Route planning method, related equipment for route planning method, and robot

Abstract

Embodiments of the present application disclose a path planning method, related equipment for the path planning method, and a robot, relating to the technical field of path planning, the path planning method includes acquiring first distance information between two adjacent obstacles, determining obstacle cluster type information based on the first distance information, and determining a travel path based on the obstacle cluster type information. The path planning method provided by embodiments of the present application can classify obstacle cluster types in the robot's work area based on the distance between obstacles, for example, it can classify the robot's work area into areas with a high density of obstacles, and when determining a travel path based on this, the obstacle cluster type information can be used as a basis to avoid areas with a high density of obstacles as much as possible, thereby improving the robot's travel efficiency, further improving the robot's work efficiency, and improving the user experience.

Description

Technical Field

[0001] [Cross-reference to Related Applications] This application claims the priority of a Chinese patent application filed with the China National Intellectual Property Administration on February 27, 2023 , with the application number 202310212528.4 , and the invention title " Route planning method, related equipment for route planning method, and robot ", the entire content of which is incorporated herein by reference.

[0002] The embodiments of this application relate to the technical field of path planning, and in particular, to a path planning method, a computer-readable storage medium, a control device, and a robot.

Background Art

[0003] With the development of science and technology, more and more smart robots are put into people's daily lives. The inventors find that although robots need to perform path planning during the running process, there may be relatively many obstacles in the user's home, which slows down the running speed of the robot and reduces the user experience. Taking a sweeping robot as an example, the sweeping robot has already been widely applied in daily life. It can liberate people from house cleaning work, effectively reduce the workload of people in house cleaning, and relieve the fatigue of people during the house cleaning process. However, when there are relatively many obstacles in the working area of the sweeping robot, it will affect the running speed of the obstacles and further affect the sweeping speed, thus affecting the user experience.

Summary of the Invention

[0004] This application aims to solve at least one of the technical problems existing in the prior art or related technologies. For this purpose, the first aspect of this application provides a path planning method. The second aspect of this application provides a computer-readable storage medium. The third aspect of this application provides a control device. The fourth aspect of this application provides a robot.

[0005] In view of this, according to the first embodiment of the present invention, a route planning method is provided, A step of obtaining first distance information between two adjacent obstacles, The steps include determining obstacle cluster type information based on the first distance information, A route planning method is provided, which includes the step of determining a travel route based on the aforementioned obstacle cluster type information.

[0006] In one viable embodiment, the step of determining obstacle cluster type information based on the first distance information is: The process includes the step of determining the obstacle cluster type information of the area where the obstacles are located as an obstacle-dense area if the distance between two adjacent obstacles is less than a first threshold.

[0007] In one viable embodiment, the route planning method is: The steps include: clustering obstacles whose interval is smaller than a first threshold to obtain obstacle clusters; The steps include obtaining second distance information between two adjacent obstacle clusters, The method further includes the step of determining obstacle cluster type information based on the second distance information.

[0008] In one viable embodiment, the step of determining obstacle cluster type information based on the second distance information is: The process includes the step of determining the area where the obstacle clusters are located as an obstacle-dense area if the distance between two adjacent obstacle clusters is less than a second threshold, The value of the second threshold is greater than the value of the first threshold.

[0009] In one viable embodiment, the step of determining a travel path based on the obstacle cluster type information is: The process includes the step of determining a first travel path based on the obstacle cluster type information in response to a movement command, The aforementioned first travel path is alternated with areas of densely packed obstacles.

[0010] In one viable embodiment, the step of determining a travel path based on the obstacle cluster type information is: A step of determining a second travel path based on the obstacle cluster type information in response to a high-speed mapping command, wherein the second travel path first passes through an area other than the obstacle-dense area, then passes through the obstacle-dense area, and / or A step of determining a third travel path based on the obstacle cluster type information in response to a high-speed cleaning command, wherein the third travel path is located in an area other than an area of ​​densely packed obstacles.

[0011] In one viable embodiment, the route planning method is: If the robot enters an area with a high density of obstacles, the steps include improving the control accuracy of the robot, and / or The method further includes a step of improving the recognition accuracy of the robot when the robot enters an area with a high density of obstacles, The control accuracy includes the robot's travel control accuracy and / or cleaning control accuracy, and the recognition accuracy includes the obstacle recognition accuracy.

[0012] In one viable embodiment, the route planning method is: Steps to respond to a cleaning order, If the area of ​​the obstacle-dense region is greater than a third threshold, the robot is divided into the obstacle-dense region and the easy-to-clean region. The method further includes the step of controlling the robot to clean the area with a high concentration of obstacles and the area that is easy to clean, respectively.

[0013] According to a second embodiment of the present invention, A computer-readable storage medium is provided, which stores a computer program that implements the route planning method described in any one of the above technical proposals.

[0014] According to a third aspect of the embodiment of the present application, there is provided a control device, including a memory storing a computer program, and a processor for executing the computer program, wherein when the processor executes the computer program, a control device is provided that realizes the path planning method described in any one of the technical solutions.

[0015] According to a fourth aspect of the embodiment of the present application, there is provided a robot, including an obstacle detection device for obtaining position information of obstacles, a processor connected to the obstacle detection device and realizing the path planning method described in any one of the technical solutions, and a navigation unit connected to the processor.

[0016] Compared with the prior art, the present application includes at least the following beneficial effects. The path planning method provided by the embodiment of the present application first obtains first distance information between two adjacent obstacles, then classifies obstacle cluster types based on the first distance information, and further determines a driving route based on the obstacle cluster type information to realize the planning of the driving route. By the path planning method provided by the embodiment of the present application, the obstacle cluster types in the working area of the robot can be classified based on the distances between obstacles. For example, an obstacle dense area can be classified in the working area of the robot. When determining the driving route based on this, the obstacle dense area can be avoided as much as possible based on the obstacle cluster type information, improving the driving efficiency of the robot, further improving the working efficiency of the robot, and improving the user experience.

Brief Description of the Drawings

[0017] Various other advantages and benefits will become apparent to those skilled in the art by reading the following detailed description of the preferred embodiments. The drawings are only for the purpose of illustrating the preferred embodiments and should not be construed as limiting the present application. Also, throughout the drawings, the same reference numerals are used to indicate the same members.

[0018] [Figure 1] It is a flowchart of the schematic steps of the route planning method in an embodiment provided by the present application. [Figure 2] It is a schematic diagram of an implementation state of the route planning method in an embodiment provided by the present application. [Figure 3] It is a schematic diagram of another implementation state of the route planning method in an embodiment provided by the present application. [Figure 4] It is a structural block diagram of a computer-readable storage medium in an embodiment provided by the present application. [Figure 5] It is a structural block diagram of a control device in an embodiment provided by the present application.

Mode for Carrying Out the Invention

[0019] To better understand the above technical solution, the technical solution of the embodiment of the present application will be described in detail below through the drawings and specific embodiments. The embodiments of the present application and the specific features in the embodiments are to describe the technical solution of the embodiment of the present application in detail, and do not limit the technical solution of the present application. It should be understood that, unless there is a contradiction, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0020] As shown in FIG. 1, according to the first aspect of the embodiment of the present application, a route planning method is provided, step 101 of obtaining the first distance information between two adjacent obstacles, step 102 of determining the obstacle cluster type information based on the first distance information, step 103 of determining the driving route based on the obstacle cluster type information.

[0021] The path planning method provided by the embodiment of the present invention first acquires first distance information between two adjacent obstacles, then classifies obstacle cluster types based on the first distance information, and further determines a travel path based on the obstacle cluster type information to plan the travel path. The path planning method provided by the embodiment of the present invention can classify obstacle cluster types in the robot's work area based on the distance between obstacles. For example, it can classify areas with a high density of obstacles in the robot's work area. When determining a travel path based on this, the obstacle cluster type information can be used as a basis to avoid areas with a high density of obstacles as much as possible, thereby improving the robot's travel efficiency, further improving the robot's work efficiency, and enhancing the user experience.

[0022] As can be understood, prior to the step of obtaining first distance information between two adjacent obstacles, the path planning method may further include a step of obtaining obstacle position information within the robot's work area, and the step of further obtaining obstacle position information within the robot's work area may include a step of marking information such as the position and type of the obstacles on a map based on sensor data detected by an LDS (Laser Distance Sensor), physical Bumper (buffer), Cliff sensor, Wall Sensor, Vision detection sensor, etc.

[0023] In one viable embodiment, the step of determining obstacle cluster type information based on first distance information includes determining the obstacle cluster type information of the area where the obstacles are located as an obstacle cluster area if the distance between two adjacent obstacles is less than a first threshold.

[0024] In this proposed technology, a specific step for determining obstacle cluster type information is further provided. If the distance between two adjacent obstacles is less than a first threshold, the distance between these two obstacles can be considered small, and it is relatively difficult for a robot to pass between these two obstacles. Therefore, the obstacle cluster type information for the area where these two obstacles are located can be determined as an obstacle-dense area.

[0025] As can be understood, the specific value of the first threshold can exhibit a positive correlation with the size of the robot; that is, the larger the robot, the larger the value of the first threshold.

[0026] In one viable embodiment, a path planning method further includes the steps of: clustering obstacles whose intervals are less than a first threshold to obtain obstacle clusters; obtaining second distance information between two adjacent obstacle clusters; and determining obstacle cluster type information based on the second distance information.

[0027] As further considered in the proposed technology, there may be multiple obstacles within the robot's work area, and the arrangement of these obstacles may be complex and disordered. For example, relying solely on the distance between two adjacent obstacles may not allow for accurate determination. Therefore, the embodiment of the present application first clusters obstacles into obstacle clusters, then obtains a second distance between two adjacent obstacle clusters, and further divides the robot's work area based on this second distance information. For example, it can divide the robot's work area into areas with a high density of obstacles. Based on this, when determining the travel path, the obstacle cluster type information can be used as a basis to avoid areas with a high density of obstacles as much as possible, improving the robot's travel efficiency, further improving the robot's work efficiency, and enhancing the user experience. Based on this, point-like obstacle information can be flattened and grouped, making it easier for the robot to plan its travel path so as to avoid areas with a high density of obstacles as much as possible.

[0028] To make it clear, let's consider the robot as a cleaning robot or robotic vacuum cleaner, and apply the robot to a home environment. Within the user's living area, there may be many obstacles, such as toys placed freely. In the embodiment of this invention, obstacle cluster type information may be determined based on the distance between two adjacent players, or based on the distance between obstacle clusters formed by multiple toy clusters. Furthermore, the travel path can be determined based on the obstacle cluster type information, allowing the robot to avoid areas with many obstacles as much as possible and improving the robot's travel efficiency.

[0029] As shown in Figure 2, area A contains multiple obstacles, and area B contains two obstacles. The distance between two adjacent obstacles in area A is less than the first threshold, and the distance between two adjacent obstacles in area B is similarly less than the first threshold. However, the distance between the obstacles in area B and the obstacles in area A is greater than the first threshold. Therefore, two obstacle clusters, A and B, are separated, and obstacle cluster type information can be determined based on the distance between the two obstacle clusters.

[0030] In one viable embodiment, the step of determining obstacle cluster type information based on second distance information includes determining the obstacle cluster type information of the region in which two obstacle clusters are located as an obstacle-dense region if the distance between two adjacent obstacle clusters is less than a second threshold, wherein the value of the second threshold is greater than the first threshold.

[0031] In this proposed technology, a specific step is provided to determine obstacle cluster type information based on second distance information. If the distance between two adjacent obstacle clusters is less than a second threshold, the distance between these two obstacle clusters can be considered small, and it is equally difficult for a robot to pass between these two obstacle clusters. Therefore, the obstacle cluster type information for the region where these two obstacle clusters are located can be determined as an obstacle-dense region.

[0032] In one viable embodiment, the step of determining a travel path based on obstacle cluster type information includes, in response to a move command, determining a first travel path based on obstacle cluster type information, of which the first travel path is arranged alternately with areas of dense obstacles.

[0033] In this proposed technology, a specific step is provided to determine the travel path based on obstacle cluster type information. In some work situations, the robot only needs to move between fixed points. For example, if the robot's battery is low and it needs to return to a charging station to recharge, the robot can determine a first travel path based on obstacle cluster type information. This first travel path bypasses areas with dense obstacles, allowing the robot to avoid passing through such areas during its movement. This improves the robot's efficiency, for example, allowing it to recharge as quickly as possible, avoiding machine jams, making the machine's trajectory smoother, increasing its speed, and improving the user experience.

[0034] As can be understood, movement commands may be transmitted to the robot by a control device, or they may be automatically generated by the robot itself. For example, a robot can automatically generate movement commands when it recognizes that its electrical charge is relatively low.

[0035] In one executable embodiment, the step of determining a travel path based on obstacle cluster type information includes, in response to a high-speed mapping command, determining a second travel path based on obstacle cluster type information, wherein the second travel path first passes through areas other than obstacle-dense areas, and then passes through obstacle-dense areas.

[0036] In this proposed technology, during the use of the robot, especially when the robot is first deployed or when it enters a workspace for the first time, the robot needs to move within the workspace to create a map of the workspace. In this case, a high-speed mapping command can be sent to the robot, and the robot can respond to the high-speed mapping command by combining it with obstacle cluster type information to determine a second travel path. The second travel path first passes through areas other than areas with dense obstacles, and then passes through areas with dense obstacles. In other words, the robot first passes through areas with relatively few or relatively scattered obstacles, and then passes through areas with dense obstacles. Based on this, the efficiency of mapping can be improved, and the user can avoid feeling that the robot is moving slowly for a long time in areas with dense obstacles, which would lead the user to think that the robot's movement efficiency is low and thus degrade the user's perception of the robot. Based on this, the robot can quickly map areas with few obstacles, and then enter and map areas with dense obstacles, improving the user experience.

[0037] In one viable embodiment, the step of determining a travel path based on obstacle cluster type information includes, in response to a high-speed cleaning command, determining a third travel path based on obstacle cluster type information, wherein the third travel path is located in an area other than an obstacle-dense area.

[0038] In this proposed technology, a specific step is provided to determine the travel path based on obstacle cluster type information. In some work situations, the user may need the robot to complete the task as quickly as possible. Taking a cleaning robot as an example, the user may want to complete the cleaning task as quickly as possible. In this case, the user can send a high-speed cleaning command to the robot. In this case, considering that the robot's cleaning speed is greatly affected if it travels through an area with a high density of obstacles, a third travel path can be planned in an area other than the area with a high density of obstacles. The cleaning robot can then perform high-speed cleaning along the third travel path, eliminating the need for the cleaning robot to enter the area with a high density of obstacles and improving cleaning efficiency.

[0039] In one viable embodiment, the path planning method further includes a step of improving the control accuracy of the robot if the robot enters an area with a high density of obstacles, the control accuracy including the robot's travel control accuracy and / or cleaning control accuracy.

[0040] In this proposed technology, after determining an obstacle-dense area using first distance information between two adjacent obstacles or second distance information between two adjacent obstacle clusters, different control methods can be used depending on the area in which the robot is located. Specifically, when the robot enters an obstacle-dense area, there is a possibility that a relatively large number of obstacles are present around the robot. In this case, the accuracy of the robot's movement control can be improved, allowing it to depart from obstacles with more precise movements. For example, when attempting to pass through several narrow passages, it can try multiple times, slowly changing its angle slightly each time, and can be designed to move along walls to depart from obstacles. This also improves the accuracy of the robot's cleaning control, thereby improving the cleaning efficiency of obstacle-dense areas. Furthermore, when the robot is not located in an obstacle-dense area, high-precision control is not required. By setting it in this way, the amount of data processing required for robot control can be reduced, resulting in greater energy savings.

[0041] In one viable embodiment, the path planning method further includes a step of improving the robot's recognition accuracy when the robot enters an area with a high density of obstacles, the recognition accuracy including obstacle recognition accuracy.

[0042] In this proposed technology, after determining an obstacle-dense area using first distance information between two adjacent obstacles or second distance information between two adjacent obstacle clusters, different recognition methods can be used if the area in which the robot is located is different. Specifically, when a robot enters an obstacle-dense area, it is possible that there are a relatively large number of obstacles around the robot. In this case, the robot's recognition accuracy can be improved, allowing the robot to acquire information about obstacles around it as quickly as possible, and based on this, the robot's driving posture can be adjusted as quickly as possible, thereby enabling the robot to efficiently pass through or work within the obstacle-dense area.

[0043] In one viable embodiment, the path planning method further includes the steps of responding to a cleaning command, dividing the area where the robot is located into an obstacle-dense area and an easy-to-clean area if the area of ​​the obstacle-dense area is greater than a third threshold, and controlling the robot to clean the obstacle-dense area and the easy-to-clean area, respectively.

[0044] In this proposed technology, if a user needs a robot to perform work across the entire work area of ​​the robot, for example, if the robot is a cleaning robot and the user needs to clean the entire area, the user can send a cleaning command. In this case, after determining the area of ​​densely packed obstacles, the robot can further recognize the area of ​​densely packed obstacles. If the area of ​​the densely packed obstacles is greater than a third threshold, it indicates that the area of ​​densely packed obstacles is large. In this case, it will take a long time for the robot to clean the densely packed obstacles, so the robot can be controlled to clean the densely packed obstacles and the easy-to-clean areas separately. For example, the easy-to-clean areas can be cleaned first, and then the densely packed obstacles can be cleaned. Based on this, cleaning efficiency can be improved on the one hand, and the robot can avoid frequently switching between moving between densely packed obstacles and easy-to-clean areas. This makes the robot's movement pattern uniform within a certain period of time, resulting in greater energy savings, a reduction in the amount of control data processed by the robot, a reduction in the operating pressure of the robot processor, and an improvement in the robot's response efficiency.

[0045] As shown in Figure 3, since there are relatively many obstacles in area C in the diagram, area C is an obstacle puzzle area, and since there are relatively few obstacles in area D, area D is an easy-to-clean area. Based on this, the robot can be controlled to clean areas C and D respectively. For example, area D can be cleaned first, and then area C can be cleaned.

[0046] As shown in Figure 4, according to a second embodiment of the present invention, a computer-readable storage medium 201 is provided, the computer-readable storage medium 201 stores a computer program 202, and realizes the path planning method of any one of the above-mentioned technical proposals.

[0047] The computer-readable storage medium 201 provided by the embodiment of the present application has all the beneficial effects of the path planning method of any one of the aforementioned technical proposals in order to implement the path planning method of the aforementioned technical proposals.

[0048] The computer-readable storage medium 201 provided by the embodiment of the present invention first acquires first distance information between two adjacent obstacles, then classifies obstacle cluster types based on the first distance information, and further determines a travel path based on the obstacle cluster type information to realize a travel path plan. The path planning method provided by the embodiment of the present invention can classify obstacle cluster types in the robot's work area based on the distance between obstacles, for example, it can classify the robot's work area into areas with a high density of obstacles. Based on this, when determining a travel path, the obstacle cluster type information can be used as a basis to avoid areas with a high density of obstacles as much as possible, thereby improving the robot's travel efficiency, further improving the robot's work efficiency, and enhancing the user experience.

[0049] Based on this understanding, the proposed technology of the present application may be embodied in the form of a software product that can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard disk), which includes several instructions for causing a computer device (such as a personal computer, server, or network device) to perform the method described in each embodiment of the present application.

[0050] As shown in Figure 5, according to a third embodiment of the present invention, a control device is provided which includes a memory 301 in which a computer program is stored and a processor 302 that executes the computer program, wherein the processor 302 implements the path planning method of any one of the above-mentioned technical proposals when executing the computer program.

[0051] The control device provided by the embodiment of the present application, in order to implement the route planning method of any one of the aforementioned technical proposals, has all the beneficial effects of the route planning method of the aforementioned technical proposals.

[0052] The control device provided by the embodiment of the present invention first acquires first distance information between two adjacent obstacles, then classifies obstacle cluster types based on the first distance information, and further determines a travel path based on the obstacle cluster type information to realize a travel path plan. The path planning method provided by the embodiment of the present invention can classify obstacle cluster types in the robot's work area based on the distance between obstacles. For example, it can classify the robot's work area into areas with a high density of obstacles. Based on this, when determining a travel path, the obstacle cluster type information can be used as a basis to avoid areas with a high density of obstacles as much as possible, thereby improving the robot's travel efficiency, further improving the robot's work efficiency, and enhancing the user experience.

[0053] In some examples, the control device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, and the like. The user interface may include a display, an input unit such as a keyboard, and the selectable user interface may further include a USB interface, a card reader interface, and the like. The network interface may optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface), and the like.

[0054] In exemplary embodiments, the control device may further include input / output interfaces and display devices, and each functional unit can communicate with one another via a bus. A computer program is stored in the memory, and the processor is used to execute the program stored in the memory and to perform the method in the embodiment.

[0055] The storage medium may further include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device of the method and supports the execution of information processing programs and other software and / or programs. The network communication module is used to enable communication between each assembly within the storage medium and communication between other hardware and software within the information processing physical device.

[0056] From the above description of the embodiments, it will be clear to those skilled in the art that the present invention can be implemented in the form of software and a necessary general-purpose hardware platform, or it can be implemented in hardware.

[0057] This application will be described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions may be provided to the processor of a general-purpose computer, a dedicated computer, an embedded computer, or other programmable data processing device to generate a machine, the instructions executed by the processor of the computer or other programmable data processing device, which generates a device used to realize a function specified in one flow of a flowchart or one or more blocks of multiple flows and / or block diagrams.

[0058] A fourth embodiment of the present invention provides a robot comprising: an obstacle detection device used to acquire location information of obstacles; a processor connected to the obstacle detection device and implementing the path planning method of any one of the above-mentioned technical proposals; and a navigation unit connected to the processor.

[0059] The robot provided by the embodiment of the present application implements the path planning method of any one of the aforementioned technical proposals, and therefore possesses all the beneficial effects of the path planning method of the aforementioned technical proposals, which are omitted from this description.

[0060] The robot provided by the embodiment of the present invention includes an obstacle detection device, a processor, and a navigation unit. The obstacle detection device is used to acquire location information of obstacles, the processor can acquire location information of obstacles, the processor can acquire first distance information between two adjacent obstacles, then classify obstacle cluster types based on the first distance information, and further determine a travel path based on the obstacle cluster type information to plan the travel path. After determining the travel path, the processor can transmit the travel path to the navigation unit, which can control the movement of the robot based on the travel path. According to the robot provided by the embodiment of the present invention, obstacle cluster types can be classified in the robot's work area based on the distance between obstacles. For example, areas with a high density of obstacles can be classified in the robot's work area. Based on this, when determining the travel path, the obstacle cluster type information can be used as a basis to avoid areas with a high density of obstacles as much as possible, improving the robot's travel efficiency, further improving the robot's work efficiency, and enhancing the user experience.

[0061] In some examples, the robot body for performing cleaning tasks includes a travel unit for driving and moving the robot body, and a cleaning unit for cleaning the floor surface.

[0062] In some examples, the robot body may further include a base station for charging the robot body.

[0063] In this application, the terms “first,” “second,” and “third” are used solely for illustrative purposes and should not be understood as indicating or implying relative importance, and the term “multiple” refers to two or more unless explicitly defined. Terms such as “attachment,” “connection,” “joining,” and “fixing” should all be understood in a broad sense; for example, “connection” may be a fixed connection, a detachable connection, or an integrated connection, and “joining” may be a direct connection or an indirect connection via an intermediary. A person skilled in the art may understand the specific meaning of the terms in this application depending on the specific circumstances.

[0064] In the description of this application, it should be understood that terms indicating direction or positional relationships, such as "up," "down," "left," "right," "front," and "back," refer to the direction or positional relationships shown based on the drawings, and are merely used to simplify the description and make it easier to explain this application. They do not indicate or suggest that the device or unit being referred to must have a specific direction, be configured in a specific direction, or be operated in a specific direction, and therefore should not be understood as limiting this application.

[0065] In this specification, the terms “one embodiment,” “several embodiments,” and “specific embodiments” mean that the specific features, structures, materials, or properties described in combination with such embodiments or examples are included in at least one embodiment or example of this application. In this specification, the descriptive expressions of such terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or properties described may be combined in an appropriate manner in any one or more embodiments or examples.

[0066] The foregoing are merely preferred embodiments of the present application and do not limit it, and various modifications and changes are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and principles of the present application should be included in the claims of the present application.

Claims

1. A route planning method, A step of obtaining first distance information between two adjacent obstacles, The steps include determining obstacle cluster type information based on the first distance information, A route planning method comprising the step of determining a travel route based on the aforementioned obstacle cluster type information.

2. The step of determining obstacle cluster type information based on the first distance information is: The path planning method according to claim 1, further comprising the step of determining the obstacle cluster type information of the area where the obstacles are located as an obstacle-dense area if the distance between two adjacent obstacles is less than a first threshold.

3. The steps include: clustering obstacles whose interval is smaller than a first threshold to obtain obstacle clusters; The steps include obtaining second distance information between two adjacent obstacle clusters, The route planning method according to claim 1, further comprising the step of determining obstacle cluster type information based on the second distance information.

4. The step of determining obstacle cluster type information based on the second distance information is as follows: The process includes the step of determining the area where the obstacle clusters are located as an obstacle-dense area if the distance between two adjacent obstacle clusters is less than a second threshold, The path planning method according to claim 3, wherein the value of the second threshold is greater than the first threshold.

5. The step of determining the travel path based on the aforementioned obstacle cluster type information is: The process includes the step of determining a first travel path based on the obstacle cluster type information in response to a movement command, The route planning method according to any one of claims 1 to 4, wherein the first travel path is alternately arranged with areas of densely packed obstacles.

6. The step of determining the travel path based on the aforementioned obstacle cluster type information is: A step of determining a second travel path based on the obstacle cluster type information in response to a high-speed mapping command, wherein the second travel path first passes through an area other than the obstacle-dense area, then passes through the obstacle-dense area, and / or A route planning method according to any one of claims 1 to 4, comprising the step of determining a third travel path based on the obstacle cluster type information in response to a high-speed cleaning command, wherein the third travel path is located in an area other than an area of ​​densely packed obstacles.

7. If the robot enters an area with a high density of obstacles, the steps include improving the control accuracy of the robot, and / or The method further includes a step of improving the recognition accuracy of the robot when the robot enters an area with a high density of obstacles, The path planning method according to any one of claims 1 to 4, wherein the control accuracy includes the robot's travel control accuracy and / or cleaning control accuracy, and the recognition accuracy includes obstacle recognition accuracy.

8. Steps to respond to a cleaning order, If the area of ​​the densely packed obstacle region is greater than a third threshold, the robot is divided into the densely packed obstacle region and the easily cleanable region. A path planning method according to any one of claims 1 to 4, further comprising the step of controlling the robot to clean the area with a high concentration of obstacles and the area that is easy to clean, respectively.

9. A computer-readable storage medium storing a computer program that implements the route planning method described in any one of claims 1 to 8.

10. A control device, The memory in which the computer program is stored, A processor that executes the aforementioned computer program, The processor is a control device that implements the path planning method described in any one of claims 1 to 8 when executing the computer program.

11. It is a robot, An obstacle detection device for acquiring location information of obstacles, A processor connected to the obstacle detection device and realizing the path planning method described in any one of claims 1 to 8, A robot including a navigation unit connected to the aforementioned processor.

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