Control method and apparatus for cleaning robot, and cleaning robot and medium

Abstract

Provided in the present disclosure are a control method and apparatus for a cleaning robot, and a cleaning robot and a medium. The cleaning robot comprises: a body (101), an extension mechanism (102) disposed on the body (101), and an image sensor (103) disposed on the extension mechanism (102). The control method comprises: acquiring environment data; when an object to be recognized is discovered on the basis of the environment data, on the basis of the environment data and an image capture strategy for said object, controlling an extension mechanism (102) to adjust the pose thereof; and during the process of the extension mechanism (102) adjusting the pose thereof, controlling an image sensor (103) to perform at least one image capture of said object.

Description

Control methods and devices for cleaning robots, cleaning robots and media

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese patent application No. 2025103089710, filed on March 14, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of cleaning robot technology, and in particular to a control method, device, cleaning robot, and medium for a cleaning robot. Background Technology

[0004] Cleaning robots (such as robotic vacuum cleaners) are equipped with sensors (such as image sensors) that can capture image information of objects to be identified (such as dirty liquids, paper balls, or data cables), and then identify them based on the image information so that the robotic vacuum cleaner can take appropriate avoidance or cleaning strategies based on the identification results.

[0005] Currently, image sensors are typically fixed on the side of the robot vacuum cleaner, facing the direction of its movement, so that the image sensors can collect image information of objects to be identified on the ground.

[0006] However, because the position and orientation of the image sensor are fixed relative to the robot's body, the coverage area of ​​the image sensor is limited, which in turn results in a limited amount of image information of the object to be identified that the image sensor can collect. Summary of the Invention

[0007] According to some embodiments of the present disclosure, a control method, apparatus, cleaning robot, and medium for a cleaning robot can acquire more image information for an object to be identified when the cleaning robot is fixed or moves within a small area.

[0008] To achieve the above objectives, the embodiments of this disclosure adopt the following technical solutions:

[0009] Firstly, a control method for a cleaning robot is provided. The cleaning robot includes: a main body, an extension mechanism disposed on the main body, and an image sensor disposed on the extension mechanism. The control method can be applied to a processor within the main body responsible for processing functions, or to a logic node, logic module, or software capable of implementing the processor's functions. Taking the application of the control method to a processor as an example, the control method includes: acquiring environmental data; upon detecting an object to be identified based on the environmental data, controlling the extension mechanism to adjust its posture according to the environmental data and an image acquisition strategy for the object to be identified; and controlling the image sensor to acquire an image of the object to be identified at least once during the posture adjustment process of the extension mechanism.

[0010] It is understood that adjusting the posture includes the extension mechanism driving the image sensor to move relative to the object to be identified, and / or changing the image acquisition perspective.

[0011] In this embodiment of the present disclosure, the cleaning robot has an extension mechanism on its main body, and an image sensor is provided on the extension mechanism. Thus, when the processor in the main body detects an object to be identified based on environmental data, it controls the extension mechanism to modulate its posture according to the environmental data and the image acquisition strategy for the object to be identified. During the posture adjustment process of the extension mechanism, it controls the image sensor to acquire an image of the object to be identified at least once. Thus, when the cleaning robot is fixed or moves within a small area, it can acquire more image information for the object to be identified.

[0012] It is understandable that the processor controls the movement of the extension mechanism by using environmental data and image acquisition strategies for the object to be identified. This allows the micro-motions of the extension mechanism to replace the large-scale movements of the cleaning robot, thereby expanding the field of view for acquiring image data of the object to be identified. Compared to the large-scale movements of the cleaning robot, this reduces the energy consumption of the cleaning robot and / or the complexity of path planning.

[0013] In some implementations, since the cleaning robot can collect more image information about the object to be identified while it is stationary or moving within a small area, it can acquire more image information about the object to be identified by micro-movement of the extension mechanism when the cleaning robot is performing a task (such as cleaning). This avoids interrupting the task by requiring the cleaning robot to move a large area to acquire more image information about the object to be identified.

[0014] It is also understandable that, since the processor controls the extension mechanism to adjust its posture based on environmental data and the image acquisition strategy for the object to be identified, the processor can directly use the image acquisition strategy to control the extension mechanism to adjust its posture, avoiding the processor from performing complex analysis to generate the image acquisition strategy and reducing the processor's computational overhead.

[0015] In some embodiments, the body also includes an environmental sensor; acquiring environmental data includes: when the extension mechanism is in a folded state, controlling the environmental sensor to sense the environment as the body moves along a preset path to obtain environmental data.

[0016] In some implementations, acquiring environmental data includes: when the extension mechanism is in the deployed state, controlling the image sensor to acquire images of the environment as the body moves along a preset path to obtain environmental data.

[0017] In some embodiments, when an object to be identified is detected based on environmental data, before controlling the extension mechanism to adjust its posture based on the environmental data and an image acquisition strategy for the object to be identified, the method provided in the first aspect further includes: acquiring the image acquisition strategy from an electronic device, which is an electronic device that establishes a wireless connection with the cleaning robot.

[0018] In some implementations, the image acquisition strategy is an image acquisition strategy determined from a set of candidate image acquisition strategies.

[0019] In some implementations, the method provided in the first aspect further includes: determining the shape features of the object to be identified based on environmental data; and determining an image acquisition strategy from a set of candidate image acquisition strategies based on the shape features of the object to be identified.

[0020] In some implementations, the method provided in the first aspect further includes: receiving an operation instruction from a user; and determining an image acquisition strategy from a set of candidate image acquisition strategies based on the operation instruction.

[0021] In some implementations, the image acquisition strategy includes at least one of the following parameters: pitch angle, which is the angle between the central axis of the sensing range of the image sensor and the ground normal; height, which is the height between the image sensor and the ground; yaw angle, which is the angle between the projection of the central axis of the sensing range of the image sensor onto the ground and a reference line, where the reference line is the line connecting the center of the projection of the body onto the ground and the center of the projection of the object to be identified onto the ground; and distance, which is the distance between the projection of the image sensor onto the ground and the edge or center position of the object to be identified.

[0022] In some implementations, controlling the adjustment posture of the extension mechanism includes: controlling the extension mechanism to adjust its posture when the extension mechanism is in the deployed state.

[0023] In a second aspect, a robot is provided, comprising: a body, an extension mechanism disposed on the body, and an image sensor disposed on the extension mechanism. The body includes a processor for implementing the various methods provided in the first aspect.

[0024] Thirdly, a control device is provided for implementing the various methods provided in the first aspect. The control device can be a processor as described in the first aspect or any implementation thereof, or a device containing such a processor, such as a chip. The control device includes modules, units, or means that implement the methods described above. These modules, units, or means can be implemented in hardware, software, or by hardware executing corresponding software. The hardware or software includes one or more modules or units corresponding to the functions described above.

[0025] In some possible designs, the control device may include a processing module. This processing module can be used to implement the processing functions described in the first aspect above and in any possible implementation thereof.

[0026] Fourthly, a control device is provided, comprising: at least one processor; the processor being configured to execute computer programs or instructions to cause the control device to perform the various methods provided in the first aspect above.

[0027] In one possible implementation, the control device further includes a memory. Optionally, the memory is coupled to the processor; the memory may be integrated with the processor, or it may be independent of the processor. Optionally, the processor is used to execute computer programs or instructions stored in the memory.

[0028] In some implementations, the memory is independent of the control device.

[0029] In some implementations, the control device also includes a communication interface for communicating with modules outside the control device, such as electronic devices (e.g., terminals or user equipment (UE)) that establish wireless communication connections with the robot.

[0030] The control device may be a processor as described in the first aspect or any implementation thereof, or a device containing the processor, such as a chip.

[0031] Fifthly, a computer-readable storage medium is provided that stores a computer program or instructions that, when executed on a processor, enable the processor to perform the methods described in the first aspect or any implementation thereof.

[0032] In a sixth aspect, a computer program product containing instructions is provided, which, when run on a computer, enables the computer to perform the method of the first aspect or any implementation thereof.

[0033] In a seventh aspect, a control device (e.g., a chip or chip system) is provided, the control device including a processor for implementing the functions involved in the first aspect or any implementation thereof.

[0034] In some possible designs, the control device includes a memory for storing necessary program instructions and data.

[0035] In some possible designs, when the control device is a chip system, it can be composed of chips or may include chips and other discrete components.

[0036] The technical effects of any of the design methods in aspects two through seven can be found in the technical effects of aspect one mentioned above, and will not be repeated here. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 is a structural schematic diagram of a cleaning robot according to some embodiments of the present disclosure;

[0039] Figure 2 is a structural schematic diagram of another cleaning robot according to some embodiments of the present disclosure;

[0040] Figure 3 is a flowchart illustrating a control method for a cleaning robot according to some embodiments of the present disclosure;

[0041] Figure 4 is a schematic diagram of the parameters in an image acquisition strategy according to some embodiments of the present disclosure;

[0042] Figure 5 is a schematic diagram of an extension mechanism according to some embodiments of the present disclosure acquiring image data of an object to be identified during posture adjustment.

[0043] Figure 6 is a schematic diagram of another extension mechanism according to some embodiments of the present disclosure acquiring image data of the object to be identified during the posture adjustment process;

[0044] Figure 7 is a schematic diagram of the electrical structure of a cleaning robot according to some embodiments of the present disclosure.

[0045] Figure label:

[0046] 10. Cleaning robot; 20. Dirt; 30. Ground; 101. Body; 102. Extension mechanism; 103. Image sensor; 104. Processor; 105. Memory; 301. Ground normal; 1011. Environmental sensor; 1012. Tank; 1021. Support arm; 1022. First joint; 1023. First working arm; 1024. Second joint; 1025. Second working arm; 1031. Central axis. Embodiments of the present invention

[0047] To facilitate understanding of the embodiments of this disclosure, the following points will be explained before introducing the embodiments of this disclosure.

[0048] 1. To make the above-mentioned objects, features, and advantages of the embodiments of this disclosure more apparent and understandable, the embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described in the specific embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0049] 2. "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device or apparatus. This disclosure does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. The one or more memories can be separate settings or integrated into the encoder or decoder, processor, or apparatus. The one or more memories can also be partially separate settings and partially integrated into the decoder, processor, or apparatus. The type of memory can be any form of storage medium, and this disclosure does not limit this.

[0050] 3. In the embodiments of this disclosure, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the fact that the device or apparatus will make corresponding processing under certain objective circumstances. They are not time limits, nor do they require the device or apparatus to make a judgment action when implementing it, nor do they imply any other limitations.

[0051] 4. The establishment of a wireless connection involved in the embodiments of this disclosure may refer to a wireless connection established through a "communication protocol". The "communication protocol" may refer to a standard protocol in the field of communication, such as the Long Term Evolution (LTE) protocol, the New Radio (NR) protocol, the Wireless Fidelity (Wi-Fi) protocol, the Bluetooth protocol, and related protocols applied to future communication systems. The embodiments of this disclosure do not limit this.

[0052] 5. In the description of the embodiments of this disclosure, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this disclosure, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. To facilitate a clear description of the technical solutions of the embodiments of this disclosure, the terms "first" and "second" are used in the embodiments of this disclosure to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this disclosure, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this disclosure should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0053] 6. In the embodiments of this disclosure, the use of terms such as “middle,” “upper,” “lower,” “front,” “rear,” “vertical,” “horizontal,” “top,” “bottom,” “inner,” or “outer” to indicate the orientation or positional relationship of the constituent elements in the drawings is only for the convenience of description and is not intended to indicate or imply that the elements or structures referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation on the solutions disclosed in the embodiments of this disclosure.

[0054] The positional relationships of the constituent elements in the attached figures may be appropriately changed according to the direction of each constituent element, and are not limited to the positional relationships of the constituent elements in the attached figures described above. This will be explained uniformly here and will not be repeated below.

[0055] 7. In embodiments of this disclosure, "parallel," "perpendicular," and "equal" include: the described situation and situations similar to the described situation, where the range of similarity is within an acceptable deviation range. The acceptable deviation range is determined, for example, by the measurement under discussion and the error associated with the measurement of a particular quantity (i.e., the limitations of the measurement system). For example, "parallel" includes absolute parallelism and approximate parallelism, where the acceptable deviation range for approximate parallelism can be, for example, a deviation within 5°; "perpendicular" includes absolute perpendicularity and approximate perpendicularity, where the acceptable deviation range for approximate perpendicularity can also be, for example, a deviation within 5°. "Equal" includes absolute equality and approximate equality, where the acceptable deviation range for approximate equality can be, for example, a difference between the two equals being less than or equal to 5% of either one.

[0056] 8. In embodiments of this disclosure, "about," "approximately," "basically," or "approximately" includes: the stated value, and the average value within an acceptable range of deviation from the specified value. The acceptable range of deviation may be determined by the measurement under discussion, and the error associated with the measurement of the specific quantity (i.e., limitations of the measurement system).

[0057] 9. In the embodiments disclosed herein, unless otherwise explicitly stated, the terms "installed," "adjacent," "connected," or "linked," etc., should be interpreted broadly. For example, the term "linked" includes mechanical connections or electrical connections. In physical form, the aforementioned mechanical or electrical connections can refer to direct connections, indirect connections through intermediate components, or internal communication between two components. In some embodiments, the forms of the aforementioned connections include fixed connections, detachable connections, or integral connections.

[0058] The following is an introduction to cleaning robots.

[0059] Cleaning robots are intelligent cleaning devices with self-moving capabilities, such as sweeping robots, mopping robots, sweeping and mopping robots, floor polishing robots, or lawn mowing robots.

[0060] Figure 1 is a schematic diagram of the structure of a cleaning robot according to some embodiments of the present disclosure. It should be noted that Figure 1 is an example of a floor sweeping robot, and the structure and shape of the floor sweeping robot shown in Figure 1 are for illustrative purposes only and are not intended to be limiting.

[0061] As shown in Figure 1, the cleaning robot 10 may include a body 101, and an environmental sensor 1011, a processor, a drive module, a cleaning component, a power module, and a human-machine interaction module disposed within the body 101. As shown in Figure 1, the body 101 has an approximately circular shape, but may also have other shapes, including but not limited to an approximately D-shaped shape with a circular front and rear, and a rectangular or square shape with a circular front and rear.

[0062] For example, the environmental sensor 1011 may include a lidar, a collision sensor, or an image sensor (such as a red-green-blue (RGB) camera) to provide the processor with various positional information and environmental data of the machine. For instance, an RGB camera and / or a lidar sensor may be located on the front of the machine body 101 to more accurately sense the environment (such as the ground) in front of the cleaning robot 10.

[0063] It is understood that the main body 101 may also be equipped with a signal transmitting / receiving device, which is used to transmit signals to the base station and / or user equipment (UE) that interface with the cleaning robot 10. For example, the signal transmitting / receiving device may be an infrared transmitting / receiving device, which may include one or more infrared transmitters / receivers. Of course, it may also be a device that receives signals through wireless communication, including but not limited to Bluetooth or Wi-Fi.

[0064] For example, the processor can be located on a circuit board within the main body 101. The processor is the control center of the cleaning robot 10; it can be a single processor or a collective term for multiple processing elements. For example, the processor can be a central processing unit. Alternatively, the processor may include a central processing unit and an application processor. The application processor can create a real-time map of the environment in which the cleaning robot 10 is located, based on obstacle information fed back from the laser rangefinder and pre-configured localization algorithms, such as simultaneous localization and mapping.

[0065] It is understandable that the circuit board also includes non-transitory memory (such as hard disks, flash memory, and random access memory).

[0066] For example, the drive module can manipulate the body 101 to travel across the ground based on drive commands with distance and angle information. For example, the cleaning components may include dry cleaning components and / or wet cleaning components; specific structures can be found in related technologies. When the cleaning robot 10 is in working mode, i.e., performing a cleaning task, it can clean the target surface (such as the ground) using the cleaning components.

[0067] For example, the power module may include a rechargeable battery, such as a nickel-metal hydride battery or a lithium battery. The rechargeable battery can be charged by connecting to electrodes on the base station via charging electrodes located on the side or bottom of the main body 101.

[0068] For example, the human-computer interaction module may include buttons on the main unit panel for users to select functions. Optionally, the human-computer interaction module may also include at least one of the following: a display screen, indicator lights, and a speaker, for displaying the current machine mode or function selection options to the user. Furthermore, the display screen may be a touch screen for users to select functions.

[0069] Optionally, the human-computer interaction module may also include a microphone for receiving user voice commands to enable voice control. In some embodiments, the user may also interact with the cleaning robot 10 through a client (i.e., application (APP)) installed on the UE that establishes a communication connection with the cleaning robot 10.

[0070] The following details how the cleaning robot 10 identifies dirt.

[0071] As shown in Figure 1, since the environmental sensor 1011 can be positioned on the side of the body 101 in the direction of travel, the viewing angle (or sensing range) of the image captured by the environmental sensor 1011 (e.g., an RGB camera) includes the ground, thereby capturing image data of dirt 20 on the ground. This image data includes, for example, the shape (e.g., granular or water-like), color, height, or surface roughness of the dirt 20. The processor can then identify the dirt 20 based on the aforementioned image data. For example, assuming that the image data of the dirt 20 determines that the dirt 20 is colorless, flat, and reflective, the processor can identify the dirt 20 as a water stain. As another example, assuming that the image data of the dirt 20 determines that the dirt 20 has an irregular shape and a raised surface resembling fabric, the processor can identify the dirt 20 as a wad of paper or fabric.

[0072] It is understandable that the cleaning robot 10 can identify dirt by directly capturing image features such as the shape, color, or reflectivity of the dirt 20 itself through an RGB camera. In this way, the processor can directly judge the appearance of the dirt 20, and the accuracy of identifying the dirt 20 is relatively high.

[0073] However, since the position and orientation of the RGB camera relative to the main body 101 are relatively fixed, the sensing range of the RGB camera is limited, which in turn leads to a limited amount of image information that the RGB camera can acquire.

[0074] It is understandable that, since the position and orientation of the RGB camera relative to the main body 101 are relatively fixed, the angle (or pitch angle) of the RGB camera relative to the ground is also fixed, and the installation position of the RGB camera cannot be higher than the main body 101. This will cause the RGB camera to only collect image data of the object to be identified (such as dirty liquid, paper balls, cat litter, or data cables) at a fixed angle. Consequently, when the cleaning robot 10 is fixed or moves within a small area, the sensing range of the RGB camera is limited, and it cannot obtain more image information for the object to be identified.

[0075] Based on this, the present disclosure provides the following technical solution, which enables the collection of more image information for the object to be identified when the cleaning robot is fixed or moves within a small area.

[0076] In some embodiments, the cleaning robot includes: a body, an extension mechanism disposed on the body, and an image sensor disposed on the extension mechanism. The body includes a processor that acquires environmental data; when the processor detects an object to be identified based on the environmental data, the processor controls the extension mechanism to adjust its posture according to the environmental data and an image acquisition strategy for the object to be identified; during the posture adjustment process of the extension mechanism, the processor controls the image sensor to acquire at least one image of the object to be identified.

[0077] It is understood that adjusting the posture includes the extension mechanism driving the image sensor to move relative to the object to be identified, and / or changing the image acquisition perspective.

[0078] In this embodiment of the present disclosure, the cleaning robot has an extension mechanism on its main body, and an image sensor is provided on the extension mechanism. Thus, when the processor in the main body detects an object to be identified based on environmental data, it controls the extension mechanism to adjust its posture according to the environmental data and the image acquisition strategy for the object to be identified. During the posture adjustment process of the extension mechanism, it controls the image sensor to acquire an image of the object to be identified at least once. Thus, when the cleaning robot is fixed or moves within a small area, it can acquire more image information for the object to be identified.

[0079] It is understandable that the processor controls the movement of the extension mechanism by using environmental data and image acquisition strategies for the object to be identified. This allows the micro-motions of the extension mechanism to replace the large-scale movements of the cleaning robot, thereby expanding the field of view for acquiring image data of the object to be identified. Compared to the large-scale movements of the cleaning robot, this reduces the energy consumption of the cleaning robot and / or the complexity of path planning.

[0080] In some implementations, since the cleaning robot can collect more image information about the object to be identified while it is stationary or moving within a small area, it can acquire more image information about the object to be identified by micro-movement of the extension mechanism when the cleaning robot is performing a task (such as cleaning). This avoids interrupting the task by requiring the cleaning robot to move a large area to acquire more image information about the object to be identified.

[0081] It is also understandable that, since the processor controls the extension mechanism to adjust its posture based on environmental data and the image acquisition strategy for the object to be identified, the processor can directly use the image acquisition strategy to control the extension mechanism to adjust its posture, avoiding the processor from performing complex analysis to generate the image acquisition strategy and reducing the processor's computational overhead.

[0082] The following is an introduction to the cleaning robot equipped with an extension mechanism.

[0083] It should be understood that the extension mechanism in this embodiment is a mechanism module capable of extending beyond the body 101 and rotating with multiple degrees of freedom. For example, the extension mechanism can be a robotic arm with multiple degrees of freedom. In other words, the extension mechanism can include two states: a folded state and an unfolded state. An illustrative description is provided below with reference to Figure 2.

[0084] Figure 2 is a schematic diagram of the structure of another cleaning robot according to some embodiments of the present disclosure. As shown in Figure 2(a), the surface of the body 101 is also provided with a groove 1012 for accommodating the extension mechanism 102. It can be understood that the extension mechanism 102 can be in a folded state and accommodated in the groove 1012.

[0085] It is understood that the position, shape, etc. of the groove 1012 in Figure 2(a) are merely examples, and the embodiments disclosed herein do not impose specific limitations on them.

[0086] As shown in Figure 2(b), the extension mechanism 102 includes: a support arm 1021, a first joint 1022, a first working arm 1023, a second joint 1024, and a second working arm 1025. The support arm 1021 can be connected to the body 101 via a mechanical joint, primarily responsible for the extension mechanism 102 being received into the groove 1012 (or returning to the hopper), or extending out of the groove 1012 (or exiting the hopper). It can be understood that the joint connecting the support arm 1021 and the body 101 can also drive the extension mechanism 102 to rotate, for example, circumferentially along the body 101. In some embodiments, after the extension mechanism 102 exits the groove 1012, the joint connecting the support arm 1021 and the body 101 can maintain a fixed angle.

[0087] As shown in Figure 2(b), the first joint 1022 and the second joint 1024 are the main joints, which determine the posture of the first working arm 1023 relative to the second working arm 1025, and also determine the position of the end (i.e. the end point) of the second working arm 1025 in space.

[0088] As shown in Figure 2(b), an image sensor 103 is provided at the end of the second working arm 1025. It should be understood that the image sensor 103 can be an image sensor capable of acquiring at least one of image, color, or distance. For example, the image sensor 103 may include one or more of an RGB camera, an infrared sensor, or a depth sensor, and this disclosure does not specifically limit it.

[0089] It is understood that the extension mechanism 102 shown in Figure 2(b) is only an example. The extension mechanism 102 may also include more working arms and more joints, or the extension mechanism 102 may only include one working arm, or the extension mechanism 102 may not include the support arm 1021. This disclosure does not specifically limit this.

[0090] In some embodiments, the working arm in the extension mechanism 102 may also be a cylinder or other polyhedron, and this disclosure does not specifically limit this.

[0091] In addition, other devices, such as grippers, may be provided at the end of the second working arm 1025, but this embodiment does not specifically limit this.

[0092] As shown in Figure 2(c), the image sensor 103 can also be disposed on the side of the second working arm 1025. It is understood that this embodiment does not specifically limit the mounting position of the image sensor 103 on the extension mechanism 102.

[0093] The control scheme of the cleaning robot described above will be explained in detail below with reference to Figures 3 to 6.

[0094] It should be understood that the names of the parameters or information related to the various devices or modules in the embodiments of this disclosure are just examples, and other names may be used in actual implementation. This disclosure does not specifically limit these names.

[0095] In some embodiments, the present disclosure uses a processor as an example for description, but the present disclosure does not limit this. The execution entity of each method embodiment in the present disclosure can be a device or module, a device included in the device or module, or a device including the device or module. For example, the execution entity can be a control device or control module including a processor, an electrical appliance or device including the control device or control module, or a control chip included in the processor. It is understood that each method embodiment in the present disclosure can be implemented by a logic node, logic module, or software capable of implementing some or all of the functions of the control device.

[0096] Figure 3 is a flowchart illustrating a control method for a cleaning robot according to some embodiments of the present disclosure. As shown in Figure 3, the method includes the following steps.

[0097] S301, The processor acquires environmental data.

[0098] S302. When the processor detects an object to be identified based on environmental data, the processor controls the extension mechanism to adjust its posture based on the environmental data and the image acquisition strategy for the object to be identified.

[0099] S303. During the process of adjusting the posture of the extension mechanism, the processor controls the image sensor to acquire an image of the object to be identified at least once.

[0100] The following sections will explain steps S301 to S302 respectively.

[0101] For step S301.

[0102] As can be understood from the relevant description in Figure 2, the extension mechanism 102 can retract into its compartment, i.e., be in a folded state. In this state, the cleaning robot 10 can acquire environmental data based on the environmental sensor 1011.

[0103] In some embodiments, the body 101 further includes an environmental sensor 1011; the processor acquires environmental data (i.e., step S301), including:

[0104] When the extension mechanism 102 is in a folded state, the processor controls the environmental sensor 1011 to sense the environment and obtain environmental data as the body 101 moves along a preset path.

[0105] As can be understood, as shown in the description of the environmental sensor 1011 in Figure 1, the environmental data may include one or more of image data, ranging data, or radar sensing data. This embodiment does not specifically limit the data in this way.

[0106] In some embodiments, the movement of the body 101 along a preset path can refer to the preset path corresponding to the cleaning robot 10 performing a task. For example, the body 101 can first perform basic cleaning functions (without the extension mechanism 102 leaving the compartment). During the cleaning process, the cleaning robot 10 will first clean along the edge, and then perform zigzag filling cleaning, avoiding obstacles when encountered. In some embodiments, the preset path can also be the preset path of the cleaning robot 10 during the above-mentioned cleaning task, so that the processor can control the image sensor 103 on the extension mechanism 102 to perform supplementary image scanning according to the dirt detection situation.

[0107] In other words, the processor can sense the environment and obtain environmental data through the environmental sensor 1011 during or after the cleaning robot 10 is performing a task, while the robot body 101 is moving along a preset path.

[0108] In another possible implementation, the processor acquires environmental data (i.e., step S301), including:

[0109] When the extension mechanism 102 is in the extended state, the processor controls the image sensor 103 to acquire images of the environment as the body 101 moves along a preset path, thereby obtaining environmental data.

[0110] It is understood that when the extension mechanism 102 is in the extended state, the field of view of the image sensor 103 will not be obstructed, and thus the image sensor 103 can also acquire images of the environment and obtain environmental data.

[0111] In other words, the processor can perceive the environment through the image sensor 103 and obtain environmental data while the cleaning robot 10 is moving along a preset path during the process of the robot 10 performing a task or after the task is completed, when the robot body 101 is in the extended state of the extension mechanism 102, thereby improving the processor's flexibility in acquiring environmental data.

[0112] It is understood that the extension mechanism 102 is in the deployed state, which can be achieved through a human-machine interface module. For example, a user can drive the extension mechanism 102 out of its compartment and into the deployed state via buttons or a touchscreen on the main unit panel. Alternatively, a user can send operation commands to the main unit 101 via voice commands or an app installed on the UE to drive the extension mechanism 102 out of its compartment and into the deployed state.

[0113] It should be understood that the above explanations regarding the human-computer interaction module can be found in the relevant explanations in Figure 1, and will not be repeated here.

[0114] Optionally, when the extension mechanism 102 is in the extended state, the processor image sensor 103 and the environment sensor 1011 acquire images of the environment and obtain environmental data as the body 101 moves along a preset path.

[0115] In other words, when the extension mechanism 102 is in the deployed state, the processor can acquire environmental data through the environmental sensor 1011 and the image sensor 103, thereby improving the processor's flexibility in acquiring environmental data.

[0116] For step S302.

[0117] It is understood that in step S302, the image acquisition strategy can be used by the processor to control the extension mechanism 102 to adjust its posture. Adjusting the posture includes the extension mechanism 102 driving the image sensor 103 to move relative to the object to be identified, and / or changing the image acquisition perspective.

[0118] It is understood that adjusting the posture in step S302 includes: the extension mechanism 102 driving the image sensor 103 to move relative to the object to be identified, and / or changing the image acquisition perspective.

[0119] The image acquisition strategy and posture adjustment are explained below.

[0120] For image acquisition strategies

[0121] In some implementations, before the processor controls the extension mechanism to adjust its posture based on the environmental data and the image acquisition strategy for the object to be identified (i.e., before step S302), when the processor detects the object to be identified based on the environmental data, the method shown in FIG3 further includes:

[0122] The processor obtains the image acquisition strategy from the electronic device, which is the same electronic device that establishes a wireless connection with the cleaning robot.

[0123] It can be understood that the processor obtaining the image acquisition strategy from the electronic device can mean that the processor obtains the image acquisition strategy from the electronic device through a transceiver. The transceiver can be a signal transmitting and / or receiving device, as shown in the relevant description in Figure 1, which will not be repeated here.

[0124] In some embodiments, the electronic device may be, for example, a user's UE, and the user's UE and the main body 101 may establish a wireless connection in advance, for example, through Bluetooth or Wi-Fi.

[0125] It can also be understood that the electronic device can be any device other than the UE. For example, the base station corresponding to the cleaning robot 10 can also have environmental perception capabilities, and thus send environmental data to the processor. Alternatively, the electronic device could be a surveillance camera that has a pre-established wireless connection with the cleaning robot 10, and thus send environmental data to the processor.

[0126] It should be understood that the embodiments disclosed herein do not specifically limit the electronic devices described above.

[0127] It is understood that the processor can obtain the image acquisition strategy from the electronic device in various ways. For example, the processor sends a request to the electronic device to acquire the image strategy. Accordingly, the electronic device receives the request from the processor. Alternatively, the electronic device sends the image strategy to the processor. Correspondingly, the processor receives the image strategy from the electronic device.

[0128] For example, an electronic device can directly send image policies to the processor. Alternatively, the electronic device can periodically send image policies to the processor. Or, the electronic device can send image policies to the processor based on conditional triggers (such as detecting dirt or garbage in the environment that needs cleaning), and this disclosure does not specifically limit this approach.

[0129] In other words, the processor can obtain the image acquisition strategy from the electronic device that establishes a wireless communication connection with the cleaning robot 10, thereby avoiding the processor from generating the corresponding image acquisition strategy based on environmental data and reducing computational overhead.

[0130] In some implementations, the image acquisition strategy is an image acquisition strategy determined from a set of candidate image acquisition strategies.

[0131] It is understandable that the candidate image acquisition strategy set includes multiple candidate image acquisition strategies, and these multiple candidate image acquisition strategies are different.

[0132] In other words, the processor can pre-obtain a set of candidate image acquisition strategies and determine the image acquisition strategy from this set, avoiding the need for the processor to generate the image acquisition strategy and reducing computational overhead. Selecting an image acquisition strategy from the candidate set allows for choices that match the currently executing cleaning task or are selected by the user, thereby improving the user experience.

[0133] The following describes several implementation methods for processors to determine image acquisition strategies from a set of candidate image acquisition strategies.

[0134] Method 1

[0135] In some embodiments, the method shown in FIG3 further includes:

[0136] The processor determines the features of the object to be identified based on environmental data. The features of the object to be identified include at least one of the following: shape features, color features, or reflection features.

[0137] The processor determines the image acquisition strategy from the set of candidate image acquisition strategies based on the shape features of the object to be identified.

[0138] It is understandable that the candidate image acquisition strategy set includes multiple candidate image acquisition strategies, and different candidate image acquisition strategies can correspond to the features of the object to be identified. For example, for granular shapes, they usually correspond to crumpled paper, fabric, cat litter, or data cables, etc. The shape features of these objects are relatively stable at different angles, so the image acquisition strategy with fewer acquisitions can be selected from the candidate image acquisition strategies. As another example, for water-like objects, they usually correspond to dirt, water stains, etc. The shape features of these objects are different at different angles, so the image acquisition strategy with more acquisitions or larger acquisition angle deviations can be selected from the candidate image acquisition strategies.

[0139] In other words, the processor can select an image acquisition strategy that matches the features of the object to be identified from the candidate image acquisition strategies, thereby improving the acquisition of image information of the object to be identified without increasing image acquisition overhead.

[0140] Method 2

[0141] In some embodiments, the method shown in FIG3 further includes:

[0142] The processor receives user operation commands;

[0143] The processor determines the image acquisition strategy from the set of candidate image acquisition strategies based on the operation instructions.

[0144] As can be understood, referring to the relevant description of the human-computer interaction module in Figure 1, the user can select an image acquisition strategy from the candidate image acquisition strategies via buttons or a touchscreen on the host panel. The processor then responds to the user's operation command and determines the image acquisition strategy indicated by that operation command from the candidate strategies. Alternatively, the user can send an operation command to the host 101 via voice command or an app installed on the UE. The processor then responds to the user's operation command and selects the image acquisition strategy indicated by that operation command from the candidate strategies.

[0145] For example, the candidate image acquisition strategy may include the following candidate image acquisition strategies.

[0146] 1. Preset mode: The number of acquisitions is 3, with pitch angles of 45°, 90°, and 135° respectively. The pitch angle is the angle between the central axis of the sensing range of the image sensor 103 and the ground normal. For details, please refer to the relevant explanation in Figure 4, which will not be repeated here.

[0147] 2. Fine mode: The number of data acquisitions is 5, with pitch angles of 15°, 45°, 90°, 120°, and 135° respectively.

[0148] 3. Fast mode: 1 acquisition attempt, 75° pitch angle.

[0149] In other words, the processor can receive user operation instructions and select the image acquisition strategy preferred by the user from the candidate image acquisition strategies, thereby improving the user experience.

[0150] Understandably, users can also customize the mode, for example, selecting 2 data acquisitions with pitch angles of 15 degrees and 75 degrees respectively. Or, for example, selecting 6 data acquisitions with pitch angles of 15°, 45°, 75°, 90°, 120°, and 135° respectively.

[0151] In other words, users can personalize image acquisition strategies, increasing the processor's flexibility in acquiring image acquisition strategies.

[0152] The parameters included in the image acquisition strategy are explained below.

[0153] In some implementations, the image acquisition strategy includes at least one of the following parameters:

[0154] Pitch angle, which is the angle between the central axis of the sensing range of the image sensor and the ground normal;

[0155] Height, which refers to the height between the image sensor and the ground;

[0156] Yaw angle is the angle between the projection of the central axis of the sensing range of the image sensor onto the ground and the reference line. The reference line is the line connecting the center of the projection of the body onto the ground and the center of the projection of the object to be identified onto the ground.

[0157] And distance, which is the distance between the projection of the image sensor on the ground and the edge or center of the object to be identified.

[0158] As shown in Figure 4, the ground 30 corresponds to two orthogonal X-axis and Y-axis, with the center point (or origin) of the X-axis and Y-axis approximately located at the center of the irregularly shaped dirt 20. In some embodiments, the ground normal 301 is perpendicular to the ground 30. Furthermore, the X-axis in Figure 4 can be understood as the line connecting the center of the projection of the object to be identified onto the ground (i.e., the center of the dirt 20) and the center of the projection of the body 101 onto the ground, or the X-axis coincides with this connection.

[0159] As shown in Figure 4(a), the angle α between the central axis 1031 of the sensing range of the image sensor 103 and the ground normal 301 is the pitch angle. It can be understood that as the rotation angle of the second working arm 1025 increases, this pitch angle decreases.

[0160] As shown in Figure 4(b), the projection of the image sensor 103 onto the ground 30 is point A, and the distance h between point A and the image sensor 103 is the height.

[0161] As shown in Figure 4(b), the angle β between the line connecting point A and the origin and the X-axis is the yaw angle.

[0162] As shown in Figure 4(b), the length d between point A and the origin is the distance.

[0163] It is understood that Figure 4 is merely an example for ease of understanding. For instance, in some embodiments, the distance may also refer to the length between point A and the edge of the dirt 20. This disclosure does not specifically limit this aspect.

[0164] It should be understood that any difference in the pitch angle, altitude, yaw angle, and distance will result in a different angle between the central axis 1031 of the sensing range of the image sensor 103 and the object to be identified, thus obtaining image information of the object to be identified at different angles.

[0165] For adjusting posture

[0166] It is understandable that different postures of the extension mechanism 102 mean different spatial positions of the ends of the extension mechanism 102, and also mean different image acquisition angles for the image sensor 103 to acquire image data of the object to be identified.

[0167] In some implementations, the processor controls the epitaxial mechanism 102 to adjust its orientation, including:

[0168] When the epitaxial mechanism 102 is in the deployed state, the processor controls the epitaxial mechanism 102 to adjust its posture.

[0169] As can be understood from the relevant description in Figure 2, when the extension mechanism 102 is in a folded state, it will obstruct the field of view of the image sensor 103, thus preventing image acquisition of the object to be identified. In other words, the processor will only control the end of the extension mechanism 102 to move toward the object to be identified when the extension mechanism 102 is in an unfolded state.

[0170] It can also be understood that the processor can detect the object to be identified based on environmental data, and when the extension mechanism 102 is in a folded state, control the extension mechanism 102 to move from the folded state to the unfolded state. Alternatively, the processor can detect the object to be identified based on environmental data, and when the extension mechanism 102 is inside the slot 1012, control the extension mechanism 102 to extend out of the slot 1012 and be in the unfolded state.

[0171] In some embodiments, the extension mechanism 102 may already be in an deployed state. For example, the extension mechanism 102 may be deployed via a human-machine interface module. For instance, a user can drive the extension mechanism 102 out of its compartment and into the deployed state via buttons or a touchscreen on the host panel. Alternatively, a user can send an operation command to the host 101 via voice commands or an app installed on the UE to drive the extension mechanism 102 out of its compartment and into the deployed state.

[0172] It should be understood that in the embodiments of this disclosure, the processor controls the movement of the epitaxial mechanism 102 by sending a control command to a processing unit (e.g., a DSP or MCU) that drives the motor of the epitaxial mechanism 102, and then the processing unit sends a drive signal to the motor based on the control command, thereby driving the epitaxial mechanism 102 to move. Alternatively, the processor in the embodiments of this disclosure may integrate a processing unit for driving the motor of the epitaxial mechanism 102, and the embodiments of this disclosure do not specifically limit this.

[0173] In some embodiments, the control instructions may be angle control instructions for each joint of the extension mechanism (which can also be understood as control instructions for adjusting the posture of the extension mechanism), or information related to spatial position, which are parsed by the processing unit. This disclosure does not specifically limit the scope of the control instructions.

[0174] It is understood that during the posture adjustment process, the end of the extension mechanism 102 may be located in different spatial positions. At this time, the processor can control the image sensor 103 to perform at least one image acquisition of the object to be identified. The following is a schematic diagram of the image data of the object to be identified acquired by the extension mechanism 102 during the posture adjustment process.

[0175] Figure 5 is a schematic diagram of an extension mechanism 102 acquiring image data of an object to be identified during posture adjustment according to some embodiments of the present disclosure. As shown in Figure 5(a), the image sensor 103 is located on the side of the second working arm 1025. In this case, during the process of the processor controlling the second working arm 1025 to adjust its posture, the end of the second working arm 1025 moves above the dirt 20 (i.e., spatial position #1), the second working arm 1025 is parallel to the ground, and the angle between the central axis 1031 of the sensing range of the image sensor 103 and the ground normal 301 is 0, i.e., the pitch angle is 0.

[0176] As shown in Figure 5(a), the distance between the body 101 and the dirt 20 is relatively close at this time. The image sensor 103 can collect image data of the dirt 20 at close range, which can improve the resolution of the image data.

[0177] As shown in Figure 5(b), the distance between the main body 101 and the dirt 20 is relatively far at this time. During the process of the processor controlling the second working arm 1025 to adjust its posture, the end of the second working arm 1025 moves to the spatial position #2. The angle between the central axis 1031 of the sensing range of the image sensor 103 and the ground normal 301 is approximately 45° (i.e., the pitch angle is approximately 45°). At this time, the processor can control the image sensor 103 to perform image acquisition.

[0178] It is understood that, due to the different orientations of the extension mechanism 102, the pitch angle, distance, and height of the image sensor 103 for acquiring images of the dirt 20 are different between (a) and (b) in Figure 5 above.

[0179] Figure 6 is a schematic diagram of another extension mechanism according to some embodiments of the present disclosure acquiring image data of an object to be identified during posture adjustment. As shown in Figure 6(a), the image sensor 103 is located at the end of the second working arm 1025. In this case, relative to the position of the image sensor 103 in Figure 5, the processor controls the second working arm 1025 to make slight posture movements, thereby acquiring images of the dirt 20 at close range. As shown in Figure 6(a), during the process of the processor controlling the second working arm 1025 to adjust its posture, the end of the second working arm 1025 moves to between the dirt 20 and the body 101 (i.e., spatial position #3). The angle between the central axis 1031 of the sensing range of the image sensor 103 and the ground normal 301 is 75°. At this time, the processor can control the image sensor 103 to acquire images.

[0180] As shown in Figure 6(b), relative to Figure 6(a), during the process of the processor controlling the second working arm 1025 to adjust its posture, the end of the second working arm 1025 moves to spatial position #4 (i.e. above the edge of the dirt 20). At this time, the pitch angle is small, and the image sensor 103 is closer to the dirt 20, so it can collect image data of the dirt 20 at close range.

[0181] For step S303

[0182] As can be understood, as described in the aforementioned step S302 regarding the processor controlling the extension mechanism 102 to adjust its posture, the processor can, during the process of adjusting the posture of the extension mechanism 102, control the image sensor 103 to acquire images of the object to be identified when it is determined that the posture of the extension mechanism 102 is the desired posture (e.g., the end of the extension mechanism 102 has moved to spatial position #1 or spatial position #2, etc.).

[0183] In some embodiments, the processor determines that the posture of the epitaxial mechanism 102 is the desired posture by: the processing unit driving the motor of the epitaxial mechanism 102 can feed back to the processor that the processing unit has completed the control command, thereby determining that the posture of the epitaxial mechanism 102 is the desired posture; or, the processor can determine that the posture of the epitaxial mechanism 102 is the desired posture by the encoder inside the motor; or, the body 101 and / or the epitaxial mechanism 102 can be provided with an optical module, and the processor can determine that the posture of the epitaxial mechanism 102 is the desired posture by the optical module. This disclosure does not specifically limit this aspect.

[0184] In some embodiments, when the processor determines that the end of the extension mechanism 102 has moved to each of at least one spatial position, the image sensor 103 can be controlled to acquire image data of the object to be identified.

[0185] In this embodiment of the present disclosure, the cleaning robot 10 has an extension mechanism 102 on its body 101 and an image sensor 103 on the extension mechanism 102. Thus, the processor in the body 101 can control the extension mechanism 102 to adjust its posture according to the environmental data and the image acquisition strategy for the object to be identified when the object to be identified is detected based on the environmental data. During the posture adjustment process of the extension mechanism 102, the image sensor 103 is controlled to acquire at least one image of the object to be identified. Thus, when the cleaning robot 10 is fixed or moves within a small area, more image information can be acquired for the object to be identified.

[0186] It is understood that the processor controls the movement of the extension mechanism 102 by using environmental data and image acquisition strategies for the object to be identified. This enables the micro-motion of the extension mechanism 102 to replace the large-scale movement of the cleaning robot 10, thereby expanding the field of view for acquiring image data of the object to be identified. Compared with the large-scale movement of the cleaning robot 10, this can reduce the energy consumption of the cleaning robot 10 and / or the complexity of path planning.

[0187] In some embodiments, since the cleaning robot 10 can collect more image information about the object to be identified when it is fixed or moves within a small area, more image information about the object to be identified can be obtained by micro-movement of the extension mechanism 102 when the cleaning robot 10 is performing a task (such as a cleaning task). This avoids interrupting the task by causing the cleaning robot 10 to move a large area due to the need to obtain more image information about the object to be identified.

[0188] It is also understandable that since the processor controls the extension mechanism 102 to adjust its posture based on environmental data and the image acquisition strategy for the object to be identified, the processor can directly use the image acquisition strategy to control the extension mechanism 102 to adjust its posture, avoiding the processor performing complex analysis to generate the image acquisition strategy and reducing the processor's computational overhead.

[0189] The method embodiments provided in this disclosure have been described above. Accordingly, this disclosure also provides a control device for a cleaning robot, which is used to implement the various methods described above. This control device may be a processor as described in the above method embodiments, or a device or apparatus including the processor, or a component that can be used with the processor.

[0190] Figure 7 is a schematic diagram of the electrical structure of a cleaning robot according to some embodiments of the present disclosure. As shown in Figure 7, the control device of the cleaning robot 10 may include modules or units for implementing the methods described in the embodiments above. In one possible design, the control device includes a processor 104. Optionally, the control device may also include a memory 105 for storing device computer programs, such as program code and / or data.

[0191] The control device can be at least one module within the control device described in the above embodiments. For example, at least one module within the control device can be a circuit or a chip within the control device.

[0192] For example, in one embodiment, the processor 104 is configured to: acquire environmental data; if an object to be identified is found based on the environmental data, control the extension mechanism 102 to adjust its posture based on the environmental data and the image acquisition strategy for the object to be identified; and control the image sensor 103 to perform at least one image acquisition of the object to be identified during the process of the extension mechanism 102 adjusting its posture.

[0193] In one possible design, when the control device is a circuit or chip in the control device, the function of the processor 104 can be implemented by one or more processors.

[0194] It is understood that the division of units in the aforementioned control device is merely a logical functional division; one function may correspond to one functional unit, or two or more functions may be integrated into one functional unit. In actual implementation, all or some units may be integrated into one physical entity, or they may be distributed across different physical entities. Furthermore, the aforementioned functional units may be implemented in hardware, software, or a combination of both. Whether a function is executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for specific applications, but such implementation should not be considered beyond the scope of this disclosure.

[0195] In one example, the functional unit in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as: one or more application-specific integrated circuits (ASICs), or one or more central processing units (CPUs), one or more microcontroller units (MCUs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0196] In one example, memory 105 may include random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory and / or registers, etc.

[0197] Furthermore, the control device can execute the above control method, so the technical effects it can achieve can be referred to the above method embodiments, and will not be repeated here.

[0198] In some embodiments, this disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a computer, implement the functions of the above-described method embodiments.

[0199] In some embodiments, this disclosure also provides a computer program product that, when executed by a computer, implements the functions of the above-described method embodiments.

[0200] In the above embodiments, implementation can be achieved, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this disclosure is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device including one or more servers, data centers, etc., that can be integrated with the medium. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0201] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0202] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0203] In the several embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0204] In the various embodiments of this disclosure, each functional unit can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0205] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0206] Although this disclosure has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed disclosure. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0207] Although this disclosure has been described in conjunction with specific features and embodiments, it will be apparent that various modifications and combinations can be made therein without departing from the scope of this disclosure. Accordingly, this specification and drawings are merely illustrative descriptions of the disclosure as defined by the appended claims and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this disclosure. It is obvious that those skilled in the art can make various alterations and modifications to this disclosure without departing from its scope. Thus, if such modifications and modifications of this disclosure fall within the scope of the claims of this disclosure and their equivalents, this disclosure is also intended to include such modifications and modifications.

Claims

1. A control method for a cleaning robot, the cleaning robot comprising: The method comprises: a body, an extension mechanism disposed on the body, and an image sensor disposed on the extension mechanism; the body being a body; an extension mechanism disposed on the extension mechanism; and the method comprising: Acquire environmental data; When an object to be identified is detected based on the environmental data, the extension mechanism is controlled to adjust its posture based on the environmental data and the image acquisition strategy for the object to be identified. During the posture adjustment process of the extension mechanism, the image sensor is controlled to acquire an image of the object to be identified at least once.

2. The method of claim 1, wherein, The body also includes environmental sensors; the acquisition of environmental data includes: When the extension mechanism is in a folded state, the environmental sensor is controlled to sense the environment and obtain environmental data as the body moves along a preset path.

3. The method of claim 1 or 2, wherein, The acquisition of environmental data includes: When the extension mechanism is in the deployed state, the image sensor is controlled to acquire images of the environment as the body moves along a preset path, thereby obtaining the environmental data.

4. The method of claim 1 or 2, wherein, Before controlling the adjustment posture of the extension mechanism based on the environmental data and the image acquisition strategy for the object to be identified, when the object to be identified is detected based on the environmental data, the method further includes: The image acquisition strategy is obtained from an electronic device, which is an electronic device that establishes a wireless connection with the cleaning robot.

5. The method of claim 1 or 2, wherein, The image acquisition strategy is an image acquisition strategy determined from a set of candidate image acquisition strategies.

6. The method according to claim 5, further comprising: Based on the environmental data, the features of the object to be identified are determined, and the features of the object to be identified include at least one of the following: shape features, color features, or reflection features; Based on the shape features of the object to be identified, the image acquisition strategy is determined from the set of candidate image acquisition strategies.

7. The method according to claim 5, further comprising: Receive user operation instructions; According to the operation instructions, the image acquisition strategy is determined from the set of candidate image acquisition strategies.

8. The method according to claim 1 or 2, wherein, The image acquisition strategy includes at least one of the following parameters: Pitch angle, which is the angle between the central axis of the sensing range of the image sensor and the ground normal; Height, whereby the height is the distance between the image sensor and the ground; Yaw angle, which is the angle between the projection of the central axis of the sensing range of the image sensor onto the ground and a reference line, wherein the reference line is the line connecting the center of the projection of the body onto the ground and the center of the projection of the object to be identified onto the ground. And distance, which is the distance between the projection of the image sensor on the ground and the edge or center of the object to be identified.

9. The method according to claim 1 or 2, wherein, The control of the extension mechanism to adjust its posture includes: When the extension mechanism is in the deployed state, control the extension mechanism to adjust its posture.

10. A control device for a cleaning robot, the cleaning robot comprising: The control device includes a body, an extension mechanism disposed on the body, and an image sensor disposed on the extension mechanism. The control device includes a control module configured to: Acquire environmental data; When an object to be identified is detected based on the environmental data, the extension mechanism is controlled to adjust its posture based on the environmental data and the image acquisition strategy for the object to be identified. During the posture adjustment process of the extension mechanism, the image sensor is controlled to acquire an image of the object to be identified at least once.

11. The control device according to claim 10, wherein, The body also includes environmental sensors; the control module is configured to: When the extension mechanism is in a folded state, the environmental sensor is controlled to sense the environment and obtain environmental data as the body moves along a preset path.

12. The control device according to claim 10 or 11, wherein, The control module is configured as follows: When the extension mechanism is in the deployed state, the image sensor is controlled to acquire images of the environment as the body moves along a preset path, thereby obtaining the environmental data.

13. The control device according to claim 10 or 11, wherein, The control module is also configured to: The image acquisition strategy is obtained from an electronic device, which is an electronic device that establishes a wireless connection with the cleaning robot.

14. The control device according to claim 10 or 11, wherein, The image acquisition strategy is an image acquisition strategy determined from a set of candidate image acquisition strategies.

15. The control device according to claim 14, wherein, The control module is also configured to: Based on the environmental data, the features of the object to be identified are determined, and the features of the object to be identified include at least one of the following: shape features, color features, or reflection features; Based on the shape features of the object to be identified, the image acquisition strategy is determined from the set of candidate image acquisition strategies.

16. The control device according to claim 15, wherein, The control module is also configured to: Receive user operation instructions; According to the operation instructions, the image acquisition strategy is determined from the set of candidate image acquisition strategies.

17. The control device according to claim 10 or 11, wherein, The image acquisition strategy includes at least one of the following parameters: Pitch angle, which is the angle between the central axis of the sensing range of the image sensor and the ground normal; Height, whereby the height is the distance between the image sensor and the ground; Yaw angle, which is the angle between the projection of the central axis of the sensing range of the image sensor onto the ground and a reference line, wherein the reference line is the line connecting the center of the projection of the body onto the ground and the center of the projection of the object to be identified onto the ground. And distance, which is the distance between the projection of the image sensor on the ground and the edge or center of the object to be identified.

18. The control device according to claim 10 or 11, wherein, The control module is configured as follows: When the extension mechanism is in the deployed state, control the extension mechanism to adjust its posture.

19. A cleaning robot, comprising: The system includes a body, an extension mechanism disposed on the body, and an image sensor disposed on the extension mechanism, wherein the body includes a processor configured to perform a control method according to any one of claims 1-9 by means of logic circuits and / or executing instructions.

20. A computer-readable storage medium comprising instructions that, when executed by a processor, cause the control method according to any one of claims 1-9 to be implemented.

21. A computer program product comprising instructions that, when executed on a computer, cause the computer to perform the control method according to any one of claims 1-9.

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