Local path planning method and vehicle

Abstract

The present invention discloses a local path planning method and a vehicle. The method includes: obtaining the current position of the vehicle and its reference path, and obtaining multiple sampling layers based on a dynamic programming algorithm; if the current position is not on the reference path, determining whether the vehicle in the local path planned by the dynamic programming algorithm is within the first layer of the multiple sampling layers to return to the reference path; if the vehicle is within the first layer to return to the reference path, determining a first splicing point on the local path and a second splicing point on the reference path based on the current position and the reference path; determining a sampling segment based on the first local path segment, the second local path segment, and the line segment formed by the first splicing point to the second splicing point. In the embodiment of the present invention, by using the line segment formed by the local path segment and the splicing point to determine the sampling segment, the reference path can be well restored and covered, and at the same time, the efficiency of path generation is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous driving, and in particular, to a local path planning method and a vehicle. Background Art

[0002] With the development of artificial intelligence technology and modern manufacturing industry, autonomous driving technology has gradually entered people's daily lives and imperceptibly changed people's travel modes. Unmanned driving technology can be briefly divided into several aspects: perception, prediction, positioning, decision-making, planning, and control. Planning usually refers to path planning methods, and the main task is to reasonably explore the environmental space according to the current vehicle information and finally plan a path that is convenient for the controller to execute and collision-free.

[0003] For most common vehicle local path planning schemes, the principle of generating paths is mostly to tend to minimize the path length on the premise of ensuring that the vehicle does not collide, so that the vehicle tends to take some "shortcut" paths and then tries to obtain the lowest possible cost. For some unmanned vehicles applied to special scenarios, such as cleaning vehicles, mine-sweeping and bomb-disposal vehicles, etc., their working goals are often to perform "scene coverage" as much as possible. At this time, it is not a very appropriate scheme to evaluate local path planning only at the cost of path length.

[0004] The inventors found that: the existing algorithms do not consider making full use of the reference path, and the sampling points between layers are all connected by curves. The curve cannot fully restore the reference path. When the reference path is relatively "irregular", the locally planned path will deviate greatly from the reference path. Summary of the Invention

[0005] Embodiments of the present invention aim to solve at least one of the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a local path planning method, including: obtaining the current position of a vehicle and its reference path, and obtaining a plurality of sampling layers based on a dynamic programming algorithm, wherein each sampling layer has a plurality of sampling points; if the current position is not on the reference path, determining whether the vehicle returns to the reference path within the first layer of the plurality of sampling layers in the local path planned by the dynamic programming algorithm; if the vehicle returns to the reference path within the first layer, determining a first splicing point on the local path based on the current position and the reference path, and a second splicing point on the reference path; determining a first local path segment from the current position to the splicing point based on the first splicing point, and determining a second local path segment based on the sampling point closest to the reference path in the direction from the splicing point to the reference path; determining a sampling segment based on the first local path segment, the second local path segment, and the line segment formed by the first splicing point to the second splicing point.

[0007] In a second aspect, an embodiment of the present invention provides a local path planning device, including: an acquisition module, configured to acquire the current position of a vehicle and its reference path, and obtain a plurality of sampling layers based on a dynamic programming algorithm, wherein each sampling layer has a plurality of sampling points; a judgment module, configured to judge whether the vehicle returns to the reference path within the first layer of the plurality of sampling layers in the local path planned by the dynamic programming algorithm if the current position is not on the reference path; a first determination module, configured to determine a first splicing point on the local path and a second splicing point on the reference path based on the current position and the reference path if the vehicle returns to the reference path within the first layer; a second determination module, configured to determine a first local path segment from the current position to the splicing point based on the first splicing point, and determine a second local path segment based on the sampling point closest to the reference path in the direction from the splicing point to the reference path; a third determination module, configured to determine a sampling segment based on the first local path segment, the second local path segment, and the line segment formed by the first splicing point to the second splicing point.

[0008] In a third aspect, an embodiment of the present invention provides an electronic device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the steps of the local path planning method according to any embodiment of the present invention.

[0009] In a fourth aspect, an embodiment of the present invention provides a storage medium, on which a computer program is stored, characterized in that when the program is executed by a processor, the steps of the local path planning method according to any embodiment of the present invention are implemented.

[0010] In a fifth aspect, an embodiment of the present invention further provides a computer program product, the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is enabled to execute the steps of the local path planning method according to any embodiment of the present invention.

[0011] In a sixth aspect, an embodiment of the present invention further provides a mobile tool, the mobile tool includes the electronic device described in the third aspect.

[0012] In this application, when the local path returns to the reference path within the first layer, the sampling segment is determined based on the line segment formed by the local path segment and the splicing point, which can well restore and cover the reference path, without temporarily generating a curve for connection, and at the same time makes the path generation more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0014] Figure 1 Flowchart of a local path planning method provided by an embodiment of the present invention;

[0015] Figure 2 Schematic diagram of the return reference path within the first layer of a local path planning method provided by the present invention;

[0016] Figure 3 Schematic diagram of the splitting of the return reference path within the first layer of a local path planning method provided by the present invention;

[0017] Figure 4 Schematic diagram of the sampling improvement in dynamic programming of a local path planning method provided by the present invention;

[0018] Figure 5 Schematic diagram of the splicing point of the local path and the reference path of a local path planning method provided by the present invention;

[0019] Figure 6 Schematic diagram of the local path outside the first layer of a local path planning method provided by the present invention;

[0020] Figure 7 Schematic diagram of the local path within the first layer of a local path planning method provided by the present invention;

[0021] Figure 8 Schematic diagram of the local path of a local path planning method provided by the present invention;

[0022] Figure 9 Schematic diagram of the structure of a local path planning execution device provided by the present invention;

[0023] Figure 10 Schematic diagram of the structure of an embodiment of the electronic device of the present invention. Detailed implementation manners

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0025] Those skilled in the art know that the embodiments of the present application can be implemented as a system, device, equipment, method, or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0026] For ease of understanding, the following technical terms related to the present application are explained: The "mobile device" referred to in the present application includes, but is not limited to, vehicles with a total of six levels of autonomous driving technology from L0 to L5.

[0027] In some embodiments, the mobile tool can be a vehicle device or a robot device with the following various functions:

[0028] (1) Passenger-carrying function, such as a family car, a bus, etc.;

[0029] (2) Cargo-carrying function, such as a general freight truck, a van, a semi-trailer truck, an enclosed truck, a tank truck, a flatbed truck, a container truck, a dump truck, a special-structured truck, etc.;

[0030] (3) Tool function, such as a logistics distribution vehicle, an automated guided vehicle (AGV), a patrol vehicle, a crane, a hoist, an excavator, a bulldozer, a forklift, a roller, a loader, an off-road engineering vehicle, an armored engineering vehicle, a sewage treatment vehicle, a sanitation vehicle, a vacuum cleaner truck, a floor washing vehicle, a sprinkler truck, a floor sweeping robot, a food delivery robot, a shopping guide robot, a lawn mower, a golf cart, etc.;

[0031] (4) Entertainment function, such as an entertainment vehicle, an autonomous driving device in an amusement park, a balance bike, etc.;

[0032] (5) Special rescue function, such as a fire truck, an ambulance, an electric power repair vehicle, an engineering rescue vehicle, etc.

[0033] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0034] The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0035] In the present invention, "module", "device", "system", etc. refer to related entities applied to a computer, such as hardware, a combination of hardware and software, software, or software in execution, etc. Specifically, for example, an element can be, but is not limited to, a process running on a processor, a processor, an object, an executable element, an execution thread, a program, and / or a computer. Also, an application program or a script program running on a server, and the server can both be elements. One or more elements can be in an execution process and / or thread, and the elements can be localized on one computer and / or distributed between two or more computers, and can be run by various computer-readable media. The elements can also communicate through local and / or remote processes according to a signal having one or more data packets, for example, a signal from data that interacts with another element in a local system, a distributed system, and / or interacts with other systems through a signal on a network of the Internet.

[0036] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising" and "including" not only include those elements, but also include other elements not explicitly listed, or also include elements inherent to such a process, method, article, or device. Without more limitations, an element defined by the statement "including..." does not exclude the existence of additional identical elements in the process, method, article, or device including the said element.

[0037] As Figure 1 shown is a flowchart of a local path planning method provided by an embodiment of the present invention, including the following steps:

[0038] S11: Obtain the current position of the vehicle and its reference path, and obtain multiple sampling layers based on the dynamic programming algorithm, where each sampling layer has multiple sampling points;

[0039] S12: If the current position is not on the reference path, determine whether the vehicle in the local path planned by the dynamic programming algorithm returns to the reference path within the first layer of the multiple sampling layers;

[0040] S13: If the vehicle returns to the reference path within the first layer, determine a first splicing point on the local path based on the current position and the reference path, and a second splicing point on the reference path;

[0041] S14: Determine a first local path segment from the current position to the first splicing point based on the first splicing point, and determine a second local path segment based on the sampling point closest to the reference path in the direction of the second splicing point to the reference path;

[0042] S15: Determine a sampling segment based on the first local path segment, the second local path segment, and the line segment formed by the first splicing point to the second splicing point.

[0043] In this embodiment, for step S11, the current position of the vehicle is obtained according to the in-vehicle positioning device, and the reference path that has been planned in the autonomous vehicle is obtained. Multiple sampling layers are calculated through a dynamic programming algorithm. Each sampling layer consists of multiple sampling points. This dynamic programming algorithm is a common path planning method. The calculation process of the dynamic programming algorithm mainly includes sampling, connection, and screening. Sampling means sampling along the direction of the reference path to obtain a series of straight lines perpendicular to the reference path, and then sampling a series of transverse sampling points on these straight lines respectively according to the principle of equal interval or equal quantity. Connection means using a certain curve connection method to connect the sampling points on adjacent layers in pairs. Screening means that through a certain selection criterion, for each sampling point on each layer, only the optimal connection curve connecting to the upper layer is selected and retained.

[0044] For step S12, if the current position of the vehicle obtained by the in-vehicle positioning device is not on the reference path, it is judged whether the vehicle in the local path planned by the dynamic programming algorithm returns to the reference path within the first layer of the multiple sampling layers.

[0045] For step S13, if the vehicle in the local path returns to the reference path within the first layer of the multiple sampling layers, the first splicing point on the local path and the second splicing point on the reference path are determined according to the current position of the vehicle and the reference path, as Figure 2 shown, where and respectively represent the "splicing points" located on the local path and the reference path, that is, it can be approximately considered that the local path "returns" to the reference path at the point. In order to restore the reference path as much as possible, so as much of the reference path as possible is used as sampling during each planning. Considering connecting all points in the S1 layer with a curve for the vehicle, obviously the point in the S1 layer located on the reference path is the position to return to the reference path fastest. If the splicing point is before the S1 layer, it means that finding this splicing point is still useful (it can return to the reference path earlier at the splicing point). If the splicing point is after the S1 layer, then it is meaningless because directly connecting to the point in the S1 layer located on the reference path will return to the reference path earlier.

[0046] For step S14, the first local path segment from the current position of the vehicle to the first splicing point is determined according to the first splicing point on the local path, and the second local path segment is determined according to the second splicing point and the sampling point closest to the reference path in the reference path direction, as Figure 3As shown, where represents the starting point of the reference path, i.e., the search starting point, which can be the current position of the vehicle itself or the end point of the "bonding line"; is the sampling point on the first layer S1 of the reference path; the first splicing point on the local path The second splicing point on the reference path Based on the first splicing point The second splicing point Determine the first local path segment The second local path segment

[0047] For step S15, according to the first local path segment, the second local path segment, and the line segment formed by the first splicing point to the second splicing point to determine the sampling segment; determine And After that, the local path segment And the reference path segment Are spliced, and the result after splicing is used as the first-layer sampling segment.

[0048] The method of the embodiment of the present application determines the sampling segment based on the line segment formed by the local path segment and the splicing point when returning to the reference path within the first layer, which can well restore and cover the reference path, does not need to temporarily generate a curve for connection, and at the same time makes the path generation more efficient.

[0049] Furthermore, if there are sampling points on the adjacent sampling layers obtained by the dynamic programming algorithm that are located on the reference path, the reference path segment determined based on the sampling points located on the reference path on the adjacent sampling layers is used as the sampling segment. As Figure 4 Shown, where Represents the sampling point on the S i-1 Layer located on the reference path P ref Shown by a white circle, Represents the sampling point on the S i Layer located on the reference path P ref Also shown by a white circle. When generating a sampling curve at a certain sampling point on the S i Layer, it will consider generating a curve pointing to the current sampling point from all the sampling points on the S i-1 Layer. There are many ways to generate a curve (such as Bezier curve, circular arc curve, etc., just choose the appropriate one). If exactly two sampling points are both located on the reference path, that is Pointing to At this time, the curve generation is no longer called, but the segment of the reference path between And Is used as the sampling, that is Figure 5 In The path segments represented. The evaluation of all sampled paths curve is as follows:

[0050]

[0051] Among them, C L represents the length cost of the sampled path curve, and its magnitude is proportional to the path length; C obs represents the collision cost of the sampled path curve, which is a maximum value when there is a collision, otherwise it is 0. The smaller the evaluation value, the higher the priority of the sampled path. The setting of the above cost values only represents an example and can be adjusted according to the effect in actual use. It can be seen from the above evaluation function that when the sampled path is a segment of the reference path, its cost value is only related to whether there is a collision. When there is no collision, its evaluation value must be less than that of the other sampled paths, that is, the higher the priority.

[0052] It should be noted that if the sampled point is not on the reference path, a curve is generated through the start and end points of the curve. There are many ways to do this, and commonly used ones include spline curves, polynomial curves, etc. In the present invention, a 3rd-order Bezier curve is used for connection. If there is exactly an obstacle between two sampled points, the sampled curve adopts the "post-check" method, that is, after generating the curve, the collision check of the obstacle is performed based on the curve result. If there is a collision, the cost of the curve is set to infinity.

[0053] Furthermore, obtain the adjacent first local path point and second local path point on the local path, and determine multiple points to be inspected on the reference path; when the distance between the second local path point and a certain point to be inspected is less than or equal to the preset distance threshold, the first angle between the first straight line formed by the first local path point and the second local path point and the second straight line formed by the second local path point and a certain point to be inspected is less than or equal to the preset angle threshold, and the second angle formed by the second straight line and the reference path direction is less than or equal to the preset angle threshold, the second local path point is determined as the first splicing point on the local path, and a certain point to be inspected is determined as the second splicing point on the reference path, as Figure 5 shown. Assume and respectively represent two adjacent points on the local path, represents the point to be inspected on the reference path, represents and the distance between them; θ represents the angle from to and the angle difference; γ represents the angle from to and the angle difference between the angle from And The criteria for whether it can be used as the splicing point of the local path and the reference path respectively are as follows:

[0054]

[0055] Among them, d represents the distance threshold, generally the upper limit of the distance between waypoints of the local path, where the upper limit is related to the position control accuracy; η represents the angle threshold, generally the upper limit of the angular difference between waypoints of the local path.

[0056] In some alternative embodiments, a binary search method is used to determine multiple points to be investigated on the reference path. For example, by traversing all points of the actual path, for each point A, a segment of the reference path is given, and within the start and end points [B,,,,,,,,C] of the segment, a point D closest to A is found through binary search, that is, [B,,,,D,,,,C]. After the traversal, the point closest to the reference path among all traversed points is selected as the splicing point.

[0057] Furthermore, if the vehicle does not return to the reference path within the first floor, the line segment formed by directly connecting the current position of the vehicle to the point on the reference path on the first floor is used as the sampling segment, as Figure 6 shown. For the first floor, the local path returns to the reference path only outside the first floor (between S0 and S1). At this time, the way to cover the reference path as much as possible should be to directly connect the sampling points on the reference path of the S1 layer from the starting point.

[0058] It should be noted that when the local path returns to the reference path within the first floor, as Figure 7 shown, if directly connecting the sampling points on the reference path of the S1 layer, a part of the reference path will be "omitted" compared to the current reference path. In particular, if the shape of the reference path is special, directly connecting the sampling points on the reference path of the S1 layer will also cause a large difference between the actual driving path and the reference path.

[0059] In some alternative embodiments, the dynamic programming algorithm mainly uses Bezier curves / arc curves to generate sampling segments between adjacent sampling points, calculates the cost values of the sampling segments between adjacent sampling points using a cost function, and then determines the optimal path according to the total cost values of the paths formed by the sampling points. The cost function is related to whether the sampling points are on the reference path, the length of the sampling segment, and the collision cost. When the sampling points are not on the reference path, a curve is generated through the start and end points of the curve, and there are many ways, such as spline curves, polynomial curves, etc. In the present invention, a 3rd-order Bezier curve is used for connection, and this is not limited herein.

[0060] Please refer to Figure 8, which is a partial path schematic diagram provided by an embodiment of the present invention.

[0061] As Figure 8 shown, the present invention is based on an assumption premise that there are only three "monotonic types" of relationships between the local path and the reference path during the first-layer sampling, namely "close", "far", and "coincide". That is, once the two paths intersect within the first-layer sampling distance, they will not separate again. Due to the sampling method designed in the present invention using reference path segments, in most scenarios, the planning of the unmanned vehicle satisfies the above assumption.

[0062] Through the local planning scheme designed by the present invention, for the "irregular" reference path scenario mentioned above, the obtained partial path schematic diagram is as Figure 8 shown. Compared with the traditional planning scheme, there is an obvious improvement in the coverage of the reference path.

[0063] Figure 9 which is a structural schematic diagram of a local path planning device provided by an embodiment of the present invention. The system can execute the local path planning method described in any of the above embodiments and is configured in the terminal.

[0064] A local path planning device 100 provided in this embodiment includes: an acquisition module 110, a judgment module 120, a first determination module 130, a second determination module 140, and a third determination module 150.

[0065] Among them, the acquisition module 110 is used to acquire the current position of the vehicle and obtain multiple sampling layers based on the dynamic programming algorithm. Each sampling layer has multiple sampling points; the judgment module 120 is used to judge whether the vehicle returns to the reference path within the first layer of the multiple sampling layers in the local path planned by the dynamic programming algorithm if the current position is not on the reference path; the first determination module 130 is used to determine a first splicing point on the local path and a second splicing point on the reference path based on the current position and the reference path if the vehicle returns to the reference path within the first layer; the second determination module 140 is used to determine a first local path segment from the current position to the first splicing point based on the first splicing point, and determine a second local path segment based on the sampling point closest to the reference path in the direction of the second splicing point to the reference path; the third determination module 150 is used to determine a sampling segment based on the first local path segment, the second local path segment, and the line segment formed by the first splicing point to the second splicing point.

[0066] A local path planning device provided by an embodiment in the present application further includes: a fourth determination module (not shown in the figure). Among them, the fourth determination module is used to, if there are sampling points on the adjacent sampling layers obtained by the dynamic programming algorithm that are located on the reference path; use the reference path segment determined based on the sampling points located on the reference path on the adjacent sampling layers as the sampling segment.

[0067] A local path planning device provided by an embodiment in the present application further includes: a fifth determination module (not shown in the figure) and a sixth determination module (not shown in the figure). Among them, the fifth determination module is used to obtain adjacent first local path point and second local path point on the local path, and determine multiple points to be inspected on the reference path; the sixth determination module is used to, when the distance between the second local path point and a certain point to be inspected is less than or equal to a preset distance threshold, the first angle formed by the first straight line formed by the first local path point and the second local path point and the second straight line formed by the second local path point and the certain point to be inspected is less than or equal to a preset angle threshold, and the second angle formed by the second straight line and the reference path direction is less than or equal to a preset angle threshold, determine the second local path point as the first splicing point on the local path, and determine the certain point to be inspected as the second splicing point on the reference path.

[0068] The fifth determination module in a local path planning device provided by an embodiment in the present application includes: a seventh determination module (not shown in the figure). Among them, the seventh determination module is used to determine multiple points to be inspected on the reference path by using the binary search method.

[0069] The acquisition module in a local path planning device provided by an embodiment in the present application includes: a generation module (not shown in the figure), a calculation module (not shown in the figure), and an eighth determination module (not shown in the figure). Among them, the acquisition module is used to generate sampling segments between adjacent sampling points by using Bezier curves / arc curves; the calculation module is used to calculate the cost values of the sampling segments between adjacent sampling points based on a cost function; the eighth determination module is used to determine the optimal path based on the total cost values of the paths formed by connecting the sampling points.

[0070] The cost function in the embodiment of the present application is related to whether the sampling points are located on the reference path, the length of the sampling segment, and the collision cost.

[0071] An embodiment of the present invention further provides a non-volatile computer storage medium, which stores computer-executable instructions that can execute the local path planning method in any of the above method embodiments; as an implementation manner, the non-volatile computer storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set to: obtain a planned reference path; calculate an initial value of a lateral control amount at least based on the reference path, perform control sampling based on the initial value to obtain a plurality of control sampling points; calculate the lateral control amount at least based on the plurality of control sampling points to obtain a plurality of pure tracking paths; perform state sampling on the plurality of pure tracking paths respectively to obtain a plurality of state sampling points corresponding to the plurality of pure tracking paths; calculate the cost value of the plurality of sampling points corresponding to the plurality of pure tracking paths at least based on the plurality of state sampling points and the reference path, and output the lateral control amount corresponding to the pure tracking path with the minimum cost value.

[0072] As a non-volatile computer-readable storage medium, it can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions / modules corresponding to the methods in the embodiments of the present invention. One or more program instructions are stored in the non-volatile computer-readable storage medium and, when executed by a processor, execute the local path planning method in any of the above method embodiments.

[0073] An embodiment of the present invention further provides an electronic device, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of local path planning.

[0074] In some embodiments, an embodiment of the present invention further provides a mobile device, including a body and the electronic device installed on the body according to any of the foregoing embodiments. Among them, the mobile device can be an autonomous vehicle, such as an autonomous sweeper, an autonomous floor washer, an autonomous logistics vehicle, an autonomous passenger vehicle, an autonomous sanitation vehicle, an autonomous minibus / bus, a truck, a mining vehicle, etc., or it can also be a robot, etc.

[0075] In some embodiments, an embodiment of the present invention further provides a computer program product, which, when running on a computer, enables the computer to execute the method for local path planning described in any one of the embodiments of the present invention.

[0076] In some embodiments, the embodiments of the present invention further provide a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to execute any one of the above methods based on local path planning.

[0077] Figure 10 FIG. 4 is a schematic hardware structure diagram of an electronic device for the local path planning method provided by another embodiment of the present application. As Figure 10 shown, the device includes one or more processors 1010 and a memory 1020. Figure 10 Taking one processor 1010 as an example. The device for the local path planning method may further include an input device 1030 and an output device 1040.

[0078] The processor 1010, the memory 1020, the input device 1030, and the output device 1040 may be connected through a bus or other means. Figure 10 Taking connection through a bus as an example.

[0079] The memory 1020, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the local path planning method in the embodiments of the present application. The processor 1010 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 1020, that is, implements the local path planning method in the above method embodiments.

[0080] The memory 1020 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data, etc. In addition, the memory 1020 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 1020 may optionally include a memory remotely provided with respect to the processor 1010, and these remote memories may be connected to the mobile device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0081] The input device 1030 may receive input digital or character information. The output device 1040 may include a display device such as a display screen.

[0082] The one or more modules are stored in the memory 1020 and, when executed by the one or more processors 1010, perform the local path planning method in any of the above method embodiments.

[0083] The above product can execute the method provided in the embodiments of the present application, and has functional modules and beneficial effects corresponding to the execution of the method. For technical details not described in detail in this embodiment, reference can be made to the method provided in the embodiments of the present application.

[0084] The non-volatile computer-readable storage medium may include a storage program area and a storage data area. Among them, the storage program area may store an operating system and application programs required for at least one function; the storage data area may store data created according to the use of the device, etc. In addition, the non-volatile computer-readable storage medium may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the non-volatile computer-readable storage medium may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the device through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0085] An embodiment of the present invention further provides an electronic device, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the local path planning method in any embodiment of the present invention.

[0086] The electronic device in the embodiments of the present application exists in various forms, including but not limited to:

[0087] (1) Mobile communication device: Such devices are characterized by having mobile communication functions and mainly providing voice and data communication.

[0088] Such terminals include: smart phones, multimedia phones, functional phones, and low-end phones, etc.

[0089] (2) Ultra-mobile personal computer device: Such devices belong to the category of personal computers and have computing and processing functions, and

[0090] generally also have the characteristic of mobile Internet access. Such terminals include: PDA, MID, and UMPC devices, etc., such as tablet computers.

[0091] (3) Portable entertainment device: Such devices can display and play multimedia content. Such devices include: audio, video

[0092] Players, handheld game consoles, e-books, as well as smart toys and portable vehicle navigation devices.

[0093] (4) Other mobile devices with data processing functions.

[0094] In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising" and "including" not only include those elements, but also include other elements not explicitly listed, or also include elements inherent to such a process, method, article or device. Without more limitations, the elements defined by the statement "comprising..." do not exclude the existence of additional identical elements in the process, method, article or device including the said elements.

[0095] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course also by hardware. Based on such an understanding, the above technical solutions, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A local path planning method, characterized in that, Including: Obtain the current position of the vehicle and its reference path, and obtain multiple sampling layers based on the dynamic programming algorithm. Among them, each sampling layer has multiple sampling points; If the current position is not on the reference path, determine whether the vehicle in the local path planned by the dynamic programming algorithm returns to the reference path within the first layer of the multiple sampling layers; If the vehicle returns to the reference path within the first layer, determine a first splicing point on the local path and a second splicing point on the reference path based on the current position and the reference path, including: obtain the first local path point and the second local path point adjacent on the local path, and determine multiple points to be inspected on the reference path; when the distance between the second local path point and a certain point to be inspected is less than or equal to a preset distance threshold, the first angle between the first straight line formed by the first local path point and the second local path point and the second straight line formed by the second local path point and the certain point to be inspected is less than or equal to a preset angle threshold, and the second angle formed by the second straight line and the reference path direction is less than or equal to a preset angle threshold, determine the second local path point as the first splicing point on the local path, and determine the certain point to be inspected as the second splicing point on the reference path; Determine a first local path segment from the current position to the first splicing point based on the first splicing point, and determine a second local path segment based on the sampling point closest to the reference path in the direction of the reference path from the second splicing point; Determine a sampling segment based on the first local path segment, the second local path segment, and the line segment formed by the first splicing point to the second splicing point; 2. The method according to claim 1, characterized in that The method further includes: If there are sampling points on the reference path on adjacent sampling layers obtained by the dynamic programming algorithm; Use the reference path segment determined based on the sampling points on the reference path on the adjacent sampling layers as the sampling segment; 3. The method according to claim 1, characterized in that, The determination of multiple points to be inspected on the reference path includes: Use the binary search method to determine multiple points to be inspected on the reference path; 4. The method according to claim 1, characterized in that, After determining whether the vehicle in the local path planned by the dynamic programming algorithm returns to the reference path within the first layer of the multiple sampling layers, the method further includes: If the vehicle does not return to the reference path within the first layer; Use the line segment formed by directly connecting the current position to the point on the first layer that is on the reference path as the sampling segment; 5. The method according to any one of claims 1-4, characterized in that, The obtaining of multiple sampling layers based on the dynamic programming algorithm includes: Use Bezier curves / arc curves to generate sampling segments between adjacent sampling points; Calculate the cost value of the sampling segments between adjacent sampling points based on the cost function; Determine the optimal path based on the total cost value of each path formed by the sampling points; 6. The method according to claim 5, characterized in that, The cost function is related to whether the sampling point is on the reference path, the length of the sampling segment, and the collision cost; 7. A local path planning device, characterized in that, Including: An obtaining module for obtaining the current position of the vehicle and its reference path, and obtaining multiple sampling layers based on the dynamic programming algorithm. Among them, each sampling layer has multiple sampling points; A judgment module, configured to, if the current position is not on the reference path, judge whether the vehicle in the local path planned by the dynamic programming algorithm is within the first layer of the multiple sampling layers and return to the reference path; A first determination module, configured to, if the vehicle returns to the reference path within the first layer, determine a first splicing point on the local path and a second splicing point on the reference path based on the current position and the reference path, including: obtaining adjacent first and second local path points on the local path, and determining multiple points to be inspected on the reference path; when the distance between the second local path point and a certain point to be inspected is less than or equal to a preset distance threshold, the first angle between the first straight line formed by the first local path point and the second local path point and the second straight line formed by the second local path point and the certain point to be inspected is less than or equal to a preset angle threshold, and the second angle between the second straight line and the reference path direction is less than or equal to a preset angle threshold, determining the second local path point as the first splicing point on the local path, and determining the certain point to be inspected as the second splicing point on the reference path; A second determination module, configured to determine a first local path segment from the current position to the first splicing point based on the first splicing point, and determine a second local path segment based on the sampling point closest to the reference path in the direction of the reference path from the second splicing point; A third determination module, configured to determine a sampling segment based on the first local path segment, the second local path segment, and the line segment formed by the first splicing point to the second splicing point; 8. An electronic device, comprising: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the method according to any one of claims 1 to 6.

9. A storage medium, on which a computer program is stored, characterized in that When the program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is enabled to execute the steps of the method according to any one of claims 1 to 6.

11. A mobile tool, the mobile tool includes the electronic device according to claim 8.

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