Mobile robot and control method, device and electronic equipment thereof

Abstract

This invention discloses a mobile robot and its control method, device, and electronic equipment. The mobile robot is equipped with a lidar. The method includes: acquiring point cloud data collected by the lidar; obtaining a target line segment based on the point cloud data; determining the target feature position of the mobile robot based on the target line segment; and controlling the mobile robot based on the target feature position. This method can reduce computational requirements and lower costs.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more particularly to a mobile robot and its control method, apparatus, and electronic equipment. Background Technology

[0002] As the operating scenarios of photovoltaic cleaning robots become increasingly diversified, the working conditions that robots need to adapt to and detect are gradually increasing. Relying solely on the sensors on the robot is usually insufficient to provide enough reliable information.

[0003] In related technologies, to accommodate this situation, image processing methods using cameras are often employed to identify features in the environment. However, this method requires a certain level of platform computing power and has high hardware costs. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, a first objective of this invention is to provide a control method for a mobile robot to reduce costs.

[0005] The second objective of this invention is to provide an electronic device.

[0006] The third objective of this invention is to provide a control device for a mobile robot.

[0007] The fourth objective of this invention is to provide a mobile robot.

[0008] To achieve the above objectives, a first aspect of the present invention provides a control method for a mobile robot, wherein the mobile robot is equipped with a lidar, and the method includes: acquiring point cloud data collected by the lidar; obtaining a target line segment based on the point cloud data; determining the target feature position of the mobile robot based on the target line segment; and controlling the mobile robot based on the target feature position.

[0009] To achieve the above objectives, a second aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the above-described control method for a mobile robot.

[0010] To achieve the above objectives, a third aspect of the present invention provides a control device for a mobile robot, the device comprising: an acquisition module for acquiring point cloud data collected by a lidar; a calculation module for obtaining a target line segment based on the point cloud data; a determination module for determining the target feature position of the robot based on the target line segment; and a control module for controlling the mobile robot based on the target feature position.

[0011] To achieve the above objectives, a fourth aspect of the present invention provides a mobile robot, including the control device for the mobile robot described above.

[0012] According to embodiments of the present invention, a mobile robot and its control method, apparatus, and electronic device acquire point cloud data collected by a lidar on the mobile robot, obtain target line segments based on the point cloud data, and determine the target feature position of the mobile robot based on the target line segments. This achieves the goal of obtaining the target feature position by only processing the line segments, which is less difficult to process, requires less computation, and reduces costs.

[0013] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0014] Figure 1 This is a flowchart of a control method for a mobile robot according to one or more embodiments of the present invention;

[0015] Figure 2 This is a flowchart of a control method for a mobile robot, as exemplified by the present invention.

[0016] Figure 3 This is a flowchart of a control method for a mobile robot according to one or more embodiments of the present invention;

[0017] Figure 4 This is a schematic diagram illustrating interior point calculation as an example of the present invention;

[0018] Figure 5 This is a schematic diagram illustrating the filtering of point cloud data as an example of the present invention;

[0019] Figure 6 This is a flowchart of a control method for a mobile robot according to one or more embodiments of the present invention;

[0020] Figure 7 This is a schematic diagram of a preset feature library as an example of the present invention;

[0021] Figure 8 This is a schematic diagram illustrating an example of a cable tray warping.

[0022] Figure 9 This is an example of a point cloud map of an impassable cable tray;

[0023] Figure 10 This is a schematic diagram of a control method for a mobile robot according to an example of the present invention;

[0024] Figure 11 This is a schematic diagram of a control method for a mobile robot, another example of the present invention;

[0025] Figure 12 This is a schematic diagram of a control method for a mobile robot, which is yet another example of the present invention.

[0026] Figure 13 This is a schematic diagram of a control method for a mobile robot, which is yet another example of the present invention.

[0027] Figure 14 This is a structural block diagram of the control device for a mobile robot according to an embodiment of the present invention;

[0028] Figure 15 This is a structural block diagram of the mobile robot according to an embodiment of the present invention. Detailed Implementation

[0029] The following description, with reference to the accompanying drawings, describes a mobile robot, its control method, apparatus, and electronic device according to embodiments of the present invention, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described with reference to the accompanying drawings are exemplary and should not be construed as limiting the present invention.

[0030] Figure 1 This is a flowchart of a control method for a mobile robot according to one or more embodiments of the present invention.

[0031] It should be noted that the mobile robot is equipped with a lidar.

[0032] like Figure 1 As shown, the control method for the mobile robot includes:

[0033] S11, acquire point cloud data collected by lidar.

[0034] S12, obtain the target line segment based on the point cloud data.

[0035] S13, determine the target feature position of the mobile robot based on the target line segment, and control the mobile robot based on the target feature position.

[0036] Specifically, a lidar sensor can be tilted and mounted on the mobile robot. The mobile robot acquires point cloud data through the lidar sensor.

[0037] After acquiring the point cloud data, the mobile robot processes the point cloud data, including removing interference data and performing fitting and extraction on the point cloud data.

[0038] After processing the point cloud data to obtain the target line segment, the target feature position of the mobile robot can be determined based on the target line segment. For example, the target line segment can be substituted into a preset feature library for matching, and the target feature position can be determined based on the matching result. Alternatively, the target line segment can be analyzed to obtain its features, and the target feature position can be determined based on the features of the target line segment.

[0039] If the target feature location is matched, it is determined that motion data fusion is needed to control the mobile robot; if the target feature location is not matched, it is determined that motion data fusion is not needed to control the mobile robot.

[0040] When the target feature location is determined through matching, fuzzy motion control is performed by combining the point cloud class of motion data to control the mobile robot, change the robot's motion state, and optimize the robot's motion. For example, if a stopping position is matched, the robot decelerates first and then stops; if a reversing position is matched, the robot decelerates first and then reverses direction; when a climbing scenario is matched, an acceleration ratio is fitted based on the calculated climbing angle, and a deceleration ratio is fitted when the robot is in the air ahead (close to the top) to achieve stable control of the robot's operation.

[0041] As an example, see Figure 2 First, a point cloud feature library is established. This library is based on the length of feature line segments, such as the width of the sensor panel, component width, and bridge width. In other words, it establishes a specific feature relationship between a particular line segment or group of line segments. During the operation of the mobile robot, point cloud data is collected using LiDAR. Target line segments are then identified from this data and matched against the point cloud feature library. The matching results determine the target feature position, which needs to be determined by combining motion data. Finally, by combining the point cloud data with the motion data, fuzzy motion control is performed to change the robot's motion state and optimize its movement.

[0042] Therefore, by acquiring point cloud data collected by LiDAR, the target line segment can be obtained from the point cloud data, the target feature position of the mobile robot can be determined from the target line segment, and the mobile robot can be controlled based on the target feature position. By using point cloud data collected by LiDAR and then using the point cloud data to obtain the target line segment, the target feature position can be obtained by only processing the line segment. The processing difficulty is low, the computational requirements are small, and the cost is reduced.

[0043] In one or more embodiments of the present invention, see Figure 3 The target line segment is obtained based on the point cloud data, including:

[0044] S31 connects any two points in the point cloud data to obtain a set of lines.

[0045] Specifically, after obtaining point cloud data using lidar, a straight line is drawn between every two points in the point cloud data to obtain a set of straight lines.

[0046] S32, for at least a portion of the lines in the line set, calculate the percentage of interior points for each line.

[0047] The ratio of inlier points to all points in the point cloud data is defined as the ratio of inlier points to all points in the point cloud data. Inlier points are points in the point cloud data whose distance from the corresponding straight line is less than a first distance threshold.

[0048] Specifically, after obtaining the set of lines, for each line in the set, the values ​​of parameters k and b corresponding to the line y = kx + b are obtained. After calculating the values ​​of the parameters corresponding to the lines, the distance between the points in the point cloud data and the lines is calculated based on the parameters of the lines and the data of the points in the point cloud data. Then, the proportion of interior points of the lines is calculated based on the distance.

[0049] See Figure 4 In the example shown, assuming we are currently calculating the percentage of interior points of line L1, we calculate the distance between each point in the point cloud data and line L1 based on the data of the points in the point cloud data and the parameters k1 and b1 of line L1. Based on the distance, we determine the interior points in the point cloud data that correspond to line L1, and thus obtain the percentage of interior points corresponding to line L1. Similarly, assuming we are currently calculating the percentage of interior points of line L2, we calculate the distance between each point in the point cloud data and line L2 based on the data of the points in the point cloud data and the parameters k2 and b2 of line L2. Based on the distance, we determine the interior points in the point cloud data that correspond to line L2, and thus obtain the percentage of interior points corresponding to line L2.

[0050] For further explanation of the calculation of the proportion of interior points using line L1, please refer to [link / reference]. Figure 4 The example shown is the distance between point (x0, y0) and line L1. The calculated distance d is compared with a preset first distance threshold d_thred. When d <= d_thred, the point (x0, y0) is considered an interior point. This process is repeated for each point in the point cloud data. For each point identified as an interior point, the interior point count corresponding to line L1 is incremented by 1, thus obtaining the number of interior points n corresponding to line L1. After obtaining the number of interior points n, the total number of points m in the point cloud data is obtained, and the percentage of interior points corresponding to line L1 is then calculated.

[0051] S33, determine the target straight line from the straight line set based on the proportion of interior points.

[0052] Specifically, after obtaining the percentage of interior points corresponding to each line in the line set, the target line is determined according to the percentage of interior points based on the preset rules. For example, the maximum percentage of interior points can be obtained, and the target line is determined to be the line corresponding to the percentage of interior points with the maximum value. Alternatively, the percentage of interior points corresponding to each line can be compared with the preset value p_thred, which is the optimal percentage, and the line corresponding to the percentage of interior points greater than p_thred is determined as the target line.

[0053] S34, based on the boundary of the point cloud data, performs a cut operation on the target straight line to obtain the target line segment.

[0054] Therefore, it is possible to obtain the target line segment based on point cloud data.

[0055] It should be noted that in the process of obtaining the target line from the line set, it is possible to traverse all lines in the line set, or it is possible to traverse only a portion of the lines in the line set. For example, when higher precision is required, it is possible to traverse all lines in the line set. In this case, the proportion of interior points for each line in the line set is calculated, and the target line is determined based on the calculated proportion of interior points. When higher calculation speed is required and overflow and over-computation are prevented, it is possible to traverse only a portion of the lines in the line set. In this case, when calculating the proportion of interior points, the calculation ends when the number of times the proportion of interior points is calculated reaches a set threshold, and the target line is determined based on the calculated proportion of interior points.

[0056] In one or more embodiments of the present invention, before obtaining the set of lines, the point cloud data is further filtered so that the distance between any point in the filtered point cloud data and its nearest point is less than a second distance threshold.

[0057] Specifically, after acquiring the point cloud data collected by the lidar, the point cloud data is first filtered according to the rule that the distance between any point and its nearest point is less than a second distance threshold. This results in point clouds with smaller intervals, which are then used as the data point set. For details, please refer to [link to relevant documentation]. Figure 5 The specific example shown.

[0058] After filtering the point cloud data to obtain a data point set, the target line segment can be obtained from the data point set. The method for obtaining the target line segment from the data point set can be found in [link to documentation]. Figure 3 The specific embodiment shown differs in that... Figure 3 The point cloud data in the method shown is replaced with a data point set.

[0059] This allows for the removal of noise points that are far apart from each other in point cloud data, thereby improving the accuracy of controlling mobile robots.

[0060] In one or more embodiments of the present invention, see Figure 6 Determine the target feature location of the mobile robot based on the target line segment, including:

[0061] S61, Match the target line segment with a preset feature library, wherein the preset feature library includes a first preset feature position and its corresponding feature line segment.

[0062] S62, when there is a feature line segment in the preset feature library that successfully matches the target line segment, the first preset feature position corresponding to the feature line segment is taken as the target feature position.

[0063] S63, when there is no feature segment in the preset feature library that successfully matches the target line segment, if the target line segment meets the conditions corresponding to the second preset feature position, then the corresponding second preset feature position is taken as the target feature position.

[0064] The aforementioned matching of the target line segment with a preset feature library can be interpreted as matching the target line segment with feature line segments in the preset feature library based on their length. Specifically, after obtaining the target line segment, a brute-force search is performed on the preset feature library based on the target line segment, matching its length with feature line segments in the library. The matching result determines whether a successful match has been achieved. See [example example]. Figure 7 The first preset feature positions include components, reversing positions, cable trays, and stop positions. If the length of the target line segment matches the length of the component feature line segment, then the matched feature line segment is determined, and the target feature position is the component. If the length of the target line segment matches the length of the reversing position feature line segment, then the matched feature line segment is determined, and the target feature position is the reversing position. If the length of the target line segment matches the length of the cable tray feature line segment, then the matched feature line segment is determined, and the target feature position is the cable tray. If the length of the target line segment matches the length of the stop position feature line segment, then the matched feature line segment is determined, and the target feature position is the stop position. Therefore, matching based on the lengths of feature line segments and target line segments can be achieved, further reducing computational requirements.

[0065] Optionally, the preset feature library can also be set to include the first preset feature position and its corresponding feature segment group. If it is still based on Figure 7 The example shown illustrates that components, reversing positions, cable trays, and stop positions can all be configured to correspond to a set of feature line segments. When the registration rate between the target line segment and a certain set of feature line segments is greater than a set threshold, the target line segment is considered to match the set of feature line segments, and the first preset feature position corresponding to the set of feature line segments is determined as the target feature position.

[0066] In one or more embodiments of the present invention, the first preset feature position includes at least one of a stopping position, a reversing position, a normal cable tray position, and a normal photovoltaic module position; the second preset feature position includes at least one of an uphill position, a downhill position, a faulty cable tray position, and a faulty photovoltaic module position; controlling the mobile robot according to the target feature position includes:

[0067] When the target feature location is a stop position, a faulty bridge position, or a faulty photovoltaic module position, control the mobile robot to stop.

[0068] When the target feature position is a reversing position, control the mobile robot to reverse direction.

[0069] When the target feature is located at the bridge position, control the mobile robot to move along the bridge position.

[0070] When the target feature location is a normal photovoltaic module location, control the mobile robot to clean the corresponding photovoltaic module.

[0071] When the target feature is located on an uphill slope, control the mobile robot to go uphill.

[0072] When the target feature is located on a downhill slope, control the mobile robot to go downhill.

[0073] Among them, the stopping position and the reversing position can be distinguished by the difference in the width or number of the stopping position sensing baffle and the reversing position sensing baffle. When the mobile robot detects the reversing position, it changes its running direction and returns. When it detects the stopping position, it stops in time.

[0074] When a point cloud state that does not match the feature library is encountered during operation, it is considered a faulty cable tray location and is impassable. As an example, Figure 8 This is a diagram illustrating a cable tray that is bent upwards and cannot be used. Figure 9 Point cloud map of impassable bridges.

[0075] If the effective photovoltaic module length is incorrect or there are too many breaks in the middle during operation, it is considered to have encountered a faulty photovoltaic module and will stop moving forward.

[0076] In one or more embodiments of the present invention, the condition corresponding to the uphill position is that as the target line segment of the mobile robot's movement becomes shorter, the mobile robot is controlled to go uphill, including: at a first moment, obtaining a first distance between the mobile robot and the road segment ahead based on the current point cloud data, and obtaining a first running speed of the mobile robot at the first moment; at a second moment after the first moment, obtaining a second distance between the mobile robot and the road segment ahead based on the current point cloud data, and obtaining a second running speed of the mobile robot at the second moment; calculating the climbing angle based on the first distance, the first running speed, the second distance, the second running speed, the first moment, and the second moment; and performing acceleration control on the mobile robot based on the climbing angle.

[0077] The robot's running speed is calculated in real time and can be obtained by Kalman filtering fusion calculation using acceleration integral and encoder feedback.

[0078] After obtaining the point cloud data at the first time step, the distance from each point in the point cloud data to the LiDAR is obtained. Based on this distance, the median point p1 in the point cloud data is calculated, and the first distance at the first time step is obtained based on the distance from the median point p1 to the LiDAR. Similarly, after obtaining the point cloud data at the second time step, the second distance is obtained using the same method.

[0079] Specifically, during the robot's operation, at the first moment, the robot's first operating speed is obtained, and the first distance is calculated based on the current point cloud data. At the second moment, the robot's second operating speed is obtained, and the second distance is calculated based on the current point cloud data. It is then determined whether the first and second distances are equal.

[0080] Because the lidar is tilted, if the first distance equals the second distance, it indicates that the mobile robot is in a position similar to... Figure 10 In the state shown, the second distance is taken as the new first distance, and the second running speed is taken as the new first running speed. The second running speed and the second distance are obtained again, and it is determined whether the first distance and the second distance are equal.

[0081] If the first distance is greater than the second distance, it means that the mobile robot is in the following position: Figure 11 The uphill position shown is then combined with the point cloud class of motion data to perform fuzzy motion control, thereby controlling the mobile robot, changing the robot's motion state, and optimizing the robot's motion.

[0082] Specifically, after determining that the first distance is greater than the second distance, the second distance is taken as the new first distance, and the second running speed is taken as the new first running speed. The second running speed and the second distance are then re-obtained, and the first distance and the second distance are compared to determine if they are equal. This process continues until the first distance is re-equal to the second distance. At this point, it indicates that the mobile robot is in a state of... Figure 12 The state shown.

[0083] After determining the mobile robot's location, Figure 12 After the state shown, the climbing angle is calculated. Where a is the climbing angle, d1 is the first distance, d2 is the second distance, v1 is the first running speed, v2 is the second running speed, t1 is the first time point, and t2 is the second time point, as shown below. Figure 13 As shown.

[0084] The robot employs acceleration and deceleration control strategies based on the climbing angle to ensure a smooth ascent.

[0085] Therefore, when the target feature location is uphill, fuzzy motion control can be performed by combining the point cloud class of motion data to control the mobile robot, change the robot's motion state, and optimize the robot's motion.

[0086] Correspondingly, if the first distance is less than the second distance, it indicates that the mobile robot is in a downhill position, and the control method during the downhill process is similar to that during the uphill process.

[0087] In one or more embodiments of the present invention, when the target feature position is a normal photovoltaic module position, the method further includes: if the length of the target line segment is greater than the length of the photovoltaic module and is continuous, then perform fuzzy control on the speed-regulating motor of the mobile robot to adjust the length of the target line segment; if the length of the target line segment cannot be adjusted to be equal to the length of the photovoltaic module, then control the mobile robot to overcome the obstacle; if the length of the target line segment is adjusted to be equal to the length of the photovoltaic module and is continuous, then execute the step of controlling the mobile robot to clean the corresponding photovoltaic module.

[0088] The aforementioned fuzzy control of the mobile robot's speed-regulating motor allows for acceleration and deceleration control of the robot, attempting to reduce the length of the target line segment. If the length of the target line segment cannot be adjusted to equal the length of the photovoltaic module, the mobile robot is considered stuck at an obstacle, and obstacle-crossing control is initiated.

[0089] Therefore, when the target feature position is a normal photovoltaic module position, the obstacle can be judged based on the length of the target line segment, and when it is judged to be an obstacle, the mobile robot can be controlled to overcome the obstacle.

[0090] In one or more embodiments of the present invention, when the target feature location is a normal photovoltaic module location, the method further includes: controlling the shuttle vehicle of the mobile robot to move to the edge of the corresponding photovoltaic module, and then performing the step of controlling the mobile robot to clean the corresponding photovoltaic module.

[0091] Specifically, after determining that the target feature location is a normal photovoltaic module location, the photovoltaic module to be cleaned is identified. Then, a shuttle vehicle is moved to the edge of the photovoltaic module to be cleaned using sub-meter level positioning. The laser radar of the mobile robot on the shuttle vehicle is used to scan the photovoltaic module, and the control is based on the length of the obtained line segment to ensure that the mobile robot's posture and distance are correct, and to ensure that the mobile robot is upright and does not drop the equipment during movement.

[0092] Therefore, when the target feature position is a normal photovoltaic module, the laser radar can be used to scan the photovoltaic module, and the control can be based on the scanning results to achieve the alignment of the shuttle vehicle and ensure the correct posture and distance of the mobile robot.

[0093] In summary, the mobile robot control method of this invention acquires point cloud data collected by the LiDAR on the mobile robot, obtains target line segments based on the point cloud data, and determines the target feature position of the mobile robot based on the target line segments. This method achieves target feature position determination by processing only the line segments, resulting in lower processing difficulty, less computational requirements, and reduced costs. Furthermore, using feature recognition to complete position recognition during robot operation further reduces computational requirements and costs. Moreover, it can achieve feature scene recognition by fusing line segments with distances fitted from the point cloud with robot motion control data, eliminating the need for mapping. The matching library only involves line segment lengths, making it simple and universal. Furthermore, the use of only LiDAR as the sensor further reduces costs.

[0094] Furthermore, the present invention proposes an electronic device.

[0095] In this embodiment of the invention, the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the above-described control method for the mobile robot.

[0096] The electronic device of this invention, by implementing the aforementioned mobile robot control method, can obtain the target feature position simply by processing line segments. This reduces processing difficulty, computational requirements, and costs. Furthermore, using feature recognition to identify the robot's position during operation further reduces computational demands and costs. Moreover, feature scene recognition can be achieved solely through the fusion of line segments with distances fitted from point clouds and robot motion control data, eliminating the need for mapping. The matching library only involves line segment lengths, making it simple and universal. Furthermore, the use of only LiDAR sensors further reduces costs.

[0097] Furthermore, the present invention proposes a control device for a mobile robot.

[0098] Figure 14 This is a structural block diagram of the control device for a mobile robot according to an embodiment of the present invention.

[0099] like Figure 14 As shown, the control device 100 for the mobile robot includes: an acquisition module 101 for acquiring point cloud data collected by a lidar; a calculation module 102 for obtaining a target line segment based on the point cloud data; a determination module 103 for determining the target feature position of the robot based on the target line segment; and a control module 104 for controlling the mobile robot based on the target feature position.

[0100] It should be noted that other specific embodiments of the control device for the mobile robot in this invention can be found in the control method for the mobile robot described above.

[0101] The mobile robot control device of this invention acquires point cloud data collected by a LiDAR on the mobile robot, obtains target line segments based on the point cloud data, and determines the target feature position of the mobile robot based on the target line segments. This achieves target feature position determination by processing only the line segments, resulting in lower processing difficulty, less computational requirements, and reduced costs. Furthermore, using feature recognition to complete position recognition during robot operation further reduces computational requirements and costs. Moreover, feature scene recognition can be achieved by fusing line segments with distances fitted from the point cloud with robot motion control data, eliminating the need for mapping. The matching library only involves line segment lengths, making it simple and universal. Furthermore, the sensor only needs to be a LiDAR, further reducing costs.

[0102] Furthermore, the present invention proposes a mobile robot.

[0103] Figure 15 This is a structural block diagram of the mobile robot according to an embodiment of the present invention.

[0104] like Figure 15 As shown, the mobile robot 10 includes the aforementioned mobile robot control device 100.

[0105] The mobile robot of this invention, through the control device described in the previous embodiment, can acquire point cloud data collected by the lidar on the mobile robot, obtain target line segments based on the point cloud data, and determine the target feature position of the mobile robot based on the target line segments. This achieves target feature position determination by processing only the line segments, resulting in lower processing difficulty, less computational requirements, and reduced costs. Furthermore, using feature recognition to complete position recognition during robot operation further reduces computational requirements and costs. Moreover, feature scene recognition can be achieved by fusing line segments with distances fitted from the point cloud with robot motion control data, eliminating the need for mapping. The matching library only involves line segment lengths, making it simple and universal. Furthermore, the use of only lidar as the sensor further reduces costs.

[0106] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein can be considered as a ordered list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0107] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0108] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0109] In the description of this specification, the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and should not be construed as limiting the present invention.

[0110] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0111] In this specification, unless otherwise stated, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly defined. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0112] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0113] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A control method for a mobile robot, characterized in that, The mobile robot is equipped with a lidar, and the method includes: Acquire the point cloud data collected by the lidar; The target line segment is obtained based on the point cloud data; The target feature position of the mobile robot is determined based on the target line segment, and the mobile robot is controlled based on the target feature position. The step of obtaining the target line segment based on the point cloud data includes: Connect any two points in the point cloud data to obtain a set of lines; for at least a portion of the lines in the set of lines, calculate the percentage of interior points for each line, where the percentage of interior points is the ratio of the number of interior points to all points in the point cloud data, and the interior points are points in the point cloud data whose distance to the corresponding line is less than a first distance threshold; determine the target line from the set of lines based on the percentage of interior points; and perform a truncation operation on the target line according to the boundary of the point cloud data to obtain the target line segment. Determining the target feature position of the mobile robot based on the target line segment includes: The target line segment is matched with a preset feature library, wherein the preset feature library includes a first preset feature position and its corresponding feature line segment; when there is a feature line segment in the preset feature library that successfully matches the target line segment, the first preset feature position corresponding to the feature line segment is taken as the target feature position, wherein the first preset feature position includes at least one of the following: stop position, commutation position, normal cable tray position, and normal photovoltaic module position.

2. The control method for a mobile robot according to claim 1, characterized in that, Before obtaining the set of lines, the point cloud data is also filtered so that the distance between any point in the filtered point cloud data and its nearest point is less than a second distance threshold.

3. The control method for a mobile robot according to claim 1, characterized in that, The target line is the line corresponding to the percentage of the interior point with the largest value.

4. The control method for a mobile robot according to claim 1, characterized in that, The step of determining the target feature position of the mobile robot based on the target line segment further includes: When there is no feature segment in the preset feature library that successfully matches the target line segment, if the target line segment meets the conditions corresponding to the second preset feature position, then the corresponding second preset feature position is taken as the target feature position.

5. The control method for a mobile robot according to claim 4, characterized in that, The second preset feature location includes at least one of an uphill location, a downhill location, a faulty cable tray location, and a faulty photovoltaic module location. Controlling the mobile robot based on the target feature location includes: When the target feature location is the stop position, the faulty bridge position, or the faulty photovoltaic module position, control the mobile robot to stop. When the target feature position is the reversing position, control the mobile robot to reverse direction; When the target feature is located at the bridge position, control the mobile robot to move along the bridge position; When the target feature location is the normal photovoltaic module location, the mobile robot is controlled to clean the corresponding photovoltaic module; When the target feature is located on an uphill slope, control the mobile robot to go uphill; When the target feature is located at the downhill position, control the mobile robot to go downhill.

6. The control method for a mobile robot according to claim 5, characterized in that, The condition corresponding to the uphill position is that the target line segment becomes shorter as the mobile robot moves. Controlling the mobile robot to go uphill includes: At the first moment, the first distance between the mobile robot and the road segment ahead is obtained based on the current point cloud data, and the first running speed of the mobile robot at the first moment is obtained; At the second moment after the first moment, the second distance between the mobile robot and the road segment ahead is obtained based on the current point cloud data, and the second running speed of the mobile robot at the second moment is obtained; The climbing angle is calculated based on the first distance, the first running speed, the second distance, the second running speed, the first time moment, and the second time moment. The mobile robot is accelerated based on the climbing angle.

7. The control method for a mobile robot according to claim 5, characterized in that, When the target feature location is a normal photovoltaic module location, the method further includes: If the length of the target line segment is greater than the length of the photovoltaic module and is continuous, then fuzzy control is applied to the speed-regulating motor of the mobile robot to adjust the length of the target line segment. If the length of the target line segment cannot be adjusted to be equal to the length of the photovoltaic module, then the mobile robot is controlled to overcome the obstacle; If the length of the target line segment is adjusted to be equal to the length of the photovoltaic module and continuous, then the step of controlling the mobile robot to clean the corresponding photovoltaic module is executed.

8. The control method for a mobile robot according to claim 5, characterized in that, When the target feature location is a normal photovoltaic module location, the method further includes: The mobile robot's shuttle vehicle is controlled to move to the edge of the corresponding photovoltaic module, and then the step of controlling the mobile robot to clean the corresponding photovoltaic module is performed.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the control method for the mobile robot as described in any one of claims 1-8.

10. A control device for a mobile robot, characterized in that, The device includes: The acquisition module is used to acquire point cloud data collected by the lidar. The calculation module is used to obtain the target line segment based on the point cloud data; The determination module is used to determine the target feature position of the robot based on the target line segment; A control module is used to control the mobile robot based on the target feature position; The step of obtaining the target line segment based on the point cloud data includes: Connect any two points in the point cloud data to obtain a set of lines; for at least a portion of the lines in the set of lines, calculate the percentage of interior points for each line, where the percentage of interior points is the ratio of the number of interior points to all points in the point cloud data, and the interior points are points in the point cloud data whose distance to the corresponding line is less than a first distance threshold; determine the target line from the set of lines based on the percentage of interior points; and perform a truncation operation on the target line according to the boundary of the point cloud data to obtain the target line segment. Determining the target feature position of the mobile robot based on the target line segment includes: The target line segment is matched with a preset feature library, wherein the preset feature library includes a first preset feature position and its corresponding feature line segment; when there is a feature line segment in the preset feature library that successfully matches the target line segment, the first preset feature position corresponding to the feature line segment is taken as the target feature position, wherein the first preset feature position includes at least one of the following: stop position, commutation position, normal cable tray position, and normal photovoltaic module position.

11. A mobile robot, characterized in that, Includes the control device for the mobile robot as described in claim 10.

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