Improved sampling path planning method, device, equipment, medium and product

Abstract

The invention relates to the technical field of unmanned aerial vehicle path planning, and discloses an improved sampling path planning method, device, equipment, medium and product, and the method comprises the steps: setting a path planning space, a starting point and a target point, and obtaining a first random sampling point from the path planning space; finding an adjacent point closest to the first random sampling point from the existing path; determining a first path point from the adjacent point to the first random sampling point according to a preset step length; judging the feasibility of the first path point, and calculating a sampling improved path generation factor based on the feasibility; determining a second path point according to the sampling improved path generation factor; connecting the second path point and the adjacent point to obtain a first path; and if the first path is feasible, adding the first path into the existing path. According to the invention, the path generation efficiency and performance in a complex application environment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle path planning, and in particular to an improved sampling path planning method, device, equipment, medium and product. Background Art

[0002] Sampling-based path planning methods do not need to establish an environmental space model in advance, but obtain paths through random sampling. For example, the probability map method converts the actual continuous space planning problem into a topological space planning problem by randomly sampling in the planning space; the Rapidly-exploring Random Trees (RRT) algorithm randomly samples in the planning space and expands the starting point as the root node in a tree manner to obtain a path from the starting point to the sub-target point. The significant advantage is that the complexity of the algorithm does not change in a high-dimensional environment, and it is widely used in path planning in unfamiliar high-dimensional environments.

[0003] In related technologies, the improvement of sampling path planning algorithms is mainly from the perspectives of sampling space and path generation efficiency, such as KD-RRT, bidirectional RRT or RRT*. For obstacle processing, point set restriction is mainly used, or the paths that do not meet the requirements are deleted after the paths are generated. For example, when dealing with complex application scenarios such as high-speed bridge inspection, there are many beams and columns under the bridge, and the environment is relatively complex. If the post-generation elimination method is used, the algorithm efficiency will be low to a certain extent. Summary of the invention

[0004] In view of this, the present invention provides an improved sampling path planning method, device, equipment, medium and product to improve the efficiency and performance of path generation in complex application environments.

[0005] In a first aspect, the present invention provides an improved sampling path planning method, the method comprising: setting a path planning space, a starting point and a target point, and obtaining a first random sampling point from the path planning space; finding an adjacent point closest to the first random sampling point from an existing path; determining a first path point from the adjacent point to the first random sampling point according to a preset step size; judging the feasibility of the first path point, and obtaining a sampling improved path generation factor based on the feasibility calculation; determining a second path point according to the sampling improved path generation factor; connecting the second path point and the adjacent point to obtain a first path; if the first path is feasible, adding the first path to the existing path.

[0006] In an optional embodiment, the feasibility of the first path point is judged, and a sampling improved path generation factor is obtained based on the feasibility calculation, including: if the first path point is feasible, the feasible probability value is 1; if the first path point is not feasible, the feasible probability value is 0; setting a step probability factor, the step probability factor is a random number between (0, 1); obtaining a roadblock radius parameter; and based on the feasible probability value, the step probability factor and the roadblock radius parameter, calculating the sampling improved path generation factor.

[0007] In an optional implementation, the method further includes: if the first path is not feasible, selecting a second random sampling point to generate a second path.

[0008] In an optional implementation, the method further includes: calculating the distance between the second path point and the obstacle, and if the distance is greater than a preset threshold, the first path is feasible.

[0009] In an optional embodiment, the method further includes: if the second path point is in a target point area, ending the path generation; the target point area includes the target point and an area with a preset radius from the target point.

[0010] In an optional implementation, the path planning space includes a two-dimensional area of ​​a preset range.

[0011] In a second aspect, the present invention provides an improved sampling path planning device, the device comprising: a sampling point acquisition module, used to set a path planning space, a starting point and a target point, and obtain a first random sampling point from the path planning space; an adjacent point acquisition module, used to find an adjacent point closest to the first random sampling point from an existing path; a first path point module, used to determine the first path point according to a preset step size from an adjacent point to the first random sampling point; a feasibility judgment module, used to judge the feasibility of the first path point, and obtain a sampling improved path generation factor based on the feasibility calculation; a second path point module, used to determine the second path point according to the sampling improved path generation factor; a path acquisition module, used to connect the second path point and the adjacent point to obtain the first path; and a path adding module, used to add the first path to the existing path if the first path is feasible.

[0012] In an optional embodiment, the feasibility judgment module includes: a first feasibility judgment unit, which is used to determine that if the first path point is feasible, the feasible probability value is 1. A second feasibility judgment unit, which is used to determine that if the first path point is not feasible, the feasible probability value is 0. A third feasibility judgment unit, which is used to set a step probability factor, and the step probability factor is a random number between (0, 1). A fourth feasibility judgment unit, which is used to obtain a roadblock radius parameter. A fifth feasibility judgment unit, which is used to calculate a sampling improved path generation factor based on the feasible probability value, the step probability factor and the roadblock radius parameter.

[0013] In an optional implementation, the path adding module is further used to: if the first path is not feasible, select a second random sampling point to generate a second path.

[0014] In an optional implementation, the path adding module is further used to calculate the distance between the second path point and the obstacle, and if the distance is greater than a preset threshold, the first path is feasible.

[0015] In an optional embodiment, the device further includes: a path ending module, which is used to end the path generation if the second path point is in the target point area; the target point area includes the target point and an area with a preset radius from the target point.

[0016] In an optional implementation, the sampling point acquisition module is further used for: the path planning space includes a two-dimensional area of ​​a preset range.

[0017] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the improved sampling path planning method of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the improved sampling path planning method of the first aspect or any corresponding embodiment thereof.

[0019] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the improved sampling path planning method of the above-mentioned first aspect or any corresponding embodiment thereof.

[0020] The technical solution provided by this application may have the following beneficial effects:

[0021] The improved sampling path planning method provided by the present invention first initializes the path planning environment and generates random sampling points. Find the point in the existing path that is closest to the random sampling point to provide a reference for generating new path points. Generate new path points between adjacent points and random sampling points, and gradually build the path. Evaluate the feasibility of the new path points, calculate the improvement factor, and improve the feasibility of the path. Adjust the path points according to the improvement factor to make it more likely to avoid obstacles. Connect the adjusted path points with the adjacent points to form a new path segment. Evaluate the feasibility of the new path segment, add it to the existing path, and gradually build a complete path. The above scheme introduces a sampling improvement factor for complex application scenarios, which can adjust some infeasible path points that fall into obstacle areas to feasible path points, efficiently generate feasible paths in complex environments, and improve the efficiency and performance of path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a flowchart of an improved sampling path planning method according to an embodiment of the present invention;

[0024] Figure 2 is a flow chart of an improved sampling path planning method provided by an optional embodiment of the present invention;

[0025] Figure 3 is an example diagram of an improved sampling path planning method provided by an optional embodiment of the present invention;

[0026] Figure 4 is a structural block diagram of an improved sampling path planning method and device according to an embodiment of the present invention;

[0027] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0029] In the description of the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between two items, or an association relationship between the two items, or a relationship between indication and being indicated, configuration and being configured, and the like.

[0030] According to an embodiment of the present invention, an embodiment of an improved sampling path planning method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] In this embodiment, an improved sampling path planning method is provided. Figure 1 is a flow chart of an improved sampling path planning method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0032] Step S101, setting a path planning space, a starting point and a target point, and obtaining a first random sampling point from the path planning space.

[0033] Optionally, the path planning space includes a two-dimensional area of ​​a preset range.

[0034] Define a two-dimensional area as the path planning space M, which includes all possible path points and obstacles. Set the starting point and target point in the path planning space M, and the starting point is denoted by X. start , the target point is recorded as X goal And, generate the first random sampling point in the path planning space, denoted as X rand The sampling point is a candidate point in the path planning process and is used to explore the path.

[0035] Step S102: Find the neighboring point closest to the first random sampling point from the existing path.

[0036] The existing path is a part of the path that has been generated, recorded as path T, which may only have the starting point at the beginning. Find the distance X from the existing path T rand The nearest neighbor point, denoted by X near This point is the random sampling point X in the existing path T. rand The closest point is used to determine the new waypoint.

[0037] Step S103, determining a first path point from an adjacent point to a first random sampling point according to a preset step length.

[0038] Set a fixed step size stepsize to control the accuracy of path point generation. near To X randGenerate the first path point X on the path with a step size of stepsize new , the calculation method is:

[0039] X new =X near +stepsize*e

[0040]

[0041] Among them, e is from X near To X rand This formula ensures that the first path point X new In X near and X rand On the straight line between near is stepsize.

[0042] Step S104, determining the feasibility of the first path point, and obtaining a sampling improved path generation factor based on the feasibility calculation.

[0043] The feasibility is determined based on the first path X new Is it in the obstacle area? new If it is in the obstacle area, it is not feasible; otherwise, it is feasible. Evaluate whether the newly generated path point is feasible, and calculate a sampling improvement path generation factor P based on the feasibility. r , used to adjust path points and improve the feasibility of the path.

[0044] Step S105, determining a second path point according to the sampled improved path generation factor.

[0045] Improve the path generation factor P based on sampling r Adjust the first path point X new , get the second path point X new2 , the calculation formula is:

[0046]

[0047] Step S106, connecting the second path point and the adjacent point to obtain the first path.

[0048] X near and X new2 Connect to form a new path segment, which is the first path E i The path segment is a part of the path planning process that is used to gradually build the complete path.

[0049] Step S107: if the first path is feasible, add the first path to the existing path.

[0050] Optionally, the distance between the second path point and the obstacle is calculated, and if the distance is greater than a preset threshold, the first path is feasible.

[0051] Calculate X new2 The shortest distance to the obstacle. If the distance is greater than the preset threshold, the path is considered feasible. If the path is feasible, the new path segment is added to the existing path. The preset threshold is based on the specific requirements of the task and the characteristics of the environment. For example, in a high-speed bridge inspection, it may be necessary to maintain a safety distance of at least 2 meters to avoid collision.

[0052] The improved sampling path planning method provided in this embodiment first initializes the path planning environment and generates random sampling points. Find the point in the existing path that is closest to the random sampling point to provide a reference for generating new path points. Generate new path points between adjacent points and random sampling points, and gradually build the path. Evaluate the feasibility of the new path points, calculate the improvement factor, and improve the feasibility of the path. Adjust the path points according to the improvement factor to make it more likely to avoid obstacles. Connect the adjusted path points with the adjacent points to form a new path segment. Evaluate the feasibility of the new path segment, add it to the existing path, and gradually build a complete path. This method introduces a sampling improvement factor for complex application scenarios, which can adjust some infeasible path points that fall into obstacle areas to feasible path points, efficiently generate feasible paths in complex environments, and improve the efficiency and performance of path planning.

[0053] In an optional implementation, the process of step S104 includes the following steps:

[0054] Step S1041: if the first path point is feasible, the feasible probability value is 1.

[0055] Determine the first path point X new Is it in an obstacle-free area? new Feasible, that is, not in the obstacle area, then it is marked as feasible, and the feasible probability value is P a Set to 1.

[0056] Step S1042: If the first path point is not feasible, the feasible probability value is 0.

[0057] If X new In the area where the obstacle is located, it is marked as infeasible, and the feasible probability value P a Set to 0.

[0058] Step S1043, setting the step length probability factor, the step length probability factor is a random number between (0, 1).

[0059] Introduce random numbers, step probability factor P eIt is a random number between (0,1) used to adjust the generation of path points to make the path generation process more flexible.

[0060] Step S1044, obtaining the roadblock radius parameter.

[0061] Get the barrier radius parameter R under load application environment b , if the roadblock is of irregular shape, its equivalent radius is calculated. Optionally, the minimum enclosing sphere of the roadblock is calculated, and its radius can be used as the equivalent radius. The minimum enclosing sphere is the smallest sphere that can completely contain the roadblock. For example, in the inspection of a high-speed bridge, there is an irregularly shaped beam column as a roadblock. The geometric shape data of the beam column is obtained through laser scanning technology, and then the radius of its minimum enclosing sphere is calculated to be 3 meters. This 3-meter radius will be applied to the path planning method as a roadblock radius parameter. During the path planning process, it will be ensured that the path point maintains a safe distance of at least 2 meters from the beam column, thereby improving the feasibility and safety of the path.

[0062] Step S1045, based on the feasible probability value, the step probability factor and the obstacle radius parameter, the sampling improved path generation factor is calculated.

[0063] Considering the feasibility, randomness and influence of obstacles of the path points, the sampling improved path generation factor P is calculated. r , used to adjust the path points and improve the feasibility of the path. The calculation formula is:

[0064] P r =P a *P e *R b

[0065] This implementation can efficiently generate feasible paths in complex application scenarios, thereby improving the efficiency and performance of path planning.

[0066] In an optional implementation, if the first path is not feasible, a second random sampling point is selected to generate a second path.

[0067] When the first path point X new or the adjusted waypoint X new2 With adjacent point X near When the path formed by the connection is not feasible, it means that there are obstacles at the current path point or path segment and it is impossible to pass directly. At this time, in order to continue to explore feasible paths, a new random sampling point needs to be re-selected. Using the new random sampling point, the process of path point generation and feasibility evaluation is re-executed to generate another possible feasible path. This process is a repeated application of the original path generation logic, and the purpose is to find a feasible path that avoids obstacles through continuous attempts.

[0068] This embodiment can flexibly adjust the strategy and improve the success rate and efficiency of path planning when facing complex environments and obstacles by continuously trying new random sampling points until a feasible path is generated.

[0069] In an alternative embodiment, when the second path point is within the target point area, the path generation is terminated; the target point area includes the target point and an area with a preset radius from the target point.

[0070] During the path generation process, check whether the second path point X new2 has approached or reached the target point X goal . The target point area not only includes the target point itself but also a circular area centered on the target point with a preset radius R g .

[0071] If |X new2 - X goa l| < Rg, then the second path point Xnew 2 is within the target point area, and it is considered that the path has successfully reached the target point, so the path generation process can be terminated.

[0072] This embodiment can terminate the path generation in advance when the path point approaches the target point, improving the efficiency of path planning, especially in scenarios where the target point area is large or the path planning task has high real-time requirements, such as high-speed bridge inspection. It not only optimizes the path generation process but also ensures that it can be quickly completed while meeting the task requirements, saving computing resources.

[0073] Figure 2 is a flowchart of an improved sampling path planning method provided by an alternative embodiment of the present invention. First, set the path planning starting point as X start , the planning target point as X goal , obtain the point X through sampling rand , find the point X rand nearest to X from the existing path T near , and generate the path point X new . Check whether the newly generated path point X new is within the obstacle area. If it is not within the obstacle area, continue to the next step. If it is within the obstacle area, adjust the path point. Calculate the sampling improvement path generation factor P r and generate the path point X new2 . Check whether the path formed by X new2 and X near is feasible. If the path is feasible, continue to the next step. If the path is not feasible, return for resampling. If it is feasible, add the path point X new2 and the path E iAdd to path T to check whether the current path has reached or is close to the target area. If it has reached the target area, end the process. If it has not reached the target area, return to resampling and continue path planning.

[0074] Figure 3 : is an example diagram of the improved sampling path planning method provided by an optional embodiment of the present invention. The current application scenario is a high-speed bridge inspection scenario. The path planning space is M, the range is in the area of ​​20*20, and the path planning starting point is set to X. start =(1,1), the planning target point is X goal =(19,19), a sampling point in the process is X rand =(12.5,14.5), find the distance X in the existing path T rand The nearest point X near =(10,7), stepsize=4,X new =(11.485,10.7125) is in the obstacle area, then P a =1,P e is a random number between (0,1), when P e =0.8, beam-column radius parameter R b =3,P r =P a *P e *R b =2.4, we can calculate X new2 =(10.594, 8.485), from X near To X new2 The path is feasible and can be added to the path. If the target point is not reached, sampling and point selection will be continued to generate subsequent paths.

[0075] In this embodiment, an improved sampling path planning method device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0076] This embodiment provides an improved sampling path planning method and device, such as Figure 4 As shown, including:

[0077] The sampling point acquisition module 401 is used to set the path planning space, the starting point and the target point, and obtain the first random sampling point from the path planning space;

[0078] The adjacent point acquisition module 402 is used to find the adjacent point closest to the first random sampling point from the existing path;

[0079] A first path point module 403, configured to determine a first path point from an adjacent point to a first random sampling point according to a preset step length;

[0080] A feasibility judgment module 404 is used to judge the feasibility of the first path point and obtain a sampling improved path generation factor based on the feasibility calculation;

[0081] A second path point module 405, for determining a second path point according to the sampled improved path generation factor;

[0082] A path acquisition module 406, configured to connect the second path point and the adjacent point to obtain the first path;

[0083] The path adding module 407 is used to add the first path to the existing path if the first path is feasible.

[0084] In an optional implementation, the feasibility determination module 404 includes:

[0085] The first feasibility judgment unit is used to determine that if the first path point is feasible, the feasible probability value is 1.

[0086] The second feasibility judgment unit is used to determine that if the first path point is not feasible, the feasible probability value is 0.

[0087] The third feasibility judgment unit is used to set a step length probability factor, and the step length probability factor is a random number between (0, 1).

[0088] The fourth feasibility judgment unit is used to obtain the roadblock radius parameter.

[0089] The fifth feasibility judgment unit is used to calculate the sampling improved path generation factor based on the feasible probability value, the step probability factor and the roadblock radius parameter.

[0090] In an optional implementation, the path adding module 407 is further used to:

[0091] If the first path is not feasible, a second random sampling point is selected to generate a second path.

[0092] In an optional implementation, the path adding module 407 is further used to:

[0093] The distance between the second path point and the obstacle is calculated. If the distance is greater than a preset threshold, the first path is feasible.

[0094] In an optional embodiment, the device further comprises:

[0095] The path ending module is used to end the path generation if the second path point is in the target point area; the target point area includes the target point and an area with a preset radius from the target point.

[0096] In an optional implementation, the sampling point acquisition module 401 is further configured to: the path planning space includes a two-dimensional area of ​​a preset range.

[0097] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0098] The improved sampling path planning method device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0099] The embodiment of the present invention also provides a computer device having the above Figure 4 The improved sampling path planning method and device shown.

[0100] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0101] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0102] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0103] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0104] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0105] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0106] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0107] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0108] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0109] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. An improved sampling path planning method, characterized in that: The method comprises: Setting a path planning space, a starting point and a target point, and obtaining a first random sampling point from the path planning space; Find the nearest neighboring point to the first random sampling point from the existing path; Determine a first path point from the adjacent point to the first random sampling point according to a preset step length; Determine the feasibility of the first path point, and calculate a sampling improved path generation factor based on the feasibility; Determining a second path point according to the sampled improved path generation factor; Connecting the second path point and the adjacent point to obtain a first path; If the first path is feasible, the first path is added to the existing path.

2. The method according to claim 1, characterized in that The determining the feasibility of the first path point and obtaining a sampling improved path generation factor based on the feasibility calculation includes: If the first path point is feasible, the feasible probability value is 1; If the first path point is not feasible, the feasible probability value is 0; Set a step length probability factor, where the step length probability factor is a random number between (0, 1); Get the roadblock radius parameter; Based on the feasible probability value, the step probability factor and the barrier radius parameter, a sampling improved path generation factor is calculated.

3. The method according to claim 1, characterized in that The method further comprises: If the first path is not feasible, a second random sampling point is selected to generate a second path.

4. The method according to claim 3, characterized in that: The method further comprises: The distance between the second path point and the obstacle is calculated. If the distance is greater than a preset threshold, the first path is feasible.

5. The method according to claim 1, characterized in that The method further comprises: If the second path point is in the target point area, the path generation is terminated; the target point area includes the target point and an area with a preset radius from the target point.

6. The method according to claim 1, characterized in that The path planning space includes a two-dimensional area of ​​a preset range.

7. An improved sampling path planning device, characterized in that: The device comprises: A sampling point acquisition module, used to set a path planning space, a starting point and a target point, and obtain a first random sampling point from the path planning space; An adjacent point acquisition module, used to find the adjacent point closest to the first random sampling point from the existing path; A first path point module, configured to determine a first path point from the adjacent point to the first random sampling point according to a preset step length; A feasibility judgment module, used to judge the feasibility of the first path point, and obtain a sampling improved path generation factor based on the feasibility calculation; A second path point module, configured to determine a second path point according to the sampled improved path generation factor; A path acquisition module, used to connect the second path point and the adjacent point to obtain a first path; A path adding module is used for adding the first path to the existing path if the first path is feasible.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the improved sampling path planning method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the improved sampling path planning method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the improved sampling path planning method according to any one of claims 1 to 6.

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