CN117570991A - Path planning method and device, electronic equipment and storage medium - Google Patents

Abstract

The application discloses a path planning method, a path planning device, electronic equipment and a storage medium. The method specifically comprises the following steps: acquiring starting point information, end point information and barrier information in a target space; performing grid division on the target space, and marking barrier information to obtain a target grid three-dimensional map; determining at least two target expansion nodes in the target grid three-dimensional map according to the starting point information, the end point information and the barrier information, and storing the target expansion nodes in a candidate node table; loading the target expansion node in the candidate node table into the screening node table according to a preset heuristic function; and determining a target path according to the screening node table. According to the technical scheme, the two-way search is carried out from the starting point and the end point through the heuristic function, a practical and usable method is provided for path planning in the three-dimensional space, the improved heuristic function can further improve the searching efficiency, the waste of calculation resources caused by a large number of searching errors is reduced, and the algorithm time is reduced.

Description

Path planning method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a path planning method, apparatus, electronic device, and storage medium.

Background

With the continuous development and exploration of artificial intelligence technology, more and more manufacturers introduce artificial intelligence technology into products. Path planning, an important existence in technologies such as automatic driving and robotics, has become one of the subjects of intensive research by all parties. The application of conventional two-dimensional path planning algorithms in three-dimensional space has been exhausted, and more optimized path planning algorithms are required for typical three-dimensional environments.

Currently, path planning in three-dimensional environments often employs a two-step strategy. The method comprises the steps of firstly, sensing and modeling an environment, and secondly, searching an optimal path through a path planning algorithm. However, the searching mode of the traditional visual method, the rapid expansion random tree and other algorithms in the three-dimensional space is low in efficiency and long in algorithm time.

Disclosure of Invention

The application provides a path planning method, a path planning device, electronic equipment and a storage medium, so that the path searching efficiency in a three-dimensional environment is improved, and the algorithm time is reduced.

According to an aspect of the present application, there is provided a path planning method, the method including:

acquiring starting point information, end point information and barrier information in a target space;

performing grid division on the target space, and marking barrier information to obtain a target grid three-dimensional map;

determining at least two target expansion nodes in the target grid three-dimensional map according to the starting point information, the end point information and the barrier information, and storing the target expansion nodes in a candidate node table;

loading the target expansion node in the candidate node table into the screening node table according to a preset heuristic function;

and determining a target path according to the screening node table.

According to another aspect of the present application, there is provided a path planning apparatus, the apparatus comprising:

the information acquisition module is used for acquiring starting point information, end point information and barrier information in the target space;

the map construction module is used for carrying out grid division on the target space and marking barrier information to obtain a target grid three-dimensional map;

the node determining module is used for determining at least two target expansion nodes in the target grid three-dimensional map according to the starting point information, the end point information and the barrier information, and storing the target expansion nodes in the candidate node table;

the node loading module is used for loading the target expansion node in the candidate node table into the screening node table according to a preset heuristic function;

and the path determining module is used for determining a target path according to the screening node table.

According to another aspect of the present application, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the path planning method described in any one of the embodiments of the present application.

According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a path planning method according to any embodiment of the present application.

According to the technical scheme, the two-way search is carried out from the starting point and the end point through the heuristic function, a practical and usable method is provided for path planning in the three-dimensional space, the improved heuristic function can further improve the searching efficiency, the waste of calculation resources caused by a large number of searching errors is reduced, and the algorithm time is reduced.

It should be understood that the description of this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a path planning method according to a first embodiment of the present application;

FIG. 2 is a schematic diagram of a grid node exploration provided in accordance with a second embodiment of the present application;

fig. 3 is a schematic structural diagram of a path planning apparatus according to a third embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device implementing a path planning method according to an embodiment of the present application.

Detailed Description

In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a path planning method according to an embodiment of the present application, where the method may be applied to a case of path planning in a three-dimensional space, and the method may be performed by a path planning apparatus, where the path planning apparatus may be implemented in a form of hardware and/or software, and the path planning apparatus may be configured in an electronic device. As shown in fig. 1, the method includes:

s110, acquiring starting point information, end point information and barrier information in a target space.

The target space may be a space where path planning is required, where an obstacle may exist, and a three-dimensional path without obstacle and collision is required to be planned from the start point to the end point of the target space. The starting point information may be position information of the starting point in the target space, and similarly, the end point information may be position information of the end point in the target space, for example, the starting point information and the end point information may be coordinate data in the target space, and of course, in general, both the starting point and the end point of the path planning are known in advance, the obstacle information may be known in advance, or may be determined after being searched in the target space, the search of the obstacle in the three-dimensional space may adopt an obstacle determining algorithm in the related art, and the determining path of the obstacle information is not limited in the embodiment of the present application.

And S120, performing grid division on the target space, and marking barrier information to obtain a target grid three-dimensional map.

The target space is a three-dimensional space, and the target space is rasterized. For example, a grid method may be first used to divide the three-dimensional space into a plurality of horizontal planes perpendicular to the Z axis (vertical direction), and then grid-divide all the horizontal planes. The divided grids may be sized according to the obstacle size. In order to improve the efficiency and accuracy of path planning, the grids are not made larger than the barriers as much as possible, otherwise, the problem of wasting the planning space is easily caused. Known obstacle information is then marked in the constructed grids to determine which grids are occupied by the obstacle, and which grids are also understood to be in existence. The marked grid map is saved as a target grid three-dimensional map.

S130, determining at least two target expansion nodes in the target grid three-dimensional map according to the starting point information, the end point information and the obstacle information, and storing the target expansion nodes in a candidate node table.

Where a node may be an intersection between grids, it will be appreciated that in three dimensions three mutually perpendicular lines divide the space into 8 parts and that the three lines intersect at a point. Similarly, in the target grid three-dimensional map, 8 grids are arranged in close proximity around one intersection point, namely, the vertex of each grid can be one intersection point, and the intersection points among the grids are used as nodes for path planning. Then the path from the start point to the end point can be formed by several nodes connected. Thus, nodes of the valid paths that may exist that are explored between the start point and the end point may all be stored in the candidate node table. The candidate node table may be a library table for storing nodes that can be extended between the start point and the end point, and the extension is understood to be a constraint condition. It should be noted that many nodes between the start point and the end point cannot be expanded, for example, there is an obstacle or the advancing direction is not expected, and the like. Constraints may include, but are not limited to, forward direction, inter-node turning radius, obstacle condition, and dynamics constraints, among others. Nodes meeting these constraints may be stored in a candidate node library table, that is, nodes that actually get available paths are all present in the candidate node table.

And S140, loading the target expansion node in the candidate node table into the screening node table according to a preset heuristic function.

The heuristic function can be used for guiding node expansion between the starting point and the end point so as to reduce blindness of the node expansion. For example, a conventional a-algorithm calculates cost values for a plurality of available paths from a start point to an end point, thereby finding an optimal path. However, random expansion is relatively blind, wastes search time and computational resources, and is slow in algorithm convergence. The exploration of the nodes can be inspired with the help of heuristic functions. Moreover, the process of searching nodes by the traditional algorithm is unidirectional, and each embodiment of the application can adopt a bidirectional searching mode, namely searching from a starting point to an end point and searching from the end point to the starting point at the same time. That is, nodes meeting the constraint conditions are stored in the candidate node table, regardless of the start point-to-end point search process or the end point-to-start point search process. And then, according to the heuristic function, selecting a node with lower cost (a short consumed path) from the target expansion nodes of the candidate node list, and storing the node into the screened node list.

S150, determining a target path according to the screening node table.

It can be understood that under the exploration in two directions, if the exploration processes in the two directions are both explored to the same node, that is, when the same node appears in the screening node table, a more appropriate theoretical optimal path appears, and the theoretical optimal path is taken as a target path to plan.

In an alternative embodiment, the candidate node table includes a forward candidate table and a reverse candidate table, and the filtering node table includes a forward filtering table and a reverse filtering table.

The forward candidate table may be a candidate table for storing the target expansion node in the process of searching from the start point to the end point, and the reverse candidate table may be a candidate table for storing the target expansion node in the process of searching from the end point to the start point. Similarly, the forward filtering table may be a library table stored by the target expansion node in the filtering forward candidate table, and the reverse filtering table may be a library table stored by the target expansion node in the filtering reverse candidate table.

Further, the determining at least two target expansion nodes in the target grid three-dimensional map according to the start point information, the end point information and the obstacle information and storing the target expansion nodes in the candidate node table may include: determining at least one target expansion node around a starting point grid corresponding to the starting point information according to the starting point information and the obstacle information; according to the terminal information and the obstacle information, determining at least one target expansion node around the terminal grid corresponding to the terminal information; and respectively storing each target expansion node into a forward candidate table corresponding to the starting point and a reverse candidate table corresponding to the end point.

It will be appreciated that the two-way exploration process begins from the start point and the end point respectively and begins under the guidance of the heuristic function, and the nodes without the influence of the obstacle are stored in the candidate node table. Accordingly, the nodes in the process of searching from the starting point to the end point are stored in the forward candidate table, and the nodes in the process of searching from the end point to the starting point are stored in the reverse candidate table.

In an optional embodiment, the loading the target extension node in the candidate node table into the screening node table according to the preset heuristic function may include: according to the heuristic function, loading the target expansion node with the minimum valuation value in the forward candidate table into a forward screening table; and loading the target expansion node with the minimum valuation value in the reverse candidate table into the reverse screening table according to the heuristic function.

Under the guidance of the heuristic function, the heuristic function is searched from the starting point and the end point to the opposite side at the same time, and each searching step in the searching process, the target expansion node with the minimum valuation value calculated by the heuristic function is loaded into the screening node table from the candidate node table. That is, the node with the smallest valuation value in the forward candidate table is loaded to the forward filtering table, and the node with the smallest valuation value in the reverse candidate table is loaded to the reverse filtering table.

In particular, the heuristic function may be:

f(n)＝h(n)+g(n)；

where f (n) is an estimated value, g (n) is a fixed cost value from a start point to an end point, h (n) is an estimated cost from the start point to the end point, dx is a distance between the start point and the end point in the x-axis direction, dy is a distance between the start point and the end point in the y-axis direction, and dz is a distance between the start point and the end point in the z-axis direction.

In an alternative embodiment, the determining the target path according to the screening node table may include: if at least one identical target expansion node exists in the forward screening table and the reverse screening table, determining a target path according to the identical target expansion node.

It will be appreciated that nodes screened from the start point to the end point (forward direction) and from the end point to the start point (backward direction) are stored separately, and if the same target expansion node appears in the two tables stored separately, it is stated that the two directions are connected to form a path on the node, that is, a path is obtained. This path may be used as a result of path planning, i.e. a standard path.

Further, the determining, according to the same target extension node, a target path may include: determining an initial path in the target grid three-dimensional map according to the same target expansion node; and carrying out smoothing treatment on the initial path to obtain a target path.

It should be noted that, since the paths between the nodes in the grid map are not smooth curves, after the path planning result is obtained in the foregoing embodiment, the path smoothing process may be further performed to obtain a relatively used path as the target path. Of course, the smoothing process may use any path smoothing algorithm in the related art, for example, a three-dimensional Du Binsi (Dubins) curve algorithm, which is not limited in this embodiment of the present application.

According to the technical scheme, the two-way search is carried out from the starting point and the end point through the heuristic function, a practical and usable method is provided for path planning in the three-dimensional space, the improved heuristic function can further improve the searching efficiency, the waste of calculation resources caused by a large number of searching errors is reduced, and the algorithm time is reduced.

Example two

Fig. 2 is a schematic diagram of a grid node search provided in a second embodiment of the present application, which is a preferred embodiment provided on the basis of the foregoing embodiments. The method specifically comprises the following steps:

step 1, dividing a three-dimensional space by adopting a grid method, firstly dividing the three-dimensional space into a plurality of horizontal planes perpendicular to a z-axis, and then dividing grids of each horizontal plane.

And 2, marking the grids occupied by the barriers in the divided grids.

And 3, establishing a bidirectional OPEN/CLOSED table, and respectively putting a starting point and an end point into the corresponding OPEN table (corresponding to the candidate node table in the previous embodiment).

And 4, establishing an area to be expanded (26 nodes around the area) according to the position coordinates (x_s, y_s, z_s) of the starting point and the ending point, and judging whether the area has an obstacle or not, if so, not expanding the area.

And 5, judging whether the two OPEN tables are empty, and ending the operation if the two OPEN tables are empty.

Step 6, placing the points meeting the constraint conditions in the region to be expanded into two OPEN tables

And 7, selecting a node with the minimum f value in the OPEN table according to the heuristic function, putting the node into the CLOSED table (which is equivalent to the screening node table in the previous embodiment), judging whether the forward OPEN table and the reverse CLOSED table and whether the reverse OPEN table and the forward CLOSED table are repeated or not, and executing the step 8 if a group of the nodes are repeated, otherwise executing the step 5.

And 8, backtracking from the forward and reverse CLOSED tables to the corresponding initial nodes to obtain a final path.

And 9, acquiring turning points in the path, and smoothing between every two points by adopting a dubins curve to obtain a final path.

It should be noted that, as shown in fig. 2, for the three-dimensional space, the range of each search is adjacent 26 nodes except the current node. As shown in fig. 3, assuming that node No. 13 is the current node, the path planning algorithm expands all nodes around node No. 13 without any constraint, and selects a node to be passed next based on its f-value (valuation value).

The cost from the start point S to the target point (end point) G via the node n can be estimated by setting the valuation function f (n) =g (n) +h (n), where G (n) represents the cost from the start point to the node n, and G (n) is a certain value. h (n) represents the estimated cost from node n to target point G. The bi-directional a-algorithm is a great optimization of the search, starting with the start point and the end point and searching separately until the path nodes meet.

Of course, h (n) may be specifically set, for example:

according to the method and the device for searching the paths in the three-dimensional space, the paths in the three-dimensional space can be searched, the efficiency of the paths is improved by adopting the improved heuristic function, and the paths subjected to evaluation processing can be better adapted to path planning in practical problems.

Example III

Fig. 3 is a schematic structural diagram of a path planning apparatus according to a third embodiment of the present application. As shown in fig. 3, the apparatus 300 includes:

an information acquisition module 310 for acquiring start point information, end point information, and obstacle information in a target space;

the map construction module 320 is configured to perform grid division on the target space, and mark obstacle information to obtain a target grid three-dimensional map;

the node determining module 330 is configured to determine at least two target expansion nodes in the target grid three-dimensional map according to the start point information, the end point information and the obstacle information, and store the two target expansion nodes in the candidate node table;

the node loading module 340 is configured to load the target extension node in the candidate node table into the screening node table according to a preset heuristic function;

the path determining module 350 is configured to determine a target path according to the screening node table.

According to the technical scheme, the two-way search is carried out from the starting point and the end point through the heuristic function, a practical and usable method is provided for path planning in the three-dimensional space, the improved heuristic function can further improve the searching efficiency, the waste of calculation resources caused by a large number of searching errors is reduced, and the algorithm time is reduced.

In an alternative embodiment, the candidate node table includes a forward candidate table and a reverse candidate table, and the filtering node table includes a forward filtering table and a reverse filtering table.

In an alternative embodiment, the node determining module 330 may include:

the starting point grid determining unit is used for determining at least one target expansion node around the starting point grid corresponding to the starting point information according to the starting point information and the obstacle information;

the terminal grid determining unit is used for determining at least one target expansion node around the terminal grid corresponding to the terminal information according to the terminal information and the barrier information;

and the table storage unit is used for storing each target expansion node into a forward candidate table corresponding to the starting point and a reverse candidate table corresponding to the end point respectively.

In an alternative embodiment, the node loading module 340 may include:

the forward table loading unit is used for loading the target expansion node with the minimum valuation value in the forward candidate table into the forward screening table according to the heuristic function;

and the reverse table loading unit is used for loading the target expansion node with the minimum valuation value in the reverse candidate table into the reverse screening table according to the heuristic function.

In an alternative embodiment, the path determining module 350 may be specifically configured to:

if at least one identical target expansion node exists in the forward screening table and the reverse screening table, determining a target path according to the identical target expansion node.

In an alternative embodiment, the path determining module 350 may include:

an initial path determining unit, configured to determine an initial path in the target grid three-dimensional map according to the same target extension node;

and the smoothing processing unit is used for carrying out smoothing processing on the initial path to obtain a target path.

In an alternative embodiment, the heuristic function may be:

f(n)＝h(n)+g(n)；

where f (n) is an estimated value, g (n) is a fixed cost value from a start point to an end point, h (n) is an estimated cost from the start point to the end point, dx is a distance between the start point and the end point in the x-axis direction, dy is a distance between the start point and the end point in the y-axis direction, and dz is a distance between the start point and the end point in the z-axis direction.

The path planning device provided by the embodiment of the application can execute the path planning method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of executing the path planning methods.

Example IV

Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement embodiments of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.

As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the path planning method.

In some embodiments, the path planning method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more of the steps of the path planning method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the path planning method in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solutions of the present application are achieved, and the present application is not limited herein.

The above embodiments do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application are intended to be included within the scope of the present application.

Claims (10)

1. A method of path planning, the method comprising:

acquiring starting point information, end point information and barrier information in a target space;

performing grid division on a target space, and marking the barrier information to obtain a target grid three-dimensional map;

determining at least two target expansion nodes in the target grid three-dimensional map according to the starting point information, the end point information and the barrier information, and storing the target expansion nodes in a candidate node table;

loading the target expansion node in the candidate node table into a screening node table according to a preset heuristic function;

and determining a target path according to the screening node table.

2. The method of claim 1, wherein the candidate node tables comprise a forward candidate table and a reverse candidate table, and wherein the screening node tables comprise a forward screening table and a reverse screening table.

3. The method of claim 2, wherein the determining at least two target expansion nodes in the target grid three-dimensional map based on the start point information and the end point information and the obstacle information, and storing in a candidate node table, comprises:

determining at least one target expansion node around a starting point grid corresponding to the starting point information according to the starting point information and the obstacle information;

determining at least one target expansion node around a destination grid corresponding to the destination information according to the destination information and the obstacle information;

and storing each target expansion node into the forward candidate table corresponding to the starting point and the reverse candidate table corresponding to the end point respectively.

4. The method of claim 3, wherein loading the target extension node in the candidate node table into the screening node table according to a predetermined heuristic function comprises:

according to the heuristic function, loading the target expansion node with the minimum valuation value in the forward candidate table into the forward screening table;

and loading the target expansion node with the minimum valuation value in the reverse candidate table into the reverse screening table according to the heuristic function.

5. The method of claim 4, wherein said determining a target path from said screening node table comprises:

and if at least one identical target expansion node exists in the forward screening table and the reverse screening table, determining the target path according to the identical target expansion node.

6. The method of claim 5, wherein the determining the target path from the same target extension node comprises:

determining an initial path in the target grid three-dimensional map according to the same target expansion node;

and carrying out smoothing treatment on the initial path to obtain the target path.

7. The method according to any one of claims 1-6, wherein the heuristic function is:

f(n)＝h(n)+g(n)；

wherein f (n) is an estimated value, g (n) is a fixed cost value from the starting point to the end point, h (n) is an estimated cost from the starting point to the end point, dx is a distance between the starting point and the end point in the x-axis direction, dy is a distance between the starting point and the end point in the y-axis direction, and dz is a distance between the starting point and the end point in the z-axis direction.

8. A path planning apparatus, the apparatus comprising:

the information acquisition module is used for acquiring starting point information, end point information and barrier information in the target space;

the map construction module is used for carrying out grid division on the target space and marking the barrier information to obtain a target grid three-dimensional map;

the node determining module is used for determining at least two target expansion nodes in the target grid three-dimensional map according to the starting point information, the end point information and the barrier information, and storing the target expansion nodes into a candidate node table;

the node loading module is used for loading the target expansion node in the candidate node table into the screening node table according to a preset heuristic function;

and the path determining module is used for determining a target path according to the screening node table.

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the path planning method of any one of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to implement the path planning method of any one of claims 1-7 when executed.