Autonomous mobile device, method, storage medium and program product

Abstract

An autonomous mobile device, a method, a storage medium and a program product. The method for operating the autonomous mobile device comprises: acquiring a surrounding map of an autonomous mobile device (10) in a work area (S100); determining an area occupied by an obstacle in the surrounding map (S200); determining whether the obstacle is a discrete obstacle, wherein the discrete obstacle represents an obstacle whose occupied area in the surrounding map has a size less than a threshold size (S300); in response to determining that the obstacle is a discrete obstacle, calculating an obstacle distance that represents the distance between the autonomous mobile device (10) and the obstacle (S400); and comparing the obstacle distance with a safety distance threshold, and in response to the obstacle distance being less than or equal to the safety distance threshold, executing a discrete obstacle avoidance scheme by means of a moving assembly (102) to avoid the obstacle, wherein the safety distance threshold is a preset value for avoiding collisions between the autonomous mobile device (10) and the obstacle (S500).

Description

Autonomous mobile device, method, storage medium and program product TECHNICAL FIELD

[0001] The present disclosure relates to autonomous mobile devices, methods, storage media and program products, in particular to navigation and obstacle avoidance in a working area of an autonomous mobile device. BACKGROUND

[0002] With the continuous development of autonomous mobile device technology, various autonomous mobile devices have emerged for various purposes, including but not limited to cleaning robots (such as intelligent sweeping robots, intelligent mopping robots, window-cleaning robots), companion mobile robots (such as intelligent electronic pets, nanny robots), service mobile robots (such as reception robots in hotels, inns, meeting places), industrial inspection intelligent devices (such as power inspection robots, intelligent forklifts, etc.), security robots (such as household or commercial security robots), etc.

[0003] In order to complete a preset task such as cleaning in a set working area, an autonomous mobile device needs to obtain a map of the working area and determine the pose of the autonomous mobile device itself. A commonly used method for creating a map and determining a pose includes a simultaneous localization and mapping (SLAM) technique. In the technology of laser SLAM, a 360-degree rotating scanning mechanical laser radar (hereinafter referred to as a 360-degree laser radar) is often used to detect object information in the surrounding working area to achieve the creation of a map and the determination of a pose. Specifically, the 360-degree laser radar, when rotating around a rotation axis perpendicular to the ground, scans the surrounding in the horizontal plane by emitting laser and receiving reflected laser reflected back by objects (hereinafter referred to as or obstacles) in the working area, thereby detecting objects in the surrounding environment and achieving SLAM.

[0004] However, the 360-degree laser radar has many defects, such as high cost, including manufacturing cost and debugging cost; and in operation, the rotation of the mechanical turntable causes physical wear, resulting in serious wear and tear of the laser radar device, with an average failure time of only 1000-3000 hours; and the rotating mechanism of the mechanical laser radar has potential failure risks, for example, its rotating mechanism is easily entangled by light-weight and easily-entangled debris such as hair and thread, which causes it to be prone to failure. In addition, the 360-degree laser radar is usually arranged on the top of the autonomous mobile device and protrudes from the main body of the autonomous mobile device, which increases the height of the autonomous mobile device and can affect the movement of the autonomous mobile device in the environment. For example, an autonomous mobile device with a protruding 360-degree laser radar tower installed on the top may be stuck at the bottom of a sofa when entering the bottom of the sofa for cleaning due to the protruding 360-degree laser radar tower. SUMMARY

[0005] The present disclosure aims to overcome or at least alleviate the deficiencies existing in the prior art, and provide an autonomous mobile device, a method, a storage medium and a program product.

[0006] According to a first aspect of the present disclosure, an autonomous mobile device with a single-point laser device is provided, characterized in that the single-point laser device is fixedly mounted on the autonomous mobile device relative to a predetermined direction of the autonomous mobile device, and the autonomous mobile device comprises: a mobile assembly configured to move the autonomous mobile device on a plane in a working area; the single-point laser device fixedly mounted on the autonomous mobile device relative to the predetermined direction of the autonomous mobile device; a memory storing instructions; and a processor configured to invoke the instructions to perform the following steps: S100: obtaining a perimeter map of the autonomous mobile device in the working area; S200: determining an area occupied by an obstacle in the perimeter map; S300: judging whether the obstacle is a discrete obstacle, wherein the discrete obstacle represents an obstacle whose occupied area in the perimeter map is smaller than a threshold size; S400: in response to judging that the obstacle is the discrete obstacle, calculating an obstacle distance representing a distance between the autonomous mobile device and the obstacle; and S500: comparing the obstacle distance with a safety distance threshold, and in response to the obstacle distance being smaller than or equal to the safety distance threshold, controlling the mobile assembly to perform a discrete obstacle avoidance scheme to avoid the obstacle, wherein the safety distance threshold is a preset value for avoiding collision between the autonomous mobile device and the obstacle.

[0007] In some implementations, the perimeter map is a local map selected from an existing historical global map of the working area.

[0008] In some implementations, the single-point laser device is configured to obtain distance data, wherein the distance data represents a distance between the single-point laser device and an object in the working area in the predetermined direction. The processor is further configured to: obtain point cloud data corresponding to at least a portion of the working area according to the distance data; and generate or update the perimeter map according to the point cloud data.

[0009] In some implementations, the processor is further configured to: cause the autonomous mobile device to perform a spot rotation at one or more positions in the working area by the mobile assembly; and obtain at least a portion of the point cloud data during each of the spot rotations.

[0010] In some implementations, the perimeter map includes a location of the autonomous mobile device in the working area, and the perimeter map has a set shape and a set size, the set shape and the set size being determined according to at least one of: a motion parameter of the autonomous mobile device, a set time interval for selecting the perimeter map, a physical shape of the autonomous mobile device, and a user input.

[0011] In some implementations, the motion parameter of the autonomous mobile device includes a travel speed of the autonomous mobile device, wherein the set shape is a rectangle, the set size includes a side length of the rectangle, wherein a side length of a side of the rectangle that is parallel to a travel direction of the autonomous mobile device is proportional to the travel speed, the set shape is a circle, the set size includes a radius of the circle, wherein the radius of the circle is proportional to the travel speed, or the set shape is an ellipse, the set size includes a major axis of the ellipse, wherein the major axis of the ellipse is parallel to the travel direction of the autonomous mobile device, and the major axis of the ellipse is proportional to the travel speed.

[0012] In some implementations, the processor is further configured to update a global map of the working area with point cloud data obtained from distance data obtained by the single-point laser device.

[0013] In some implementations, the processor is further configured to generate a plurality of perimeter maps at set time intervals during travel of the autonomous mobile device, and perform steps S200 and subsequent steps based on each of the plurality of perimeter maps.

[0014] In some implementations, in a case where the set shape and the set size of the perimeter map exceed an edge of the global map, the edge of the global map is taken as an edge of the perimeter map.

[0015] In some implementations, the safety distance threshold is proportional to a travel speed of the autonomous mobile device.

[0016] In some implementations, the obstacle distance calculated in the step S400 represents a distance between the autonomous mobile device and the obstacle further comprises determining a plurality of predicted arrival positions where the autonomous mobile device is to arrive subsequently within the set time interval following the originally planned operating route of the autonomous mobile device, and calculating an estimated obstacle distance between the autonomous mobile device and the discrete obstacle at each of the plurality of predicted arrival positions, and the step S500 comprises comparing the plurality of estimated obstacle distances, comparing the smallest estimated obstacle distance with the safety distance threshold, determining a predicted evasive position in response to the smallest estimated obstacle distance being smaller than or equal to the safety distance threshold, wherein the estimated obstacle distance between the autonomous mobile device and the discrete obstacle at the predicted evasive position is greater than or equal to the safety distance threshold, and executing the discrete obstacle evading scheme in response to the autonomous mobile device arriving at the predicted evasive position.

[0017] In some implementations, the discrete obstacle evading scheme comprises causing the autonomous mobile device to decelerate and / or turn by the moving assembly.

[0018] In some implementations, the discrete obstacle evading scheme comprises causing the autonomous mobile device to keep a predetermined evasive distance from the discrete obstacle and travel around the discrete obstacle by the moving assembly.

[0019] According to a second aspect of the present disclosure, there is provided a method performed by an autonomous mobile device, the autonomous mobile device comprising a moving assembly for moving the autonomous mobile device on a plane in a working area and a processor, the method comprising the following steps: S100: obtaining, by the processor, a perimeter map of the autonomous mobile device in the working area; S200: determining, by the processor, an area occupied by an obstacle in the perimeter map; S300: determining, by the processor, whether the obstacle is a discrete obstacle, wherein the discrete obstacle represents an obstacle whose occupied area in the perimeter map is smaller than a threshold size; S400: in response to determining that the obstacle is the discrete obstacle, calculating, by the processor, an obstacle distance representing a distance between the autonomous mobile device and the obstacle; and S500: comparing, by the processor, the obstacle distance with a safety distance threshold, and in response to the obstacle distance being smaller than or equal to the safety distance threshold, executing, by the moving assembly, a discrete obstacle evading scheme to avoid the obstacle, wherein the safety distance threshold is a preset value for avoiding collision between the autonomous mobile device and the obstacle.

[0020] In some implementations, the autonomous mobile device further includes a single-point laser device fixedly mounted on the autonomous mobile device relative to a predetermined direction of the autonomous mobile device, the single-point laser device configured to obtain distance data, wherein the distance data represents a distance between the single-point laser device and an object in the working area in the predetermined direction. The method further includes obtaining point cloud data corresponding to at least a portion of the working area according to the distance data, and generating or updating the perimeter map according to the point cloud data.

[0021] In some implementations, the method further includes causing the autonomous mobile device to spin on the spot at one or more locations in the working area by a moving component, and obtaining at least a portion of the point cloud data during each of the spins on the spot.

[0022] In some implementations, the method further includes updating a global map of the working area with point cloud data obtained from distance data obtained by the single-point laser device.

[0023] In some implementations, the method further includes generating a plurality of perimeter maps at set time intervals during travel of the autonomous mobile device by the processor, and for each of the plurality of perimeter maps, performing steps S200 and subsequent steps.

[0024] According to a third aspect of the present disclosure, there is provided a computer-readable storage medium, characterized in that the storage medium stores a computer program including instructions which, when executed by a processor of an autonomous mobile device, implement the steps of the method of the second aspect described above.

[0025] According to a fourth aspect of the present disclosure, there is provided a computer program product, characterized in that the program product includes instructions which, when executed by a processor of an autonomous mobile device, implement the steps of the method of the second aspect described above.

[0026] According to the technical solutions of the present disclosure, in the case of using a single-point laser device, identification and avoidance of discrete obstacles such as chair legs in the working area can be achieved, thereby avoiding unwanted collisions between the autonomous mobile device and the obstacles, and avoiding damage to the autonomous mobile device and / or the objects. In addition, since such identification and avoidance can be achieved without using a 360-degree laser radar, the consumption of computing power can be reduced.

[0027] Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0028] FIG. 1 illustrates a block diagram of components in an autonomous mobile device according to an exemplary embodiment.

[0029] FIG. 2 illustrates a schematic diagram of an autonomous mobile device installed with a single-point laser device according to an exemplary embodiment.

[0030] FIG. 3 illustrates a schematic diagram of an autonomous mobile device operating in a work area according to an exemplary embodiment.

[0031] FIG. 4 illustrates a flowchart of an operating method of an autonomous mobile device according to an exemplary embodiment.

[0032] FIG. 5A illustrates a schematic diagram of a surrounding map according to an exemplary embodiment.

[0033] FIGS. 5B to 5D illustrate schematic diagrams of a surrounding map according to another exemplary embodiment.

[0034] FIGS. 6A to 6E illustrate different setting ways of shape and size of a surrounding map according to an exemplary embodiment.

[0035] FIG. 7 illustrates determination of a plurality of estimated obstacle distances at a plurality of possible positions according to an exemplary embodiment. DETAILED DESCRIPTION

[0036] Various exemplary embodiments, features, and aspects of the present disclosure will be described hereinafter with reference to the accompanying drawings. The same reference numbers in different drawings denote the same or similar elements. Although various aspects of embodiments are illustrated in the drawings, the drawings are not necessarily drawn to scale unless specifically noted.

[0037] The term "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0038] In addition, for the purpose of convenience and brevity, detailed descriptions of well-known functions and structures incorporated in the disclosure will be omitted. It will be appreciated that those skilled in the art can practice the present disclosure without these specific details. In some instances, well-known methods, structures, and techniques have not been described in detail in order to avoid obscuring the subject matter of the present disclosure.

[0039] FIG. 1 shows a block diagram of components in an autonomous mobile device according to an example embodiment. The autonomous mobile device 10 can be an intelligent self-moving device performing a task, such as an industrial inspection intelligent device performing a patrolling task, a logistics robot or AGV (Automated Guided Vehicle) forklift performing a logistics carrying task, an intelligent shopping cart performing a following task, a cleaning robot performing a floor cleaning task, etc. The autonomous mobile device 10 includes a moving component 102 for enabling the autonomous mobile device 10 to move in a working area, a single-point laser device 104 fixedly mounted on the autonomous mobile device 10, a memory 106 storing instructions, and a processor 108 for implementing various functions of the autonomous mobile device 10. In addition, the autonomous mobile device 10 can also include other components for completing a task, such as a brush head for cleaning or a tray for carrying objects, or a display for displaying information to a user, etc., which are not limited by the present disclosure. In addition, the autonomous mobile device 10 can also include a battery for storing electric energy, a motion sensor 110 for detecting motion parameters, and a communication device for communicating with a base station or other devices, etc., which are not limited by the present disclosure.

[0040] FIG. 2 shows a schematic diagram of an autonomous mobile device mounted with a single-point laser device according to an example embodiment. The single-point laser device 104 is fixedly mounted on the autonomous mobile device 10 in a predetermined direction relative to the autonomous mobile device 10. In the illustrative embodiment, the single-point laser device 104 is fixedly mounted on a front end of the autonomous mobile device 10 and faces a front direction of the autonomous mobile device 10. The single-point laser device 104 emits a laser (a point laser beam) towards the predetermined direction (in this embodiment, the front direction), and the reflected laser after passing through an obstacle in the predetermined direction can be received by the single-point laser device 104. The wavelength range of the laser emitted by the single-point laser device 104 can be visible light, or invisible light, such as infrared light. Based on the emitted laser and the received reflected laser, distance data between the autonomous mobile device 10 and an obstacle in the predetermined direction in the working area can be calculated in real time.

[0041] In exemplary embodiments, a time of flight (TOF) method is employed to calculate the distance data between the autonomous mobile device 10 and the obstacles. A single-point laser device 104 (such as the STP-23L by Leica, the LR-X series by Keyence, etc.) integrates a laser emitter, a laser receiver, and a processing chip that is capable of recording the time of emission of the laser and the time of reception of the reflected laser, and accurately calculates the time difference of the same laser beam from the time of emission to the time of reception, i.e., the time of flight of the laser. Since the speed of light is known, the distance between the autonomous mobile device 10 and the obstacle can be calculated based on the speed of light and the aforementioned time difference, and the distance data can be directly output. In some embodiments, the single-point laser device 104 is communicatively connected to the processor 108, and the distance data output by the single-point laser device 104 is received by the processor 108. In other embodiments, a triangulation method can also be employed to calculate the distance, and the functions of detection, data processing, and calculation can also be completed by other elements such as the circuit on the single-point laser device 104. The present disclosure does not make specific limitations in this regard.

[0042] The processor 108 can also record the orientation of the autonomous mobile device when the point laser beam is emitted, thereby determining the direction of the obstacle irradiated by the point laser beam relative to the autonomous mobile device. Based on the distance data and the relative direction of the obstacle relative to the autonomous mobile device, the relative coordinates of the irradiated part of the obstacle relative to the autonomous mobile device can be determined.

[0043] When the laser emitted by the single-point laser device 104 irradiates the obstacle, a "spot" of the laser is formed on the side surface of the obstacle facing the autonomous mobile device, which can be recorded as a "laser point". As described above, the laser point can represent the relative coordinates of the irradiated part of the object relative to the autonomous mobile device. When the autonomous mobile device 10 rotates, the single-point laser device 104 rotates with it and emits laser and receives reflected laser during this period. Thus, multiple distance data can be obtained, each of which can correspond to a specific orientation of the autonomous mobile device 10, i.e., during one rotation, the single-point laser device 104 can obtain multiple "laser points" in multiple orientations. The set of "laser points" separated by the single-point laser device 104 in the corresponding orientations and with the corresponding distance data constitutes a "point cloud"; and the relative coordinates of the point cloud relative to the single-point laser device 104 (i.e., the autonomous mobile device 10) constitute the point cloud data, which correspondingly represent the coordinate positions of the obstacles irradiated by the laser in the surrounding environment of the autonomous mobile device 10. Thus, without using a 360-degree laser radar but only using a single-point laser device, through the rotation of the autonomous mobile device in place, the point cloud data representing the coordinate positions of the surrounding obstacles can also be obtained, so as to detect the objects in the surrounding environment and achieve SLAM.

[0044] Figure 3 shows a schematic view of an autonomous mobile device operating in a working area according to an example embodiment. The autonomous mobile device 10 operates in a working area 20; typically, the working area 20 is an enclosed space defined by walls 22, usually an indoor space of a house, in which there are usually a plurality of furniture and appliances placed on the ground, such as a table 24, a chair 26 and a cabinet 28, etc. As an example, the autonomous mobile device 10 in this example is a cleaning robot operating in the indoor space. Since the map used by the autonomous mobile device 10 is usually a historical map that it has established during its operation in the same working area 20, and the height of the cleaning robot is usually around 100mm, and the height of the single-point laser device 104 mounted on the cleaning robot from the ground is in the range of 80mm to 180mm depending on the structure, the table 24 and the chair 26 detected at this height range in the map are represented by 241-244 for the table legs and 261-264 for the chair legs in Figure 3, which only occupy a very small area in the map (the area occupied by the table legs 241-244 or the chair legs 261-264 in the map is much smaller than the upper surface of the table 24 or the chair 26, which corresponds to an actual area of, for example, about 100cm 2 in the real world); in contrast, the cabinet 28 occupies a larger area in the map (corresponding to an actual area of, for example, more than 10,000cm 2 in the real world) because its main body is directly in contact with the ground. Therefore, the table legs, chair legs and other obstacles occupy much smaller areas in the map than other obstacles that have a large area in contact with the ground, such as walls, floor beds, air conditioners, and the cabinet 28 mentioned above, and thus the table legs 261-264 and the chair legs 261-264 can be considered as discrete obstacles. A discrete obstacle in this disclosure refers to an obstacle whose occupied area in the perimeter map is smaller than a threshold size, such as the threshold size can be set to 1mm 2 in the map; those skilled in the art can understand that this threshold size can be converted to an actual size in the actual working area based on the scale relationship between the map and the actual working area, such as if the length scale between the map and the actual working area is 1:100 (thus the area scale is 1:10000), then the above-mentioned threshold size of 1mm 2 in the map is equivalent to an actual size of 100cm 2 in the actual working area, so the threshold size in the map and the threshold size in the actual working area are essentially the same. Based on the above example, any area smaller than 1mm 2 in the map or any area smaller than 100cm 2The obstacles of the discrete obstacle belong to the discrete obstacle. Of course, the above-mentioned area as a threshold size is only an example of judging the discrete obstacle, and those skilled in the art can understand that the length quantity can also be used as the dimension of the threshold size (for example, the threshold size can be set to 1mm in length on the map), and then the length quantity of the obstacle is compared with the threshold size to determine whether the obstacle belongs to the discrete obstacle, such as the length of the detected obstacle, or the longest length of the obstacle side length, or the diameter of the circular obstacle, etc. For the grid map, in order to facilitate calculation, the threshold size can also be converted into the number of grids; for example, if it is set that the length of each side of a grid corresponds to the length of the actual working area of 6cm, and if the threshold size is set to 18cm in length of the actual working area, which corresponds to 3 grid lengths, then whether the obstacle is a discrete obstacle can be simply judged by judging the number of grids occupied by the obstacle in the map, so although the number of grids is used as the parameter for judging the discrete obstacle, its essence is still the length dimension. The disclosure does not limit the dimension of the threshold size and the specific parameter for judging whether it is a discrete obstacle. In contrast to the discrete obstacle, that is, the obstacle occupies an area in the perimeter map whose size is greater than or equal to the threshold size, which is the ordinary obstacle.

[0045] During the operation of the autonomous mobile device 10 (e.g. cleaning the floor by a cleaning robot), the orientation of the single-point laser device 104 can not change or only change in a small range for a period of time, thus it is not able to obtain the "point cloud data" of each direction in the surrounding environment as the 360-degree laser radar does, but only the limited "point cloud data" in the predetermined direction in front of the single-point laser device 104. Since the cross-sectional area of the point laser beam emitted by the single-point laser device 104 is very small (the spot width of the point laser beam emitted by the emitter at the maximum measurement distance such as 6m is about 10cm in the horizontal direction), it can not cover the working width of the autonomous mobile device 10 (for example, the width of a cleaning robot is usually greater than 30cm), thus when the autonomous mobile device is normally running, the single-point laser device 104 thereof can only detect the distance between the obstacle in front of the running direction and the obstacle in the side front of the running direction, and can not detect or accurately detect the distance between the obstacle in the side front of the running direction, thus such obstacle can collide with the autonomous mobile device, causing damage to the autonomous mobile device and the obstacle. For example, as shown in FIG. 3, the predetermined direction of the single-point laser device 104 is upward in the picture, i.e. toward the top of the picture, at this time, the nearby chair leg 261, although in the direction of the movement of the autonomous mobile device performing the working task, is not in the light path of the point laser beam of the single-point laser device 104 due to being in the side front of the running direction, and the reflection cross section of the chair leg 261 to the single-point laser device 104 is small, thus when the autonomous mobile device 10 continues to move straight, it is very likely to cause the right front side of the autonomous mobile device 10 to collide with the chair leg 261. On the other hand, since the cross section (the area of the discrete obstacle perpendicular to the incident point laser beam) of the reflection of the point laser beam by the discrete obstacle such as the table leg or the chair leg is relatively small, the point cloud generated is also relatively small; and since the technology of detecting distance by laser itself can cause noise to interfere with the measurement accuracy, in the case of small point cloud, the actual point cloud can be removed as noise data, or the noise can be calculated as actual point cloud, thus even when the autonomous mobile device 10 rotates in place to obtain the point cloud data of the surrounding environment, it can not detect or accurately detect the discrete obstacle far away or the distance thereof. Therefore, before the autonomous mobile device 10 travels to a position close to the chair leg 261 as shown in FIG. 3, even if the discrete obstacle such as the chair leg is scanned by the single-point laser device, a small amount of point cloud can be obtained, and this part of the point cloud can also be excluded as noise or error, thus the autonomous mobile device can collide with the discrete obstacle due to not recording the discrete obstacle.

[0046] To be able to timely detect the discrete obstacles (e.g., table legs 241-244 and chair legs 261-264) in the working area 20 and effectively avoid collision in advance, the present disclosure provides a method for operating an autonomous mobile device, the flowchart of which is shown in FIG. 4. The method comprises: step S100, acquiring, by the processor 108, a peripheral map of the autonomous mobile device 10 in the working area; step S200, determining, by the processor 108, an area occupied by an obstacle in the peripheral map; step S300, judging, by the processor 108, whether the obstacle is a discrete obstacle, as described above, the discrete obstacle of the present disclosure refers to an obstacle whose occupied area in the peripheral map is smaller than a threshold size, which will not be described here again; step S400, in response to judging that the obstacle is the discrete obstacle, calculating, by the processor 108, an obstacle distance representing the distance between the autonomous mobile device and the obstacle; and step S500, comparing, by the processor 108, the obstacle distance with a safety distance threshold, and in response to the obstacle distance being smaller than or equal to the safety distance threshold, controlling the mobile assembly to execute a discrete obstacle avoidance scheme to avoid the obstacle. The safety distance threshold is a preset value for avoiding collision between the autonomous mobile device and the obstacle.

[0047] In embodiment one, the peripheral map of the autonomous mobile device acquired in step S100 can be a partial map selected from a known global map of the working area 20 as the peripheral map. For example, the autonomous mobile device 10 has once worked in the working area 20 and has acquired a global map of the working area 20 based on SLAM algorithm from various data acquired by various sensors, so that when the autonomous mobile device 10 works in the same working area 20 again, the global map of the working area 20 can be directly obtained. For another example, the processor 108 can receive an existing global map of the same working area 20 from a base station or a server or other autonomous mobile devices through a communication device on the autonomous mobile device 10. Since the two cases are both to acquire an existing global map of the working area 20, the global map can also be called a historical map or a historical global map. The peripheral map acquired in the present embodiment is a partial map selected from the global map. For example, a partial map with a set shape and a set size around the position of the autonomous mobile device itself can be selected. FIG. 5A shows a peripheral map 30 obtained by selecting a partial map from a global map.

[0048] In the embodiment two, the acquired surrounding map in step S100 can be a surrounding map calculated from the point cloud data continuously obtained by the single-point laser device 104 during the running of the autonomous mobile device 10 and continuously updated and improved. In the case where the above historical map is not available or the historical map is not accurate, the autonomous mobile device 10 needs to calculate the distance data between the autonomous mobile device 10 and the obstacles in real time by the single-point laser device 104 during the running, and generate a new surrounding map from the distance data. In this process, since the autonomous mobile device is constantly moving (including straight running and rotation), the single-point laser device is also constantly emitting and receiving laser, so the single-point laser device will constantly obtain multiple distance data of the obstacles at different positions and different orientations, and update and improve the surrounding map in real time. In this process, the outline, shape, position, and area occupied by the obstacles in the surrounding map in the working area 20 are gradually improved; as the surrounding map is gradually updated, the obstacles on the map become more and more accurate. For the convenience of subsequent description, in this embodiment, acquiring the surrounding map includes updating the surrounding map.

[0049] Please note that whether the autonomous mobile device rotates or not, the single-point laser device will emit laser to the front of the predetermined direction of the single-point laser device and receive the reflected laser along with the running of the autonomous mobile device, that is, the single-point laser device acquires the distance data between the single-point laser device and the obstacles in front of the predetermined direction of the single-point laser device in real time and all the time. However, in the process of rotation of the autonomous mobile device, the single-point laser device will emit laser to different directions in the surrounding environment and receive the reflected laser of the obstacles in multiple directions in the environment along with the rotation of the autonomous mobile device, so as to obtain multiple distance data between the autonomous mobile device and different objects in the surrounding environment. In order to better illustrate the technical solutions of the present disclosure, the distance data obtained when the autonomous mobile device rotates in place is taken as an example for introduction.

[0050] The autonomous mobile device 10 rotates in place by an integer multiple of 360 degrees, such as one rotation, two rotations, or five rotations, etc., to comprehensively and uniformly scan the surrounding environment. Alternatively, the autonomous mobile device rotates in place by 360 degrees. The selected rotation angle can be adjusted based on the estimated position and the estimated orientation or the preset path planning instruction. The present disclosure does not limit the rotation angle.

[0051] Exemplarily, the effective detection range of the single-point laser device 104 is to detect the distance of the obstacles within the range of 0.2m to 6m away from it. In some embodiments, a detection range threshold can be set and the distance data beyond the detection range threshold can be discarded from the obtained plurality of distance data, while only the plurality of distance data within the detection range threshold is reserved. For example, the autonomous mobile device 10 performs a 360-degree in-place rotation and sets the detection range threshold to 0.2m to 2m, thereby being able to obtain the point cloud data corresponding to the environment within the circular range with a radius of 0.2m to 2m, and generate or update the surrounding map within the corresponding detection range based on these point cloud data.

[0052] Figures 5B-5D illustrate the process of acquiring the perimeter map 31-33 using the single-point laser device 104. In embodiment two, as shown in Figure 5B, the autonomous mobile device 10, in operation, acquires, in real-time, a point cloud data of a portion of the work area 20 at the location P0(e.g., by rotating in place) to form the perimeter map 31, in which each of the chair legs 261, 262, and 264 is only partially illuminated by the single-point laser device 104 toward the autonomous mobile device 10, thus each occupies 2 or 3 grid cells, while the chair leg 263 is not illuminated due to the occlusion by 261; meanwhile, a portion of the cabinet 28 is also occluded by each of the chair legs and is not represented in the perimeter map 31 as an obstacle. At this time, the actual area occupied by the chair legs 261-264 and the cabinet 28 in the environment 20 is not correctly represented in the perimeter map 31. Then, as shown in Figure 5C, the autonomous mobile device moves to the location P1, and acquires, in real-time, another set of point cloud data of the portion of the work area 20 (e.g., by rotating in place) to update the previously obtained perimeter map 31 to obtain the perimeter map 32. Specifically, at this time, another portion of each of the chair legs 261, 262, and 264 and the cabinet 28 is illuminated by the single-point laser device 104, and a portion of the chair leg 263 is also illuminated. Thus, in the updated perimeter map 32, each of the chair legs 261 and 262 occupies 4 grid cells, the chair leg 263 occupies 2 grid cells, the chair leg 264 occupies 3 grid cells, and the cabinet 28 occupies 36 grid cells. As the autonomous mobile device 10 continues to travel to the location P2, as shown in Figure 5D, another set of point cloud data of the portion of the work area 20 is acquired in real-time (e.g., by rotating in place) to update the previously obtained perimeter map 32 to obtain the perimeter map 33. At this time, although the lower half of the chair legs 261, 262 can not be included in the point cloud data obtained during this rotation in place, it has already been included in the previously obtained perimeter maps 31 and 32; by rotating in place at P2, the upper half of the chair legs 263 and 264 and the bottom of the cabinet 28 are all illuminated and reflected in the updated perimeter map 33. Thus, all portions of the chair legs 261-264 and the cabinet 28 are accurately and fully reflected in the updated perimeter map 33, in which each of the chair legs 261-264 occupies 4 grid cells, and the cabinet 28 occupies 39 grid cells. With respect to the actual area occupied by the chair legs 261-264 and the cabinet 28 in the environment 20, the latest perimeter map 33 is more accurate than the earlier perimeter map 31 in this embodiment. In other words, as the autonomous mobile device operates, the perimeter map is gradually updated and refined, and the determination of the discrete obstacles becomes more and more accurate.

[0053] It can be understood that the surrounding map obtained in step S100 includes the position of the autonomous mobile device 10 in the working area, and the surrounding map has a set shape and a set size. The set size and the set shape of the surrounding map can be determined according to a plurality of factors.

[0054] For example, the set shape and the set size of the surrounding map can be determined according to the motion parameters of the autonomous mobile device 10. The autonomous mobile device 10 can have a motion sensor such as an IMU (Inertial Measurement Unit), which can obtain acceleration and angular velocity, and then respectively integrate the acceleration and the angular velocity to obtain velocity and angle. The shape and the size of the surrounding map can be changed based on these motion parameters. For example, FIG. 6A shows a part of the working area of the autonomous mobile device 10 and a rectangular surrounding map 60, 61. The length of the side R1 of the rectangle parallel to the direction of travel F of the autonomous mobile device 10 is proportional to the travel speed v. For example, when the autonomous mobile device 10 travels at a first speed v1, the preset surrounding map 60 can be a square with a side length of 2 m, which is centered at the current position of the autonomous mobile device 10. When the autonomous mobile device 10 travels at a second speed v2 faster than the first speed v1 (for example, v2 is 1.5 times v1), the preset surrounding map 61 can be a rectangle with a first side R1 of 3 m and a second side R2 of 2 m, which is composed of the first side and the second side perpendicular to each other, and the first side R1 is parallel to the direction of travel F of the autonomous mobile device, and the second side R2 is perpendicular to the direction of travel F of the autonomous mobile device. At this time, the length of the first side R1 of the surrounding map in the direction of travel of the autonomous mobile device 10 increases, and the length of the first side R1 is proportional to the travel speed of the autonomous mobile device, for example, 3 m; and the length of the second side R2 perpendicular to the direction of travel is still 2 m.

[0055] For another example, the surrounding map can be a circle with the current position of the autonomous mobile device 10 as the center, and the radius of the circle is proportional to the travel speed of the autonomous mobile device 10. As shown in FIG. 6B, when the autonomous mobile device 10 travels at a first speed v1, the surrounding map 63 can be a circle with a radius R3 of 1 m, which is centered at the current position of the autonomous mobile device 10. When the autonomous mobile device 10 travels at a second speed v2 faster than the first speed v1, if the second speed v2 is 1.2 times the first speed v1, the surrounding map 64 can be a circle with a radius R3 of 1.2 m, which is centered at the current position of the autonomous mobile device 10.

[0056] For another example, the perimeter map can be an ellipse centered at the current position of the autonomous mobile device 10, with the major axis R5 of the ellipse parallel to the direction of travel F of the autonomous mobile device 10, and the major axis R5 of the ellipse proportional to the speed of travel. As shown in FIG. 6C, the perimeter map 65 has an elliptical shape with the major axis R5 parallel to the direction of travel F of the autonomous mobile device 10, and the minor axis R6 perpendicular to the direction of travel F of the autonomous mobile device 10. When the autonomous mobile device 10 travels at a first speed vl, the length of the major axis R5 of the perimeter map 65 is 1 m, and the length of the minor axis R6 is 0.6 m. When the autonomous mobile device 10 travels at a second speed v2 that is 1.2 times the first speed vl, the perimeter map 66 can be an ellipse with the length of the major axis R5 increased to 1.2 m, and the length of the minor axis R6 still 0.6 m.

[0057] In exemplary embodiments, the shape and size of the perimeter map can also be determined according to the set time interval At for generating the perimeter map. The set time interval At of the present disclosure refers to the time interval between the generation of two adjacent perimeter maps of the autonomous mobile device in the working area. For example, when the set interval for performing step S100 is long, the size of the perimeter map can be large. Alternatively, the set size and shape of the perimeter map can be determined by taking into account both the set time interval At for generating the two adjacent perimeter maps and the motion parameters of the autonomous mobile device 10. For example, when the autonomous mobile device 10 travels at a speed of 0.2 m / s, and the set time interval At for generating the perimeter map is 5 s, the perimeter map generated at this time can be a square of 2.5 m x 2.5 m centered at the current position of the autonomous mobile device 10. In this way, no matter how the autonomous mobile device 10 travels before the next local map is generated, it can at most travel a distance of 1 m, which is less than the side length 2.5 m of the perimeter map, and thus will not exceed the range covered by the perimeter map.

[0058] In exemplary embodiments, the shape and size of the perimeter map can also be determined according to the physical shape of the autonomous mobile device 10 itself. For example, if the autonomous mobile device 10 is a D-shaped device with a front straight edge and a rear circular edge, the perimeter map can also be generated in a D shape with the straight edge of the D shape parallel to the front straight edge of the autonomous mobile device 10 and in front of the direction of travel F.

[0059] In an exemplary embodiment, the set shape and the set size of the perimeter map can also be determined according to an input from the user to the autonomous mobile device 10. The input can be a value corresponding to an operating mode of the autonomous mobile device 10, for example, in the case that the autonomous mobile device 10 is a cleaning robot, there can be a "deep cleaning mode" with a slower traveling speed and a "normal cleaning mode" with a faster traveling speed; in the case that the user selects the "deep cleaning mode", the set size of the perimeter map corresponding thereto can be smaller than the set size corresponding to the "normal cleaning mode". The input can also be a value corresponding to a cleaning mode related to the ground material, for example, the traveling speed of the cleaning robot in a "carpet cleaning mode" is usually slower than in a "floor cleaning mode", in the case that the user selects the "carpet cleaning mode", the set size of the perimeter map corresponding to the input can be smaller than the set size corresponding to the "floor cleaning mode". The input can also be a value corresponding to a region to which the environment 20 belongs, for example, when the user inputs that the working environment in which the autonomous mobile device 10 is located belongs to a "corridor" region, since the space of the "corridor" region is more narrow than other regions such as a "living room" region, the set size of the perimeter map corresponding to the "corridor" region is smaller than the set size corresponding to the "living room" region.

[0060] In the above-mentioned embodiment one, when the perimeter map is a partial map selected from a historical global map of an existing working region, and at least a part of the perimeter map determined according to the set shape and the set size exceeds the edge of the global map, the edge of the global map is used to replace the edge of the perimeter map. For example, in an embodiment, as shown in FIG. 6D, the perimeter map obtained according to the set size is a dashed frame 67' around the autonomous mobile device 10, the front edge of which exceeds the edge of the global map, the part of the perimeter map exceeding the edge of the global map is actually invalid, at this time, the edge of the global map intersecting with the part of the perimeter map exceeding the edge of the global map (i.e. the edge line E1 of the global map in FIG. 6D and FIG. 6E) can be used to replace the front edge of the perimeter map 67', to form the perimeter map 67 of the autonomous mobile device at this time as shown in FIG. 6E; as shown in FIG. 6E, the perimeter map at this time is a closed shape composed of the left side, the right side and the lower side of the original perimeter map determined according to the set size and the edge line E1 of the global map.

[0061] In step S200, a region occupied by obstacles in the perimeter map is determined. In an exemplary embodiment, as shown in FIG. 5A, the obtained perimeter map 30 is a grid map, which generally includes several types of regions: an unknown region composed of a plurality of adjacent unknown positions, an obstacle-occupied region (simply referred to as an obstacle region) composed of a plurality of adjacent obstacle positions, and a reachable region (simply referred to as a reachable region) composed of a plurality of adjacent reachable positions. A reachable position is a position that the autonomous mobile device can reach or has passed through. Exemplarily, before the map is established, the state values of all coordinate positions in the map are set to initial values (generally consistent with the state value of an unknown position) at the start; when the autonomous mobile device runs in the working area, the state value of each position reached by the autonomous mobile device is updated according to the data detected by various sensors, or the state values of coordinate positions on a trajectory passed through by the autonomous mobile device in a period of time are updated according to the trajectory, and a region is composed of a plurality of coordinate positions having a common state value and being adjacent to each other. For example, the state value of an unknown position is preset to 75 (such as a pixel value or a grayscale value; in this embodiment, the initial value of all positions at the start is 75); the state value of a coordinate corresponding to a reachable position is set to 0 (such as a pixel value or a grayscale value, represented by white color in the figure); and the state value of an obstacle position is set or updated to 100 (such as a pixel value or a grayscale value, represented by black color in the figure). In this disclosure, the obstacle region, i.e., the grid occupied by the obstacle, can also be referred to as the region being occupied, or the region being in an "occupied" state. As shown in the perimeter map 30 in FIG. 5A, each of the chair legs 261-264 occupies 4 grids, and a part of the cabinet 28 occupies 39 grids.

[0062] In step S300, it is determined whether the obstacle is a discrete obstacle, wherein the discrete obstacle represents an obstacle whose occupied region in the perimeter map is smaller than a threshold size.

[0063] In an exemplary embodiment, the autonomous mobile device 10 can be a cleaning robot used in the working area 20 of a home environment, as described in the foregoing embodiments. As shown in FIG. 3, the table legs 241-244 and the chair legs 261-264 can be considered as discrete obstacles because the sizes of the regions occupied by the table legs 241-244 and the chair legs 261-264 in the perimeter map are smaller than the threshold size, while the wall 22 and the cabinet 28 can be considered as common obstacles because the sizes of the regions occupied by the wall 22 and the cabinet 28 in the perimeter map are greater than or equal to the threshold size. The dimension of the threshold size and how to determine whether an obstacle belongs to a discrete obstacle or a common obstacle according to the threshold size are described in the foregoing related content, which will not be repeated here.

[0064] In practical applications, different processing methods can be used for common obstacles and discrete obstacles. For example, in some embodiments, for a common obstacle, the autonomous mobile device 10 can run to a certain distance range close to it (such as detected by a proximity sensor that the autonomous mobile device is away from it) or collide with it once (such as detected by a collision sensor), then turn and continue to run in an edge-following mode along a direction parallel to the edge of the common obstacle (for example, wall 22), so as to more fully clean the area close to the edge of the common obstacle; and for discrete obstacles, such as wooden and furniture-painted table legs and chair legs, it is necessary to avoid frequent collisions between the autonomous mobile device 10 and the obstacles during the work (for example, cleaning) process as much as possible, so as to prevent damage to the table or chair and damage to the autonomous mobile device by these discrete obstacles.

[0065] In the example embodiment, as shown in the perimeter map 30 of FIG. 5A, the threshold size can be set to 3x3 = 9 grids, that is, only when the length and width of the obstacle are both less than 3 grids, the obstacle is determined to be a discrete obstacle. According to the foregoing setting, 1 grid length is 6 cm, and the threshold size is 3 grid length, that is, 18 cm, so the obstacle with a length and width less than 18 cm is determined to be a discrete obstacle. As shown in FIG. 5A, each of the chair legs 261-264 occupies 2x2 = 4 grids, that is, its length in the actual working area is greater than 6 cm and less than or equal to 12 cm. Since the length and width of the chair legs 261-264 are both less than the threshold size, the chair legs 261-264 are determined to be discrete obstacles; and the cabinet 28 occupies 13x3 = 39 grids in FIG. 5A, its length is 78 cm, and its width is 18 cm, both of which do not meet the condition of "less than the threshold size", so the cabinet 28 is determined to be a common obstacle. In the example embodiment, the threshold size for determining a discrete obstacle can be pre-set (for example, before the autonomous mobile device 10 is shipped) or set by a user according to, for example, the environment used by the autonomous mobile device 10, which is not limited in the present disclosure.

[0066] As a specific implementation, any occupied grid A can be found in the perimeter map, and then its perimeter range is defined, for example, a 3x3 grid range is selected as its perimeter range with the occupied grid A as the center, and the occupancy of other grids in the perimeter range is determined; if the number of occupied grids in the other 8 grids in the perimeter range is less than 4 grids, the obstacle corresponding to the grid A can be determined to be a discrete obstacle; otherwise, it is determined to be a common obstacle.

[0067] In the second embodiment, the discrete obstacle is still determined according to the above method, but since the position, formation, size and area of the obstacle in the surrounding map are established and gradually updated by the autonomous mobile device during operation, the discrete obstacle determined at the beginning may not be accurate, but after a period of operation, the determination of the discrete obstacle will be more and more accurate as the surrounding map is updated and improved.

[0068] It can be understood that if it is determined in step 300 that there is no discrete obstacle, the processor 108 will skip the following steps S400 and S500, and end the method shown in FIG. 4.

[0069] In step S400, in the exemplary embodiment, if it is determined that a certain obstacle is a discrete obstacle, the obstacle distance between the autonomous mobile device 10 and the discrete obstacle is further calculated. In the embodiment in which the surrounding map is a grid map, the obstacle distance can be calculated based on the number of grids between the discrete obstacle and the autonomous mobile device in the surrounding map and the size of each grid in the surrounding map. For example, in the surrounding map 30 shown in FIG. 5A, the center C1 of the autonomous mobile device 10 is separated by 8 grids horizontally and 8 grids vertically from the discrete obstacle 261, and in the case where the length of one grid is set to correspond to the length of the actual working area of 6 cm, it is known that the center C1 of the autonomous mobile device 10 is separated by 48 cm horizontally and 48 cm vertically from the discrete obstacle 261 in the working area, and the straight-line obstacle distance L1 between the center C1 of the autonomous mobile device 10 and the discrete obstacle 261 is 48 x V2 = 67 cm. In another embodiment, the actual obstacle distance length can not be calculated, but the number of grids between the grid where the center C1 of the autonomous mobile device 10 is located and the discrete obstacle 261 can be directly recorded for subsequent comparison with the threshold value.

[0070] In other embodiments, the obstacle distance between the autonomous mobile device and the discrete obstacle can also be directly calculated. For example, as shown in FIG. 5D, in the embodiment in which the surrounding map 33 is obtained by point cloud data, since the single-point laser device 104 itself is a ranging device, and the single-point laser device 104 has detected the discrete obstacle 261 at least once, and has obtained the distance data between the discrete obstacle 261 and the discrete obstacle 261 at the same time, the corresponding "distance data" from the single-point laser device 104 can be directly used as the obstacle distance between the autonomous mobile device 10 and the discrete obstacle 261.

[0071] In some embodiments, the physical size of the autonomous mobile device 10 itself can also be considered when calculating the obstacle distance. For example, in the perimeter map 30 of the grid shown in FIG. 5A, only one grid in the center of the autonomous mobile device 10 can be marked as the position of the autonomous mobile device 10, and in the case of calculating the obstacle distance based on the number of grids between the center and the discrete obstacle 261, the physical size of the autonomous mobile device 10 itself can be considered, and then the actual distance of the outermost edge of the autonomous mobile device 10 from the discrete obstacle 261 is obtained as the obstacle distance L1. Assuming that the autonomous mobile device 10 is a cylinder with a radius of 30 cm, in the above-mentioned embodiment, the final obstacle distance should be corrected to 67 cm - 30 cm = 37 cm. Alternatively, as shown in FIG. 5D, in the embodiment of obtaining the perimeter map 33 through point cloud data, the installation position of the single-point laser device 104 on the autonomous mobile device 10 needs to be considered when calculating the obstacle distance. If the single-point laser device 104 is installed at the front edge of the autonomous mobile device 10 as shown in FIG. 2 and the installation direction is also forward, the corresponding "distance data" measured in front of the single-point laser device 104 can be directly used as the obstacle distance. If the single-point laser device 104 is installed at the center of the top surface of the autonomous mobile device 10 and the installation direction is forward, the corresponding "distance data" measured in front of the single-point laser device 104 needs to be subtracted by the radius of the autonomous mobile device 10 as the obstacle distance. Of course, those skilled in the art should understand that the problem caused by the radius of the autonomous mobile device itself can also be solved by adjusting the safety distance threshold, for example, if the single-point laser device 104 is installed at the center of the top surface of the autonomous mobile device 10 and the installation direction is forward, the corresponding "distance data" measured in front of the single-point laser device 104 can be directly used as the obstacle distance, but the "safety distance threshold" mentioned above is increased by the radius of the autonomous mobile device as a new safety distance threshold to compare with the obstacle distance.

[0072] In step S500, in an exemplary embodiment, the calculated obstacle distance is compared with a safety distance threshold; if the calculated obstacle distance is less than or equal to the safety distance threshold, it means that avoidance needs to be performed to avoid collision between the autonomous mobile device 10 and the discrete obstacle. The safety distance threshold can ensure whether the autonomous mobile device 10 collides with the discrete obstacle; it can be set in advance (for example, at the production of the autonomous mobile device 10), or it can be set by the user according to the environment used by the autonomous mobile device 10, and the present disclosure does not limit this.

[0073] In some embodiments, the safety distance threshold can be adjusted according to the motion parameters of the autonomous mobile device 10, such as setting the safety distance threshold according to the running speed of the autonomous mobile device 10. For example, when the autonomous mobile device 10 travels at a speed of 0.2 m / s, the safety distance threshold can be set to 0.1 m, while when the autonomous mobile device 10 travels at a faster speed of 0.5 m / s, the safety distance threshold can be set larger, for example, to 0.3 m, so as to ensure that the autonomous mobile device 10 and the discrete obstacle do not collide. In some other embodiments, the safety distance threshold can be adjusted according to the working mode of the autonomous mobile device 10. For example, in the case of the autonomous mobile device 10 being a cleaning robot, if it is set to "carpet cleaning mode", since the resistance of the carpet material to the cleaning robot is greater than that of the floor, the cleaning robot is more likely to slow down, so the safety distance threshold can be set smaller than that of "floor cleaning mode", for example, to 0.1 m; while if it is set to "floor cleaning mode", since the friction of the floor material is smaller, the cleaning robot needs a longer distance to slow down, so the safety distance threshold can be set larger, for example, to 0.3 m. In some other embodiments, if only the number of grids between the center C1 of the autonomous mobile device 10 and the discrete obstacle 261 is recorded in step S400 without being converted into an actual distance, the safety distance threshold can also be set to the number of grids instead of a specific distance value in step S500, and a comparison between the numbers of grids is made. Those skilled in the art can also set the safety distance threshold according to other factors as needed, which is not limited in the present disclosure.

[0074] In the case where it is determined that the obstacle distance is less than or equal to the safety distance threshold, the autonomous mobile device 10 executes an avoidance scheme, which is pre-set by the producer or user before the autonomous mobile device 10 starts working. In an exemplary embodiment, the avoidance scheme can include slowing down or retreating the autonomous mobile device 10, so as to avoid collision between the autonomous mobile device and the discrete obstacle. In some embodiments, the avoidance scheme can also include turning the autonomous mobile device 10 to move away from the discrete obstacle.

[0075] In some other embodiments, the evading scheme includes slowing down and changing the direction of the autonomous mobile device 10 to travel around the discrete obstacle at a predetermined evading distance. The evading distance can be less than or equal to the safety distance threshold, i.e. in the case where it is determined that the obstacle distance between the autonomous mobile device 10 and the discrete obstacle is less than or equal to the safety distance threshold, the autonomous mobile device 10 can continue to travel until the distance between the autonomous mobile device and the discrete obstacle reaches the predetermined evading distance; then the autonomous mobile device 10 can proceed while changing direction so that the obstacle distance between the autonomous mobile device and the discrete obstacle remains at the predetermined evading distance (greater than or equal to the evading distance), so as to avoid collision with the discrete obstacle during the process of passing around the discrete obstacle. The evading distance can be equal to or greater than the safety distance threshold. In this case, in the case where it is determined that the obstacle distance between the autonomous mobile device 10 and the discrete obstacle is less than or equal to the safety distance threshold, the autonomous mobile device 10 needs to be slowed down to make the obstacle distance between the autonomous mobile device and the discrete obstacle reach the predetermined evading distance, and then pass around the discrete obstacle at the evading distance as described above.

[0076] In embodiments, the evading distance can be a fixed value set by the producer or user. In some embodiments, the evading distance can be adjusted according to the motion parameters of the autonomous mobile device 10. For example, when the autonomous mobile device 10 travels at a speed of 0.2 m / s, the evading distance can be set to 0.15 m, and when the autonomous mobile device 10 travels at a speed of 0.5 m / s, the evading distance can be set to be larger, for example, 0.3 m, so that the autonomous mobile device has enough distance to evade the discrete obstacle. In some other embodiments, the evading distance can be adjusted according to the working mode of the autonomous mobile device 10. For example, in the case of a cleaning robot, if it is set to "carpet cleaning mode", since the resistance of the carpet material to the cleaning robot is greater than that of the floor, the cleaning robot is more likely to evade the discrete obstacle, so the evading distance can be set to be smaller, for example, 0.15 m; and if it is set to "floor cleaning mode", since the friction of the floor material is smaller, the cleaning robot needs a longer distance to evade the discrete obstacle, so the evading distance can be set to be larger, for example, 0.2 m. Those skilled in the art can also set the evading distance according to other factors as needed, which is not limited in the present disclosure.

[0077] By the method shown in FIG. 4, the detection of obstacles in the surrounding environment can be realized on the autonomous mobile device 10 using a single-point laser device, and the discrete obstacles can be identified and evaded if necessary, so as to avoid unwanted collisions between the autonomous mobile device and the obstacles. At the same time, it is not necessary to read the global map of the entire environment, and the consumption of computing power is reduced while realizing obstacle evasion.

[0078] In some embodiments, the obstacle distance in step S400 above representing the distance between the autonomous mobile device and the obstacle further comprises determining a plurality of predicted arrival locations where the autonomous mobile device is expected to arrive subsequently within the set time interval following the originally planned operating route, and calculating an estimated obstacle distance between the autonomous mobile device and the discrete obstacle at each of the plurality of predicted arrival locations. And in step S500 above further comprising comparing the plurality of estimated obstacle distances, comparing the smallest estimated obstacle distance with the safety distance threshold, determining a predicted evasive location where the estimated obstacle distance between the autonomous mobile device and the discrete obstacle is greater than or equal to the safety distance threshold in the case that the smallest estimated obstacle distance is less than or equal to the safety distance threshold, and executing the discrete obstacle evasive scheme when the autonomous mobile device arrives at the predicted evasive location.

[0079] In order to avoid collision with discrete obstacles near the originally planned running route, when calculating the obstacle distance, not only the actual distance data between the autonomous mobile device 10 and the discrete obstacles in the working area is calculated in real time, but also a plurality of estimated obstacle distances between the autonomous mobile device 10 and the discrete obstacles after the autonomous mobile device 10 continues to travel along the originally planned running route for a plurality of time points (referred to as prediction time points) according to the current motion parameters are calculated, and the minimum estimated obstacle distance is obtained by comparison, and then the minimum estimated obstacle distance is compared with the safety distance threshold, so as to determine the avoidance position in advance and plan the pre-avoidance route, so that the autonomous mobile device can avoid collision with the possible discrete obstacles near the originally planned running route before the next actual distance data is obtained, and the collision risk is reduced. In an exemplary embodiment, as shown in FIG. 7, it is assumed that at t0, the autonomous mobile device 10 obtains the surrounding map of the autonomous mobile device in the working area at position P3 of the surrounding map 70, thereby performing the above steps (not described again here) and calculating the actual distance data L3 between the autonomous mobile device 10 and the discrete obstacles in the working area in real time (i.e. step S400), and according to the setting, the autonomous mobile device should obtain the next surrounding map again at t1 after a set time interval At from t0. Between t0 and t1, a plurality of prediction time points (for example, t01, t02, …) are set. Since the autonomous mobile device 10 obtains the surrounding map at position P3 at t0, the autonomous mobile device 10 travels forward along the F direction in the surrounding map as shown in FIG. 7 at a speed of 0.6 m / s, and then reaches the next prediction time point t01 (for example, t01=t0+10 seconds) according to the originally planned running route (assuming that the time consumed for the original rotation for obtaining the surrounding map is 8s, and other time consumption except the action of the original rotation of the autonomous mobile device is ignored, then as shown in FIG. 7, the P4 position is 1.2 m above the P3 position), and then reaches the next prediction time point t02 (for example, t02=t01+2 seconds) according to the originally planned running route (the P5 position is 1.2 m above the P4 position), then P4 and P5 are the predicted arrival positions that the autonomous mobile device 10 can reach according to the originally planned running route. Optionally, the coverage range of the surrounding map 70 can be set to cover the farthest position that the autonomous mobile device 10 can reach before t1 is reached by the processor 108 from t0 for a set time interval At, and the predicted arrival position that the autonomous mobile device 10 can reach according to the originally planned running route includes the farthest position (for example, P5).Then, based on the predicted arrival positions P3, P4, P5 and the motion parameters (such as the speed and direction of the autonomous mobile device at P3, P4, P5, etc.) of various motion sensors, the estimated obstacle distances L3, L4, L5… (L3, L4, L5 respectively correspond to the estimated obstacle distances between the predicted arrival positions P3, P4, P5 and the discrete obstacle 261) between the autonomous mobile device 10 and the discrete obstacle 261 at these predicted arrival positions (such as P3, P4, P5) can be pre-calculated. Those skilled in the art can understand that the positions P3, P4, P5 that the autonomous mobile device 10 can run to can also be selected in other ways, and the present disclosure does not limit this.

[0080] In the subsequent step S500, in some embodiments, the estimated obstacle distances between the autonomous mobile device 10 and the discrete obstacle 261 at the predicted arrival positions within the range of the surrounding map 70 are compared, the minimum estimated obstacle distance Lmin is found, and the minimum estimated obstacle distance Lmin is compared with the safety distance threshold. As shown in FIG. 7, L4 is determined as the minimum estimated obstacle distance Lmin. At this time, not only the obstacle distance L3 between the autonomous mobile device 10 and the discrete obstacle in the working area calculated needs to be compared with the safety distance threshold, but also the possibility of collision between the autonomous mobile device and the discrete obstacle that has been judged needs to be considered according to the originally planned running route, and the minimum estimated obstacle distance Lmin (L4 in this embodiment) needs to be compared with the safety distance threshold. If L4 is less than or equal to the safety distance threshold, it means that even if the obstacle distance L3 between the autonomous mobile device 10 and the discrete obstacle 261 at the current position P3 is large enough, there is still a risk of collision with the discrete obstacle 261 at the predicted arrival position P4 during the subsequent advancement of the autonomous mobile device 10 according to the originally planned running route; and the avoidance scheme must be executed before the autonomous mobile device 10 travels to the predicted arrival position P4. At this time, the processor determines the estimated avoidance position P3’ according to the current motion parameters of the autonomous mobile device 10 and / or the originally planned running route within the set time interval At; wherein at the estimated avoidance position P3’, the estimated obstacle distance between the autonomous mobile device 10 and the discrete obstacle 261 is greater than or equal to the safety distance threshold. When the autonomous mobile device 10 continues to travel to the estimated avoidance position P3’ according to the originally planned running route, the avoidance scheme is executed, for example, to bypass the discrete obstacle 261 with the avoidance route shown in FIG. 7.

[0081] In the above embodiment, by calculating the distances between the multiple predicted arrival positions within the set time interval At between the acquisition of two adjacent surrounding maps and the discrete obstacle respectively, the collision between the autonomous mobile device and the discrete obstacle can be more reliably avoided.

[0082] In some embodiments, the method shown in FIG. 4 is performed multiple times at a set time interval At during the operation of the autonomous mobile device 10 in the environment 20 to detect and avoid discrete obstacles constantly. The set time interval At can be related to the motion parameters of the autonomous mobile device 10. For example, the set time interval At can be set to 10s when the autonomous mobile device 10 travels at a speed of 0.2m / s, while the set time interval At can be set to 4s when the autonomous mobile device 10 travels at a speed of 0.5m / s. In further embodiments, the set time interval At can be related to the shape and size of the peripheral map 30, 30’ used to generate the peripheral map 30, 30’. Alternatively, the set time interval At is set such that the maximum distance the autonomous mobile device 10 can travel within the set time interval At does not exceed the range of the peripheral map, so that each acquired peripheral map covers the maximum travel range of the autonomous mobile device within the set time interval. In this way, the detection and avoidance of discrete obstacles can be performed for any time point during the operation of the autonomous mobile device 10.

[0083] In some embodiments, with the global map, the distance data or point cloud data from the single-point laser device 104 can be used to update the global map. For example, the chair leg 261 is marked in the peripheral map 30 acquired by step S100 shown in FIG. 5A. According to the method shown in FIG. 4, when the obstacle distance between the autonomous mobile device 10 and the chair leg 261 is calculated to be less than or equal to the safety distance threshold according to the peripheral map 30, the autonomous mobile device 10 is caused to travel around the chair leg 261 at a predetermined avoidance distance. The distance data of the single-point laser device 104 can be acquired during the turning and compared with the peripheral map 30, and the location, formation, size, area occupied by the obstacle in the global map is updated using the distance data if necessary. For example, the Field Of View (FOV) of the single-point laser device 104 will cover the location of the chair leg 264 during the turning around the chair leg 261; if the distance data acquired from the single-point laser device 104 during the turning does not indicate the presence of any obstacle, it means that the chair 26 can have been moved away. In such a case, the distance data from the single-point laser device 104 can be used to update the global map, so that the next peripheral map acquired after the autonomous mobile device 10 continues to move forward for a set time interval At can be obtained from the updated global map without including the discrete obstacle 264. By updating the global map using the distance data from the single-point laser device 104, unnecessary avoidance actions can be avoided, or new discrete obstacles can be detected for necessary avoidance actions, so that the method shown in FIG. 4 can be performed more efficiently and accurately.

[0084] In some embodiments, a non-transitory computer readable storage medium or program product including instructions embodied therein is also provided, which can be executed on a processor to complete the above-described method for operating an autonomous mobile device. The processor includes, but is not limited to, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic elements, etc.

[0085] With respect to the autonomous mobile device in the above-described embodiments, the specific manner in which each component performs operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

[0086] The above has described various embodiments of the present disclosure, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical application, or improvement of the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. An autonomous mobile device having a single point laser device, characterized by, The single-point laser device is fixedly mounted on the autonomous mobile device in a predetermined direction relative to the autonomous mobile device, the autonomous mobile device comprising: A mobile component configured to move the autonomous mobile device on a plane within the work area; A single-point laser device fixedly mounted on the autonomous mobile device in a predetermined direction relative to the autonomous mobile device; Memory, which stores instructions; and A processor configured to invoke the instructions to perform the following steps: S100: Obtain the surrounding map of the autonomous mobile device in the working area; S200: Determine the area occupied by obstacles in the surrounding map; S300: Determine whether the obstacle is a discrete obstacle, wherein the discrete obstacle refers to an obstacle whose area in the surrounding map is smaller than a threshold size; S400: In response to determining that the obstacle is the discrete obstacle, calculate the obstacle distance representing the distance between the autonomous mobile device and the obstacle; and S500: Compare the obstacle distance with a safe distance threshold, and in response to the obstacle distance being less than or equal to the safe distance threshold, control the mobile component to execute a discrete obstacle avoidance scheme to avoid the obstacle, wherein the safe distance threshold is a preset value for avoiding collisions between the autonomous mobile device and the obstacle.

2. The autonomous mobile device of claim 1, wherein, The surrounding map is a local map selected from the existing historical global map of the work area.

3. The autonomous mobile device of claim 1, wherein, The single-point laser device is configured to obtain distance data, wherein the distance data represents the distance between the single-point laser device and an object in the working area in the predetermined direction; and The processor is also configured to perform: Based on the distance data, point cloud data corresponding to at least a portion of the working area is obtained; and The surrounding map is generated or updated based on the point cloud data.

4. The autonomous mobile device of claim 3, wherein, The processor is also configured to: The autonomous mobile device is rotated in place at one or more locations within the work area via a moving component; and At least a portion of the point cloud data is obtained during each of the in-situ rotations.

5. The autonomous mobile device of claim 2 or 3, wherein, The surrounding map includes the location of the autonomous mobile device in the work area, and the surrounding map has a set shape and a set size. The defined shape and the defined size are determined based on at least one of the following: The motion parameters of the autonomous mobile device Select a set time interval from the surrounding map. The physical shape of the autonomous mobile device, and User input.

6. The autonomous mobile device of claim 5, wherein, The motion parameters of the autonomous mobile device include the travel speed of the autonomous mobile device, wherein, The defined shape is a rectangle, and the defined dimensions include the side length of the rectangle. The side length of the rectangle parallel to the direction of travel of the autonomous mobile device is proportional to the travel speed. The set shape is circular, and the set size includes the radius of the circle, wherein the radius of the circle is proportional to the traveling speed, or The set shape is an ellipse, and the set size includes a long axis of the ellipse, wherein the long axis of the ellipse is parallel to a travel direction of the autonomous mobile device, and the long axis of the ellipse is proportional to the travel speed.

7. The autonomous mobile device of claim 1, wherein, The processor is further configured to update a global map of the working area using point cloud data obtained from distance data obtained by the single-point laser device.

8. The autonomous mobile device of claim 1, wherein, The processor is further configured to: generate a plurality of perimeter maps at set time intervals during travel of the autonomous mobile device, and perform steps S200 and subsequent steps based on each of the plurality of perimeter maps.

9. The autonomous mobile device of claim 2, wherein, In a case where the set shape and the set size of the perimeter map exceed an edge of the global map, the edge of the global map is taken as an edge of the perimeter map.

10. The autonomous mobile device of claim 1, wherein, The safety distance threshold is proportional to a travel speed of the autonomous mobile device.

11. The autonomous mobile device of claim 5, wherein, The obstacle distance in step S400, which represents a distance between the autonomous mobile device and the obstacle, further includes: determining a plurality of predicted arrival positions at which the autonomous mobile device is to arrive subsequently within the set time interval according to the originally planned operating route, and calculating an estimated obstacle distance between the autonomous mobile device and the discrete obstacle at each of the plurality of predicted arrival positions, Step S500 includes: comparing the plurality of estimated obstacle distances, and comparing the smallest estimated obstacle distance with the safety distance threshold, in response to the smallest estimated obstacle distance being less than or equal to the safety distance threshold, determining an estimated avoidance position at which the estimated obstacle distance between the autonomous mobile device and the discrete obstacle is greater than or equal to the safety distance threshold, and in response to the autonomous mobile device arriving at the estimated avoidance position, performing the discrete obstacle avoidance scheme.

12. The autonomous mobile device of claim 1, wherein, The discrete obstacle avoidance scheme includes causing the autonomous mobile device to decelerate and / or turn through the movement assembly.

13. The autonomous mobile device of claim 1, wherein, The discrete obstacle avoidance scheme includes causing the autonomous mobile device to travel at a predetermined avoidance distance from the discrete obstacle and bypass the discrete obstacle through the movement assembly.

14. A method run by an autonomous mobile device, the method comprising: The autonomous mobile device includes a movement assembly for moving the autonomous mobile device on a plane in a working area and a processor, The method includes the following steps: S100: obtaining, by the processor, a perimeter map of the autonomous mobile device in the working area; S200: determining, by the processor, an area occupied by an obstacle in the perimeter map; S300: determining, by the processor, whether the obstacle is a discrete obstacle, wherein the discrete obstacle represents an obstacle whose occupied area in the perimeter map is smaller than a threshold size; S400: in response to determining that the obstacle is the discrete obstacle, calculating, by the processor, an obstacle distance representing a distance between the autonomous mobile device and the obstacle; and S500: in response to determining that the obstacle is the discrete obstacle, performing, by the processor, the discrete obstacle avoidance scheme. S500: comparing, by the processor, the obstacle distance to a safety distance threshold, and in response to the obstacle distance being less than or equal to the safety distance threshold, executing, by the mobile component, a discrete obstacle avoidance scheme to avoid the obstacle, wherein the safety distance threshold is a pre-set value for avoiding collision of the autonomous mobile device with the obstacle.

15. A computer-readable storage medium, characterized in that, The storage medium stores a computer program including instructions which, when executed by a processor of an autonomous mobile device, implement the steps of the method of claim 14.

16. A computer program product, characterised in that, The program product includes instructions which, when executed by a processor of an autonomous mobile device, implement the steps of the method of claim 14.

Images