Control method and device of autonomous mobile equipment, equipment, medium and program product

Abstract

The invention relates to the technical field of autonomous mobile equipment, and provides an autonomous mobile equipment control method and device, equipment, a medium and a program product. The method comprises the following steps: if a current environment is detected to be an open environment, determining a target size for initializing a grid map, and initializing a first grid map of which the size is the target size by taking a current first position of the autonomous mobile equipment as a reference point; taking the first grid map as a global grid map of the autonomous mobile device, and updating the occupation state of grids in the global grid map according to sensing data acquired by the autonomous mobile device in real time through an environment sensor; and performing movement control on the autonomous mobile device according to the global grid map. According to the invention, the system overhead of path planning of the autonomous mobile device can be reduced.

Description

Technical Field

[0001] This invention relates to the field of autonomous mobile device technology, and in particular to a control method for autonomous mobile devices, a control device for autonomous mobile devices, an autonomous mobile device, a computer-readable storage medium, and a computer program product. Background Technology

[0002] With the continuous development of technology, the use of autonomous mobile devices for automated operations can greatly improve work efficiency. For example, lawnmower robots, as a type of autonomous mobile device, integrate technologies such as motion control, multi-sensor fusion, and path planning, and are widely used in the maintenance of home lawns and the mowing of large lawns. It should be noted that the path planning of autonomous mobile devices is inseparable from the mapping of the environment. However, in related technologies, when facing open working environments such as grasslands or golf courses, autonomous mobile devices often cannot construct a global map covering the entire working environment, resulting in the inability of autonomous mobile devices to move effectively. Summary of the Invention

[0003] This invention provides a control method for autonomous mobile devices, a control device for autonomous mobile devices, an autonomous mobile device, a computer-readable storage medium, and a computer program product, which enable autonomous mobile devices to move effectively in open environments.

[0004] In a first aspect, the control method for an autonomous mobile device provided by the present invention includes: If the current environment is detected to be an open environment, the target size for initializing the raster map is determined, and the first raster map with the target size is initialized with the current first position of the autonomous mobile device as the reference point. The first grid map is used as the global grid map of the autonomous mobile device, and the occupancy status of the grid in the global grid map is updated according to the perception data collected in real time by the autonomous mobile device through the environmental sensor. Motion control is performed on autonomous mobile devices based on a global grid map.

[0005] Secondly, the control device for an autonomous mobile device provided by the present invention includes: The map building module is used to determine the target size for initializing the raster map if the current environment is detected to be an open environment, and to initialize the first raster map with the target size using the current first position of the autonomous mobile device as the reference point. The map maintenance module is used to use the first grid map as the global grid map of the autonomous mobile device, and to update the occupancy status of the grid in the global grid map based on the perception data collected in real time by the autonomous mobile device through environmental sensors. The motion control module is used to control the movement of autonomous mobile devices based on a global grid map.

[0006] Thirdly, the autonomous mobile device provided by the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control method of the autonomous mobile device provided by the present invention.

[0007] Fourthly, the computer-readable storage medium provided by the present invention stores a computer program, which, when executed by a processor, implements the control method for the autonomous mobile device provided by the present invention.

[0008] Fifthly, the computer program product provided by the present invention includes a computer program that, when executed by a processor, implements the control method for the autonomous mobile device provided by the present invention.

[0009] The control scheme for autonomous mobile devices provided by this invention, if the current environment is detected to be open, determines the target size for initializing the grid map, and initializes a first grid map with the target size using the current first position of the autonomous mobile device as a reference point. This first grid map is then used as the global grid map for the autonomous mobile device, and the occupancy status of the grids in the global grid map is updated based on the perception data collected in real time by the autonomous mobile device through environmental sensors. Based on the global grid map, movement control is performed on the autonomous mobile device. Compared to related technologies that cannot perform effective autonomous movement in open environments, this scheme does not substantially construct a global grid map when the autonomous mobile device faces an open environment. Instead, it constructs a local grid map with the autonomous mobile device's current position as a reference point, and uses this local grid map as the global grid map. This allows the autonomous mobile device to still possess path planning capabilities in open environments, thereby effectively performing movement control and achieving stable navigation and obstacle avoidance in open environments. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a control method for an autonomous mobile device provided in an embodiment of the present invention; Figure 2 This is another schematic flowchart of the control method for an autonomous mobile device provided in an embodiment of the present invention; Figure 3This is an example diagram of the corner points of the grid map in an embodiment of the present invention; Figure 4 yes Figure 1 Detailed process diagram of S130; Figure 5 This is an example diagram of the autonomous mobile device performing path replanning within a global grid map, provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the structure of the control device for an autonomous mobile device provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of the autonomous mobile device provided in an embodiment of the present invention; Figure 8 This is an example diagram of the product form of the autonomous mobile device provided in the embodiments of the present invention. Detailed Implementation

[0012] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0013] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0014] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0015] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0016] Furthermore, in the description of this invention and the appended claims, the terms “second”, “third”, etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0018] This invention provides a control method for an autonomous mobile device, a control device for an autonomous mobile device, an autonomous mobile device, a computer-readable storage medium, and a computer program product. The control method for the autonomous mobile device can be executed by the control device of the autonomous mobile device, or by an autonomous mobile device integrating the control device. The control method includes: if the current environment is detected to be an open environment, determining a target size for initializing a grid map, and initializing a first grid map with the target size using the current first position of the autonomous mobile device as a reference point; using the first grid map as the global grid map of the autonomous mobile device, and updating the occupancy status of the grids in the global grid map based on real-time sensing data collected by the autonomous mobile device through environmental sensors; and performing motion control on the autonomous mobile device based on the global grid map.

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a control method for an autonomous mobile device provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the control method for this autonomous mobile device can be described as follows: In S110, if the current environment is detected to be an open environment, the target size for initializing the grid map is determined, and the first grid map with the target size is initialized with the current first position of the autonomous mobile device as the reference point.

[0021] The following explanation uses the autonomous mobile device as an example to illustrate the control method for autonomous mobile devices.

[0022] Autonomous mobile devices are intelligent devices that can autonomously sense their environment, plan their paths, and move along those planned paths. They include, but are not limited to, mobile robots that use differential speed models, four-wheel four-turn models, omnidirectional movement models, etc., such as sweeping robots, logistics handling robots, lawn mowing robots, etc. They can also be robots that use wheeled legs, quadrupedal movement, etc.

[0023] It should be noted that when an autonomous mobile device moves autonomously, it first performs global path planning based on the static obstacle information recorded in a pre-built global grid map (such as a grid map), obtaining an original planned path from the starting point to the target point that avoids static obstacles, and then moves according to this original planned path. In other words, only after the autonomous mobile device has built an accurate global grid map can it achieve efficient and safe path planning, and thus achieve autonomous movement.

[0024] However, in vast, open environments such as grasslands and golf courses where significant reference points are lacking, traditional mapping methods often fail to construct a global raster map covering the entire environment, leading to path planning failures and consequently affecting the normal operation of autonomous mobile devices. Therefore, this invention provides a control method for autonomous mobile devices suitable for open environments, enabling them to move effectively autonomously in such conditions.

[0025] Among them, open environment refers to an environment that lacks significant feature points or reference points that can be used for precise positioning, such as large areas of flat lawn (such as naturally formed grasslands, artificially constructed golf courses, etc.), deserts, snowfields, etc.

[0026] In this embodiment of the invention, when the autonomous mobile device moves autonomously according to the user-planned path, it can perceive the surrounding environment in real time through configured environmental sensors to detect whether the environment it is in is open. If the current environment is detected to be open, the grid map initialization mechanism is triggered. First, the target size for initializing the grid map is determined, and the current position is obtained through the configured positioning module and recorded as the first position. Using this first position as a reference point, a blank grid map with a size equal to the target size is initialized and recorded as the first grid map. The reference point can be the center point of the grid map, any corner point of the grid map, or the midpoint of the edge of the grid map, which can be set according to the actual application scenario. For example, in this embodiment of the invention, the reference point is set as the center point of the initialized grid map to ensure that the autonomous mobile device has the ability to expand the map in all directions in a balanced manner after initializing the grid map.

[0027] In S120, the first grid map is used as the global grid map of the autonomous mobile device, and the occupancy status of the grids in the global grid map is updated according to the perception data collected in real time by the autonomous mobile device through the environmental sensors.

[0028] In this embodiment of the invention, the autonomous mobile device uses the first grid map initialized above as a global grid map. It should be noted that the first grid map is not a real global grid map, but is used as a global grid map.

[0029] At this point, although the first grid map is blank, it already has a spatial coordinate framework. Autonomous mobile devices can dynamically update the occupancy status of each grid in the global grid map through real-time sensing data collected by environmental sensors, so that the global grid map can be gradually filled with environmental information and achieve effective modeling of the open environment.

[0030] The environmental sensors include, but are not limited to, one or more combinations of LiDAR, millimeter-wave radar, ultrasonic sensors, visual cameras, and infrared sensors. The autonomous mobile device can filter and extract features from multi-source sensor data using a configured multi-source sensor fusion algorithm, thereby identifying obstacles in the environment and marking their corresponding locations on a global grid map as occupied. Understandably, as the autonomous mobile device continues to move in an open environment, blank areas in the global grid map will gradually be filled, forming a progressively refined model of the open environment, effectively supporting path planning and obstacle avoidance decisions.

[0031] In S130, the autonomous mobile device is controlled for movement based on the global grid map.

[0032] In this embodiment of the invention, the autonomous mobile device performs real-time dynamic analysis of the global grid map, evaluates the coverage of the global grid map, and determines whether the global grid map meets preset usage conditions. If the global grid map meets the preset usage conditions, movement control is performed based on the global grid map, and the global grid map is continuously updated; if the global grid map does not meet the preset usage conditions, the autonomous mobile device continues to update the global grid map until the global grid map meets the preset usage conditions.

[0033] It should be noted that the above usage conditions are not specifically limited in the embodiments of the present invention, and can be flexibly set according to actual needs. For example, the usage conditions configured in the embodiments of the present invention are: the coverage of the global grid map is not lower than a preset threshold and the distribution of obstacles tends to be stable.

[0034] Furthermore, when autonomous mobile devices perform movement control based on a global grid map, they can use the user-planned path as a reference. Based on the global grid map, they employ global path planning algorithms such as A*, Dijkstra's algorithm, or A-search, comprehensively considering kinematic constraints, environmental topology, and path optimality indices to generate a smooth and executable global path. Accordingly, the autonomous mobile device can then move autonomously according to this global path until it reaches the endpoint of the user-planned path.

[0035] Alternatively, in one embodiment, please refer to Figure 2 After using the first grid map as the global grid map for the autonomous mobile device, it also includes: In S140, when the map update conditions are met, a second grid map with the target size is initialized using the current second location of the autonomous mobile device as a reference point. In S150, the second grid map is used as the global grid map.

[0036] It is understandable that when an autonomous mobile device moves continuously in an open environment, the existing global grid map may not fully cover the newly arrived area, causing the autonomous mobile device to be unable to continue effective movement control. Therefore, to avoid movement control failure due to insufficient coverage of the global grid map, this embodiment of the invention further introduces a dynamic update mechanism for the global grid map.

[0037] When the autonomous mobile device recognizes that the map update conditions are met, it obtains its current location through the positioning module and records it as the second location. Using this second location as a reference point, it re-initializes a blank raster map with the target size and records it as the second raster map. This second raster map is used as the global raster map, that is, it is used as the global raster map. Based on this, it continues to update the state of the raster in the global raster map to achieve continuous coverage of the new area.

[0038] It should be noted that the map update conditions are not limited in the embodiments of the present invention and can be flexibly configured according to the actual application scenario. For example, the map update conditions can be configured as follows: when the distance between the autonomous mobile device and the boundary of the global grid map is less than the distance threshold.

[0039] As the autonomous mobile device moves, the global grid map is continuously updated to ensure that its coverage always meets the needs of mobile control, thereby ensuring the reliability of path planning and ensuring that the autonomous mobile device can continuously and effectively perform mobile control in open environments.

[0040] Optionally, in one embodiment, before using the second raster map as the global raster map, the method further includes: Identify the shared grid cells between the second grid map and the global grid map; Remove the occupancy status of non-shared grid cells from the global grid map, and copy the occupancy status of shared grid cells from the global grid map to the second grid map.

[0041] To ensure that the updated global grid map can support path planning as soon as possible, this embodiment of the invention further introduces a map state transition mechanism.

[0042] Before using the second grid map as the new global grid map, the autonomous mobile device first compares the coverage areas of the second grid map and the current global grid map to determine the shared grids. Then, it deletes the occupancy status of non-shared grids in the current global grid map to avoid invalid data interfering with path planning. At the same time, it copies the occupancy status of shared grids in the original global grid map to the corresponding positions in the second grid map, thereby inheriting the confirmed occupancy information in the current global grid map. Then, the second grid map is put into use as the new global grid map, so that the state between the old and new global grid maps can be smoothly transitioned, avoiding environmental perception gaps caused by map reset.

[0043] For example, please refer to Figure 3 Assuming the target size for initializing the grid map is determined to be 50×50 grid cells, and the reference point is configured as the center point of the grid map, if the first position of the autonomous mobile device at time T1 is (0, 0), the center point of the first grid map initialized at this time is located at (0, 0), and its corner points are [(25,25),(25,-25),(-25, -25),(-25, 25)]. This first grid map is used as the global grid map, and the grid state in it is updated in real time. When the autonomous mobile device moves to the second position (1,1) at time T2 and meets the map update conditions, the second grid map is reinitialized based on the second position (1,1). Its center point is shifted to (1,1), and its corner points are adjusted accordingly to [(26,26),(26,-24),(-24,-24),(-24,26)], forming a new coverage area that partially overlaps with the original map. By comparing the coordinate ranges of the first grid map and the second grid map, the grids within the range of [(25,25),(25,-24),(-24,-24),(-24,25)] are identified as shared grids. The occupancy status of the grids within this range in the current global grid map is copied to the corresponding position in the second grid map. At the same time, the occupancy status of non-shared grids in the current global grid map is deleted. Then, the second grid map is used as the new global grid map.

[0044] By introducing a map state transition mechanism, the continuity of environmental perception is effectively preserved, preventing the loss of known obstacle information due to map updates, thereby ensuring the stability and real-time performance of path planning. Simultaneously, this mechanism reduces the computational overhead of frequent map reconstruction, ensuring the continuous and reliable operation of autonomous mobile devices in open environments.

[0045] Optionally, in one embodiment, the map update conditions include: The usage time of the first grid map as a global grid map reaches the first duration threshold; and / or The first grid map, as a global grid map, controls the movement of autonomous mobile devices when the movement distance reaches the first distance threshold.

[0046] The embodiments of the present invention set a dual threshold mechanism of duration threshold and distance threshold. When either condition is triggered, or both are triggered simultaneously, the map update process is initiated to ensure that the global raster map maintains a high environmental matching accuracy even after the device has been running for a long time or moved over a large area.

[0047] The first duration threshold can be set based on the stability period of sensor data fusion, and the first distance threshold can be set in combination with the motion characteristics and environmental scale of the autonomous mobile device. For example, when the first duration threshold is set to 30 seconds and the first distance threshold is set to 10 grid cells, the autonomous mobile device can trigger the map update mechanism to reinitialize the second grid map with the current position of the autonomous mobile device as the reference point when it has used the first grid map as the global grid map for 30 seconds, or when the cumulative movement distance reaches 10 grid cells, or when both conditions are met.

[0048] Through the synergistic effect of the above dual threshold mechanisms, the problem of inaccurate environmental modeling caused by the high-speed movement of autonomous mobile devices or the lack of updates for a long time can be avoided, thereby improving the adaptability of autonomous mobile devices in open and dynamic environments.

[0049] Alternatively, in one embodiment, please refer to Figure 4 Based on the global grid map, motion control is performed on the autonomous mobile device, including: In S1301, the user planning path of the autonomous mobile device is obtained. The user planning path includes a multi-point drawing path or a point-to-point path. In S1302, the endpoint of the user-planned path is located within the global grid map; In S1303, the first movement path is planned using a global path planning algorithm based on the global grid map, according to the end path point and the current first pose of the autonomous mobile device. In S1304, the first moving path and the remaining path after the end path point in the user-planned path point are spliced ​​together to obtain the first target moving path, and the autonomous mobile device is controlled to move according to the first target moving path.

[0050] Multi-point drawing path refers to a path formed by users continuously drawing multiple points along the way through the interactive interface, while point-to-point path refers to a straight path formed by connecting the start and end points with a straight line, where the user only sets the start and end points.

[0051] Pose refers to the spatial position and attitude of an autonomous mobile device, including coordinate position and heading angle, and is the basic input for path planning and motion control.

[0052] In this embodiment of the invention, when the autonomous mobile device performs movement control based on a global grid map, it obtains the current user-planned path and determines the end path point of the user-planned path in the global grid map, which is the last reachable path point in the overlapping part of the user-planned path and the global grid map. Furthermore, the autonomous mobile device also obtains its current first pose and, combined with the spatial relationship between the end path point and the first pose, uses a global path planning algorithm such as A*, Dijkstra's algorithm, or A* search to plan a movement path from the current position to the end path point in the global grid map, denoted as the first movement path. Subsequently, the autonomous mobile device concatenates the first movement path with the remaining paths in the user-planned path after the end path point to form a complete path, denoted as the first target movement path. This first target movement path is used as the navigation basis for the autonomous mobile device for movement control, thereby ensuring that the autonomous mobile device moves along the user's planned intention while taking into account environmental obstacles and global optimality, ultimately achieving accurate, efficient, and safe path tracking control.

[0053] For example, please refer to Figure 5 The user-planned path is a point-to-point straight path P0 leading to the destination B. The autonomous mobile device is currently located at point A on this path, but part of the path extends beyond the boundary of the global grid map. After acquiring this path, the autonomous mobile device determines its end path point C within the global grid map and, combined with its current first pose, uses a global path algorithm to plan a first movement path P1 from its current position to point C. Subsequently, this first movement path is concatenated with the remaining path after point C in the original user-planned path to generate the first target movement path P2, and movement control is performed accordingly.

[0054] Through the above motion control methods, autonomous mobile devices can effectively integrate user intent and environmental constraints in open environments, achieving smooth, continuous, and safe path tracking.

[0055] Optionally, in one embodiment, the control method for the autonomous mobile device provided by the present invention further includes: If a dynamic obstacle is detected on the current actual movement path of the autonomous mobile device, a path point is selected from the part of the first target movement path located in the global grid map as an obstacle avoidance look-ahead point. Based on the obstacle avoidance look-ahead point and the current second pose of the autonomous mobile device, a second movement path for avoiding dynamic obstacles is planned using a global path planning algorithm based on a global grid map. The remaining paths after the obstacle avoidance look-ahead point in the second movement path and the first target movement path are spliced ​​together to obtain the second target movement path, and the autonomous mobile device is controlled to move according to the second target movement path.

[0056] Dynamic obstacles refer to objects that appear or move dynamically during the movement of autonomous mobile devices. Their positions and motion states are uncertain, such as pedestrians, pets, and other mobile devices, making them difficult to avoid using a pre-built global grid map.

[0057] It should be noted that during actual movement, various factors may cause a certain deviation between the current actual movement path of the autonomous mobile device and the first target movement path, such as uneven ground friction, slippage of the walking mechanism, or sensor errors. The current actual movement path refers to the path corresponding to the actual position of the autonomous mobile device at a certain moment and its subsequent direction of travel, which may not completely coincide with the first target movement path.

[0058] In this embodiment of the invention, the autonomous mobile device perceives the surrounding environment in real time through the configured environmental sensors and detects whether there are dynamic obstacles on its current actual movement path. If there are dynamic obstacles, a path replanning mechanism is triggered to select a path point that is not occupied by dynamic obstacles from the part of the first target movement path located in the global grid map as the obstacle avoidance look-ahead point. This obstacle avoidance look-ahead point is also the target point of the current path planning.

[0059] Environmental sensors include, but are not limited to, one or more of LiDAR, depth cameras, and ultrasonic sensors. For example, an autonomous mobile device can use LiDAR alone to scan its environment and detect the presence of dynamic obstacles on its current path; it can also combine a depth camera and ultrasonic sensors for multimodal perception to comprehensively detect the presence of dynamic obstacles on its current actual movement path. For instance, an autonomous mobile device can use LiDAR to scan its surroundings in real time, constructing point cloud data, combining this with RGB-D information acquired by a depth camera to accurately identify the location of dynamic obstacles, and using ultrasonic sensors to supplement near-field blind spot detection, ensuring the comprehensiveness and reliability of environmental perception.

[0060] After selecting an obstacle avoidance look-ahead point, the autonomous mobile device temporarily marks the area corresponding to the dynamic obstacle in the global grid map as an impassable area. Based on the updated map information and the spatial relationship between the second pose and the obstacle avoidance look-ahead point, a global path planning algorithm is used to replan a feasible path from the current pose to the obstacle avoidance look-ahead point, ensuring that the area occupied by the dynamic obstacle is avoided. This feasible path is recorded as the second movement path. The autonomous mobile device can use global path planning algorithms such as A*, Dijkstra's algorithm, or A-search, comprehensively considering kinematic constraints, environmental topology, and path optimality indices to generate a smooth and executable second movement path.

[0061] Subsequently, the autonomous mobile device seamlessly stitches the second movement path with the remaining path of the first target movement path after the obstacle avoidance look-ahead point, forming a complete second target movement path. Movement control is then performed based on this second target movement path, allowing the device to return to the first target movement path after avoiding dynamic obstacles. During the movement along the second movement path, the autonomous mobile device switches to obstacle avoidance mode, moving at a set speed below the normal cruising speed to ensure safe passage through complex dynamic environments.

[0062] Optionally, in one embodiment, selecting path points as obstacle avoidance look-ahead points from the portion of the first target's movement path located in the global grid map includes: From the portion of the first target's movement path located on the global grid map, select path points whose distance from the autonomous mobile device is used as reference distances as obstacle avoidance look-ahead points.

[0063] This invention provides an optional obstacle avoidance look-ahead point selection method.

[0064] Specifically, the autonomous mobile device selects a path point from the portion of the first target's movement path located in the global grid map, with the distance between the autonomous mobile device and the path point as a reference distance, as an obstacle avoidance look-ahead point. This reference distance can be dynamically adjusted according to the device's movement speed and the sensor's perception range to ensure that the obstacle avoidance look-ahead point is located within a reliable perception area and has sufficient path adjustment margin, thereby improving the timeliness and reliability of obstacle avoidance decisions.

[0065] For example, when an autonomous mobile device operates at a higher speed, the reference distance can be appropriately increased to predict path trends in advance; conversely, when the autonomous mobile device operates at a lower speed, the reference distance can be reduced accordingly to improve path tracking accuracy. Furthermore, autonomous mobile devices can also reduce the reference distance when ambient light conditions weaken or sensor confidence decreases, thereby mitigating obstacle avoidance risks caused by perception uncertainties.

[0066] By dynamically adjusting the reference distance as described above, the selection of the obstacle avoidance look-ahead point is made more adaptable to the real-time operating state, taking into account both the path tracking efficiency and the obstacle avoidance response sensitivity, effectively improving the operating safety and fluency of the autonomous mobile device in a complex dynamic environment.

[0067] Optionally, in one embodiment, the control method for the autonomous mobile device provided by the present invention further includes: If the planning of the second moving path fails, increase the reference distance, and transfer to the step of selecting a path point whose distance from the autonomous mobile device is the reference distance as the obstacle avoidance look-ahead point from the part of the first target moving path located in the global grid map.

[0068] It can be understood that in the actual environment, due to limited space or occlusion by other obstacles, it may be impossible to generate an effective second moving path for the obstacle avoidance look-ahead point at the current reference distance, resulting in the failure of planning the second moving path.

[0069] Correspondingly, to ensure that a feasible second moving path can be successfully planned, the autonomous mobile device will increase the reference distance, and return to the step of selecting a path point whose distance from the autonomous mobile device is the reference distance as the obstacle avoidance look-ahead point from the part of the first target moving path located in the global grid map, and re-select a farther path point as the new obstacle avoidance look-ahead point to expand the exploration range of path replanning and improve the avoidance feasibility.

[0070] For example, when the reference distance is adjusted from L1 to L2, where L1 < L2, the obstacle avoidance look-ahead point moves backward from P1 to P2, making the planned end point more backward, thereby providing a more ample space margin for the replanning of the second moving path and increasing the success rate of generating the second moving path. When the reference distance increases to L2, the obstacle avoidance look-ahead point migrates from P1 to P2. Although it may slightly sacrifice the immediate responsiveness, it significantly improves the global vision and adjustment space of path planning.

[0071] Through the mechanism of gradually increasing the reference distance as described above, the autonomous mobile device can skip local infeasible areas and find an alternative path that is passable in a global sense. This mechanism enhances the fault tolerance and environmental adaptability of path planning, can effectively avoid decision-making deadlocks caused by local insolvability, achieves a better balance between time and space constraints, and maintains the continuity and overall efficiency of the mobile task.

[0072] Optionally, in one embodiment, if a dynamic obstacle is detected on the current actual moving path of the autonomous mobile device, the selection of a path point as the obstacle avoidance look-ahead point from the part of the first target moving path located in the global grid map includes: If a dynamic obstacle is detected on the current actual moving path of the autonomous mobile device, control the autonomous mobile device to stop moving; If the time spent in motion reaches the second time threshold, and there are still dynamic obstacles on the actual movement path, then a path point is selected from the portion of the first target movement path located in the global grid map as an obstacle avoidance look-ahead point.

[0073] In this embodiment of the invention, when the autonomous mobile device detects a dynamic obstacle on its current actual movement path, it does not immediately replan the path. Instead, it first stops moving and initiates a timing mechanism to determine the continued existence of the dynamic obstacle. When the stop time reaches a preset second duration threshold, and the dynamic obstacle still exists on the current path, the autonomous mobile device determines that the path needs to be replanned to bypass the obstacle. Accordingly, the autonomous mobile device further selects path points from the portion of the first target movement path located in the global grid map as obstacle avoidance look-ahead points for path replanning. For details, please refer to the relevant descriptions in the above embodiments; they will not be repeated here.

[0074] It should be noted that the value of the second duration threshold is not specifically limited in this embodiment of the invention. For example, it can be statically set to 3 seconds, 5 seconds, or 10 seconds according to actual needs; or the value of the second duration threshold can be dynamically determined. For example, the autonomous mobile device also acquires the moving direction and speed of the dynamic obstacle, predicts its future trajectory through a kinematic model, and estimates the duration for which the obstacle occupies the first target moving path, i.e., the departure duration. Then, the autonomous mobile device dynamically adjusts the second duration threshold for waiting according to the predicted departure duration. If the departure duration is short, a shorter second duration threshold is set so that the device can start replanning after a short wait; if the departure duration is long, the second duration threshold is extended accordingly to avoid frequent starts and stops or premature abandonment of the original path.

[0075] Optionally, in one embodiment, if a dynamic obstacle is detected on the current actual movement path of the autonomous mobile device, the autonomous mobile device is controlled to stop moving, including: If a dynamic obstacle is detected on the current actual movement path of the autonomous mobile device, the offset distance of the autonomous mobile device relative to the first target movement path is obtained. If the offset distance is less than or equal to the second distance threshold, the autonomous mobile device is controlled to stop moving.

[0076] It should be noted that when the dynamic obstacle detected by the autonomous mobile device is an elongated obstacle (such as a car), the autonomous mobile device may be unable to detect the complete outline of the dynamic obstacle through environmental sensors. This may result in the risk of collision with the dynamic obstacle even after the autonomous mobile device replans its path, thus triggering the stop mechanism again and causing obstacle avoidance to be unsuccessful. To address this, this embodiment of the invention further introduces an offset distance determination mechanism as an additional condition for whether the stop mechanism is triggered.

[0077] In this embodiment of the invention, after the autonomous mobile device completes a first target obstacle avoidance movement path as described in the above embodiments, while controlling the movement of the autonomous mobile device according to the first target movement path, it continues to detect in real time whether there are dynamic obstacles on its actual movement path through environmental sensors. Unlike the above embodiments, when the autonomous mobile device detects a dynamic obstacle again, it does not directly enter a stopped state. Instead, it first calculates its current offset distance relative to the first target movement path. If the offset distance is less than or equal to a preset second distance threshold, it indicates that the detected dynamic obstacle is not the same dynamic obstacle detected previously, but a newly appeared dynamic obstacle. Only then is the stopping mechanism triggered, and a new round of obstacle avoidance replanning process initiated. Please refer to the relevant descriptions in the above embodiments for details, which will not be repeated here. Furthermore, if the offset distance is greater than the second distance threshold, it is determined that the currently detected dynamic obstacle is still the same dynamic obstacle previously identified. In this case, the stopping mechanism is not triggered, but the process proceeds to the step of selecting path points from the portion of the first target movement path located in the global grid map as obstacle avoidance look-ahead points, directly replanning the path. Please refer to the relevant descriptions in the above embodiments for details, which will not be repeated here.

[0078] The autonomous mobile device can calculate the offset distance as follows: Obtain the current actual location coordinates of the autonomous mobile device, and calculate the distance from the point to the straight line as the offset distance by combining the path line equation of the first target's movement path; if the first target's movement path is a curve, then use piecewise linear fitting and take the nearest line segment for calculation.

[0079] Furthermore, the embodiments of the present invention do not impose specific restrictions on the setting of the second distance threshold, which can be adjusted according to the actual movement state. For example, it can be set to a larger value when running at low speed and a smaller value when running at high speed, so as to balance safety and driving efficiency.

[0080] By introducing an offset distance determination mechanism, the above approach effectively avoids repeated obstacle avoidance decisions caused by incomplete perception of the outline of the same elongated dynamic obstacle, thus improving the stability and continuity of path planning. Simultaneously, this mechanism enhances the autonomous mobile device's ability to distinguish between new and old obstacles, enabling it to respond promptly to real threats in complex dynamic environments while reducing unnecessary pauses and replanning, thereby achieving smarter and smoother obstacle avoidance.

[0081] Optionally, in one embodiment, if the offset distance is less than or equal to a second distance threshold, controlling the autonomous mobile device to stop moving includes: If the offset distance is less than or equal to the second distance threshold, the probability of the dynamic obstacle actively leaving is obtained; If the probability of actively leaving exceeds a preset threshold, the autonomous mobile device will be controlled to stop moving.

[0082] In this embodiment of the invention, to avoid frequent starts and stops caused by brief stays of dynamic obstacles, an active departure probability assessment mechanism is further introduced as an additional condition for whether to trigger the stop mechanism.

[0083] In this scenario, after the autonomous mobile device detects that the offset distance is less than or equal to the second distance threshold, it does not immediately enter a stopped state. Instead, it further predicts the probability of actively leaving the detected dynamic obstacle according to the configured departure probability prediction strategy, thereby obtaining the probability of actively leaving the dynamic obstacle. No specific restrictions are placed on the configuration of the departure probability prediction strategy here.

[0084] As mentioned above, after obtaining the probability of the dynamic obstacle leaving actively, the autonomous mobile device further determines whether the probability of the dynamic obstacle leaving actively is greater than a preset probability threshold. If the probability of the dynamic obstacle leaving actively is greater than the probability threshold, it is considered that the obstacle will leave on its own in a short time. At this time, the autonomous mobile device is controlled to enter a stop state and wait for the dynamic obstacle to leave on its own.

[0085] As an optional implementation, the departure probability prediction strategy can be configured as follows: The autonomous mobile device first identifies the type of dynamic obstacle, obtains the obstacle type, and then determines the active departure probability corresponding to the obstacle type based on the preset type-probability mapping relationship.

[0086] For example, obstacles with high autonomous mobility, such as pedestrians and animals, are assigned a relatively high probability of actively leaving; while obstacles lacking autonomous mobility, such as turned-off vehicles and stationary objects, are assigned a lower probability of actively leaving. This mapping relationship can be derived from historical observation data and dynamically adjusted according to actual application scenarios to improve the accuracy of judgment. For instance, the probability of actively leaving an obstacle type of pedestrian is set to 0.85; the probability of actively leaving an obstacle type of pet is 0.75; the probability of actively leaving an obstacle type of parked bicycle or handcart is 0.6; and the probability of actively leaving an obstacle type of turned-off vehicle is 0.3.

[0087] It should be noted that the embodiments of the present invention do not impose specific limitations on the method of obstacle type identification. Identification can be performed using methods such as deep learning-based image classification models, LiDAR point cloud analysis, or fusion of multi-sensor information. For example, an autonomous mobile device can acquire image information of dynamic obstacles through an image acquisition component, analyze the image information using a pre-trained obstacle classification model, and identify the category of the dynamic obstacle; or it can acquire point cloud data of obstacles through LiDAR, and use point cloud feature extraction and clustering algorithms to determine its type.

[0088] In other embodiments, if the probability of the dynamic obstacle actively leaving is less than or equal to a preset probability threshold, the autonomous mobile device determines that the dynamic obstacle is unlikely to leave on its own in the short term. At this time, there is no need to enter the stop waiting state. Instead, it switches to the step of selecting path points as obstacle avoidance look-ahead points from the part of the first target movement path located in the global grid map and directly replans the path. For details, please refer to the relevant descriptions in the above embodiments. They will not be repeated here.

[0089] For example, assuming the probability threshold is configured to 0.7, when an obstacle type is identified as a pedestrian, the autonomous mobile device obtains a corresponding active departure probability of 0.85, which is greater than the probability threshold of 0.7. Therefore, it is determined that the pedestrian may move out of the current path autonomously in a short period of time, and the autonomous mobile device enters a stop state. When an obstacle type is identified as a stalled vehicle, the autonomous mobile device obtains a corresponding active departure probability of 0.3, which is less than the probability threshold of 0.7. Therefore, it is determined that the stalled vehicle is unlikely to leave on its own in a short period of time. In this case, there is no need to enter a stop waiting state. Instead, the device directly selects a path point from the part of the first target's movement path located in the global grid map as an obstacle avoidance look-ahead point, initiates path replanning, and avoids the stalled vehicle.

[0090] Optionally, in one embodiment, if a dynamic obstacle is detected on the current actual movement path of the autonomous mobile device, the autonomous mobile device is controlled to stop moving, including: If a dynamic obstacle is detected within the stopping distance of the current actual movement path of the autonomous mobile device, the autonomous mobile device is controlled to stop moving.

[0091] The obstacle stopping distance is a preset safety threshold used to determine whether a dynamic obstacle poses an immediate threat to the current movement. The specific distance can be set according to actual needs.

[0092] In this embodiment of the invention, when the autonomous mobile device detects a dynamic obstacle on its current actual movement path, it further determines whether the dynamic obstacle is within the stopping distance range. If the dynamic obstacle is within the stopping distance range, it indicates that the dynamic obstacle poses an immediate collision risk to the autonomous mobile device, and it immediately enters a stopping state to avoid the collision risk and ensure operational safety. If the dynamic obstacle is outside the stopping distance range, it indicates that there is no immediate collision risk, and the autonomous mobile device can continue to move while continuously tracking the dynamic obstacle. If the dynamic obstacle subsequently enters the stopping distance range, it immediately enters a stopping state.

[0093] The autonomous mobile device acquires the current braking distance in real time and sets the stopping distance based on this distance. The stopping distance can be set to be greater than or equal to the current braking distance, ensuring that the stopping distance is always no less than the current braking distance and guaranteeing that the autonomous mobile device can safely stop in any operating state. For example, the autonomous mobile device can acquire the current braking distance in the following way: Obtain the current real-time speed and acceleration, as well as the coefficient of friction of the road surface; Based on the obtained real-time speed, acceleration, and friction coefficient, the current braking distance is calculated using a dynamic model.

[0094] By dynamically integrating real-time speed, acceleration, and road friction coefficient, the braking distance calculation accurately reflects the current operating state and environmental characteristics, thus ensuring that the obstacle avoidance distance is always physically achievable. For example, when the autonomous mobile device is operating at high speed or on a slippery surface, the calculated current braking distance is extended, and the obstacle avoidance distance is increased accordingly to reserve sufficient braking space; conversely, under low-speed or high-adhesion road conditions, the calculated current braking distance is shortened, and the obstacle avoidance distance is reduced accordingly to improve operating efficiency and avoid overly conservative obstacle avoidance responses.

[0095] Optionally, in one embodiment, determining the target size for initializing the raster map includes: The current level of structuring of the current scene is evaluated to obtain the current level of structuring of the current scene; Based on the correspondence between the degree of structuring and the composition size, the composition size corresponding to the current degree of structuring is determined as the target size.

[0096] The degree of structuring refers to the regularity and geometric alignment of the distribution of objects in a scene, reflecting the level of orderliness of the environment. The higher the degree of structuring, the more regular the arrangement of obstacles in the scene, such as city roads or indoor corridors; the lower the degree of structuring, the more disordered the arrangement of obstacles in the scene.

[0097] Accordingly, when determining the target size for initializing the raster map, the autonomous mobile device first evaluates the structuring level of the current scene to obtain the current structuring level of the current scene, and then determines the structuring size corresponding to the current structuring level as the target size based on the correspondence between the structuring level and the composition size.

[0098] The correspondence between the degree of structuring and the size of the map can be established based on preset empirical data or learning models. For example, a high degree of structuring corresponds to a smaller size of the map to improve map building efficiency and reduce computational overhead; while a low degree of structuring matches a larger size of the map to retain enough environmental details for path planning and obstacle avoidance decisions.

[0099] Optionally, in one embodiment, the control method for the autonomous mobile device provided by the present invention further includes: Acquire the sensing data from the environmental sensors of the autonomous mobile device and calculate the feature density of the sensing data. If the feature density is less than the density threshold, it is determined that the autonomous mobile device is in an open environment. Alternatively, if the autonomous mobile device uses satellite positioning, and the confidence level of its sensor feature matching-based positioning mode is less than a confidence threshold, then the autonomous mobile device is determined to be in an open environment. It can be understood that the "confidence threshold" refers to a preset numerical limit set for the reliability index of sensor feature matching-based positioning. This reliability index can be calculated based on the proportion of feature matching points, the probability distribution of positioning results, or the location estimation covariance matrix. When the positioning confidence level is lower than this threshold, it indicates that the positioning result is unreliable, thus determining that the autonomous mobile device is in an open environment.

[0100] The embodiments of the present invention further provide two optional environmental identification methods.

[0101] Firstly, autonomous mobile devices can calculate the feature density of the sensor data collected by environmental sensors and determine whether this feature density is less than a preset density threshold. If it is less, it is determined that the current environment is open. Feature density reflects the density of identifiable feature points in the environment; the lower the density, the sparser the environmental texture or the sparser the object distribution. Therefore, an open scene can be identified by setting a density threshold. The specific value of the density threshold is not limited here and can be determined according to the actual sensor performance. For example, for LiDAR, the density threshold can be set to 0.1 feature points per square meter, and for depth cameras, the density threshold can be set to 0.5 feature points per square meter, and so on.

[0102] Secondly, autonomous mobile devices can also determine the type of environment based on the positioning mode they are using. When an autonomous mobile device uses satellite positioning mode, and the confidence level of the sensor feature matching-based positioning mode is less than the confidence threshold, it is determined that the current environment is open. It should be noted that autonomous mobile devices default to using sensor feature matching positioning mode for positioning. They only switch to satellite positioning mode when stable positioning cannot be obtained through sensor feature matching. If satellite positioning is available at this time but the confidence level of feature matching positioning is low, it indicates that there are no significant features available for matching in the environment, which is consistent with the characteristics of an open environment.

[0103] It should be noted that the two environmental identification methods mentioned above can be used alone or in combination to improve the robustness of environmental determination.

[0104] As can be seen from the above, the control method for autonomous mobile devices provided by this invention, if the current environment is detected to be an open environment, determines the target size for initializing the grid map, and initializes a first grid map with the target size using the current first position of the autonomous mobile device as a reference point; the first grid map is used as the global grid map of the autonomous mobile device, and the occupancy status of the grids in the global grid map is updated according to the perception data collected in real time by the autonomous mobile device through environmental sensors; the autonomous mobile device is then controlled to move based on the global grid map. Compared with related technologies that cannot perform effective autonomous movement in open environments, this solution does not actually construct a global grid map when the autonomous mobile device faces an open environment. Instead, it constructs a local grid map with the location of the autonomous mobile device as a reference point, and uses this local grid map as the global grid map. This allows the autonomous mobile device to still have path planning capabilities in open environments, thereby effectively performing movement control and achieving stable navigation and obstacle avoidance in open environments.

[0105] To facilitate better implementation of the above-described control method for autonomous mobile devices, this embodiment of the invention also provides a corresponding control device for autonomous mobile devices. The meanings of the terms used are the same as in the above-described control method for autonomous mobile devices; for specific implementation details, please refer to the descriptions in the above method embodiments.

[0106] Please refer to Figure 6 , Figure 6 This is a schematic diagram of the structure of a control device for an autonomous mobile device provided in an embodiment of the present invention. The control device for the autonomous mobile device may include a map building module 210, a map maintenance module 220, and a movement control module 230, wherein... The map building module 210 is used to determine the target size for initializing the raster map if the current environment is detected to be an open environment, and to initialize a first raster map with the target size using the current first position of the autonomous mobile device as a reference point. The map maintenance module 220 is used to use the first grid map as the global grid map of the autonomous mobile device, and to update the occupancy status of the grid in the global grid map according to the perception data collected in real time by the autonomous mobile device through the environmental sensor. The motion control module 230 is used to control the movement of autonomous mobile devices based on a global grid map.

[0107] Optionally, in one embodiment, the map building module 210 is further configured to initialize a second grid map with a target size, using the current second location of the autonomous mobile device as a reference point, when the map update conditions are met; the map maintenance module 220 is further configured to use the second grid map as a global grid map.

[0108] Optionally, in one embodiment, the map maintenance module 220 is further configured to determine the shared grids of the second grid map and the global grid map; delete the occupancy status of non-shared grids in the global grid map; and copy the occupancy status of shared grids in the global grid map to the second grid map.

[0109] Optionally, in one embodiment, the map update conditions include: The usage time of the first grid map as a global grid map reaches the first duration threshold; and / or The first grid map, as a global grid map, controls the movement of autonomous mobile devices when the movement distance reaches the first distance threshold.

[0110] Optionally, in one embodiment, the mobility control module 230 is used to obtain the user-planned path of the autonomous mobile device, the user-planned path including a multi-point drawing path or a point-to-point path; determine the end path point of the user-planned path located in the global grid map; plan a first mobility path based on the global grid map using a global path planning algorithm according to the end path point and the current first pose of the autonomous mobile device; stitch the first mobility path and the remaining path after the end path point in the user-planned path to obtain a first target mobility path, and control the autonomous mobile device to move according to the first target mobility path.

[0111] Optionally, in one embodiment, the motion control module 230 is further configured to: if a dynamic obstacle is detected on the current actual movement path of the autonomous mobile device, select a path point from the portion of the first target movement path located in the global grid map as an obstacle avoidance look-ahead point; plan a second movement path for avoiding the dynamic obstacle based on the obstacle avoidance look-ahead point and the current second pose of the autonomous mobile device using a global path planning algorithm based on the global grid map; splice the second movement path and the remaining path after the obstacle avoidance look-ahead point in the first target movement path to obtain the second target movement path, and control the movement of the autonomous mobile device according to the second target movement path.

[0112] Optionally, in one embodiment, the motion control module 230 is used to select path points from the portion of the first target motion path located in the global grid map, with a distance between the path point and the autonomous mobile device as a reference distance, as obstacle avoidance look-ahead points.

[0113] Optionally, in one embodiment, the motion control module 230 is further configured to, if planning the second motion path fails, increase the reference distance and proceed to the step of selecting a path point from the portion of the first target motion path located in the global grid map with a distance between it and the autonomous mobile device as the reference distance as the obstacle avoidance look-ahead point.

[0114] Optionally, in one embodiment, the mobility control module 230 is configured to: if a dynamic obstacle is detected on the current actual movement path of the autonomous mobile device, control the autonomous mobile device to stop moving; if the duration of stopping movement reaches a second duration threshold and a dynamic obstacle still exists on the actual movement path, select a path point from the portion of the first target movement path located in the global grid map as an obstacle avoidance look-ahead point.

[0115] Optionally, in one embodiment, the motion control module 230 is configured to: if a dynamic obstacle is detected on the current actual movement path of the autonomous mobile device, obtain the offset distance of the autonomous mobile device relative to the first target movement path; if the offset distance is less than or equal to a second distance threshold, control the autonomous mobile device to stop moving.

[0116] Optionally, in one embodiment, the map building module 210 is used to evaluate the structuring level of the current scene to obtain the current structuring level of the current scene; and to determine the structuring size corresponding to the current structuring level as the target size according to the correspondence between the structuring level and the composition size.

[0117] Optionally, in one embodiment, the control device for the autonomous mobile device provided by the present invention further includes a ring recognition module, used to acquire the sensing data of the environmental sensors of the autonomous mobile device and calculate the feature density of the sensing data. If the feature density is less than the density threshold, it is determined that the autonomous mobile device is in an open environment; or, if the autonomous mobile device adopts a satellite positioning mode and the confidence level of the positioning mode based on sensor feature matching of the autonomous mobile device is less than the confidence level threshold, it is determined that the autonomous mobile device is in an open environment.

[0118] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0119] In one embodiment, an autonomous mobile device is provided, the internal structure of which can be as follows: Figure 7As shown, the autonomous mobile device includes a main body and a memory 310, a processor 320, a power supply 330, an environmental sensor 340, a working mechanism 350, a communication module 360, a positioning module 370, a drive wheel 380, and a bus 390, all mounted on the main body. The processor 320 is coupled to the memory 310, power supply 330, environmental sensor 340, working mechanism 350, communication module 360, positioning module 370, and drive wheel 380 via the bus 390.

[0120] Memory 310 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM). The RAM can be directly read and written by the processor 320 and can be used to store executable programs (such as machine instructions) of the operating system or other running programs, as well as user and application data. The RAM may include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), and double data rate synchronous dynamic random access memory (DDRAM). (memory, DDR SDRAM, etc.)

[0121] Non-volatile memory can also store executable programs and user and application data, and can be pre-loaded into random access memory for direct reading and writing by the processor 320. Non-volatile memory can include disk storage devices and flash memory.

[0122] The memory 310 is used to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 320. The one or more computer programs include multiple instructions that, when executed by the processor 320, enable a method for detecting dirt in a camera on an autonomous mobile device.

[0123] In other embodiments, the autonomous mobile device also includes an external memory interface for connecting to an external memory to expand the storage capacity of the autonomous mobile device.

[0124] Processor 320 may include one or more processing units, such as: application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, video codec, digital signal processor (DSP), baseband processor, and / or neural network processor. Processing units (NPUs), etc. Different processing units can be independent devices or integrated into one or more processors.

[0125] The processor 320 provides computing and control capabilities; for example, the processor 320 is used to execute computer programs stored in the memory 310 to implement the path planning method described above.

[0126] The power supply 330 is used to power autonomous mobile devices. In one embodiment of the present invention, the power supply 330 may include any one or more power supply devices of the type of battery, fuel generator, solar power generation module, wind power generation module, etc.

[0127] The environmental sensor 340 is used to acquire information for the autonomous mobile device, such as environmental information and movement information of the autonomous mobile device. In one embodiment of the present invention, the environmental sensor 340 may include a binocular camera, and may also include one or more sensors of the type such as lidar, infrared sensor, encoder, etc.

[0128] The working mechanism 350 is used to perform corresponding tasks, such as mowing, de-icing, patrolling, sweeping, and spraying pesticides. In some embodiments of the present invention, the working mechanism 350 may include a motor, a transmission mechanism, and a blade disc. When the autonomous mobile device is a lawnmower, the motor can drive the blade disc to rotate through the transmission mechanism to achieve the mowing function. The motor can also control the movement of the blades to adjust the mowing height and the mowing area.

[0129] The communication module 360 ​​is used to enable communication between the autonomous mobile device and other devices. In one embodiment of the present invention, the communication module 360 ​​can interact with other devices via wired and / or wireless communication. The aforementioned wireless communication may include one or more combinations of communication methods such as Bluetooth communication, Wi-Fi communication, and Near Field Communication (NFC).

[0130] The positioning module 370 is used to determine the location of the autonomous mobile device. In some embodiments of the present invention, the positioning module 370 may include one or more positioning modules of the type such as Global Positioning System (GPS), inertial navigation system, and real-time kinematic (RTK) carrier phase differential system.

[0131] The drive wheel 380 is used to enable the autonomous mobile device to move. In some embodiments of the present invention, the drive wheel 380 can realize the movement function of the autonomous mobile device according to the control of the processor 320. In some embodiments of the present invention, the drive wheel 380 may include a left drive wheel and a right drive wheel.

[0132] Bus 390 is used at least to provide a channel for communication between the memory 310, processor 320, power supply 330, environmental sensor 340, operating mechanism 350, communication module 360, positioning module 370, and drive wheel 380 in the autonomous mobile device.

[0133] In other embodiments of the present invention, the autonomous mobile device may further include a collision avoidance component and a steering assembly. The collision avoidance component can be used to prevent the drive wheels 380 from colliding with obstacles or the like in front of the autonomous mobile device. The steering assembly can be used to adjust the driving direction of the drive wheels 380.

[0134] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the autonomous mobile device. In other embodiments of the present invention, the autonomous mobile device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0135] In one embodiment, an autonomous mobile device is provided, including an environmental sensor, a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control method for the autonomous mobile device described in the above embodiment. For example, please refer to... Figure 8 The actual product form of the autonomous mobile device can be a lawn mowing robot. The lawn mowing robot also includes an anti-collision mechanism, an operating mechanism (not shown in the figure), and drive wheels. The operating mechanism includes a rotatable blade, which can perform lawn mowing operations while the lawn mowing robot is driven by the drive wheels to move in the lawn mowing area.

[0136] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0137] The present invention also provides a computer program product comprising a computer program that, when executed on a processor, causes the processor to implement the steps in the control method for an autonomous mobile device provided by the present invention.

[0138] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0139] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

[0140] It should be noted that when the above embodiments of the present invention are applied to specific products or technologies, and user-related data is involved, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

Claims

1. A control method of an autonomous mobile device, characterized by, The method comprises the following steps: If it is detected that the current environment is an open environment, a target size for initializing a grid map is determined, and a first grid map with the target size is initialized with a first position of the autonomous mobile device as a reference point; The first grid map is taken as a global grid map of the autonomous mobile device, and the occupancy state of a grid in the global grid map is updated according to perception data collected by an environmental sensor of the autonomous mobile device in real time; The autonomous mobile device is controlled according to the global grid map.

2. The control method of an autonomous mobile device according to claim 1, characterized by, After the first grid map is taken as the global grid map of the autonomous mobile device, the method further comprises the following steps: When a map updating condition is met, a second grid map with the target size is initialized with a second position of the autonomous mobile device as a reference point; The second grid map is taken as the global grid map.

3. The control method of an autonomous mobile device according to claim 2, characterized by, Before the second grid map is taken as the global grid map, the method further comprises the following steps: Common grids of the second grid map and the global grid map are determined; The occupancy state of a non-common grid in the global grid map is deleted, and the occupancy state of the common grid in the global grid map is copied to the second grid map.

4. The control method of an autonomous mobile device according to claim 2, characterized by, The map updating condition comprises: The use time length of the first grid map as the global grid map reaches a first time length threshold; and / or The moving distance of the first grid map as the global grid map for controlling the autonomous mobile device to move reaches a first distance threshold. 5.The control method of an autonomous mobile device according to claim 1, wherein The method of controlling the autonomous mobile device to move according to the global grid map comprises the following steps: A user planned path of the autonomous mobile device is obtained, the user planned path comprising a multi-point drawing path or a point-to-point path; An end path point of the user planned path located in the global grid map is determined; A first moving path is planned based on the global grid map according to the end path point and a first pose of the autonomous mobile device; The first moving path and the remaining path of the user planned path point located after the end path point are spliced to obtain a first target moving path, and the autonomous mobile device is controlled to move according to the first target moving path.

6. The control method of an autonomous mobile device according to claim 5, wherein The method further comprises the following steps: If it is detected that there is a dynamic obstacle on an actual moving path of the autonomous mobile device, a path point in the part of the first target moving path located in the global grid map is selected as an obstacle avoidance lookahead point; A second moving path for avoiding the dynamic obstacle is planned based on the global grid map according to the obstacle avoidance lookahead point and a second pose of the autonomous mobile device; The second moving path and the remaining path of the first target moving path located after the obstacle avoidance lookahead point are spliced to obtain a second target moving path, and the autonomous mobile device is controlled to move according to the second target moving path.

7. The control method of an autonomous mobile device according to claim 6, wherein The method of selecting a path point in the part of the first target moving path located in the global grid map as an obstacle avoidance lookahead point comprises the following steps: The path point with a distance from the autonomous mobile device as a reference distance is selected as an obstacle avoidance lookahead point from a part of the global grid map where the first target movement path is located. 8.The control method of an autonomous mobile device according to claim 7, wherein Further comprising: If the second movement path fails to be planned, the reference distance is increased, and the step of selecting the path point with a distance from the autonomous mobile device as a reference distance from a part of the global grid map where the first target movement path is located is entered. 9.The control method of an autonomous mobile device according to claim 6, wherein, If a dynamic obstacle is detected on the actual movement path of the autonomous mobile device, the path point is selected from a part of the global grid map where the first target movement path is located as an obstacle avoidance lookahead point, comprising: If a dynamic obstacle is detected on the actual movement path of the autonomous mobile device, the autonomous mobile device is controlled to stop moving; If the stop moving duration reaches a second duration threshold, and the dynamic obstacle still exists on the actual movement path, the path point is selected from a part of the global grid map where the first target movement path is located as an obstacle avoidance lookahead point. 10.The control method of an autonomous mobile device according to claim 9, wherein If a dynamic obstacle is detected on the actual movement path of the autonomous mobile device, the autonomous mobile device is controlled to stop moving, comprising: If a dynamic obstacle is detected on the actual movement path of the autonomous mobile device, an offset distance of the autonomous mobile device relative to the first target movement path is obtained; If the offset distance is less than or equal to a second distance threshold, the autonomous mobile device is controlled to stop moving. 11.The control method of an autonomous mobile device according to claim 1, wherein The target size for initializing the grid map is determined, comprising: The structured degree of the current scene is evaluated to obtain a current structured degree of the current scene; According to a correspondence between the structured degree and the mapping size, a mapping size corresponding to the current structured degree is determined as the target size. 12.The control method of an autonomous mobile device according to claim 1, wherein, Further comprising: The perception data of the environmental sensor of the autonomous mobile device is obtained, and a feature density of the perception data is calculated, and if the feature density is less than a density threshold, it is determined that the autonomous mobile device is in an open environment; Or, if the autonomous mobile device adopts a satellite positioning mode, and a confidence degree of a positioning mode based on sensor feature matching of the autonomous mobile device is less than a confidence degree threshold, it is determined that the autonomous mobile device is in an open environment.

13. A control device of an autonomous mobile device, characterized by, Comprising: A map construction module is configured to determine a target size for initializing a grid map if it is detected that the current environment is an open environment, and initialize a first grid map with the target size with a first position of the autonomous mobile device as a reference point; A map maintenance module is configured to take the first grid map as a global grid map of the autonomous mobile device, and update an occupancy state of a grid in the global grid map according to perception data collected by the environmental sensor of the autonomous mobile device in real time; A movement control module is configured to control the autonomous mobile device according to the global grid map.

14. An autonomous mobile device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, The processor executes the computer program to implement the control method of the autonomous mobile device according to any one of claims 1 to 12. The processor executes the computer program to implement the control method of the autonomous mobile device according to any one of claims 1 to 12.

15. A computer-readable storage medium, the computer-readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, implements the control method of the autonomous mobile device according to any one of claims 1 to 12.

16. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the control method of the autonomous mobile device according to any one of claims 1 to 12.

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