fitness of any individual by performing the following operations: Obtaining, from the abstract map of the park, actual site distances between sites corresponding to adjacent vector values ​​in any of the individuals; The sum of the site distances between sites corresponding to all adjacent vector values ​​is taken as the fitness of any individual.

[0060] Optionally, the iterative updating unit 302 obtains the population energy index of this iteration by performing the following operations: Based on the number of individuals in this iteration and the current individual energy of each candidate individual in this iteration, the population energy index of this iteration is obtained.

[0061] Optionally, the iterative updating unit 302 obtains the current individual energy of each candidate individual in this iteration by performing the following operations: Based on the initial individual energy of the ancestor individual associated with each candidate individual in this iteration, the number of iterations and the total number of iterations, the current individual energy of each candidate individual in this iteration is obtained respectively.

[0062] Optionally, the decoding processing unit 303 obtains the optimal path by performing the following operations on the optimal candidate individual: Each vector value in the optimal candidate individual is replaced with each associated site label value, and a path sequence between the site label values ​​is constructed based on the arrangement sequence of the vector values ​​to form an optimal path between the planning starting point and the planning end point.

[0063] For the convenience of description, the above parts are divided into modules (or units) according to their functions and described separately. Of course, when implementing this application, the functions of each module (or unit) can be implemented in the same or multiple software or hardware.

[0064] After introducing the path planning method and apparatus according to the exemplary embodiment of the present application, next, a computer device according to another exemplary embodiment of the present application is introduced.

[0065] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0066] Based on the same inventive concept as the above method embodiment, the present application embodiment also provides a computer device, see Figure 4 As shown, the computer device 400 may include at least a processor 401 and a memory 402. The memory 402 stores program codes, and when the program codes are executed by the processor 401, the processor 401 executes the steps of any one of the above path planning methods.

[0067] In some possible implementations, the computing device according to the present application may include at least one processor and at least one memory. The memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the path planning method according to various exemplary implementations of the present application described above in this specification. For example, the processor may execute the following steps: Figure 2A Follow the steps shown in .

[0068] Refer to the following Figure 5 hereinafter, a computing device 500 according to this embodiment of the present application is described. Figure 5 The computing device 500 is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0069] like Figure 5As shown, the computing device 500 is in the form of a general computing device. The components of the computing device 500 may include but are not limited to: at least one processing unit 501, at least one storage unit 502, and a bus 503 connecting different system components (including the storage unit 502 and the processing unit 501).

[0070] Bus 503 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a processor, or a local bus using any of a variety of bus architectures.

[0071] The storage unit 502 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 5021 and / or a cache memory unit 5022 , and may further include a read-only memory (ROM) 5023 .

[0072] The storage unit 502 may also include a program / utility 5025 having a set (at least one) of program modules 5024, such program modules 5024 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0073] The computing device 500 may also communicate with one or more external devices 504 (e.g., keyboards, pointing devices, etc.), may also communicate with one or more devices that enable a user to interact with the computing device 500, and / or communicate with any device that enables the computing device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 505. In addition, the computing device 500 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 506. As shown, the network adapter 506 communicates with other modules for the computing device 500 through a bus 503. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computing device 500, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0074] Based on the same inventive concept as the above method embodiments, various aspects of the path planning method provided by the present application can also be implemented in the form of a program product, which includes program code. When the program product is run on a computer device, the program code is used to enable the computer device to execute the steps of the path planning method according to various exemplary embodiments of the present application described above in this specification. For example, the computer device can execute the following steps: Figure 2AFollow the steps shown in .

[0075] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0076] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0077] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A path planning method, characterized in that: include: In response to the path planning task, a plurality of ancestor individuals of the first round of iterations are randomly generated, wherein any individual is used to represent a driving path connecting a plurality of stations in the park in series, and the starting point and the end point of the driving path are respectively the planning starting point and the planning end point in the path planning task; The following iterative operations are performed cyclically: based on the population energy index of this iteration, multiple candidate individuals of this iteration are screened, and the candidate individuals retained after the screening and new candidate individuals generated based on at least part of the retained candidate individuals are used as candidate individuals for the next iteration; Until the maximum number of iterations is met, the optimal candidate individual with the highest fitness in the last iteration is obtained, and the driving path obtained by decoding the optimal candidate individual is used as the optimal path between the planning starting point and the planning end point.

2. The method according to claim 1, characterized in that Based on the population energy index of this iteration, multiple candidate individuals of this iteration are screened, and the candidate individuals retained after the screening and new candidate individuals generated based on at least part of the retained candidate individuals are used as candidate individuals for the next iteration, including: When the population energy index of this iteration is within the first value range, the candidate individuals whose fitness ranking in this iteration is lower than the first preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals whose fitness ranking in this iteration is not lower than the second preset ranking are used as the candidate individuals for the next iteration; When the population energy index of this iteration is within the second value range, the candidate individuals whose fitness ranking in this iteration is lower than the third preset ranking are eliminated, and the other individuals retained in this iteration and the new candidate individuals generated based on the candidate individuals with the highest fitness in this iteration are used as candidate individuals for the next iteration; When the population energy index of this iteration is within the third value range, the individuals except the candidate individual with the highest fitness in this iteration are eliminated, and the candidate individual with the highest fitness in this iteration and the new candidate individual generated based on the candidate individual with the highest fitness in this iteration are used as the candidate individuals for the next iteration.

3. The method according to claim 1, characterized in that The new candidate individuals are generated by performing the following operations: Based on the fitness of a candidate individual in this iteration, the fitness of all candidate individuals and the update radius of this iteration, a candidate individual is iteratively updated to obtain a candidate individual for the next iteration.

4. The method according to claim 1, characterized in that The fitness of any individual is obtained by performing the following operations: Obtaining, from the abstract map of the park, actual site distances between sites corresponding to adjacent vector values ​​in any of the individuals; The sum of the site distances between sites corresponding to all adjacent vector values ​​is taken as the fitness of any individual.

5. The method according to claim 1, characterized in that The population energy index of this iteration is obtained by performing the following operations: Based on the number of individuals in this iteration and the current individual energy of each candidate individual in this iteration, the population energy index of this iteration is obtained.

6. The method according to claim 5, characterized in that The current individual energy of each candidate individual in this iteration is obtained by performing the following operations: Based on the initial individual energy of the ancestor individual associated with each candidate individual in this iteration, the number of iterations and the total number of iterations, the current individual energy of each candidate individual in this iteration is obtained respectively.

7. The method according to claim 1, characterized in that The optimal path is obtained by decoding the optimal candidate individuals by performing the following operations: Each vector value in the optimal candidate individual is replaced with each associated site label value, and a path sequence between the site label values ​​is constructed based on the arrangement sequence of the vector values ​​to form an optimal path between the planning starting point and the planning end point.

8. A path planning device, characterized in that: include: An initialization unit, for randomly generating a plurality of ancestor individuals of a first round of iterations in response to a path planning task, wherein any individual is used to represent a driving path connecting a plurality of sites in a park in series, and a starting point and an end point of the driving path are respectively a planning starting point and a planning end point in the path planning task; An iterative updating unit, configured to cyclically perform the following iterative operations: based on the population energy index of this iteration, screening multiple candidate individuals of this iteration, and using the candidate individuals retained after screening and new candidate individuals generated based on at least part of the retained candidate individuals as candidate individuals for the next iteration; The decoding processing unit is used to obtain the optimal candidate individual with the highest fitness in the last iteration until the maximum number of iterations is met, and the driving path obtained by decoding the optimal candidate individual is used as the optimal path between the planned starting point and the planned end point.

9. A computer device, characterized in that: The method comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: It includes program code. When the program product is run on a computer device, the program code is used to make the computer device execute the steps of the method according to any one of claims 1 to 7.

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