Robot control method

Abstract

A robot control method, comprising: acquiring first recognition information collected in a water pool by a target robot, wherein the first recognition information represents feature information obtained by means of performing image recognition on a target obstacle, and the target obstacle is an obstacle detected in the water pool by the target robot; on the basis of the first recognition information, determining the category of the target obstacle, wherein different categories are set in advance for obstacles allowed to be detected in the water pool, and control modes used by the target robot for detecting different types of obstacles are allowed to be different; and under the condition that the category of the target obstacle is a target category, on the basis of a target control mode corresponding to the target category, controlling the target robot to execute a preset task in the water pool. A water-pool robot and a computer-readable storage medium.

Description

Robot control method

[0001] This disclosure claims priority to the Chinese patent application filed with the China Patent Office on August 17, 2023, with application number 202311041067.5 and invention name “Robot Control Method”, the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0002] The embodiments of the present disclosure relate to the field of robots, and in particular, to a method for controlling a robot. Background Art

[0003] In the related art, a pool robot usually encounters various obstacles when working in a pool, and the control method of the pool robot is usually to avoid the obstacles after colliding with them, or to keep a fixed distance from the obstacles to avoid them.

[0004] If the robot avoids an obstacle after colliding with it, the obstacle may be drawn into the executing component of the pool robot, causing the executing component to be blocked; if the robot avoids the obstacle while maintaining a fixed distance, the cleaning of specific obstacles may be missed; this is a technical problem that leads to low working efficiency of the pool robot.

[0005] There is currently no effective solution to the above problems.

[0006] Summary of the Invention

[0007] An embodiment of the present disclosure provides a method for controlling a robot.

[0008] According to one embodiment of the present disclosure, a robot control method is provided, comprising: obtaining first identification information collected by a target robot in a pool, wherein the first identification information represents feature information obtained by image recognition of a target obstacle, and the target obstacle is an obstacle detected by the target robot in the pool; determining a category of the target obstacle based on the first identification information, wherein different categories are set in advance for obstacles allowed to be detected in the pool, and different control methods are allowed to be adopted by the target robot when detecting obstacles of different categories; and when the category of the target obstacle is a target category, controlling the target robot to perform a preset task in the pool based on a target control method corresponding to the target category.

[0009] According to another embodiment of the present disclosure, a robot control device is provided, including: an acquisition module, configured to acquire first identification information collected by a target robot in a pool, wherein the first identification information represents feature information obtained by image recognition of a target obstacle, and the target obstacle is an obstacle detected by the target robot in the pool; a determination module, configured to determine the category of the target obstacle based on the first identification information, wherein different categories are set in advance for obstacles allowed to be detected in the pool, and the control methods adopted by the target robot when detecting obstacles of different categories are allowed to be different; a control module, configured to control the target robot to perform a preset task in the pool based on a target control method corresponding to the target category when the category of the target obstacle is a target category.

[0010] According to another embodiment of the present disclosure, a pool robot is provided, and a control method applied to the above-mentioned robot includes: an image acquisition device, configured to acquire first identification information; a processor, configured to determine the target control mode based on the first identification information, and control the target robot to perform the preset task in the pool according to the target control mode.

[0011] According to another embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0012] According to another embodiment of the present disclosure, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] FIG1 is a hardware structure block diagram of a mobile terminal for a robot control method according to an embodiment of the present disclosure;

[0014] FIG2 is a flow chart of a method for controlling a robot according to an embodiment of the present disclosure;

[0015] FIG3 is a schematic diagram of a specific flow chart of a method for controlling a robot according to an embodiment of the present disclosure;

[0016] FIG4 is a structural block diagram of a control device of a robot according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and in conjunction with embodiments.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0019] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, for example, the mobile terminal can be a mobile robot. Taking running on a mobile terminal as an example, FIG1 is a hardware structure block diagram of a mobile terminal of a robot control method according to an embodiment of the present disclosure. As shown in FIG1 , the mobile terminal may include one or more (only one is shown in FIG1 ) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that the structure shown in FIG1 is only for illustration and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than those shown in FIG1 , or have a configuration different from that shown in FIG1 .

[0020] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the robot control method in the embodiment of the present disclosure. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0021] The transmission device 106 is used to receive or send data via a network. A specific example of the aforementioned network may include a wireless network provided by the mobile terminal's communications provider. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0022] In this embodiment, a control method running on the above-mentioned robot is provided. This positioning method can be applied to scenarios where the robot cleans water environments such as swimming pools and ponds. For example, when a pool robot is cleaning in a swimming pool and encounters an obstacle that needs to be avoided, the above-mentioned robot control method can be used.

[0023] FIG2 is a flow chart of a method for controlling a robot according to an embodiment of the present disclosure. As shown in FIG2 , the flow chart includes the following steps:

[0024] S202, obtaining first identification information collected by the target robot in the pool, wherein the first identification information represents feature information obtained by performing image recognition on a target obstacle, and the target obstacle is an obstacle detected by the target robot in the pool;

[0025] Optionally, in this embodiment, the control method of the above-mentioned robot may include but is not limited to application scenarios that require cleaning of water environments, such as swimming pool cleaning scenarios, pond cleaning scenarios, and fish tank cleaning scenarios.

[0026] It should be noted that there are obstacles in the water body in this application scenario, which will affect the cleaning process of the robot. Therefore, it is necessary to determine the obstacle category through the above-mentioned robot control method, and select the appropriate robot control method according to the determined obstacle category to achieve cleaning of the water environment.

[0027] Optionally, in this embodiment, the target robot includes, but is not limited to, an underwater robot, which can be understood as a robot operating in a water environment. The robot can be a tethered or untethered submersible, such as a device used to clean a water environment such as a swimming pool. The pool can be understood as a water environment in which a robot is required to perform a preset task. By placing the target robot in the pool, the target robot can be controlled to complete the preset task in the pool.

[0028] It should be noted that the above-mentioned preset tasks may include but are not limited to pool cleaning tasks, pool detection tasks, etc.

[0029] In an exemplary embodiment, the first identification information is used to represent feature information obtained by performing image recognition on the target obstacle. The feature information may be feature information extracted based on an underwater target recognition algorithm.

[0030] It should be noted that the above-mentioned underwater target recognition algorithm can be, but is not limited to, a target recognition algorithm based on artificial intelligence.

[0031] Artificial Intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. AI also encompasses the study of the design principles and implementation methods of various intelligent machines, enabling them to possess the capabilities of perception, reasoning, and decision-making.

[0032] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0033] Computer vision (CV) is the science of making machines "see." Specifically, it refers to machine vision, where cameras and computers replace the human eye in identifying and measuring objects, performing further image processing to create images more suitable for human observation or transmission to instrumentation. As a scientific discipline, computer vision studies related theories and technologies, aiming to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, optical character recognition (OCR), video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0034] In an exemplary embodiment, the first identification information can be determined by an object detection algorithm. Object detection involves identifying objects of interest to the user from an image / video and providing feedback on the object's location and category. These object detection algorithms include, but are not limited to, R-CNN, YOLO, and SSD. Taking R-CNN as an example, its main principle is to create a large number (over 2,000) of candidate regions for an image, then perform object recognition calculations on each of these regions and classify them using Support Vector Machines (SVMs). Building on R-CNN, there are also Fast R-CNN and Faster R-CNN. Taking the YOLO algorithm as an example, YOLO (You Only Look Once) is primarily used for fast object detection, particularly in edge devices such as autonomous vehicles and unmanned vehicles, which require rapid recognition and feedback from acquired images and videos to enable rapid control system response. Compared to R-CNN, which uses a large number of detection region proposals, YOLO adopts a one-step approach, taking the entire image as input and directly outputting the identified object region and object type. YOLO's core concept is to take the entire image as input and directly derive the object region and type at the output layer. Faster R-CNN also uses the entire image as input, but Raster R-CNN employs a two-step approach: first proposing target detection areas, then performing image recognition on each area. For example, the Single Short Multibox Detector (SSD) algorithm emerged after the emergence of R-CNN and YOLO, a superior algorithm that exploits the unique characteristics of both. YOLO's computational speed is significantly improved compared to R-CNN, but its accuracy is inferior.

[0035] The above is only an example, and this embodiment does not limit the specific type of recognition algorithm for obtaining the first identification information.

[0036] Optionally, in this embodiment, the target obstacles may be understood as obstacles that the target robot is allowed to detect in the pool, such as ladders, rags, leaves, clothes, and other obstacles that may affect the operation of the target robot.

[0037] It should be noted that the target obstacles can be classified according to their impact on the target robot.

[0038] S204, determining the category of the target obstacle based on the first identification information, wherein different categories of obstacles allowed to be detected in the pool are pre-set, and the target robot may use different control methods when detecting obstacles of different categories;

[0039] Optionally, in this embodiment, different categories of obstacles allowed to be detected in the pool can be set in advance, and the recognition model can be trained based on the feature information extracted from the obstacles of the corresponding categories to obtain a recognition algorithm that can classify the identified obstacles. Then, the category of the target obstacle is determined according to the first recognition information through the target recognition algorithm.

[0040] It should be noted that the different categories set for the obstacles mentioned above can be set manually based on prior knowledge, or can be implemented by a self-learning category generation algorithm.

[0041] For example, clothing, rags, etc. may adhere to the wheel hub of the target robot, affecting the normal operation of the robot. In this case, you can consider setting textiles such as clothing and rags as one category.

[0042] For example, escalators, stone pillars, etc. may block the movement of the robot and affect the normal operation of the robot. In this case, you can consider setting escalators, stone pillars, etc. as one of the categories.

[0043] In an exemplary embodiment, taking the above-mentioned target category as textiles as an example, when the target robot detects that there is an object A in front of it, it uses an image acquisition device to capture an image of object A to obtain image data, and then performs feature extraction and target detection operations on the image data. It can be determined that object A belongs to the textile category. At this time, the textile category is the above-mentioned target category.

[0044] It should be noted that the control methods used by the target robot to clean different types of obstacles can be different. This can be understood as the target robot adopting different cleaning strategies for different obstacles. For example, if the obstacle is a textile, a relatively large avoidance distance is set to prevent the target robot from being entangled in the obstacle. For another example, if the obstacle is an escalator, a relatively small avoidance distance is set to prevent the target robot from failing to clean the escalator for a long time.

[0045] S206 , when the category of the target obstacle is the target category, controlling the target robot to perform a preset task in the pool based on a target control method corresponding to the target category.

[0046] Optionally, in this embodiment, the target control mode and the target category have a mapping relationship.

[0047] Optionally, in this embodiment, the above-mentioned preset tasks may include but are not limited to one or more combinations of cleaning, disinfecting, and water quality testing in the pool.

[0048] Through the disclosed embodiment, the first identification information collected by the target robot in the pool is obtained, wherein the first identification information represents the characteristic information obtained by image recognition of the target obstacle, and the target obstacle is the obstacle detected by the target robot in the pool. The category of the target obstacle is determined as the target category based on the first identification information, wherein different categories are set in advance for obstacles allowed to be detected in the pool, and the control methods used by the target robot to clean obstacles of different categories are allowed to be different. The target robot is controlled to clean the pool based on the target control method corresponding to the target category. By identifying the type of obstacle and handling the obstacle according to different types. It is intended to solve the problem that when the underwater robot encounters different obstacles during actual work, it is unable to adopt an effective handling method, resulting in blockage or leakage of cleaning of the machine's execution components, and the working efficiency of the target robot can be improved. Therefore, the problem of low working efficiency of the pool robot can be solved.

[0049] As an optional solution, obtaining the first identification information collected by the target robot in the pool includes: in response to the target robot starting to execute the preset task, capturing the image of the target obstacle in the pool through a target image acquisition device to obtain target image data, wherein the target image acquisition device is set on the target robot; identifying the target image data to determine the first identification information.

[0050] Optionally, in this embodiment, the target image acquisition device can be understood as a device that allows communication with the target robot. The device can be deployed on the target robot or in a location where it can communicate normally with the robot, such as a pool wall.

[0051] As an optional solution, obtaining the first identification information collected by the target robot in the pool includes: in response to the target robot entering the pool, detecting the pool through a target sensor; when the presence of a target obstacle is detected, capturing an image of the target obstacle through a target image acquisition device to obtain target image data; and identifying the target image data to determine the first identification information.

[0052] Optionally, in this embodiment, the above-mentioned target sensor can be but is not limited to an ultrasonic sensor, and the sensing information collected by the sensor includes distance information and angle information, including but not limited to the distance between the obstacle and the target robot, and the direction angle of the obstacle relative to the target robot. That is, the above-mentioned sensing information includes distance information and angle information, the distance information is used to represent the distance between the obstacle and the target robot, and the angle information is used to represent the direction angle between the obstacle and the target robot.

[0053] Optionally, in this embodiment, when a target obstacle is detected by the target sensor, image capture of the target obstacle is started by a target image capture device, and the target image capture device may include but is not limited to an underwater camera, etc.

[0054] When the target robot moves along the edge of the pool or rotates, the sensor information collected by the sensor can obtain the line segment data features and angle features of the obstacle. The line segment data features and angle features constitute some features of the pool, such as the pool corners, walls, and other parts of the pool.

[0055] In one exemplary embodiment, ultrasonic sensors are used to collect sensor information, including certain pool features, such as outer and inner right angles, outer and inner arcs, as well as steps and ladders within the pool. Ultrasonic sensors can also detect the distance to vertical pool edges and each pool corner.

[0056] As an optional solution, in response to the target robot starting to perform the preset task, the target obstacle in the pool is imaged by a target image acquisition device to obtain target image data, including: in response to the target robot starting to perform the preset task, detecting the water depth in which the target robot is located; determining acquisition parameters of the target image acquisition device according to the water depth, and acquiring images of the target obstacle based on the acquisition parameters to obtain target image data.

[0057] Optionally, in this embodiment, a target image acquisition device operating at different water depths may be pre-configured for the target robot, and the target image acquisition device operating at different water depths has different energy consumption and / or computing capabilities.

[0058] It should be noted that the above-mentioned instrument for detecting the water depth can be a conventional physical instrument. After determining the water depth where the target robot is located, a start command is sent to the target image acquisition device corresponding to the water depth, and the corresponding target image acquisition device immediately starts working.

[0059] As an optional solution, the above method also includes: in response to the target robot being at a first water body depth, determining a first target image acquisition device among the target image acquisition devices; in response to the target robot being at a second water body depth, determining a second target image acquisition device among the target image acquisition devices, wherein the second water body depth is greater than the first water body depth, and the power consumption of the second target image acquisition device is higher than the power consumption of the first target image acquisition device.

[0060] Optionally, in this embodiment, the above-mentioned second water body depth can be understood as greater than the first water body depth, that is, when the target robot is at different water body depths, the target image acquisition device used is different. When the target robot is at a deeper water body depth, the working intensity of the target image acquisition device is relatively higher, and the power consumption is higher at this time. When the target robot is at a shallower water body depth, the working intensity of the target image acquisition device is relatively lower, and the power consumption is lower at this time.

[0061] As an optional solution, image capture of the target obstacle in the pool is performed using a target image capture device, including: when the target robot is at a first water depth, image capture of the target obstacle is performed based on the first target image capture device; in response to the target robot moving from the first water depth to the second water depth, image capture of the target obstacle is performed based on the second target image capture device.

[0062] Through this embodiment, different target image acquisition devices are used to detect obstacles at different water depths, which can reduce the resource overhead of the target robot and ensure a certain degree of accuracy.

[0063] As an optional solution, in response to the target robot starting to execute the preset task, the target obstacle in the pool is imaged by a target image acquisition device to obtain target image data, including: detecting the water cleanliness of the pool to determine the target water cleanliness; determining the acquisition parameters of the target image acquisition device according to the target water cleanliness, and acquiring the image of the target obstacle based on the acquisition parameters to obtain target image data.

[0064] Optionally, in this embodiment, the detection of the cleanliness of the water in the pool can be understood as different acquisition parameters of the target image acquisition device corresponding to when the water is relatively turbid or when the water is relatively clear.

[0065] It should be noted that the above-mentioned acquisition parameters can be understood as exposure, focal length, etc. Different acquisition parameters can be used to adapt to different water cleanliness conditions and collect clearer target image data to ensure subsequent recognition accuracy.

[0066] As an optional solution, the acquisition parameters of the target image acquisition device are determined according to the cleanliness of the target water body, and the target obstacle is imaged based on the acquisition parameters to obtain target image data, including: when the cleanliness of the target water body belongs to the first cleanliness interval, the acquisition parameters of the target image acquisition device are configured as the first acquisition parameters, and the target obstacle is imaged based on the first acquisition parameters to obtain target image data, wherein the first acquisition parameters represent acquisition parameters having a mapping relationship with the first cleanliness interval; when the cleanliness of the target water body belongs to the second cleanliness interval, the acquisition parameters of the target image acquisition device are configured as the second acquisition parameters, and the target obstacle is imaged based on the second acquisition parameters to obtain target image data, wherein the first cleanliness interval is different from the second cleanliness interval, the first acquisition parameters are different from the second acquisition parameters, and the second acquisition parameters represent acquisition parameters having a mapping relationship with the second cleanliness interval.

[0067] Optionally, in this embodiment, the first cleanliness interval and the second cleanliness interval can be pre-set manually based on prior knowledge. By binding different acquisition parameters to different cleanliness intervals, the acquisition parameters can be dynamically adjusted under different cleanliness intervals, so that the target image acquisition device can obtain clearer target image data.

[0068] As an optional solution, identifying the target image data and determining the first identification information includes: performing feature extraction on the target image data to determine target feature information of the target obstacle; preprocessing the target feature information, and inputting the preprocessed target feature information into a recognition model deployed on the target robot to determine the first identification information, wherein the preprocessing is used to increase the weight value corresponding to the feature information in the target feature information used to determine the category to which the target obstacle belongs.

[0069] Optionally, in this embodiment, the above-mentioned preprocessing is used to represent weighting the features in the target image data to increase the weight value corresponding to the feature information used to determine the category to which the target obstacle belongs in the feature information, so that the subsequent recognition accuracy corresponding to the preprocessed target feature information is higher.

[0070] Exemplarily, the above-mentioned preprocessing operations may include but are not limited to image enhancement, image denoising, image sharpening, etc.

[0071] As an optional solution, determining the category of the target obstacle according to the first identification information includes at least one of the following:

[0072] The category of the target obstacle is determined to be a first category based on the first identification information, wherein the target category includes the first category, and the first category indicates that the target obstacle is an obstacle that the target robot needs to stay away from; the category of the target obstacle is determined to be a second category based on the first identification information, wherein the target category includes the second category, and the second category indicates that the target obstacle is a fixed obstacle; the category of the target obstacle is determined to be a third category based on the first identification information, wherein the target category includes the third category, and the third category indicates that the target obstacle is an obstacle that requires a reminder message; the category of the target obstacle is determined to be a fourth category based on the first identification information, wherein the target category includes the fourth category, and the fourth category indicates that the target obstacle is an obstacle that needs to be moved by the target robot.

[0073] Optionally, in this embodiment, the first category can be understood as pre-set obstacles that require the target robot to avoid a greater distance, such as rags, clothes, etc.

[0074] Optionally, in this embodiment, the second category can be understood as obstacles that have existed for longer than a preset duration. This preset duration can be pre-set by the system or manually. Each time the obstacle is recognized, the recognition time is recorded, with the duration being the time from the earliest to the latest recognition of the obstacle. Examples include escalators and water pipes. Because the obstacle persists for an extended period, the target robot needs to perform more thorough cleaning around it and avoid it at a shorter distance.

[0075] In an exemplary embodiment, the specific type of obstacle can also be directly identified based on the image. For example, an underwater escalator can be identified as a fixed obstacle. In other words, there is no need to identify the existence duration of the obstacle each time. Instead, the first time the obstacle is identified, the type of obstacle is determined by the above-mentioned existence duration. If the obstacle is identified again in the future, the obstacle is directly determined based on image recognition.

[0076] It should be noted that the above-mentioned fixed obstacles can be understood as obstacles that exist for a period exceeding a preset period, etc.

[0077] Optionally, in this embodiment, the third category can be understood as the target robot needing to send a reminder message or play a reminder sound effect to the connected target terminal after identifying the target obstacle. For example, if a human body part is identified, the target robot needs to send a reminder message and avoid it.

[0078] Optionally, in this embodiment, the above-mentioned fourth category can be understood as an obstacle that requires the target robot to move. At this time, the target robot is configured with a corresponding mechanical control part, for example, the robotic arm of the target robot. When it is identified that the obstacle belongs to the fourth category, the robotic arm is controlled to move the obstacle to achieve the effect of clearing the area blocked by the obstacle.

[0079] As an optional solution, when the category of the target obstacle is the target category, the target robot is controlled to perform a preset task in the pool based on the target control method corresponding to the target category, including at least one of the following: when the category of the target obstacle is the first category, a first obstacle avoidance distance is set, wherein the first obstacle avoidance distance is greater than the initial obstacle avoidance distance preset for the target robot; when the target robot moves to a distance between it and the target obstacle that meets the first obstacle avoidance distance, the target robot is controlled to perform the preset task in the pool according to the first obstacle avoidance distance; when the category of the target obstacle is the second category, a second obstacle avoidance distance is set, wherein the second obstacle avoidance distance is less than the initial obstacle avoidance distance preset for the target robot. distance; when the target robot moves to a distance from the target obstacle that meets the second obstacle avoidance distance, the target robot is controlled to perform the preset task in the pool according to the second obstacle avoidance distance; when the category of the target obstacle is the third category, the position of the target obstacle is marked; a target reminder message is sent to the target terminal to indicate the position of the target obstacle in the pool, and the preset task is performed in the pool according to the initial obstacle avoidance distance preset for the target robot; when the category of the target obstacle is the fourth category, the mechanical control part of the target robot is controlled to move the target obstacle, and the position of the target obstacle before the movement is cleared, so as to control the target robot to perform the preset task in the pool.

[0080] Optionally, in this embodiment, the first obstacle avoidance distance may be pre-set based on prior knowledge, or may be adaptively set based on the size of the identified target obstacle, that is, the first obstacle avoidance distance may be adaptively adjusted based on the size of the target obstacle.

[0081] It should be noted that the above-mentioned second obstacle avoidance distance can be obtained in the following ways, including but not limited to pre-setting based on prior knowledge, adaptive setting based on the size of the identified target obstacle, etc. That is, the second obstacle avoidance distance can be adaptively adjusted according to the size of the target obstacle to complete the update.

[0082] As an optional solution, the above method also includes: obtaining an original map of the pool and the location information of the target obstacle, wherein the target robot is set to perform a preset task in the pool according to the original map; marking and updating the original map according to the location information to obtain a target map to control the target robot to perform the preset task in the pool according to the target map.

[0083] Optionally, in this embodiment, the original map of the above-mentioned pool can be pre-calibrated by a map calibration algorithm, and the original map does not mark the position of the target obstacle. When the target obstacle is identified for the first time, the position of the target obstacle needs to be marked and the original map is updated. When the target robot reaches a preset distance from the target position, the target robot is controlled to slow down, and second identification information collected by the target robot in the pool is obtained, wherein the second identification information represents feature information obtained by image recognition of the target obstacle in the process of cleaning the pool according to the target map, and the target position represents the marked position of the target obstacle in the target map; the category of the target obstacle is determined according to the second identification information, and when the category of the target obstacle is the target category, the target robot is controlled to perform the preset task in the pool according to the target obstacle avoidance distance corresponding to the target control method.

[0084] Optionally, in this embodiment, the original map is a pre-constructed map of the pool. The original map may be a bottom map of the pool, a surface map of the pool, or a combination of the bottom map and the surface map. The original map is a map created before the target robot calibrates the position of the target obstacle, or a map created during the target robot's position calibration of the target obstacle.

[0085] Specifically, the original map of the pool can be constructed as follows:

[0086] The target robot is controlled to move along the edge of the pool. During the movement of the target robot, sensor information is collected by sensors provided on the target robot to obtain raw sensor information. An original map is constructed based on the raw sensor information.

[0087] Alternatively, the target robot may be controlled to rotate once at a preset position, and sensing information may be collected through sensors provided on the target robot.

[0088] It should be noted that when collecting sensor information by moving along the edge of the pool, the features of obstacles located on the side of the target robot can be collected, and the features of obstacles located in front of the target robot can also be collected (objects detected by sensors installed on the target robot are considered obstacles, such as the pool wall, water surface, etc.).

[0089] When collecting sensor information by rotating in a circle, the features of obstacles located on the side of the target robot can be collected, the features of obstacles located in front of the target robot can be collected, or the features of obstacles located on the side and in front of the target robot can be collected at the same time.

[0090] The features of the above-mentioned obstacles include wall features located at the bottom of the pool, or features located on the water surface of the pool.

[0091] The present application is described below through specific implementation methods:

[0092] Taking the target robot as a pool robot as an example, an ultrasonic sensor and an image acquisition device are set on the pool robot. The ultrasonic sensor can detect obstacles, and the image acquisition device can complete image acquisition of the obstacles.

[0093] Pool robots can perform tasks in swimming pools, such as cleaning, disinfecting, or testing water quality.

[0094] While operating in a swimming pool, the pool robot uses ultrasonic sensors to collect sensor information within the pool (including but not limited to the distance to obstacles and the direction of obstacles relative to the pool robot) to determine whether an obstacle is detected. Upon detecting an obstacle, the robot controls the image acquisition device to capture and identify the obstacle, ultimately selecting the appropriate control method based on the type of obstacle and controlling the robot to clean the pool.

[0095] This application uses AI recognition and real-time machine positioning technology to identify obstacle types and handle them accordingly. This aims to address the problem of underwater robots encountering different obstacles during actual operation, which can lead to blockage or missed cleaning of machine execution components due to the inability to take effective measures.

[0096] This application mainly uses AI recognition technology to identify and classify obstacles and update the location of the obstacles to the map, thereby implementing different obstacle avoidance strategies for different types of obstacles. Figure 3 is a schematic diagram of the specific flow of the robot control method according to an embodiment of the present disclosure, as shown in Figure 3, including but not limited to the following steps:

[0097] S302: During the cleaning process, the pool robot collects image information within a preset range through its image acquisition module, and then matches and recognizes the collected image information through AI recognition technology to identify the type of obstacle ahead.

[0098] S304: After identifying the obstacle type, obstacle avoidance is performed based on the obstacle type. For example, if the obstacle significantly impacts the pool robot's operation, a larger avoidance distance can be set, and the pool robot can also use this larger distance when maneuvering around it. If the obstacle is a fixed, long-standing obstacle, the pool robot can use a shorter avoidance distance to clean the accumulated dust around it.

[0099] S306, after the obstacle is identified, the location information of the obstacle will be marked according to the location information of the pool robot itself.

[0100] It should be noted that this function is helpful for taking some pre-emptive action when an obstacle is encountered during the second sweep. For example, when reaching an obstacle marked previously, slowing down in advance and re-confirming the obstacle information will facilitate subsequent obstacle avoidance.

[0101] Through this embodiment, AI recognition technology is used to identify and classify obstacles and mark them on the map, so that different obstacle avoidance strategies can be set. Compared with conventional machines, the swimming pool can be cleaned more efficiently, the machine collision and entrapment rate can be reduced, and it is more intelligent.

[0102] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the relevant technology, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0103] This embodiment also provides a robot control device, which is configured to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0104] FIG4 is a structural block diagram of a control device for a robot according to an embodiment of the present disclosure. As shown in FIG4 , the device includes:

[0105] According to another embodiment of the present disclosure, there is provided a control device for a robot, comprising:

[0106] an acquisition module 402 configured to acquire first identification information collected by the target robot in the pool, wherein the first identification information represents feature information obtained by performing image recognition on a target obstacle, the target obstacle being an obstacle detected by the target robot in the pool;

[0107] a determination module 404 configured to determine a category of the target obstacle based on the first identification information, wherein different categories of obstacles allowed to be detected in the pool are pre-set, and different control methods may be used by the target robot when detecting obstacles of different categories;

[0108] The control module 406 is configured to control the target robot to perform a preset task in the pool based on a target control method corresponding to the target category when the category of the target obstacle is a target category.

[0109] As an optional solution, the device is configured to obtain the first identification information collected by the target robot in the pool in the following manner: in response to the target robot starting to execute the preset task, the target obstacle in the pool is imaged by a target image acquisition device to obtain target image data, wherein the target image acquisition device is set on the target robot; the target image data is identified to determine the first identification information.

[0110] As an optional solution, the device is configured to, in response to the target robot starting to perform the preset task, perform image capture of the target obstacle in the pool through a target image capture device to obtain target image data in the following manner: in response to the target robot starting to perform the preset task, detect the water depth in which the target robot is located; determine the capture parameters of the target image capture device according to the water depth, and perform image capture of the target obstacle based on the capture parameters to obtain target image data.

[0111] As an optional solution, the device is configured to respond to the target robot starting to execute the preset task by capturing the image of the target obstacle in the pool through a target image capture device to obtain target image data in the following manner: detecting the water cleanliness of the pool to determine the target water cleanliness; determining the capture parameters of the target image capture device according to the target water cleanliness, and capturing the image of the target obstacle based on the capture parameters to obtain target image data.

[0112] As an optional solution, the device is configured to determine the acquisition parameters of the target image acquisition device according to the cleanliness of the target water body in the following manner, and perform image acquisition on the target obstacle based on the acquisition parameters to obtain target image data: when the cleanliness of the target water body belongs to a first cleanliness interval, the acquisition parameters of the target image acquisition device are configured as first acquisition parameters, and image acquisition is performed on the target obstacle based on the first acquisition parameters to obtain the target image data, wherein the first acquisition parameters represent the acquisition parameters having a mapping relationship with the first cleanliness interval; when the cleanliness of the target water body belongs to a second cleanliness interval, the acquisition parameters of the target image acquisition device are configured as second acquisition parameters, and image acquisition is performed on the target obstacle based on the second acquisition parameters to obtain the target image data, wherein the first cleanliness interval is different from the second cleanliness interval, the first acquisition parameters are different from the second acquisition parameters, and the second acquisition parameters represent the acquisition parameters having a mapping relationship with the second cleanliness interval.

[0113] As an optional solution, the device is further configured to: obtain the first acquisition parameter and the second acquisition parameter in advance; bind the first acquisition parameter to the first cleaning interval, and bind the second acquisition parameter to the second cleaning interval.

[0114] As an optional solution, the device is configured to identify the target image data and determine the first identification information in the following manner: perform feature extraction on the target image data to determine target feature information of the target obstacle; preprocess the target feature information, and input the preprocessed target feature information into a recognition model deployed on the target robot to determine the first identification information, wherein the preprocessing is used to increase the weight value corresponding to the feature information in the target feature information used to determine the category to which the target obstacle belongs.

[0115] As an optional solution, the device is further configured to: in response to the target robot being at a first water body depth, determine a first target image acquisition device among the target image acquisition devices; in response to the target robot being at a second water body depth, determine a second target image acquisition device among the target image acquisition devices, wherein the second water body depth is greater than the first water body depth, and the power consumption of the second target image acquisition device is higher than the power consumption of the first target image acquisition device.

[0116] As an optional solution, the device is configured to capture images of the target obstacle in the pool through a target image capture device in the following manner: when the target robot is at the first water body depth, image capture of the target obstacle is performed based on the first target image capture device; in response to the target robot moving from the first water body depth to the second water body depth, image capture of the target obstacle is performed based on the second target image capture device.

[0117] As an optional solution, the device is configured to obtain the first identification information collected by the target robot in the pool in the following manner: in response to the target robot entering the pool, detecting the pool through a target sensor; when the presence of the target obstacle is detected, capturing an image of the target obstacle through the target image acquisition device to obtain the target image data; and identifying the target image data to determine the first identification information.

[0118] As an optional solution, the device is configured to detect the pool through a target sensor in response to the target robot entering the pool in the following manner: in response to the target robot entering the pool, controlling the target sensor to obtain sensing information, wherein the sensing information includes distance information and angle information, the distance information is used to represent the distance between the target obstacle and the target robot, and the angle information is used to represent the direction angle between the target obstacle and the target robot; and determining whether the target obstacle exists in the pool based on the sensing information.

[0119] As an optional solution, the device is configured to obtain the first identification information collected by the target robot in the pool in the following manner: in response to the target robot starting to execute the preset task, the target obstacle in the pool is imaged by a target image acquisition device to obtain target image data, wherein the target image acquisition device is set outside the target robot in a position allowing normal communication with the target robot; the target image data is identified to determine the first identification information.

[0120] As an optional scheme, the device is configured to determine the category of the target obstacle based on the first identification information in at least one of the following ways: determining the category of the target obstacle as a first category based on the first identification information, wherein the target category includes the first category, and the first category indicates that the target obstacle is an obstacle that the target robot needs to stay away from; determining the category of the target obstacle as a second category based on the first identification information, wherein the target category includes the second category, and the second category indicates that the target obstacle is a fixed obstacle; determining the category of the target obstacle as a third category based on the first identification information, wherein the target category includes the third category, and the third category indicates that the target obstacle is an obstacle that requires a reminder message; determining the category of the target obstacle as a fourth category based on the first identification information, wherein the target category includes the fourth category, and the fourth category indicates that the target obstacle is an obstacle that needs to be moved by the target robot.

[0121] As an optional solution, the device is configured to control the target robot to perform a preset task in the pool based on a target control method corresponding to the target category when the category of the target obstacle is the target category in at least one of the following ways: when the category of the target obstacle is the first category, setting a first obstacle avoidance distance, wherein the first obstacle avoidance distance is greater than the initial obstacle avoidance distance preset for the target robot; when the distance between the target robot and the target obstacle meets the first obstacle avoidance distance, controlling the target robot to perform the preset task in the pool according to the first obstacle avoidance distance; when the category of the target obstacle is the second category, setting a second obstacle avoidance distance, wherein the second obstacle avoidance distance is less than the initial obstacle avoidance distance preset for the target robot. distance; when the target robot moves to a distance from the target obstacle that meets the second obstacle avoidance distance, controlling the target robot to perform the preset task in the pool according to the second obstacle avoidance distance; when the category of the target obstacle is the third category, marking the position of the target obstacle; sending a target reminder message to the target terminal to indicate the position of the target obstacle in the pool, and performing the preset task in the pool according to the initial obstacle avoidance distance preset by the target robot; when the category of the target obstacle is the fourth category, controlling the mechanical control part of the target robot to move the target obstacle, and clearing the position of the target obstacle before the movement, so as to control the target robot to perform the preset task in the pool.

[0122] As an optional solution, the device is further configured to do at least one of the following: pre-set the first obstacle avoidance distance and the second obstacle avoidance distance; and update the first obstacle avoidance distance and the second obstacle avoidance distance according to the identified size of the target obstacle.

[0123] As an optional solution, the device is configured to determine that the category of the target obstacle is the second category based on the first identification information in the following manner: obtaining the preset duration; if the existence duration of the target obstacle exceeds the preset duration, determining the category of the target obstacle as the second category, wherein the existence duration represents the duration from the earliest time the target obstacle was identified to the latest time the target obstacle was identified.

[0124] As an optional solution, the device is further configured to: obtain an original map of the pool and the location information of the target obstacle, wherein the target robot is configured to perform the preset task in the pool according to the original map; mark and update the original map according to the location information to obtain a target map, so as to control the target robot to perform the preset task in the pool according to the target map.

[0125] As an optional solution, the device is further configured to: when the target robot reaches a preset distance from the target position, control the target robot to decelerate, and obtain second identification information collected by the target robot in the pool, wherein the second identification information represents feature information obtained by image recognition of the target obstacle in the process of cleaning the pool according to the target map, and the target position represents the marked position of the target obstacle in the target map; determine the category of the target obstacle based on the second identification information, and when the category of the target obstacle is the target category, control the target robot to perform the preset task in the pool according to the target obstacle avoidance distance corresponding to the target control method.

[0126] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0127] An embodiment of the present disclosure further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0128] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0129] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0130] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0131] The present disclosure also provides a pool robot, which is applicable to the robot control method of the above-mentioned embodiment. The pool robot image acquisition device is configured to acquire first identification information; the processor is configured to determine the target control mode based on the first identification information and control the target robot to perform the preset task in the pool according to the target control mode.

[0132] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0133] Obviously, those skilled in the art should understand that the modules or steps of the present disclosure described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present disclosure is not limited to any particular combination of hardware and software.

[0134] The foregoing description is merely a partial embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that the present disclosure may be modified and altered in various ways. Any modifications, equivalent substitutions, or improvements made within the principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A robot control method, comprising: Acquire first identification information collected by the target robot in the pool, wherein the first identification information represents feature information obtained by performing image recognition on a target obstacle, and the target obstacle is an obstacle detected by the target robot in the pool; Determine the category of the target obstacle according to the first identification information, wherein different categories of obstacles allowed to be detected in the pool are set in advance, and the control methods adopted by the target robot when detecting obstacles of different categories are allowed to be different; In a case where the category of the target obstacle is a target category, the target robot is controlled to perform a preset task in the pool based on a target control method corresponding to the target category.

2. The method according to claim 1, wherein: The first identification information collected by the target robot in the pool is obtained, including: In response to the target robot starting to perform the preset task, an image of the target obstacle in the pool is captured by a target image acquisition device to obtain target image data, wherein the target image acquisition device is disposed on the target robot; The target image data is identified to determine the first identification information.

3. The method according to claim 2, wherein: In response to the target robot starting to execute the preset task, the target obstacle in the pool is imaged by a target image acquisition device to obtain target image data, including: In response to the target robot starting to perform the preset task, detecting the depth of the water body where the target robot is located; The acquisition parameters of the target image acquisition device are determined according to the water body depth, and the image of the target obstacle is acquired based on the acquisition parameters to obtain target image data.

4. The method according to claim 2, wherein: In response to the target robot starting to execute the preset task, the target obstacle in the pool is imaged by a target image acquisition device to obtain target image data, including: Testing the cleanliness of the water in the pool to determine a target cleanliness of the water; The acquisition parameters of the target image acquisition device are determined according to the cleanliness of the target water body, and the image of the target obstacle is acquired based on the acquisition parameters to obtain target image data.

5. The method according to claim 4, wherein: The acquisition parameters of the target image acquisition device are determined according to the cleanliness of the target water body, and the image of the target obstacle is acquired based on the acquisition parameters to obtain the target image data, including: When the cleanliness of the target water body belongs to a first cleanliness interval, configuring the acquisition parameter of the target image acquisition device to a first acquisition parameter, and performing image acquisition on the target obstacle based on the first acquisition parameter to obtain the target image data, wherein the first acquisition parameter represents the acquisition parameter having a mapping relationship with the first cleanliness interval; When the cleanliness of the target water body belongs to the second cleanliness interval, the acquisition parameter of the target image acquisition device is configured as the second acquisition parameter, and the image of the target obstacle is acquired based on the second acquisition parameter to obtain the The target image data, wherein the first cleanliness interval is different from the second cleanliness interval, the first acquisition parameter is different from the second acquisition parameter, and the second acquisition parameter represents the acquisition parameter having a mapping relationship with the second cleanliness interval.

6. The method according to claim 5, wherein: The method further comprises: Pre-acquire the first acquisition parameter and the second acquisition parameter; The first acquisition parameter is bound to the first cleaning interval, and the second acquisition parameter is bound to the second cleaning interval.

7. The method according to claim 2, wherein: Identifying the target image data to determine the first identification information includes: Extracting features from the target image data to determine target feature information of the target obstacle; The target feature information is preprocessed, and the preprocessed target feature information is input into a recognition model deployed on the target robot to determine the first recognition information, wherein the preprocessing is used to increase a weight value corresponding to feature information in the target feature information for determining a category to which the target obstacle belongs.

8. The method according to claim 2, wherein: The method further comprises: In response to the target robot being at a first water depth, determining a first target image acquisition device among the target image acquisition devices; In response to the target robot being at a second water body depth, a second target image acquisition device among the target image acquisition devices is determined, wherein the second water body depth is greater than the first water body depth and the power consumption of the second target image acquisition device is higher than the power consumption of the first target image acquisition device.

9. The method according to claim 8, wherein: The target obstacle in the pool is imaged by a target image acquisition device, including: When the target robot is at the first water depth, performing image acquisition of the target obstacle based on the first target image acquisition device; In response to the target robot moving from the first water body depth to the second water body depth, the target obstacle is imaged based on the second target image acquisition device.

10. The method according to claim 1, wherein: Acquiring the first identification information collected by the target robot in the pool includes: In response to the target robot entering the pool, detecting the pool via a target sensor; When the target obstacle is detected, an image of the target obstacle is captured by a target image capture device to obtain the target image data; The target image data is identified to determine the first identification information.

11. The method according to claim 10, wherein: In response to the target robot entering the water pool, detecting the water pool by a target sensor includes: In response to the target robot entering the pool, controlling the target sensor to acquire sensing information, wherein the sensing information includes distance information and angle information, the distance information is used to represent the distance between the target obstacle and the target robot, and the angle information is used to represent the direction angle between the target obstacle and the target robot; Determine whether the target obstacle exists in the pool according to the sensing information.

12. The method according to claim 1, wherein: Acquiring the first identification information collected by the target robot in the pool includes: In response to the target robot starting to perform the preset task, the target obstacle in the pool is imaged by a target image acquisition device to obtain target image data, wherein the target image acquisition device is disposed outside the target robot and is in a position allowing normal communication with the target robot; The target image data is identified to determine the first identification information.

13. The method according to claim 1, wherein: Determining the category of the target obstacle according to the first identification information includes at least one of the following: Determining, according to the first identification information, that the category of the target obstacle is a first category, wherein the target category includes the first category, and the first category indicates that the target obstacle is an obstacle that the target robot needs to stay away from; Determining, according to the first identification information, that the category of the target obstacle is a second category, wherein the target category includes the second category, and the second category indicates that the target obstacle is a fixed obstacle; Determining, according to the first identification information, that the category of the target obstacle is a third category, wherein the target category includes the third category, and the third category indicates that the target obstacle is an obstacle for which a reminder message needs to be issued; The category of the target obstacle is determined to be a fourth category according to the first identification information, wherein the target category includes the fourth category, and the fourth category indicates that the target obstacle is an obstacle that needs to be moved by the target robot.

14. The method according to claim 13, wherein: In the case where the category of the target obstacle is a target category, controlling the target robot to perform a preset task in the pool based on a target control method corresponding to the target category includes at least one of the following: When the category of the target obstacle is the first category, a first obstacle avoidance distance is set, wherein the first obstacle avoidance distance is greater than an initial obstacle avoidance distance preset by the target robot; when the distance between the target robot and the target obstacle satisfies the first obstacle avoidance distance, the target robot is controlled to perform the preset task in the pool according to the first obstacle avoidance distance; When the category of the target obstacle is the second category, a second obstacle avoidance distance is set, wherein the second obstacle avoidance distance is less than an initial obstacle avoidance distance preset for the target robot; when the distance between the target robot and the target obstacle satisfies the second obstacle avoidance distance, the target robot is controlled to perform the preset task in the pool according to the second obstacle avoidance distance; In the case where the category of the target obstacle is the third category, marking the location of the target obstacle; sending a target reminder message to the target terminal to indicate the location of the target obstacle in the pool, and performing the preset task in the pool according to the preset initial obstacle avoidance distance of the target robot; When the category of the target obstacle is the fourth category, the mechanical control part of the target robot is controlled to move the target obstacle, and the position of the target obstacle before the movement is cleaned, so as to control the target robot to perform the preset task in the pool.

15. The method according to claim 14, wherein: The method further comprises at least one of the following: Presetting the first obstacle avoidance distance and the second obstacle avoidance distance; The first obstacle avoidance distance and the second obstacle avoidance distance are updated according to the identified size of the target obstacle.

16. The method according to claim 13, wherein: The determining, according to the first identification information, that the category of the target obstacle is the second category includes: Obtaining the preset duration; When the existence duration of the target obstacle exceeds the preset duration, the category of the target obstacle is determined as the second category, wherein the existence duration indicates the duration from the earliest time when the target obstacle is identified to the latest time when the target obstacle is identified.

17. The method according to claim 1, wherein: The method further comprises: Acquire an original map of the pool and location information of the target obstacle, wherein the target robot is configured to perform the preset task in the pool according to the original map; The original map is marked and updated according to the position information to obtain a target map, so as to control the target robot to perform the preset task in the pool according to the target map.

18. The method according to claim 17, wherein: The method further comprises: When the target robot reaches a preset distance from the target position, the target robot is controlled to decelerate, and second identification information collected by the target robot in the pool is obtained, wherein the second identification information represents feature information obtained by image recognition of the target obstacle in the process of cleaning the pool according to the target map, and the target position represents a marked position of the target obstacle in the target map; The category of the target obstacle is determined according to the second identification information. When the category of the target obstacle is the target category, the target robot is controlled to perform the preset task in the pool according to the target obstacle avoidance distance corresponding to the target control method.

19. A pool robot, applied to the control method of the robot as claimed in any one of claims 1 to 18, comprising: An image acquisition device configured to acquire first identification information; The processor is configured to determine the target control mode according to the first identification information, and control the target robot to perform the preset task in the pool according to the target control mode.

20. A computer-readable storage medium having a computer program stored therein, wherein: When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 18 are implemented.

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