Obstacle detection method of mobile robot

Abstract

The invention discloses an obstacle detection method for a mobile robot, and belongs to the technical field of obstacle detection, the mobile robot is provided with a line laser sensor, the line laser sensor is used for emitting line laser inclining downwards relative to the horizontal plane, and the method comprises the steps that light spot information of the line laser emitted by the line laser sensor is acquired; determining a laser spot and an obstacle point set on the ground in the emitting direction of the line laser sensor according to the light spot information; determining the distance between each obstacle point in the obstacle point set and the corresponding projection point; and determining whether an obstacle corresponding to the obstacle point set is an obstacle to be cleaned or not according to the distance between each obstacle point and the corresponding projection point. According to the method, a scheme for detecting obstacles in a specific size range by using the line laser is realized, and a relatively good obstacle detection effect is achieved.

Description

Technical Field

[0001] This application belongs to the technical field of obstacle detection, and particularly relates to an obstacle detection method for a mobile robot. Background Art

[0002] With the continuous maturity of various technologies of mobile robots such as floor-sweeping robots, mobile robots have become a powerful assistant for household cleaning. In order to further improve the cleaning effect and efficiency of mobile robots, there are mainly the following several obstacle detection schemes:

[0003] 1. Detection scheme based on optical sensors: An optical sensor is a method of detecting an object through optical principles. A mobile robot uses an optical sensor to emit light and receive the reflected light, and determines the type and position of the obstacle by analyzing information such as the intensity and color of the reflected light. Common optical sensors include infrared sensors, ultraviolet sensors, etc. These optical sensors have characteristics such as fast speed, high accuracy, and non-contact, and are suitable for obstacle detection in different scenarios. However, due to the relatively complex types of obstacles of mobile robots, it is easy to cause pollution to the dust channels, form dirt on the inner wall surface, and block the light of the sensor, which will seriously affect the detection effect of the optical sensor.

[0004] 2. Detection scheme based on ultrasonic waves: Ultrasonic detection is a method of detecting an object through ultrasonic waves. A mobile robot uses an ultrasonic sensor to emit ultrasonic waves and receive the reflected waves, and determines the distance and shape of the obstacle by analyzing information such as the time and phase of the reflected waves. Ultrasonic detection has advantages such as strong penetration and strong anti-interference ability, and is suitable for obstacle detection of different materials. However, the limitation of ultrasonic waves is that it cannot effectively distinguish between obstacle materials and carpet materials, which is easy to cause no detection, and the cost of the ultrasonic sensor itself is relatively high.

[0005] 3. Detection scheme based on image recognition: Image recognition is a method of taking an image of an object through a camera and then using image processing technology to identify the object. After a mobile robot captures an image of an obstacle through a camera, it can use image processing algorithms for preprocessing, feature extraction, and classification to identify different types of obstacles. Common image recognition algorithms include traditional methods and deep learning algorithms. The traditional method extracts image features through steps such as color space conversion, edge detection, and morphological processing, while the deep learning algorithm autonomously learns image features through models such as convolutional neural networks. However, image recognition has high requirements for computing resources, and there are many types of obstacles with different shapes, colors, etc. A large sample library is required for algorithm training, and image recognition is easily affected by lighting conditions and is difficult to use in a darker environment.

[0006] 4. Olfactory-based detection scheme: Olfactory sensors generally adopt electrochemical sensors. Their working principle is to make the gas to be detected undergo an oxidation-reduction reaction on the electrode to generate an electrolytic current, and the concentration of the gas to be detected is determined by detecting the magnitude of the current. However, currently, the types of chemical substances that olfactory sensors can detect are limited, the requirements for the concentration of specific gases are relatively high, and a reaction time is required, resulting in a certain lag, and the cost is relatively high.

[0007] 5. Detection scheme based on a micro-motion sensor: The micro-motion sensor can sense the collision and impact signals caused by obstacles in the air duct on the sensor. However, the micro-motion sensor is easily contaminated and affected by air flow fluctuations, and its sensitivity and detection effect are limited. Summary of the Invention

[0008] This application provides one to solve at least one of the above technical problems.

[0009] The technical solution adopted in this application is as follows:

[0010] An obstacle detection method for a mobile robot, where the mobile robot is provided with a line laser sensor for emitting a line laser inclined downward relative to the horizontal plane, including: obtaining the spot information of the line laser emitted by the line laser sensor; determining the laser spot on the ground in the emission direction of the line laser sensor and the obstacle point set in the emission direction of the line laser sensor according to the spot information; determining the distance between each obstacle point in the obstacle point set and the corresponding projection point, where the projection point is the intersection of the extension line of the laser corresponding to the obstacle point and the straight line where the laser spot on the ground is located; determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the distance between each obstacle point and the corresponding projection point.

[0011] In a possible implementation manner of the specification of this application, the determining the laser spot on the ground in the emission direction of the line laser sensor and the obstacle point set in the emission direction of the line laser sensor according to the spot information includes: performing a straight line detection on the spot of the line laser according to the spot information; determining the spot on the detected straight line as the laser spot on the ground in the emission direction of the line laser sensor; and determining the spot not on the straight line as an obstacle point.

[0012] In a possible implementation manner of the specification of this application, the determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the distance between each obstacle point and the corresponding projection point includes: screening the cleanable obstacle points in the obstacle point set according to a preset distance threshold and the distance between each obstacle point and the corresponding projection point; and determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the distance between the cleanable obstacle point and the corresponding projection point.

[0013] In a possible implementation manner of the specification of the present application, screening the cleanable obstacle points in the obstacle point set includes: if the distance between any obstacle point in the obstacle point set and the corresponding projection point is less than or equal to the preset distance threshold, determining the any obstacle point as the cleanable obstacle point; if the distance between any obstacle point in the obstacle point set and the corresponding projection point is greater than the preset distance threshold, determining the any obstacle point as a non-cleanable obstacle point.

[0014] In a possible implementation manner of the specification of the present application, when any obstacle point is determined as a non-cleanable obstacle point, the method further includes: assigning a special value to the distance between the non-cleanable obstacle point and the corresponding projection point.

[0015] In a possible implementation manner of the specification of the present application, determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the distance between the cleanable obstacle point and the corresponding projection point includes: calculating the distance difference between the distances between adjacent cleanable obstacle points and the corresponding projection points; calculating the obstacle detection parameter corresponding to the obstacle point set according to the distance difference; and determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the obstacle detection parameter.

[0016] In a possible implementation manner of the specification of the present application, calculating the distance difference between the distances between adjacent cleanable obstacle points and the corresponding projection points includes: calculating the distance difference between the distances between adjacent obstacle points in the obstacle point set and the corresponding projection points; when calculating, if the distance between any obstacle point in the adjacent obstacle points and the corresponding projection point is a special value, the distance difference between the distances between the adjacent obstacle points and the corresponding projection points is calculated as 0.

[0017] In a possible implementation manner of the specification of the present application, determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the obstacle detection parameter includes: if the obstacle detection parameter is less than or equal to a preset obstacle detection threshold, determining that the obstacle corresponding to the obstacle point set is not an obstacle to be cleaned; if the obstacle detection parameter is greater than the preset obstacle detection threshold, determining that the obstacle corresponding to the obstacle point set is an obstacle to be cleaned.

[0018] In a possible implementation manner of the specification of the present application, after determining that the obstacle corresponding to the obstacle point set is an obstacle to be cleaned, the method further includes: calculating the driving duration required for the mobile robot to drive to the position of the obstacle to be cleaned through the current position and the current driving speed of the mobile robot; and determining the gear shift time point of the cleaning component of the mobile robot according to the driving duration.

[0019] In a possible implementation manner of the specification of this application, after determining that the obstacle corresponding to the obstacle point set is an obstacle to be cleaned, the method further includes: determining the size of the obstacle to be cleaned according to the distance between each obstacle point and the corresponding projection point; determining the speed to be traveled by the mobile robot or the gear to be changed by the cleaning component of the mobile robot according to the size of the obstacle to be cleaned.

[0020] Due to the adoption of the above technical solution, the beneficial effects obtained by this application are as follows:

[0021] 1. Using the spot information of the line laser emitted by the line laser sensor to determine the obstacle point set corresponding to the mobile robot, and then using the distance between each obstacle point in the point set and the corresponding projection point to determine whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned, realizing the obstacle detection scheme using the line laser, and achieving a good detection effect, solving the problem of limited detection effect in the traditional obstacle detection scheme. At the same time, using the line laser sensor can also effectively reduce the cost of obstacle detection.

[0022] 2. When using the line laser for obstacle detection, the garbage that the mobile robot can suck in is screened out by presetting a distance threshold, and then the garbage to be cleaned by the mobile robot is identified through a preset obstacle detection threshold. Without increasing the calculation amount, an obstacle detection method for a specific size range is realized, and only by setting the threshold for detection, the simplicity and feasibility of the detection scheme are also ensured.

[0023] 3. When detecting an obstacle to be cleaned, according to the position of the obstacle, the position of the robot, and the current traveling speed of the robot, the time point when the robot reaches the obstacle position can be determined, thereby providing a basis for the action time of the cleaning component of the robot. When the robot has not reached the obstacle position, there is no need to turn on the strong cleaning mode, which can also play a role in energy saving and consumption reduction of the robot, thereby prolonging the battery life of the robot.

[0024] 4. When detecting an obstacle to be cleaned, the size of the obstacle can also be determined according to the distance between each obstacle point and the corresponding projection point. Then, the gear to be changed by the cleaning component of the robot and the speed to be traveled by the robot can be adjusted according to different sizes, so as to realize different cleaning schemes for obstacles of different sizes, providing a guarantee for obtaining a better cleaning effect. Description of the Drawings

[0025] The drawings described herein are used to provide a further understanding of this application and constitute a part of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0026] Figure 1 Flowchart of an obstacle detection method for a mobile robot provided by an embodiment of the present application;

[0027] Figure 2 Front view of a mobile robot equipped with a wired laser sensor provided by an embodiment of the present application;

[0028] Figure 3 Side view of a mobile robot equipped with a wired laser sensor provided by an embodiment of the present application;

[0029] Figure 4 Schematic diagram of a line laser spot on a clean ground provided by an embodiment of the present application;

[0030] Figure 5 Schematic diagram of a line laser spot on a ground with obstacles provided by an embodiment of the present application;

[0031] Figure 6 Schematic diagram of a calculation method for the distance between an obstacle point and its corresponding projection point provided by an embodiment of the present application;

[0032] Figure 7 Schematic diagram of a cleaning component of a mobile robot provided by an embodiment of the present application. Detailed implementation manners

[0033] In order to more clearly illustrate the overall concept of the present application, the following will be described in detail by way of examples in conjunction with the accompanying drawings of the specification.

[0034] In the following description, many specific details are set forth in order to fully understand the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present application is not limited by the specific embodiments disclosed below.

[0035] In addition, in the description of the present application, it should be understood that the orientation or positional relationship indicated by terms such as "top", "bottom", "inside", "outside", "axial direction", "radial direction", "circumferential direction", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0036] In this application, unless otherwise clearly defined and limited, terms such as "installed", "connected", "linked", "fixed", etc. shall be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection, an electrical connection, or a communication connection; it can be directly connected, or indirectly connected through an intermediate medium, and can be the communication between two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0037] In this application, unless otherwise clearly defined and limited, the first feature being "on" or "under" the second feature can be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. In the description of this specification, the description with reference to terms such as "embodiment", "example", "an embodiment", "example", or "specific example" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0038] The obstacle detection method in the embodiments of this application will be explained below with reference to the drawings.

[0039] Figure 1 It is a flowchart of an obstacle detection method for a mobile robot provided in an embodiment of this application. As Figure 1 shown, the obstacle detection method in the embodiments of this application at least includes the following execution steps:

[0040] Step 101: Obtain the spot information of the line laser emitted by the line laser sensor.

[0041] The obstacle detection method provided in the embodiments of this application is implemented by using the line laser sensor provided on the mobile robot.

[0042] Figure 2 It is a front view of a mobile robot equipped with a line laser sensor provided in an embodiment of this application. In Figure 2 it, the line laser sensor exists in the form of a line laser module 1, and the line laser module 1 is arranged directly in front of the mobile robot 2 so as to emit a line laser to the ground in front of the mobile robot. At the same time, the line laser module 1 includes a line laser emitting component 11 and a line laser receiving component 12. Figure 3 It is a side view of a mobile robot equipped with a line laser sensor provided in an embodiment of this application. In Figure 3As can be seen, a lens 3 is provided on the front side of the line laser module 1, and a line laser receiving lens 13 is provided on the front side of the line laser receiving component 12. At the same time, in Figure 3 It can also be clearly seen that there is an included angle α between the emission angle of the line laser emitting component and the horizontal plane, and the emission angle is downward relative to the horizontal plane, so that the line laser sensor can emit line laser to the ground in front of the mobile robot.

[0043] When the mobile robot performs obstacle detection, the line laser sensor emits line laser that is inclined downward relative to the horizontal plane. When the line laser hits the ground in front of the mobile robot, a light spot will be formed.

[0044] Figure 4 This is a schematic diagram of the line laser light spot on a clean ground provided by an embodiment of the present application. As Figure 4 shown, the mobile robot 2 emits line laser 4 to the ground in front through the line laser sensor 1 provided thereon. The line laser 4 is a laser group composed of multiple line lasers, and light spots such as S1, S2, S3... Sn are formed on the ground in front.

[0045] It should be noted that since each laser of the line laser is in the same plane, the light spots formed by the line laser on the ground are also in the same plane. If the ground in front of the mobile robot is a clean ground, the light spots formed by the line laser on the ground are Figure 4 the same as the light spots shown in

[0046] Figure 5 This is a schematic diagram of the line laser light spot on an obstacle ground provided by an embodiment of the present application. From Figure 5 it can be seen that when there is an obstacle 5 on the ground in front of the mobile robot, some of the light spots P1, P2... Pn formed after the line laser 4 hits the ground fall on the ground, and some fall on the obstacle 5.

[0047] Obtain the light spot information formed after the line laser sensor emits line laser for use in the subsequent obstacle detection process. The method for obtaining the light spot information here can determine the position of the light spot through the reflected light received by the line laser receiving component installed on the mobile robot, or can be obtained by other means. The embodiments of the present application do not limit this.

[0048] Step 102: Determine the laser light spots and obstacle point sets on the ground in the emission direction of the line laser sensor according to the light spot information.

[0049] In a possible implementation manner of the embodiment of the present application, after obtaining the spot information of the line laser, a straight-line detection is performed on the spot. The straight-line detection process here can be implemented by adopting, for example, the Hough straight-line detection algorithm and any other existing straight-line detection methods. The present application does not limit this. After that, the detected straight line is used as the plane where the ground is located, the spots on the detected straight line are determined as the laser spots on the ground in the emission direction of the line laser sensor, and the spots not on the straight line are the spots formed by the laser falling on the obstacle. These spots are used as obstacle points, and an obstacle point set is constructed for storage.

[0050] As Figure 5 shown, L is the straight line obtained by performing straight-line detection on the spot, that is, the ground. The solid dots P3, P4, P7, P8... are the spots formed by the laser falling on the obstacle. These spots are irregularly distributed along with the obstacle. The hollow dots P1, P2, P5, P6... are the spots formed by the laser falling on the flat ground. All these spots are on the straight line L, and Figure 5 in l1, l2, l3, l4…ln are the optical paths of the emission light or reflected light corresponding to P1, P2, P3, P4…Pn, that is, the light distances from each spot to the line laser sensor.

[0051] Step 103: Determine the distance between each obstacle point in the obstacle point set and its corresponding projection point.

[0052] Project each obstacle point in the obstacle point set in its light direction, that is, extend the laser corresponding to each obstacle point, and obtain the intersection points of the extended line and the straight line where the laser spot on the ground is located after extension, and use these intersection points as the corresponding projection points of the obstacle points.

[0053] After that, calculate the distance between each obstacle point and its corresponding projection point. In an example of the present application, the distance between each obstacle point and its corresponding projection point here can be represented by the Euclidean distance between the obstacle point and the corresponding projection point, and the calculation of the Euclidean distance can be realized by constructing a coordinate system.

[0054] Specifically, taking the optical center of the line laser sensor as the origin, taking the front direction of the line laser sensor as the y-axis direction, and taking the direction perpendicular to the y-axis as the x-axis direction, construct a coordinate system as Figure 6 shown, in Figure 6Among them, the solid dots P3, P4, P7, P8... are obstacle points, the hollow dots P1, P2, P5, P6... are the light spots falling on the ground, the straight line L is the ground, and l1, l2, l3, l4…ln are the optical paths of the emitted light or reflected light corresponding to P1, P2, P3, P4…Pn, that is, the light distances from each light spot to the line laser sensor. After extending the lasers corresponding to the obstacle points P3, P4, P7, P8... in the light direction, the intersection points c3, c4, c7, c8... of the extension lines and the ground are obtained, that is, the projection points corresponding to each obstacle point. In this way, the distances between the obstacle points and the projection points are the d3, d4, d7, d8... shown by the thick line segments, that is, the Euclidean distances from c3 to P3, c4 to P4, c7 to P7, c8 to P8... In the current coordinate system, the coordinates of each obstacle point and the projection point are known. Using the coordinates of each point, the distances between the aforementioned obstacle points and the projection points can be calculated.

[0055] Step 104: Determine whether the obstacles corresponding to the obstacle point set are the obstacles to be cleaned according to the distances between each obstacle point and the corresponding projection point.

[0056] In a possible implementation manner of the embodiment of the present application, after obtaining the distances between each obstacle point and the corresponding projection point, the distances are first used to screen the obstacle points, so as to screen the obstacles that can be inhaled by the mobile robot on the ground through the screening of the obstacle points, that is, to screen out the obstacles that can be cleaned.

[0057] Specifically, the sizes of the obstacles usually present in the working scenario of the mobile robot are generally small. Therefore, the distance between the laser light spot on the obstacle surface, that is, the obstacle point, and its corresponding projection point is small. Therefore, the preset distance threshold of the distance between the obstacle point and the corresponding projection point can be determined according to the size of the largest obstacle that can be inhaled by the suction port of the mobile robot. Then, through the relationship between the preset distance threshold and the distances between each obstacle point and the corresponding projection point calculated above, the obstacle point set is screened, and the obstacle points corresponding to the obstacles that can be successfully inhaled by the mobile robot are screened out from it, so as to reduce the calculation amount when determining whether it is an obstacle to be cleaned later.

[0058] Further, compare the distance between any obstacle point in the obstacle point set and the corresponding projection point with a preset distance threshold. If the distance between any obstacle point and the corresponding projection point is less than or equal to the preset distance threshold, it indicates that the obstacle corresponding to this obstacle point can be sucked in by the mobile robot. At this time, determine any obstacle point as a cleanable obstacle point. If the distance between any obstacle point and the corresponding projection point is greater than the preset distance threshold, it indicates that the obstacle corresponding to this obstacle point is larger than the maximum garbage size that the suction port of the mobile robot can suck in, that is, it cannot be successfully sucked in. At this time, determine any obstacle point as a non-cleanable obstacle point.

[0059] In an example of the present application, if any obstacle point in the obstacle point set is determined as a non-cleanable obstacle point, an assignment process is performed on this any obstacle point, and a special value is assigned to it. This special value can be used in subsequent calculations of the distance difference. For specific details, refer to the relevant parts described below. This embodiment of the present application will not elaborate here.

[0060] After the screening is completed, the distance between the screened cleanable obstacle points and the corresponding projection points can be used to determine whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned. The specific determination process is as follows:

[0061] First, calculate the distance difference between the distances of adjacent cleanable obstacle points and the corresponding projection points. To facilitate the calculation of independent cleanable obstacle points (that is, cleanable obstacle points without adjacent points), it can be achieved by calculating the distance difference between the distances of adjacent obstacle points in the obstacle point set and the corresponding projection points, that is, by calculating Figure 6 the difference between d3 and d4, d7 and d8 in. At the same time, when calculating, if any obstacle point among the adjacent obstacle points in the obstacle point set is assigned a special value, at this time, calculate the distance difference of this adjacent obstacle point as 0.

[0062] Secondly, use the calculated distance difference to continue calculating the obstacle detection parameter corresponding to the obstacle point set. In an example of the present application, the obstacle detection parameter can be characterized by the information entropy E. Here, the calculation method of the information entropy E is to perform a weighted sum on the previously calculated distance difference, and the obtained value is the obstacle detection parameter.

[0063] After that, use the relationship between the obstacle detection parameter and the preset obstacle detection threshold to determine whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned.

[0064] In an example of the present application, ideally, when the ground cleaned by the mobile robot is close to horizontal and there is no garbage on the ground, the information entropy E is close to 0. In fact, since the ground is not completely horizontal, for example, there is a carpet on the ground or the ground is a wavy wooden floor, these factors will make the information entropy E greater than 0 and close to a certain threshold, and this threshold can be set as the preset obstacle threshold. Then, it can be considered that when the obstacle detection parameter, that is, the information entropy E, is greater than the preset obstacle threshold, the obstacle corresponding to the obstacle point set is the obstacle to be cleaned, and when the obstacle detection parameter, that is, the information entropy E, is less than or equal to the preset obstacle detection threshold, the obstacle corresponding to the obstacle point set is not the obstacle to be cleaned.

[0065] It should be noted that the aforementioned preset obstacle threshold can be determined through experiments. The experimental determination method is as follows: Select a wavy wooden floor that is relatively common or has certain representativeness in daily life as the experimental ground. Then, make the mobile robot emit line laser on the experimental ground, calculate the distance between the light spot of the line laser and its corresponding projection point, and then calculate the information entropy E by performing weighted summation on this distance. Record multiple groups of data at different positions and calculate the value of the information entropy E, and take its maximum value as the preset obstacle detection threshold.

[0066] In a possible implementation manner in the embodiment of the present application, after the mobile robot detects the obstacle to be cleaned, it will clean the obstacle to be cleaned. In order to reduce the power consumption of the mobile robot, the driving duration required for the mobile robot to reach the obstacle to be cleaned can be judged based on the position of the robot, the position of the obstacle to be cleaned, and the current driving speed of the robot. Then, based on this driving duration and the time required for the gear shift of the cleaning component of the robot, it can be set that when the mobile robot reaches the position of the obstacle to be cleaned, the cleaning component completes the gear shift, that is, the gear shift time point of the cleaning component of the robot is set, so as to avoid the problem of increased power consumption caused by the mobile robot increasing the gear shift, and effectively realize the energy saving and consumption reduction of the robot.

[0067] At the same time, in a possible implementation manner in the embodiment of the present application, in order to improve the cleaning efficiency of the mobile robot for the obstacle to be cleaned, the size of the obstacle to be cleaned can also be classified by presetting multiple different obstacle detection thresholds. After determining the size of the obstacle to be cleaned, the expected driving speed of the mobile robot or the expected gear shift of the cleaning component can be determined according to this size.

[0068] For example, when the obstacle detection parameter, i.e., the information entropy E, is less than or equal to the first preset obstacle detection threshold E1, it is determined that the obstacle corresponding to the obstacle point set is not the obstacle to be cleaned; when the obstacle detection parameter, i.e., the information entropy E, is greater than the first preset obstacle detection threshold E1 and less than or equal to the second preset obstacle detection threshold E2, it is determined that the obstacle corresponding to the obstacle point set is a small obstacle with a slight severity level, and the traveling speed of the mobile robot is controlled to be reduced by 10% of the normal speed; when the obstacle detection parameter, i.e., the information entropy E, is greater than the second preset obstacle detection threshold E2 and less than or equal to the third preset obstacle detection threshold E3, it is determined that the obstacle corresponding to the obstacle point set is a medium obstacle with a medium severity level, and the traveling speed of the mobile robot is controlled to be reduced by 30% of the normal speed; when the obstacle detection parameter, i.e., the information entropy E, is greater than the third preset obstacle detection threshold E3 and less than or equal to the fourth preset obstacle detection threshold E4, it is determined that the obstacle corresponding to the obstacle point set is a large obstacle with a severe severity level, and the traveling speed of the mobile robot is controlled to be reduced by 50% of the normal speed. That is, when there is more garbage, the traveling speed of the robot is slowed down, and at the same time, the power of the cleaning component is increased or the gear of the cleaning component is adjusted to a higher level, so that the mobile robot can achieve a better cleaning effect, thereby improving the cleaning efficiency of the mobile robot.

[0069] Figure 7 Schematic diagram of a cleaning component of a mobile robot provided by an embodiment of the present application. In Figure 7 1 is a line laser sensor, 3 is a lens, 25 is a driven wheel, 26 is a left driving wheel, and 27 is a right driving wheel. Therefore, from Figure 7 it can be seen that the cleaning component is arranged at the bottom of the mobile robot 2 and includes, but is not limited to, a left brush 21, a right brush 22, a rotary brush 23, and a dust suction port 24. A dust suction fan is connected inside the dust suction port to form a negative pressure at the dust suction port to suck in obstacles. The left brush 21, the right brush 22, and the rotary brush 23 are all motor-driven components and are controlled by an embedded program to complete the set cleaning actions.

[0070] According to the technical solution disclosed in the present application, in the working scenario of a mobile robot equipped with a line laser sensor, garbage and other obstacles are set. When the mobile robot travels to a position close to the obstacle, the gear of the cleaning component will change to enhance the cleaning effect or the traveling speed of the robot will slow down. And for different sizes of garbage set in the working scenario, when the mobile robot travels to a position close to the obstacle, the change in the gear of the cleaning component or the change in the traveling speed may be inconsistent. The larger the size of the obstacle, the stronger the cleaning effect of the gear after the change or the slower the traveling speed of the robot.

[0071] What is not described in this application can be achieved by adopting or referring to existing technologies.

[0072] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0073] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for obstacle detection of a mobile robot, wherein the mobile robot is provided with a line laser sensor, and the line laser sensor is used to emit a line laser that is inclined downward relative to the horizontal plane, characterized in that, Including: Obtaining the spot information of the line laser emitted by the line laser sensor; Determining the laser spot on the ground in the emission direction of the line laser sensor and the obstacle point set in the emission direction of the line laser sensor according to the spot information; Determining the distance between each obstacle point in the obstacle point set and the corresponding projection point, where the projection point is the intersection point of the extension line of the laser corresponding to the obstacle point and the straight line where the laser spot on the ground is located; Determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the distance between each obstacle point and the corresponding projection point.

2. The method for obstacle detection of a mobile robot according to claim 1, characterized in that, The determining the laser spot on the ground in the emission direction of the line laser sensor and the obstacle point set in the emission direction of the line laser sensor according to the spot information includes: Performing straight line detection on the spot of the line laser according to the spot information; Determining the laser spot on the ground in the emission direction of the line laser sensor as the spot on the detected straight line; Determining the spots not on the straight line as obstacle points.

3. The method for obstacle detection of a mobile robot according to claim 1, characterized in that, The determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the distance between each obstacle point and the corresponding projection point includes: Screening the cleanable obstacle points in the obstacle point set according to a preset distance threshold and the distance between each obstacle point and the corresponding projection point; Determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the distance between the cleanable obstacle point and the corresponding projection point.

4. The method for obstacle detection of a mobile robot according to claim 3, characterized in that, The screening the cleanable obstacle points in the obstacle point set includes: If the distance between any obstacle point in the obstacle point set and the corresponding projection point is less than or equal to the preset distance threshold, determining the any obstacle point as the cleanable obstacle point; If the distance between any obstacle point in the obstacle point set and the corresponding projection point is greater than the preset distance threshold, determining the any obstacle point as a non-cleanable obstacle point.

5. The method for obstacle detection of a mobile robot according to claim 4, characterized in that, When any obstacle point is determined as a non-cleanable obstacle point, the method further includes: Assigning a special value to the distance between the non-cleanable obstacle point and the corresponding projection point.

6. The method for obstacle detection of a mobile robot according to claim 3, characterized in that, The determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the distance between the cleanable obstacle point and the corresponding projection point includes: Calculating the distance difference between the distances between adjacent cleanable obstacle points and the corresponding projection points; Calculating the obstacle detection parameter corresponding to the obstacle point set according to the distance difference; Determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the obstacle detection parameter.

7. The method for obstacle detection of a mobile robot according to claim 6, characterized in that, The calculating the distance difference between the distances between adjacent cleanable obstacle points and the corresponding projection points includes: Calculating the distance difference between the distances between adjacent obstacle points in the obstacle point set and the corresponding projection points; When calculating, if the distance between any obstacle point among the adjacent obstacle points and the corresponding projection point is a special value, the distance difference between the distances between the adjacent obstacle points and the corresponding projection points is calculated as 0.

8. The method for obstacle detection of a mobile robot according to claim 6, characterized in that, The determining whether the obstacle corresponding to the obstacle point set is an obstacle to be cleaned according to the obstacle detection parameter includes: If the obstacle detection parameter is less than or equal to a preset obstacle detection threshold, it is determined that the obstacle corresponding to the obstacle point set is not an obstacle to be cleaned; If the obstacle detection parameter is greater than the preset obstacle detection threshold, it is determined that the obstacle corresponding to the obstacle point set is an obstacle to be cleaned.

9. A method for obstacle detection of a mobile robot according to claim 1, characterized in that, After determining that the obstacle corresponding to the obstacle point set is an obstacle to be cleaned, the method further includes: Calculating the driving duration required for the mobile robot to drive to the position of the obstacle to be cleaned based on the current position and current driving speed of the mobile robot; Determining the gear shift time point of the cleaning component of the mobile robot according to the driving duration.

10. A method for obstacle detection of a mobile robot according to claim 1, characterized in that, After determining that the obstacle corresponding to the obstacle point set is an obstacle to be cleaned, the method further includes: Determining the size of the obstacle to be cleaned according to the distance between each obstacle point and the corresponding projection point; Determining the speed to be traveled by the mobile robot or the gear to be shifted of the cleaning component of the mobile robot according to the size of the obstacle to be cleaned.

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