A mobile robot path planning method and system for two-dimensional code navigation, a storage medium and a terminal

Abstract

The application discloses a kind of two-dimensional code navigation-oriented mobile robot path planning method, system, storage medium and terminal, comprising: defining initial node n s And target node n d And code list list, open list list, close list;According to the information defined, find target node n d And target node n d Join open list list, move to starting point along parent node, form path.The reward function mentioned in the node search of the application is based on the two-dimensional code coordinates in the map, and due to the existence of the reward function, the evaluation function will give priority to the coordinate points containing the two-dimensional code position during path planning, so that a part of the two-dimensional code coordinates is included in the overall path, enabling the mobile robot to collect two-dimensional code data, calibrate dead reckoning and further reduce errors, thereby providing a guarantee for the mobile robot to complete the task more efficiently and safely.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a method, system, storage medium, and terminal for mobile robot path planning oriented towards QR code navigation. Background Technology

[0002] With societal progress and technological advancements, mobile robots are increasingly widely used. Their market application not only improves work efficiency but also significantly reduces the cost of personnel injuries during complex and dangerous tasks, while also lowering time costs. Autonomous mobile robots operate in environments with numerous uncertainties. For a robot to move safely within such environments, it needs real-time localization and mapping technologies to perceive its external environment and its own state. Simultaneously, based on this perceived data, the robot makes its own decisions, moving with minimal cost in obstacle-filled environments under constraints. This requires path planning algorithms, and path planning, as a key technology for intelligent mobile robots, is particularly important for its adaptability to different scenarios.

[0003] In real-world scenarios, some motion states of mobile robots need to be calculated from their tracks. However, due to inherent defects in some sensors, the data calculated from the tracks may not match the actual data. In such cases, QR codes need to be introduced to correct the mobile robot's trajectory and reduce the error in track calculation.

[0004] For mobile robots that navigate to QR codes, the conventional A* algorithm mainly considers the shortest distance to the target point when planning the path, without taking into account that the mobile robot needs to collect QR code data to calibrate the trajectory. In this case, the planned path may not include the location of the QR code, which can easily lead to accumulated errors and have a significant impact on the movement of the mobile robot. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing mobile robot path planning technologies and to provide a method, system, storage medium, and terminal for mobile robot path planning oriented towards QR code navigation. This provides path planning for QR code navigation mobile robots that includes the location of the QR codes, thus avoiding situations where mobile robots cannot complete their tasks or encounter danger due to large errors in trajectory calculation.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] In a first aspect, a method for mobile robot path planning oriented towards QR code navigation is provided, the method comprising the following steps:

[0008] S1. Define the initial node n s and target node n dAnd the code list, open list, and close list;

[0009] S2. Determine if the open list is empty. If the open list is empty, path planning ends; if the open list is not empty, set the initial node n... s Set it as the parent node and proceed to step S3;

[0010] S3. Move the node n0 with the lowest overall priority in the open list to the close list; the overall priority is determined by the cost function g(n), the heuristic function h(n), and the reward function c(n), wherein the reward function c(n) prioritizes the coordinates of the location containing the QR code;

[0011] S4. Determine whether the next expansion range of node n0 includes the target node n. d If the path is included, the path planning ends; otherwise, proceed to step S5.

[0012] S5. Expand node n0 in step S3 to obtain several child nodes of n0;

[0013] S6. Determine whether the child node obtained in step S5 is in the open list. If the child node is in the open list, update the open list and set n0 as the parent node; otherwise, proceed to step S7.

[0014] S7. Check if the child node is in the closed list. If the child node is in the closed list, skip the child node; otherwise, add the child node to the open list and set it as the parent node.

[0015] S8. Repeat steps S2-S7 until the target node n is found. d and set the target node n d It is added to the open list and moves along the parent node to the starting point, forming a path.

[0016] As a preferred option, a mobile robot path planning method for QR code navigation includes the following formula for calculating the comprehensive priority:

[0017] f(n i )=g(n i )+h(n i )+C(n i )

[0018] Where g(n) i ) represents the initial node ns The distance to node n i , h(n i ) is the distance from node n i to the target node n d . When the node ni is not at the position of the QR code, the reward function c(n i ) = 0; when the node n i is at the position of the QR code, then c(n i ) is calculated as follows:

[0019]

[0020] In the formula respectively represent the coordinates and angles of the two nodes participating in the calculation, is the yaw angle weighting coefficient.

[0021] As an optional item, in a mobile robot path planning method for QR code navigation, in step S1, both the codelist list and the close list list are initialized as empty lists.

[0022] As an optional item, in a mobile robot path planning method for QR code navigation, in step S2, when the open list list is not empty, calculate the f(n) values of the surrounding nodes of the initial node n s and add them to the open list list.

[0023] As an optional item, in a mobile robot path planning method for QR code navigation, step S5 specifically includes:

[0024] Obtain several child nodes of n0 and剔除 the infeasible nodes among them, and then calculate the cost function g(n), the heuristic function h(n), the pose information and the comprehensive priority f(n).

[0025] As an optional item, in a mobile robot path planning method for QR code navigation, in step S6, when the child node is in the open list list, obtain the f(n i ) value of the child node and compare it with the f(n0)* in step S5. If f(n i ) < f(n0)*, then retain the information of the child node in the open list list; if f(n i ) > f(n0)*, then a closer path can be obtained with n0 as the parent node, and update the comprehensive priority of the child node to f(n0)*.

[0026] As an optional item, in a mobile robot path planning method for QR code navigation, step S8 also includes:

[0027] Target node n not found d Furthermore, the open list is empty, meaning there is no path.

[0028] In a second aspect, a mobile robot path planning system for QR code navigation is provided, the system comprising:

[0029] The node initialization module is configured to define the initial node n. s and target node n d And the code list, open list, and close list;

[0030] The node lookup module is configured to find the target node n. d The target node n was found d The specific process includes:

[0031] S2. Determine if the open list is empty. If the open list is empty, path planning ends; if the open list is not empty, set the initial node n... s Set it as the parent node and proceed to step S3;

[0032] S3. Move the node n0 with the lowest overall priority in the open list to the close list; the overall priority is determined by the cost function g(n), the heuristic function h(n), and the reward function c(n), wherein the reward function c(n) prioritizes the coordinates of the location containing the QR code;

[0033] S4. Determine whether the next expansion range of node n0 includes the target node n. d If the path is included, the path planning ends; otherwise, proceed to step S5.

[0034] S5. Expand node n0 in step S3 to obtain several child nodes of n0;

[0035] S6. Determine whether the child node obtained in step S5 is in the open list. If the child node is in the open list, update the open list and set n0 as the parent node; otherwise, proceed to step S7.

[0036] S7. Check if the child node is in the closed list. If the child node is in the closed list, skip the child node; otherwise, add the child node to the open list and set it as the parent node.

[0037] The path formation module is configured to reuse the node search module to repeat steps S2-S7 until the target node n is found. d and set the target node n d It is added to the open list and moves along the parent node to the starting point, forming a path.

[0038] In a third aspect, a computer storage medium is provided, on which computer instructions are stored, wherein the computer instructions, when executed, perform the relevant steps in any one of the mobile robot path planning methods for QR code navigation described above.

[0039] In a fourth aspect, a terminal is provided, including a memory and a processor, wherein the memory stores computer instructions that can be run on the processor, and the processor executes the relevant steps in any one of the mobile robot path planning methods for QR code navigation described above when running the computer instructions.

[0040] It should be further noted that the technical features corresponding to the above options can be combined or substituted to form new technical solutions if there is no conflict.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] The reward function mentioned in this invention for node search is based on the coordinates of QR codes on the map. During path planning, due to the existence of the reward function, the evaluation function will prioritize the coordinates of points containing QR code locations, thus including a portion of the QR code coordinates in the overall path. This allows the mobile robot to collect QR code data, calibrate the trajectory calculation, and reduce errors. This is beneficial for improving the positioning accuracy of mobile robots that navigate to QR codes, and provides a guarantee for mobile robots to complete tasks more efficiently and safely. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a mobile robot path planning method for QR code navigation, as shown in an embodiment of the present invention. Detailed Implementation

[0044] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] In one exemplary embodiment, a mobile robot path planning method for QR code navigation is provided. This invention is based on the hybrid ASTAR algorithm. Under the constraints of the mobile robot's body size, turning radius, and orientation, a QR code position constraint is added. The QR code coordinates are stored in a list, and a reward function is set. Based on the path planning search for the shortest path, the path containing the QR code is selected to correct the mobile robot's trajectory calculation.

[0047] Specifically, refer to Figure 1 The method includes the following steps:

[0048] S1. Define the initial node n s and target node n d The code list, open list, and close list are initialized to empty lists, and the initial node ns is added to the open list to be checked. The code list is used to store the location coordinates of the QR code on the map.

[0049] S2. Determine if the open list is empty. If the open list is empty, path planning ends, and there is no reachable target node (location) on the map. d The path; if the open list is not empty, calculate the initial node n. s Find the f(n) values ​​of the surrounding nodes, add them to the open list, and set the initial node n... s Set it as the parent node and proceed to step S3;

[0050] S3, n s The process moves the node from the open list to the closed list. Specifically, it iterates through the open list, finds the node n0 with the lowest overall priority, and moves it to the closed list as the node to be processed. The overall priority is determined by the cost function g(n), the heuristic function h(n), and the reward function c(n). During algorithm execution, the reward function c(n) is calculated using the QR code coordinate data stored in the code list, prioritizing coordinates containing QR code locations. Furthermore, the formula for calculating the overall priority is as follows:

[0051] f(n i )=g(n i )+h(n i )+C(n i )

[0052] where \(g(n\) i ) represents the distance from the initial node \(n\) s to node \(n\) i , \(h(n\) i ) is the distance from node \(n\) i to the target node \(n\) d . When the node \(n_i\) is not at the position of the QR code, the reward function \(c(n\) i ) = 0; when the node \(n\) i is at the position of the QR code, then \(c(n\) i ) is calculated as follows:

[0053]

[0054] In the formula respectively represent the coordinates and angles of the two nodes participating in the calculation, is the yaw angle weighting coefficient.

[0055] S4. Determine whether the next expansion range of the node \(n_0\) contains the target node \(n\) d . If it contains, the path planning ends, and the algorithm successfully searches for the path; otherwise, go to step S5;

[0056] S5. Expand the node \(n_0\) in step S3 to obtain several child nodes of \(n_0\) and remove the infeasible nodes, and then calculate the cost function \(g(n)\), heuristic function \(h(n)\), pose information and node information such as the comprehensive priority \(f(n)\).

[0057] S6. Determine whether the feasible child nodes obtained in step S5 are in the open list. When the child node is in the open list, obtain the \(f(n\) i ) value and compare it with \(f(n_0)^*\) in step S5. If \(f(n\) i ) < \(f(n_0)^*\), then retain the information of the child node in the open list; if \(f(n\) i ) > \(f(n_0)^*\), then a closer path can be obtained with \(n_0\) as the parent node, and update the comprehensive priority of the child node to \(f(n_0)^*\); otherwise, go to step S7;

[0058] S7. Determine whether the child node is in the close list. If the child node is in the close list, do not perform the search and skip the child node; otherwise, it means that the node has not been traversed, add the child node to the open list, and set it as the parent node;

[0059] S8. Repeat steps S2 - S7 until the target node \(n\) is foundd and set the target node n d It was added to the open list, and finally from the endpoint (i.e., the target node n) d Starting from the parent node, move along the parent node to the starting point to form a path.

[0060] Further step S8 also includes:

[0061] Target node n not found d The destination search failed, and the open list is empty, meaning there is no path.

[0062] In another exemplary embodiment, a mobile robot path planning system for QR code navigation is provided, the system comprising:

[0063] The node initialization module is configured to define the initial node n. s and target node n d And the code list, open list, and close list;

[0064] The node lookup module is configured to find the target node n. d The target node n was found d The specific process includes:

[0065] S2. Determine if the open list is empty. If the open list is empty, path planning ends; if the open list is not empty, set the initial node n... s Set it as the parent node and proceed to step S3;

[0066] S3. Move the node n0 with the lowest overall priority in the open list to the close list; the overall priority is determined by the cost function g(n), the heuristic function h(n), and the reward function c(n), wherein the reward function c(n) prioritizes the coordinates of the location containing the QR code;

[0067] S4. Determine whether the next expansion range of node n0 includes the target node n. d If the path is included, the path planning ends; otherwise, proceed to step S5.

[0068] S5. Expand node n0 in step S3 to obtain several child nodes of n0;

[0069] S6. Determine whether the child node obtained in step S5 is in the open list. If the child node is in the open list, update the open list and set n0 as the parent node; otherwise, proceed to step S7.

[0070] S7. Check if the child node is in the closed list. If the child node is in the closed list, skip the child node; otherwise, add the child node to the open list and set it as the parent node.

[0071] The path formation module is configured to reuse the node search module to repeat steps S2-S7 until the target node n is found. d and set the target node n d It is added to the open list and moves along the parent node to the starting point, forming a path.

[0072] In another exemplary embodiment, the present invention provides a computer storage medium storing computer instructions, which, when executed, perform relevant steps in the mobile robot path planning method for QR code navigation.

[0073] Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] In another exemplary embodiment, the present invention provides a terminal including a memory and a processor. The memory stores computer instructions that can be executed on the processor. When the processor executes the computer instructions, it performs the relevant steps in the mobile robot path planning method for QR code navigation.

[0075] The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0076] The embodiments of the subject matter and functional operation described in this specification can be implemented in: tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing device or for controlling the operation of a data processing device. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing device.

[0077] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by dedicated logic circuitry—such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as dedicated logic circuitry.

[0078] Suitable processors for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0079] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0080] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0081] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A mobile robot path planning method for QR code navigation, characterized in that, The method includes the following steps: S1. Define the initial node n s and target node n d And the code list, open list, and closed list; S2. Determine if the open list is empty. If the open list is empty, path planning ends; if the open list is not empty, set the initial node n... s Set it as the parent node and proceed to step S3; S3. Move the node n0 with the lowest overall priority from the open list to the closed list; the overall priority is determined by the cost function g(n), the heuristic function h(n), and the reward function c(n), wherein the reward function c(n) prioritizes the coordinates of points containing QR code locations; the formula for calculating the overall priority is as follows: f(n i )=g(n i )+h(n i )+c(n i ) Where g(n) i ) represents the initial node n s to node n i The distance, h(n) i ) is node n i to target node n d The distance, when node ni is not the location of the QR code, where the reward function c(n) is... i ) = 0; when node n i When the location is the QR code, then c(n) i The calculation is as follows: In the formula Let represent the coordinates and angles of the two nodes involved in the calculation, respectively. This is the weighting factor for the yaw angle; S4. Determine whether the next expansion range of node n0 includes the target node n. d If the path is included, the path planning ends; otherwise, proceed to step S5. S5. Expand node n0 in step S3 to obtain several child nodes of n0; S6. Determine whether the child node obtained in step S5 is in the open list. If the child node is in the open list, update the open list and set n0 as the parent node; otherwise, proceed to step S7. S7. Check if the child node is in the closed list. If the child node is in the closed list, skip the child node; otherwise, add the child node to the open list and set it as the parent node. S8. Repeat steps S2-S7 until the target node n is found. d and set the target node n d It is added to the open list and moves along the parent node to the starting point, forming a path.

2. The mobile robot path planning method for QR code navigation according to claim 1, characterized in that, In step S1, both the code list and the close list are initialized to empty lists.

3. The mobile robot path planning method for QR code navigation according to claim 1, characterized in that, In step S2, if the open list is not empty, calculate the initial node n. s The f(n) values ​​of the surrounding nodes are added to the open list.

4. The mobile robot path planning method for QR code navigation according to claim 1, characterized in that, Step S5 specifically includes: Obtain several child nodes of n0 and remove infeasible nodes. Then calculate the cost function g(n), heuristic function h(n), and pose information. And the overall priority f(n).

5. A mobile robot path planning method for QR code navigation according to claim 4, characterized in that, In step S6, when the child node is in the open list, obtain the f(n i ) value of the child node, and compare it with f(n0)* in step S5. If f(n i ) < f(n0)*, then retain the information of the child node in the open list; if f(n i ) > f(n0)*, then a closer path can be obtained with n0 as the parent node, and update the comprehensive priority of the child node to f(n0)*.

6. The mobile robot path planning method for QR code navigation according to claim 1, characterized in that, Step S8 also includes: Target node n not found d Furthermore, the open list is empty, meaning there is no path.

7. A mobile robot path planning system for QR code navigation, characterized in that, The system includes: The node initialization module is configured to define the initial node n. s and target node n d And the code list, open list, and close list; The node lookup module is configured to find the target node n. d The target node n was found d The specific process includes: S2. Determine if the open list is empty. If the open list is empty, path planning ends; if the open list is not empty, set the initial node n... s Set it as the parent node and proceed to step S3; S3. Move the node n0 with the lowest overall priority from the open list to the closed list; the overall priority is determined by the cost function g(n), the heuristic function h(n), and the reward function c(n), wherein the reward function c(n) prioritizes the coordinates of points containing QR code locations; the formula for calculating the overall priority is as follows: f(n i )=g(n i )+h(n i )+c(n i ) Where g(n) i ) represents the initial node n s to node n i The distance, h(n) i ) is node n i to target node n d The distance, when node ni is not the location of the QR code, where the reward function c(n) is... i ) = 0; when node n i When the location is the QR code, then c(n) i The calculation is as follows: In the formula Let represent the coordinates and angles of the two nodes involved in the calculation, respectively. This is the weighting factor for the yaw angle; S4. Determine whether the next expansion range of node n0 includes the target node n. d If the path is included, the path planning ends; otherwise, proceed to step S5. S5. Expand node n0 in step S3 to obtain several child nodes of n0; S6. Determine whether the child node obtained in step S5 is in the open list. If the child node is in the open list, update the open list and set n0 as the parent node; otherwise, proceed to step S7. S7. Check if the child node is in the closed list. If the child node is in the closed list, skip the child node; otherwise, add the child node to the open list and set it as the parent node. The path formation module is configured to reuse the node search module to repeat steps S2-S7 until the target node n is found. d and set the target node n d It is added to the open list and moves along the parent node to the starting point, forming a path.

8. A computer storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed, they perform the relevant steps in the mobile robot path planning method for QR code navigation as described in any one of claims 1-6.

9. A terminal, comprising a memory and a processor, wherein the memory stores computer instructions executable by the processor, characterized in that, When the processor executes computer instructions, it performs the relevant steps in the mobile robot path planning method for QR code navigation as described in any one of claims 1-6.

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