Automatic recharging method of robot, robot and computer readable storage medium

Abstract

The embodiment of the invention relates to the field of mowing robots, and discloses an automatic recharging method of a robot, the robot and a computer readable storage medium, and the automatic recharging method comprises the steps: obtaining initial point cloud data of a target area where a charging station is located; if the current environment state meets a preset condition, the initial point cloud data is processed based on a pre-stored pose of the charging station, the relative position relation of the robot relative to the charging station is obtained, and the pre-stored pose of the charging station is the pose under a world coordinate system; and controlling the robot to be recharged according to the relative position relationship. According to the method, it can be ensured that the mowing robot can still accurately move to the charging station when the point cloud data are interfered, recharging failures caused by positioning errors are reduced, and the recharging success rate is increased.

Description

[Technical field]

[0001] The present invention relates to the field of lawn mowing robots, and in particular to an automatic recharging method of a robot, the robot, and a computer-readable storage medium. [Background Technology]

[0002] Existing lawn mowing robots generally use laser radar (LiDAR) for environmental perception and charging station positioning. Through the 3D laser radar sensor, it collects the surrounding point cloud. Because the charging station is affixed with highly reflective reflective stickers, a point cloud with intensity feature information can be obtained.

[0003] However, in rainy, foggy, and hazy weather, small particles in the air (such as water droplets and dust) will scatter or absorb the laser beam emitted by the lidar, causing a large number of interference points to be mixed into the point cloud data. The laser transmission path is disturbed, causing the point cloud position to shift, which manifests as some points being "attracted" to the radar, causing the radar to misjudge the location of the charging station and causing the lawn mower robot to fail to recharge.

[0004] Therefore, there is an urgent need for a solution that can enable the lawn mower robot to recharge normally in adverse weather conditions such as rain, fog, and haze that affect the laser beam. [Summary of the invention]

[0005] The purpose of the embodiments of the present invention is to provide an automatic recharging method for a robot, a robot, and a computer-readable storage medium, which filters the point cloud of the charging station area according to the pre-stored charging station posture as a spatial constraint condition, clusters and corrects the filtered target points, and thus determines the target position of the charging station, solves the impact of meteorological conditions on the quality of the point cloud, ensures that the robot can still move to the charging station accurately when the point cloud data is interfered with, reduces recharging failures caused by positioning errors, and improves the recharging success rate.

[0006] To solve the above technical problems, an embodiment of the present invention provides an automatic recharging method, including: obtaining initial point cloud data of the target area where the charging station is located; if the current environmental state meets the preset conditions, processing the initial point cloud data based on the pre-stored posture of the charging station to obtain the relative position relationship of the robot relative to the charging station, wherein the pre-stored posture of the charging station is the posture in the world coordinate system; and controlling the robot to recharge according to the relative position relationship.

[0007] An embodiment of the present invention also provides a robot comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the automatic recharging method as described above.

[0008] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the automatic recharging method described above are implemented.

[0009] An embodiment of the present invention further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the automatic recharging method described above.

[0010] In addition, the initial point cloud data is processed based on the pre-stored posture of the charging station as described above to obtain the relative position relationship of the robot with respect to the charging station, specifically including: filtering the initial point cloud data based on the pre-stored posture of the charging station to obtain target point cloud data; clustering the target point cloud data to obtain cluster center coordinates; correcting the cluster center coordinates based on the pre-stored posture of the charging station to obtain target cluster center coordinates; and calculating the relative position relationship of the robot with respect to the charging station based on the target cluster center coordinates.

[0011] In addition, the initial point cloud data is filtered based on the pre-stored posture of the charging station as described above to obtain target point cloud data, which specifically includes: converting the initial point cloud data into a world coordinate system to determine the coordinates of each laser point in the initial point cloud data; in the world coordinate system, using the pre-stored posture of the charging station as a reference point, extending a preset length in the positive and negative directions of the X, Y, and Z axes to construct a spatial cube; obtaining the target point cloud data based on whether the positional relationship between the coordinates of each laser point in the initial point cloud data and the spatial cube meets a preset spatial association condition; wherein the preset spatial association condition is that the coordinates of each laser point in the initial point cloud data are located within the spatial cube.

[0012] In addition, clustering the target point cloud data as described above includes: converting the target point cloud data into a robot coordinate system according to the current posture of the robot; and clustering all target points in the target point cloud data in the robot coordinate system.

[0013] In addition, reflective stickers are provided on the charging station as described above, and the reflective stickers include at least a first reflective sticker and a second reflective sticker, and the cluster center point coordinates include a first cluster center point coordinate and a second cluster center point coordinate, wherein the first cluster center point coordinates are used to indicate the center point coordinates of the first reflective sticker, and the second cluster center point coordinates are used to indicate the center point coordinates of the second reflective sticker.

[0014] In addition, the cluster center point coordinates are corrected as described above to obtain the target cluster center point coordinates, including: converting the first cluster center point coordinates and the second cluster center point coordinates to the world coordinate system; determining the corrected midpoint based on the first cluster center point coordinates and the second cluster center point coordinates in the world coordinate system; correcting the first cluster center point coordinates and the second cluster center point coordinates based on the corrected midpoint and the pre-stored posture of the charging station to obtain the first target cluster center point coordinates and the second target cluster center point coordinates.

[0015] In addition, as described above, the relative position relationship of the robot relative to the charging station is calculated based on the target cluster center point coordinates, including: based on the current posture of the robot, the first target cluster center point coordinates and the second target cluster center point coordinates are converted from the world coordinate system to the robot coordinate system, and the relative position relationship of the robot relative to the charging station is calculated based on the first target cluster center point coordinates and the second target cluster center point coordinates in the robot coordinate system.

[0016] In addition, controlling the robot to recharge according to the relative position relationship as described above specifically includes: controlling the robot to perform forward, backward or rotational movements according to the relative position until the robot docks with the charging station.

[0017] Compared with the prior art, the embodiments of the present invention process the initial point cloud data of the charging station area according to the pre-stored charging station posture as a spatial constraint condition, effectively filter out noise points caused by meteorological interference and invalid points outside the charging station range, and reduce the impact of invalid data on subsequent processing; and correct the cluster center point according to the pre-stored charging station posture, so as to accurately obtain the relative position relationship of the robot with respect to the charging station, solve the impact of meteorological conditions on the point cloud quality, ensure that the robot can still move to the charging station accurately when the point cloud data is interfered with, reduce recharging failures caused by positioning errors, and reduce the recharging failure rate caused by environmental interference. [Brief Description of the Drawings]

[0018] One or more embodiments are exemplarily described by the figures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments.

[0019] Figure 1 This is a flow diagram of an automatic recharge method provided by an embodiment of the present invention. Figure 1 ;

[0020] Figure 2 This is a flow diagram of an automatic recharge method provided by an embodiment of the present invention. Figure 2 ;

[0021] Figure 3 This is a flow diagram of an automatic recharge method provided by an embodiment of the present invention. Figure 3 ;

[0022] Figure 4 This is a flow diagram of an automatic recharge method provided by an embodiment of the present invention. Figure 4 ;

[0023] Figure 5 is a schematic diagram of coordinate correction provided by an embodiment of the present invention;

[0024] Figure 6 It is a schematic diagram of the robot structure provided by one embodiment of the present invention. [Specific implementation method]

[0025] To make the objectives, technical solutions, and advantages of the present invention more apparent, various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will appreciate that many technical details are provided in the various embodiments of the present invention to help readers better understand the present application. However, even without these technical details and the various variations and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented.

[0026] In the description of the embodiments of the present invention, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly indicate the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0027] Robot lawn mower recharging refers to the process in which the robot automatically returns to the charging station to recharge after completing a task or when the battery is low. Generally speaking, when recharging, the robot uses a 3D lidar sensor to collect a point cloud around the charging station. Because the charging station is affixed with highly reflective reflective stickers, a point cloud with intensity characteristic information can be obtained. By analyzing and processing the point cloud data, the location of the charging station can be determined. However, when the robot lawn mower is in rain, fog, or haze, small molecular particles will interfere with the infrared laser beam, affecting the transmission speed of the beam. As a result, some points in the point cloud identified by the radar will be attracted to the lidar, resulting in unstable ranging, which will affect the accuracy of the subsequent clustering of reflective sticker centers, leading to inaccurate calculation of the charging station's location, poor recharging effect, or even recharging failure.

[0028] In order to solve the above problems, the present invention proposes an automatic recharging method, such as Figure 1 As shown, the specific steps include:

[0029] Step 101: Acquire initial point cloud data of the target area where the charging station is located.

[0030] Specifically, in this embodiment, the target area refers to the area where the robot mower's charging station is located. Generally, the robot mower's charging station is affixed with highly reflective stickers. The robot mower uses its onboard laser radar (LIDAR) to transmit laser beams and receive reflected signals, converting the contours of objects in the environment into three-dimensional point cloud data. The charging station's location is represented by a laser point with characteristic intensity information. Under complex meteorological conditions such as rain, fog, and haze, the laser beam can be scattered or absorbed by tiny particles in the air, resulting in a large number of noise points in the point cloud data. To improve data quality, this embodiment can employ a dynamic scanning strategy when collecting point cloud data. For example, when high humidity is detected, the laser radar's scanning frequency can be reduced to reduce noise interference, or the radar can be switched to a model with stronger anti-interference capabilities. Furthermore, since the charging station's location is represented by a laser point with characteristic intensity information, after acquiring the point cloud data, to more quickly and accurately locate the charging station's location, the acquired point cloud data can be pre-processed to filter out laser points with intensities less than 255. Alternatively, the point cloud data can be directly processed. This embodiment does not impose specific restrictions on whether or not pre-processing is performed.

[0031] Step 102: If the current environmental state meets the preset conditions, the initial point cloud data is processed based on the pre-stored posture of the charging station to obtain the relative position relationship of the robot with respect to the charging station;

[0032] The preset conditions are complex meteorological conditions such as rain, fog, and haze. When these conditions occur, the air is filled with numerous tiny particles, such as raindrops, fog droplets, and dust. When a laser beam passes through this air filled with tiny particles, two situations may occur: scattering by these particles or absorption by them. Both of these situations interfere with the formation of the laser point cloud. The end result is a large number of noise points in the acquired point cloud data, which can affect accurate 3D perception of the surrounding environment and subsequent data processing and analysis.

[0033] For an example, see Figure 2 In step 102, the initial point cloud data is processed based on the pre-stored posture of the charging station to obtain the relative position relationship of the robot with respect to the charging station. This can be achieved by the following steps:

[0034] Step 1021: Filter the initial point cloud data based on the pre-stored posture of the charging station to obtain target point cloud data.

[0035] Specifically, the pre-stored charging station pose in this embodiment refers to the pose stored by the mowing robot during map creation. When the mowing robot begins map creation, it first exits the charging station by a preset distance, reaches the map creation starting point, and begins map creation with the map creation starting point as the origin. Since the map creation starting point is the origin, the robot can use the lidar to obtain the distance and angle of the charging station relative to the map creation starting point at the map creation starting point, thereby calculating the charging station pose and saving it as the pre-stored charging station pose. The pre-stored charging station pose is accurate by default, and the pre-stored charging station pose is the pose in the world coordinate system. The pre-stored charging station pose in this embodiment includes the position coordinates and attitude angle of the charging station.

[0036] By using the pre-stored posture, the target point cloud data related to the charging station can be filtered out from the acquired point cloud data. The screening is performed based on preset spatial association conditions, which can be defined from multiple dimensions. For example, the distance condition can limit the spatial distance between the filtered target point and the pre-stored posture to a certain range to exclude interference points at a long distance; the angle condition can require that the angle between the normal direction of the target point and the normal direction of the charging station reflection surface is within a certain threshold to ensure that the filtered points come from the surface of the charging station; the height condition can filter out invalid points on the ground or in the air based on the actual height range of the charging station, etc. This embodiment does not impose specific restrictions on the preset spatial association conditions for filtering out target point cloud data.

[0037] Step 1022: Cluster the target point cloud data to obtain the coordinates of the cluster center point.

[0038] Step 1023: Correct the cluster center coordinates based on the pre-stored charging station positions to obtain the target cluster center coordinates.

[0039] Step 1024: Calculate the relative position of the robot relative to the charging station based on the coordinates of the target cluster center point.

[0040] Specifically, after obtaining target point cloud data that meets preset spatial association conditions with the charging station location, a clustering algorithm is used to cluster the target points in the target point cloud data. The clustering algorithm can divide points with similar characteristics into different categories, each category representing a potential target object. In this embodiment, the point cloud with strong characteristics reflected by the reflective tape of the charging station is clustered by clustering. In the present invention, the clustering algorithm can adopt methods such as Euclidean clustering, density clustering, superbody clustering, etc. This embodiment does not impose specific restrictions on this. These algorithms can automatically identify the cluster area where the charging station is located based on the distribution characteristics of the point cloud data.

[0041] In one example, in step 1021 , filtering the initial point cloud data based on the pre-stored posture of the charging station to obtain target point cloud data can be specifically implemented as follows.

[0042] First, the initial point cloud data is converted to the world coordinate system to determine the coordinates of each laser point in the initial point cloud data;

[0043] Specifically, the position of each laser point in the point cloud data relative to the laser radar can be obtained according to the point cloud data obtained in step 101, which is recorded as The mowing robot can obtain its position relative to the world coordinate system through real-time positioning and record it as The pre-stored position of the laser radar relative to the mowing robot is recorded as By calculation The position of the laser point in the world coordinate system can be obtained

[0044] For some examples, see Figure 3 In step 1021 of this embodiment, after determining the coordinates of each laser point in the initial point cloud data, the following steps may be further included:

[0045] Step 301: In the world coordinate system, with the location of the charging station as the reference point, a space cube is constructed by extending the preset lengths in the positive and negative directions of the X, Y, and Z axes.

[0046] Step 302: Target point cloud data is obtained based on whether the positional relationship between the coordinates of each laser point in the initial point cloud data and the spatial cube satisfies a preset spatial association condition.

[0047] The preset spatial association condition is that the coordinates of each laser point in the initial point cloud data are located within the spatial cube.

[0048] Specifically, the world coordinate system is a globally unified coordinate system that provides a unified reference framework for subsequent processing. Coordinate transformation is used to determine the first coordinates of each laser point in the point cloud data. Coordinate transformation can be performed using a variety of methods. For example, based on the robot's initial position and posture information, combined with the installation position and calibration parameters of the lidar, a coordinate transformation matrix is ​​used to convert the point cloud data in the lidar coordinate system to the world coordinate system.

[0049] Next, when screening the target point cloud data in the point cloud data based on the pre-stored posture of the charging station, the method of constructing a spatial cube is adopted. In the world coordinate system, the coordinates of the charging station in the pre-stored posture are used as the reference point, and the preset length is extended in the positive and negative directions of the X, Y, and Z axes to construct a spatial cube. The determination of the preset length requires comprehensive consideration of many factors, such as the actual size of the charging station, the measurement error of the lidar, and possible environmental interference. Generally speaking, the preset length should be slightly larger than the actual size of the charging station to ensure that possible reflection points around the charging station can be included, but not too large to avoid introducing too many noise points. For example, with the coordinates of the charging station as the reference point, 0.5 meters (preset length) are extended in the positive and negative directions of the X, Y, and Z axes to construct a spatial cube. For example, if the coordinates of the charging station are (x0, y0, z0), the boundary range of the spatial cube is [x0-0.5, x0+0.5], [y0-0.5, y0+0.5], [z0-0.5, z0+0.5]. This embodiment does not limit the specific value of the preset length.

[0050] Next, target points are filtered based on the positional relationship between the first coordinate of each laser point in the point cloud data and the spatial cube. The first coordinate of each laser point in the point cloud data is traversed to determine whether it lies within the spatial cube. If so, the laser point is filtered as the target point, ultimately generating the target point cloud data. This filtering method effectively eliminates invalid points outside the spatial cube that are irrelevant to charging station positioning, improving the purity of the target point cloud data and providing a more accurate data foundation for subsequent clustering and positioning operations.

[0051] In one example, in step 1022 , clustering the target point cloud data may include: converting the target point cloud data into a robot coordinate system; and clustering all target points in the target point cloud data in the robot coordinate system.

[0052] Specifically, the target point is acquired in the laser radar coordinate system. In order to facilitate the navigation and movement of the lawn mower robot, it is necessary to locate the charging station in the robot coordinate system of the lawn mower robot. Among them, the robot coordinate system is a coordinate system established with the lawn mower robot itself as the origin. The coordinate conversion process needs to take into account the current position and posture of the robot and the coordinate information of the target point in the laser radar coordinate system. The coordinate system conversion can be achieved through the coordinate transformation matrix. This application does not limit the specific conversion method. After completing the coordinate conversion, all target points in the target point cloud data are clustered in the robot coordinate system.

[0053] Generally speaking, the point cloud corresponding to each reflective sticker on the charging station should be clustered into a point, and the position coordinates of the point represent the location of the corresponding reflective sticker. By clustering the target point cloud data into cluster centers corresponding to the number of reflective stickers on the charging station, the positions of all reflective stickers on the charging station can be determined, and then the location of the charging station can be determined.

[0054] In one example, after clustering the target point cloud data, the method of this embodiment further includes:

[0055] Determine whether the clustering result meets the preset clustering condition; wherein the preset clustering condition is whether the number of cluster center points obtained by clustering the target point cloud data is equal to the number of reflective stickers on the charging station;

[0056] If the clustering result does not meet the preset clustering conditions, a frame of point cloud data of the target area is obtained again.

[0057] If the clustering result satisfies the preset clustering conditions, it proves that the quality of the current point cloud is good and can be used to further determine the location of the charging station.

[0058] In one example, two reflective stickers are provided on the charging station in an embodiment of the present application, the two reflective stickers are a first reflective sticker and a second reflective sticker, and the cluster center point coordinates include the first cluster center point coordinates and the second cluster center point coordinates, wherein the first cluster center point coordinates are used to indicate the center point coordinates of the first reflective sticker, and the second cluster center point coordinates are used to indicate the center point coordinates of the second reflective sticker.

[0059] In this case, see Figure 4 , for step 1023, the cluster center coordinates are corrected to obtain the target cluster center coordinates, which can be done by the following steps:

[0060] Step 401: Convert the coordinates of the first cluster center point and the second cluster center point to the world coordinate system.

[0061] Step 402: Determine a revised midpoint based on the coordinates of the first cluster center point and the coordinates of the second cluster center point.

[0062] Step 403: Correct the coordinates of the first cluster center point and the second cluster center point according to the correction midpoint and the pre-stored posture of the charging station.

[0063] Specifically, in rainy, foggy, and hazy weather, the location of the cluster center corresponding to the reflective sticker is less susceptible to interference in the Y and Z axes, but more significantly in the X axis. Therefore, the X-axis position of the cluster center needs to be corrected based on the pre-stored charging station's position, while the Y-axis position remains unchanged. Since the pre-stored charging station's position is in the world coordinate system, the cluster center coordinates must first be converted to the world coordinate system. This allows position correction to be performed within the same coordinate system. The specific details of the coordinate system conversion are not detailed here.

[0064] After the cluster center points in the world coordinate system are converted, each cluster center point converted to the world coordinate system is projected along the Z-axis direction of the world coordinate system, and the cluster center points in the three-dimensional space are mapped to the two-dimensional plane to obtain the plane coordinates corresponding to each cluster center point, thereby simplifying subsequent calculations and analysis, and eliminating possible interference factors in the vertical direction.

[0065] For an example, see Figure 5 , point A and point B are the initial first cluster center point and the second cluster center point, and the position correction method specifically includes: using the correction formula to correct the coordinates of the first cluster center point and the second cluster center point to obtain the coordinates of the first target cluster center point and the second target cluster center point;

[0066] The correction formula is:

[0067] x1′=x center -y1×tan(θ),x2′=x center +y2×tan(θ)

[0068] Where A(x1, y1) is the plane coordinate of the first cluster center, B(x2, y2) is the plane coordinate of the second cluster center; A(x1′, y1′) is the plane coordinate of the first target cluster center, and B·(x2′, y2′) is the plane coordinate of the second target cluster center;

[0069] x center is the X-axis coordinate of the corrected midpoint,

[0070] θ is the attitude angle of the charging station included in the pre-stored posture of the charging station.

[0071] Specifically, the correction in this embodiment is actually performed based on the pre-stored posture of the charging station and the correction midpoint. Through the above correction method, the influence of the actual posture of the charging station on the plane coordinates of the cluster center point is fully considered, thereby achieving accurate correction of the X-axis position of the plane coordinates of each cluster center point, further reducing the error of the lawn mower robot in positioning the charging station, thereby significantly improving the success rate of automatic recharging, and enhancing the adaptability and reliability of the lawn mower robot in complex environments.

[0072] In an example, for step 1024, the relative position relationship of the robot relative to the charging station is calculated, specifically including: converting the coordinates of the center point of the first target cluster and the coordinates of the center point of the second target cluster from the world coordinate system to the robot coordinate system, and calculating the relative position relationship of the robot relative to the charging station based on the coordinates of the center point of the first target cluster and the coordinates of the center point of the second target cluster in the robot coordinate system.

[0073] It is understandable that in order for the lawn mower robot to navigate according to the coordinates of the target cluster center point, it is also necessary to convert the coordinates of the target cluster center point from the world coordinate system to the robot coordinate system used for the lawn mower robot navigation, so that the lawn mower robot can navigate according to the relative position relationship of the robot with respect to the charging station and complete recharging.

[0074] Step 103: Control the robot to recharge according to the relative position relationship.

[0075] Specifically, after determining the relative position relationship [Δx, Δy, yaw] of the robot relative to the charging station, the target location of the charging station is determined. The mowing robot can then accurately move to the target location for docking using a path planning algorithm. The path planning algorithm generates an optimal motion path based on the robot's current position, target location, and surrounding obstacle information. Common path planning algorithms include the A* algorithm and the Dijkstra algorithm, and are not specifically limited in this embodiment.

[0076] In addition, the robot can also perform forward, backward, or rotation movements based on the relative position relationship [Δx, Δy, yaw] to gradually adjust its posture until it docks with the charging station. The specific method is as follows:

[0077] Determine the Δy value in [Δx, Δy, yaw];

[0078] If Δy≠0, it means the robot is not on the central axis of the charging station. At this time, first rotate the robot's front end in place so that the front end stops at a preset angle (to ensure that the charging pile is within the optimal scanning area of ​​the lidar). Depending on the positive or negative value of Δy, control the robot to move forward or backward in a straight line so that Δy=0 (that is, return to the central axis of the charging station). Then rotate the front end in place so that yaw=0. Then, control the robot to move forward in a straight line in the x-axis direction until it docks with the charging station (making Δx=0). If Δy≠0 and yaw≠0 during the straight-line forward process, control the robot's front end direction for fine-tuning.

[0079] If Δy = 0 (ideal value), it means the robot is exactly on the central axis of the charging station. At this time, rotate the vehicle head so that yaw = 0. Then, control the robot to move forward in a straight line along the x-axis until it docks with the charging station (making Δx = 0). If Δy ≠ 0 and yaw ≠ 0 during the straight-line movement, control the vehicle head direction for fine-tuning.

[0080] When Δy=0, yaw=0 (indicating that the robot is on the central axis of the charging station and the front of the vehicle is facing the charging station), the robot is controlled to move forward in a straight line until it docks with the charging station (making Δx=0). If Δy≠0, yaw≠0 during the straight-line movement, the direction of the vehicle head is controlled for fine-tuning.

[0081] Compared with the related art, the embodiment of the present invention screens the point cloud data of the charging station area according to the pre-stored charging station posture as a spatial constraint condition, effectively filters out the noise points caused by meteorological interference and invalid points outside the charging station range, and reduces the impact of invalid data on subsequent processing; corrects the cluster center point according to the pre-stored charging station posture, so as to accurately obtain the relative position relationship of the robot with respect to the charging station, reduce the positioning deviation caused by noise, solve the impact of meteorological conditions on the point cloud quality, ensure that the robot can still move to the charging station accurately when the point cloud data is interfered with, reduce the recharging failure caused by positioning error, and reduce the recharging failure rate caused by environmental interference.

[0082] Another embodiment of the present invention relates to a robot, such as Figure 6 As shown, it includes: at least one processor 202; and a memory 201 that is communicatively connected to the at least one processor 202; wherein the memory 201 stores instructions that can be executed by the at least one processor 202, and the instructions are executed by the at least one processor 202 to enable the at least one processor 202 to execute any of the above method embodiments.

[0083] Among them, the memory 201 and the processor 202 are connected in a bus manner. The bus can include any number of interconnected buses and bridges. The bus connects one or more processors 202 and various circuits of the memory 201 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and, therefore, are not further described in this article. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 202 is transmitted on the wireless medium via the antenna. Furthermore, the antenna also receives data and transmits the data to the processor 202.

[0084] Processor 202 is responsible for bus management and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 201 can be used to store data used by processor 202 when performing operations. It should be noted that the robot in this embodiment is the lawn mowing robot described in the aforementioned embodiments.

[0085] An embodiment of the present application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the automatic recharging method embodiment of the robot recorded in any of the above embodiments are implemented, and the same technical effect can be achieved. To avoid repetition, they will not be repeated here.

[0086] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0087] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, and storage of user personal information involved in the technical solutions disclosed herein comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken with respect to user personal information to prevent unauthorized access to user personal information data and maintain the security of user personal information and network security.

[0088] Yet another embodiment of the present invention relates to a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the automatic recharging method described in the above method embodiment.

[0089] An embodiment of the present invention also provides a computer-readable storage medium, which stores programs or instructions. When the program or instructions are executed by a processor, the various processes of the embodiment of the automatic recharge method such as any one of the above-mentioned items are implemented, and the same technical effect can be achieved. To avoid repetition, they will not be repeated here.

[0090] That is, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps in the various embodiments of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0091] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present invention, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A robot automatic recharging method, characterized in that: The automatic recharging method includes: Obtain the initial point cloud data of the target area where the charging station is located; If the current environmental state meets the preset conditions, the initial point cloud data is processed based on the pre-stored posture of the charging station to obtain the relative position relationship of the robot with respect to the charging station, wherein the pre-stored posture of the charging station is the posture in the world coordinate system; The robot is controlled to recharge according to the relative position relationship.

2. The automatic recharging method according to claim 1, characterized in that: The processing of the initial point cloud data based on the pre-stored posture of the charging station to obtain the relative position relationship of the robot with respect to the charging station specifically includes: Filtering the initial point cloud data based on the pre-stored posture of the charging station to obtain target point cloud data; Clustering the target point cloud data to obtain cluster center coordinates; Correcting the cluster center coordinates based on the pre-stored position of the charging station to obtain the target cluster center coordinates; The relative position relationship of the robot with respect to the charging station is calculated based on the coordinates of the target cluster center point.

3. The automatic recharging method according to claim 2, characterized in that: The filtering of the initial point cloud data based on the pre-stored posture of the charging station to obtain target point cloud data specifically includes: Converting the initial point cloud data into a world coordinate system to determine the coordinates of each laser point in the initial point cloud data; In the world coordinate system, taking the pre-stored posture of the charging station as the reference point, extending the preset lengths in the positive and negative directions of the X, Y, and Z axes to construct a spatial cube; Obtaining the target point cloud data according to whether the positional relationship between the coordinates of each laser point in the initial point cloud data and the space cube satisfies a preset spatial association condition; The preset spatial association condition is that the coordinates of each laser point in the initial point cloud data are located within the spatial cube.

4. The automatic recharging method according to claim 3, characterized in that: The clustering of the target point cloud data includes: According to the current posture of the robot, the target point cloud data is converted into the robot coordinate system; All target points in the target point cloud data are clustered in the robot coordinate system.

5. The automatic recharging method according to claim 2, characterized in that: The charging station is provided with reflective stickers, which include at least a first reflective sticker and a second reflective sticker. The cluster center point coordinates include a first cluster center point coordinate and a second cluster center point coordinate, wherein the first cluster center point coordinates are used to indicate the center point coordinates of the first reflective sticker, and the second cluster center point coordinates are used to indicate the center point coordinates of the second reflective sticker.

6. The automatic recharging method according to claim 5, characterized in that: The step of correcting the cluster center point coordinates to obtain the target cluster center point coordinates includes: Converting the coordinates of the first cluster center point and the second cluster center point to a world coordinate system; Determine a revised midpoint according to the coordinates of the first cluster center point and the coordinates of the second cluster center point in a world coordinate system; The first cluster center point coordinates and the second cluster center point coordinates are corrected according to the corrected midpoint and the pre-stored position of the charging station to obtain the first target cluster center point coordinates and the second target cluster center point coordinates.

7. The automatic recharging method according to claim 6, characterized in that: The calculating, based on the coordinates of the target cluster center point, a relative position relationship of the robot with respect to the charging station includes: Based on the current posture of the robot, the coordinates of the first target cluster center point and the second target cluster center point are converted from the world coordinate system to the robot coordinate system, and the relative position relationship of the robot with respect to the charging station is calculated according to the coordinates of the first target cluster center point and the second target cluster center point in the robot coordinate system.

8. The automatic recharging method according to claim 1, characterized in that: The controlling the robot to recharge according to the relative position relationship specifically includes: The robot is controlled to perform forward, backward or rotational motion according to the relative position until the robot docks with the charging station.

9. A robot, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the automatic recharging method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that A computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the automatic recharging method according to any one of claims 1 to 8 are implemented.

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