Multi-mode inertial navigation method and system for swimming pool robot

Abstract

The invention relates to the technical field of navigation and positioning, and discloses a multi-modal inertial navigation method and system for a swimming pool robot, and the method comprises the steps: constructing a multi-sensor network of the swimming pool robot, collecting the multi-modal data of the swimming pool robot in real time, constructing a swimming pool three-dimensional environment map of the swimming pool robot, and carrying out the multi-modal inertial navigation of the swimming pool robot. The method comprises the steps of determining an initial position, an underwater target position and a path coverage range of a swimming pool robot, calculating an initial underwater path of the swimming pool robot, calculating a motion adjustment parameter and a posture adjustment angle of the swimming pool robot, updating the initial underwater path, and determining an overwater target position of an overwater object corresponding to the swimming pool robot. Calculating the position distance between the initial position and the overwater target position, determining an initial overwater path of the swimming pool robot, analyzing an object flowing path of the overwater object, adjusting the initial overwater path, obtaining an overwater service path, and generating a final service path of the swimming pool robot in the application scene. The navigation accuracy of the swimming pool robot can be improved.

Description

Technical Field

[0001] The present invention relates to a multi-modal inertial navigation method and system for a swimming pool robot, belonging to the technical field of navigation and positioning. Background Art

[0002] Multimodal inertial navigation combines an inertial measurement unit (IMU) with other heterogeneous sensors (such as vision, sonar, depth gauge, GPS / Beidou, and magnetometer) to achieve high-precision and robust autonomous navigation through data fusion algorithms. Multimodal inertial navigation overcomes the limitations of single sensors and adapts to complex underwater environments (such as dynamic currents, low light levels, and turbid water).

[0003] Traditional pool robot navigation relies on infrared / ultrasonic obstacle avoidance. This approach relies on pre-programmed paths and infrared / ultrasonic sensors. This approach can only detect nearby obstacles and make simple avoidance adjustments, but it lacks the ability to fully perceive the pool's three-dimensional structure or detect distant obstacles, enabling precise path planning and autonomous navigation. This makes it difficult to provide efficient and comprehensive service. Therefore, a solution to improve the navigation accuracy of pool robots is urgently needed. Summary of the Invention

[0004] The present invention provides a multimodal inertial navigation method and system for a swimming pool robot, the main purpose of which is to improve the navigation accuracy of the swimming pool robot.

[0005] To achieve the above objectives, the present invention provides a multimodal inertial navigation method for a swimming pool robot, comprising:

[0006] Identify application scenarios for the pool robot, build a multi-sensor network for the pool robot based on the application scenarios, and collect multimodal data of the pool robot in real time based on the multi-sensor network, wherein the multimodal data includes inertial measurement data, underwater sonar positioning data, and visual sensor data;

[0007] fusing the multimodal data to obtain fused data, constructing a three-dimensional swimming pool environment map of the swimming pool robot based on the fused data, determining an initial position, an underwater target position, and a path coverage range of the swimming pool robot based on an underwater application scenario in the application scenario, and mapping the initial position, the underwater target position, and the path coverage range to the three-dimensional swimming pool environment map to calculate an initial underwater path of the swimming pool robot;

[0008] Based on the initial underwater path, collecting obstacle data of the pool robot in real time, calculating motion adjustment parameters and posture adjustment angles of the pool robot based on the obstacle data, and updating the initial underwater path based on the motion adjustment parameters and the posture adjustment angles to obtain an underwater service path;

[0009] Determining, based on the aquatic application scenario in the application scenario, an aquatic target position of the pool robot corresponding to an aquatic object, calculating a position distance between the initial position and the aquatic target position, and determining an initial aquatic path of the pool robot based on the position distance;

[0010] The flow speed and flow direction of the water object are collected in real time, the object flow path of the water object is analyzed based on the flow speed and the flow direction, and the initial water path is adjusted based on the object flow path to obtain the water service path.

[0011] Optionally, constructing a multi-sensor network of the swimming pool robot based on the application scenario includes:

[0012] Based on the application scenario, configure the multi-sensor of the swimming pool robot;

[0013] Determining a connection method and an installation layout between the multiple sensors, and determining a sensor network architecture of the multiple sensors based on the connection method and the installation layout;

[0014] Building a communication network of the sensor network architecture;

[0015] Determining sensor circuits and signal conditioning circuits of the sensor network architecture;

[0016] The multi-sensor network is obtained by integrating the multi-sensor, the communication network, the sensor circuit and the signal adjustment circuit into the sensor network architecture.

[0017] Optionally, fusing the multimodal data to obtain fused data includes:

[0018] Denoising the multimodal data to obtain denoised multi-source data;

[0019] Unifying the timestamps of the denoised multi-source data, and temporally aligning the denoised multi-source data based on the timestamps to obtain aligned multi-source data;

[0020] Constructing a spatial coordinate system for the aligned multi-source data, and mapping the aligned multi-source data to the spatial coordinate system to obtain registered multi-source data;

[0021] A multi-source feature matrix of the registered multi-source data is extracted, and based on the multi-source feature matrix, feature fusion is performed on the registered multi-source data to obtain fused data.

[0022] Optionally, determining the initial position, underwater target position, and path coverage of the swimming pool robot based on the underwater application scenario in the application scenario includes:

[0023] Collecting environmental data of the underwater application scene, and analyzing the relative position of the swimming pool robot in the underwater application scene based on the environmental data;

[0024] Matching the relative position with a three-dimensional pool environment map corresponding to the pool robot to obtain an initial position;

[0025] Analyzing service requirements of the swimming pool robot, and identifying a target area of ​​the underwater application scenario based on the service requirements;

[0026] determining an underwater target position in the target area;

[0027] A target area volume of the target area is calculated, and a path coverage range of the swimming pool robot is determined according to the target area volume.

[0028] Optionally, mapping the initial position, the underwater target position, and the path coverage range into the three-dimensional environment map of the swimming pool to calculate the initial underwater path of the swimming pool robot includes:

[0029] Dividing the three-dimensional environment map of the swimming pool into multiple map areas;

[0030] Dividing the multi-map area into an initial position area, a target area, a coverage area, and a path planning area based on the initial position, the underwater target position, and the path coverage range;

[0031] Calculating the robot's passability probability in the path planning area;

[0032] Combining the initial location area, the target area, the coverage area, and the path planning area according to the passable probability to obtain a path combination set;

[0033] Calculating the path length of each path in the path combination set;

[0034] An initial underwater path in the path combination set is screened out according to the path length.

[0035] Optionally, calculating the motion adjustment parameters and posture adjustment angle of the swimming pool robot based on the obstacle data includes:

[0036] Determine the current position, current speed, and current posture of the swimming pool robot, and extract the obstacle position and obstacle speed of the obstacle corresponding to the obstacle data;

[0037] Determining a safety radius of the swimming pool robot;

[0038] According to the current position, the current speed, the current posture, the obstacle position, the obstacle speed, and the safety radius, the motion adjustment parameters and the posture adjustment angle of the swimming pool robot are calculated using the following formula:

[0039]

[0040] Wherein, S represents the motion adjustment parameter, θ represents the posture adjustment angle, S0 represents the current speed vector of the current speed, Y represents the current position vector of the current position, Z represents the obstacle position vector of the obstacle position, R represents the safety radius vector of the safety radius, S Z The obstacle velocity vector represents the speed of the obstacle, and θ0 represents the attitude angle of the current attitude relative to the horizontal plane.

[0041] Optionally, determining the target position of the swimming pool robot corresponding to the object on the water based on the water application scenario in the application scenario includes:

[0042] Determining the water service requirements of the swimming pool robot based on the water application scenario;

[0043] collecting water object data of the water object;

[0044] identifying the object category of the water object according to the water object data;

[0045] Marking the water object based on the object category and the water service demand to obtain a water target object;

[0046] Determine the above-water target position of the above-water target object.

[0047] Optionally, determining an initial water path of the swimming pool robot based on the position distance includes:

[0048] determining a path point sequence of the swimming pool robot according to the position distance;

[0049] Determining an on-water path set of the swimming pool robot according to the path point sequence;

[0050] determining the number of waypoints in the waypoint sequence, and analyzing a steering energy consumption coefficient of the waypoint sequence;

[0051] A path cost function of the swimming pool robot is defined according to the number of path points and the steering energy consumption coefficient, wherein the path cost function includes:

[0052]

[0053] Where D(B) represents the path cost, n represents the number of path points, and b i+1 represents the path point position vector of the i+1th path point in the path point sequence, b i represents the path point position vector of the i-th path point in the path point sequence, μ represents the steering energy consumption coefficient, arctan represents the inverse tangent function, q i+1 Indicates the vertical coordinate of the path point position corresponding to the i+1th path point in the path point sequence, q i Indicates the vertical coordinate of the position of the path point corresponding to the i-th path point in the path point sequence, q i-1 Indicates the vertical coordinate of the position of the path point corresponding to the i-1th path point in the path point sequence, p i+1 Indicates the horizontal coordinate of the path point position corresponding to the i+1th path point in the path point sequence, p i Indicates the horizontal coordinate of the path point position corresponding to the i-th path point in the path point sequence, p i-1 Indicates the horizontal coordinate of the path point position corresponding to the i-1th path point in the path point sequence;

[0054] Calculating the path cost of the water path set according to the path cost function;

[0055] An initial water path in the water path set is screened out according to the path cost.

[0056] Optionally, analyzing the object flow path of the aquatic object based on the flow velocity and the flow direction includes:

[0057] Analyzing the discrete temporal positions of the aquatic object at discrete time points based on the flow velocity and the flow direction;

[0058] Analyzing environmental impact factors of the aquatic object;

[0059] Analyzing the influencing parameters of the discrete time series positions according to the environmental influencing factors;

[0060] Optimizing the discrete time series position according to the influencing parameter to obtain an optimized discrete time series position;

[0061] The optimized discrete time sequence positions are subjected to curve fitting according to the time sequence to obtain the object flow path.

[0062] In order to solve the above problems, the present invention also provides a multimodal inertial navigation system for a swimming pool robot, the system comprising:

[0063] A multimodal data acquisition module is used to identify application scenarios of the pool robot, build a multi-sensor network for the pool robot based on the application scenarios, and collect multimodal data of the pool robot in real time based on the multi-sensor network, wherein the multimodal data includes inertial measurement data, underwater sonar positioning data, and visual sensor data;

[0064] a three-dimensional map construction module, configured to fuse the multimodal data to obtain fused data, construct a three-dimensional pool environment map of the pool robot based on the fused data, determine an initial position, an underwater target position, and a path coverage range of the pool robot based on an underwater application scenario in the application scenario, and map the initial position, the underwater target position, and the path coverage range into the three-dimensional pool environment map to calculate an initial underwater path of the pool robot;

[0065] an underwater path optimization module, configured to collect obstacle data of the pool robot in real time based on the initial underwater path, calculate motion adjustment parameters and posture adjustment angles of the pool robot based on the obstacle data, and update the initial underwater path based on the motion adjustment parameters and the posture adjustment angles to obtain an underwater service path;

[0066] an aquatic path construction module, configured to determine an aquatic target position of the pool robot corresponding to an aquatic object based on the aquatic application scenario in the application scenario, calculate a positional distance between the initial position and the aquatic target position, and determine an initial aquatic path of the pool robot based on the positional distance;

[0067] a water path optimization module, configured to collect the flow speed and flow direction of the water object in real time, analyze the object flow path of the water object based on the flow speed and the flow direction, and adjust the initial water path based on the object flow path to obtain a water service path;

[0068] The final service path generation module is used to generate a final service path of the swimming pool robot in the application scenario according to the underwater service path and the above-water service path.

[0069] Compared to the problems described in the background art, the embodiments of the present invention can more accurately perceive the swimming pool environment by collecting multimodal data of the swimming pool robot in real time, providing a data basis for subsequent dynamic planning of cleaning paths and optimizing work efficiency. Optionally, the embodiments of the present invention can help the robot better adapt to the environment and plan a safe and efficient path by constructing a three-dimensional swimming pool environment map of the swimming pool robot based on the fused data. The embodiments of the present invention can help the robot better adapt to the environment and plan a safe and efficient path by constructing a three-dimensional swimming pool environment map of the swimming pool robot based on the fused data. The embodiments of the present invention can adjust the path in real time by updating the initial underwater path based on the motion adjustment parameters and the posture adjustment angle to obtain an underwater service path, effectively avoiding obstacles, adapting to a changing underwater environment, and reducing the probability of being trapped. The embodiments of the present invention can determine the initial water path of the swimming pool robot based on the position distance, and plan the shortest or optimal path from the initial position to the target position for the robot, reducing travel distance and time, and improving work efficiency. Finally, the embodiments of the present invention can adjust the initial water path based on the object flow path to obtain an water service path, which can adapt to the dynamically changing water environment, always maintain the best service state, and improve work efficiency. Therefore, the multimodal inertial navigation method and system for the swimming pool robot provided by the embodiments of the present invention can improve the navigation accuracy of the swimming pool robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic flow chart of a multimodal inertial navigation method for a swimming pool robot provided in one embodiment of the present invention;

[0071] Figure 2 A schematic diagram of modules for implementing the multimodal inertial navigation method of the swimming pool robot provided in one embodiment of the present invention.

[0072] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0073] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0074] The present invention provides a multimodal inertial navigation method for a swimming pool robot. The multimodal inertial navigation method for the swimming pool robot may be executed by at least one of the following electronic devices: a server, a terminal, or other electronic device capable of executing the method provided by the present invention. In other words, the multimodal inertial navigation method for the swimming pool robot may be executed by software or hardware installed on a terminal device or a server device. The server may include, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0075] Example 1:

[0076] Reference Figure 1 FIG. 1 is a flow chart of a multimodal inertial navigation method for a swimming pool robot according to an embodiment of the present invention. In this embodiment, the multimodal inertial navigation method for the swimming pool robot includes:

[0077] S1. Clarify the application scenarios of the swimming pool robot, build a multi-sensor network for the swimming pool robot based on the application scenarios, and collect multimodal data of the swimming pool robot in real time based on the multi-sensor network, wherein the multimodal data includes: inertial measurement data, underwater sonar positioning data, and visual sensor data.

[0078] By clarifying the application scenarios of the pool robot, the embodiments of the present invention can lay the foundation for the subsequent use of different path planning for different work tasks. The application scenarios refer to the specific environments and tasks in which the pool robot can perform its functions and roles, such as home swimming pools, public swimming pools, water parks, underwater photography, etc.

[0079] By building a multi-sensor network based on the application scenario, the pool robot can achieve precise navigation and positioning in the pool, avoiding collisions and disorientation. The multi-sensor network refers to a network system composed of multiple different types of sensors that collaboratively sense and monitor various information in a specific environment.

[0080] As an embodiment of the present invention, the multi-sensor network of the swimming pool robot is constructed based on the application scenario, including:

[0081] Based on the application scenario, configure the multi-sensor of the swimming pool robot;

[0082] Determining a connection method and an installation layout between the multiple sensors, and determining a sensor network architecture of the multiple sensors based on the connection method and the installation layout;

[0083] Building a communication network of the sensor network architecture;

[0084] Determining sensor circuits and signal conditioning circuits of the sensor network architecture;

[0085] The multi-sensor network is obtained by integrating the multi-sensor, the communication network, the sensor circuit and the signal adjustment circuit into the sensor network architecture.

[0086] Among them, the multi-sensor refers to the various types of sensors equipped on the pool robot for sensing and collecting various information in the pool environment, such as inertial measurement units, underwater sonar positioning systems, visual sensors, etc. The connection method refers to the communication and data transmission method between sensors, between sensors and controllers, and between controllers and external systems (such as user interfaces, remote monitoring platforms, etc.), such as wired connections, wireless connections, hybrid connections, etc. The installation layout refers to the physical installation position and arrangement of components such as sensors and controllers on the robot body. The sensor network architecture refers to the organizational structure and interaction method between sensors, controllers and communication module devices. The communication network refers to the network used to transmit data between various sensors, controllers and external systems on the robot. The sensor circuit refers to an electronic circuit used to connect, control and process electrical signals from various sensors. The signal adjustment circuit refers to an electronic circuit used to process and optimize sensor output signals.

[0087] Optionally, the sensor network architecture may be determined by a topology optimization algorithm, such as a genetic algorithm, simulated annealing, an ant colony algorithm, or the like.

[0088] Optionally, the communication network can be constructed using low-power wide area network technology, such as NB-IoT, LoRaWAN, etc.

[0089] The embodiment of the present invention can more accurately perceive the environment of the swimming pool by collecting the multimodal data of the swimming pool robot in real time, providing a data basis for the subsequent dynamic planning of the cleaning path and optimization of work efficiency. Among them, the multimodal data refers to the collection of data collected by multiple different types of sensors. The inertial measurement data refers to the data collected by the inertial measurement unit, such as linear acceleration and deceleration information, rotation rate, magnetic data, etc. The underwater sonar positioning data refers to the data collected by the underwater sonar system, such as distance measurement data, environmental mapping data, etc. The visual sensor data refers to the image or video data collected by the visual sensor (such as a camera) carried by the swimming pool robot.

[0090] S2. Fusing the multimodal data to obtain fused data, constructing a three-dimensional swimming pool environment map of the swimming pool robot based on the fused data, determining an initial position, an underwater target position, and a path coverage range of the swimming pool robot based on an underwater application scenario in the application scenario, and mapping the initial position, the underwater target position, and the path coverage range to the three-dimensional swimming pool environment map to calculate an initial underwater path of the swimming pool robot.

[0091] The embodiments of the present invention fuse the multimodal data to obtain fused data, which can overcome the limitations of a single modality and provide more comprehensive and accurate environmental perception. The fused data refers to the result obtained by integrating and processing data from different sensor modalities.

[0092] As an embodiment of the present invention, fusing the multimodal data to obtain fused data includes:

[0093] Denoising the multimodal data to obtain denoised multi-source data;

[0094] Unifying the timestamps of the denoised multi-source data, and temporally aligning the denoised multi-source data based on the timestamps to obtain aligned multi-source data;

[0095] Constructing a spatial coordinate system for the aligned multi-source data, and mapping the aligned multi-source data to the spatial coordinate system to obtain registered multi-source data;

[0096] A multi-source feature matrix of the registered multi-source data is extracted, and based on the multi-source feature matrix, feature fusion is performed on the registered multi-source data to obtain fused data.

[0097] Among them, the denoised multi-source data refers to a set of data from multiple different sensor modalities that has undergone denoising processing. The timestamp refers to the precise time information marked for the data sample, which is used to record the specific moment of data acquisition and generation. The aligned multi-source data refers to the data after the denoised multi-source data is synchronized and integrated in time. The spatial coordinate system refers to a reference frame used to describe and represent the position and relationship of aligned multi-source data in space. The registered multi-source data refers to a set of data from different sensor modalities that has undergone spatial registration processing. The multi-source feature matrix refers to a set of matrices used to represent data features formed after feature extraction of the registered multi-source data.

[0098] Optionally, the denoised multi-source data can be obtained by a filtering denoising algorithm, such as a low-pass filtering algorithm, a median filtering algorithm, a bilateral filtering algorithm, etc.

[0099] Optionally, the registered multi-source data can be registered through a multimodal registration network, such as constructing a multimodal registration network for the aligned multi-source data, analyzing the correspondence between the aligned multi-source data through the multimodal registration network, and mapping the aligned multi-source data to the spatial coordinate system according to the correspondence to obtain the registered multi-source data.

[0100] By constructing a three-dimensional pool environment map for the pool robot based on the fused data, embodiments of the present invention can help the robot better adapt to the environment and plan a safe and efficient path. The three-dimensional pool environment map is constructed by fusing multiple sensor data (such as inertial measurement data, underwater sonar positioning data, and visual sensor data) to create a three-dimensional digital model that comprehensively and accurately describes the pool's interior and surrounding environment.

[0101] Optionally, the three-dimensional environment map of the swimming pool may be constructed by a three-dimensional reconstruction algorithm, such as a point-based reconstruction algorithm, a grid-based reconstruction algorithm, and the like.

[0102] By determining the pool robot's initial position, underwater target position, and path coverage based on the underwater application scenario, the embodiments of the present invention provide the robot with accurate starting and target information, which serves as the basis for subsequent path planning and navigation. The initial position refers to the pool robot's location when it begins a task. The underwater target position refers to the underwater location the robot needs to reach or perform a specific task. The path coverage refers to the area of ​​water within which the robot can reach and perform its task.

[0103] As an embodiment of the present invention, the determining of the initial position, underwater target position, and path coverage of the swimming pool robot based on the underwater application scenario in the application scenario includes:

[0104] Collecting environmental data of the underwater application scene, and analyzing the relative position of the swimming pool robot in the underwater application scene based on the environmental data;

[0105] Matching the relative position with a three-dimensional pool environment map corresponding to the pool robot to obtain an initial position;

[0106] Analyzing service requirements of the swimming pool robot, and identifying a target area of ​​the underwater application scenario based on the service requirements;

[0107] determining an underwater target position in the target area;

[0108] A target area volume of the target area is calculated, and a path coverage range of the swimming pool robot is determined according to the target area volume.

[0109] The environmental data refers to information about the swimming pool's underwater environment collected by the robot's onboard sensor network, such as the pool's shape, size, wall and bottom material, inclination angle, and other information. The relative position refers to the position of the pool robot relative to a reference point or reference object (such as the pool wall) in the pool environment. The service requirements refer to the service content and functions that the user expects of the pool robot. The target area refers to the specific water area where the robot needs to perform specific tasks or services, such as the pool bottom or pool wall. The target area volume refers to the size of the space occupied by the target area.

[0110] Optionally, the relative position of the swimming pool robot in the underwater application scene can be analyzed by an environmental feature matching algorithm, such as extracting the environmental features of the environmental data, matching the environmental features with the scene features in the underwater application scene, obtaining a feature matching coefficient, and determining the relative position of the swimming pool robot based on the matching coefficient.

[0111] Optionally, the service demand may be obtained by acquiring an execution instruction received by the swimming pool robot and parsing the execution instruction to obtain the service demand of the swimming pool robot.

[0112] In this embodiment of the present invention, by mapping the initial position, the underwater target position, and the path coverage into the three-dimensional swimming pool environment map, the initial underwater path of the swimming pool robot is calculated. This allows accurate planning of the path from the starting point to the end point, avoiding unnecessary duplication or omission, and reducing navigation errors. The initial underwater path refers to the motion trajectory that initially covers the swimming pool area, as planned based on the constructed three-dimensional environment map.

[0113] As an embodiment of the present invention, mapping the initial position, the underwater target position, and the path coverage range into the three-dimensional environment map of the swimming pool to calculate the initial underwater path of the swimming pool robot includes:

[0114] Dividing the three-dimensional environment map of the swimming pool into multiple map areas;

[0115] Dividing the multi-map area into an initial position area, a target area, a coverage area, and a path planning area based on the initial position, the underwater target position, and the path coverage range;

[0116] Calculating the robot's passability probability in the path planning area;

[0117] Combining the initial location area, the target area, the coverage area, and the path planning area according to the passable probability to obtain a path combination set;

[0118] Calculating the path length of each path in the path combination set;

[0119] An initial underwater path in the path combination set is screened out according to the path length.

[0120] Among them, the multi-map area refers to dividing the three-dimensional environment map of the entire swimming pool into multiple smaller, regular areas. The initial position area refers to the area in the multi-map area that contains the initial position. The target area refers to the area in the multi-map area that contains the underwater target position. The coverage area refers to the area in the multi-map area that contains the path coverage range, and the path planning area refers to the area remaining in the multi-map area for planning the moving path. The robot passability probability refers to the possibility that the path planning area is passable for the swimming pool robot. The path combination set refers to a set consisting of multiple possible paths, each path representing a potential route from the initial position to the target position. The path length refers to the total length of the path traversed by the robot from the initial position to the target position.

[0121] Optionally, the robot's passability probability can be determined by identifying the passable range and the obstruction range of each area in the path planning area, calculating the range ratio of the passable range to the obstruction range, and determining the robot's passability probability of the path planning area based on the range ratio.

[0122] Optionally, the path combination set includes three paths A, B and C, and the path lengths of the three paths A, B and C are 200 meters, 300 meters and 400 meters respectively. Then, path A with the shortest path is selected as the initial underwater path of the pool robot.

[0123] S3. Based on the initial underwater path, obstacle data of the swimming pool robot is collected in real time. Based on the obstacle data, motion adjustment parameters and posture adjustment angles of the swimming pool robot are calculated. Based on the motion adjustment parameters and the posture adjustment angles, the initial underwater path is updated to obtain an underwater service path.

[0124] By collecting obstacle data from the pool robot in real time based on the initial underwater path, the present invention can instantly detect obstacles ahead or around it and quickly react to avoid collisions. Obstacle data refers to information collected by the robot's onboard sensors about objects in its surroundings that may hinder its movement or task execution, such as obstacle size and obstacle motion data.

[0125] By calculating the motion adjustment parameters and attitude adjustment angles of the pool robot based on the obstacle data, the embodiments of the present invention can help the robot select the optimal obstacle avoidance path, reduce unnecessary detours, and thus improve the efficiency of task execution. The motion adjustment parameters refer to a series of parameters used to control the robot's motion behavior, such as angular velocity, linear velocity, and acceleration. The attitude adjustment angle refers to the angle the robot needs to rotate relative to its original or desired attitude when adjusting its current attitude to adapt to environmental changes or perform specific tasks.

[0126] As an embodiment of the present invention, the calculating of the motion adjustment parameters and the posture adjustment angle of the swimming pool robot based on the obstacle data includes:

[0127] Determine the current position, current speed, and current posture of the swimming pool robot, and extract the obstacle position and obstacle speed of the obstacle corresponding to the obstacle data;

[0128] Determining a safety radius of the swimming pool robot;

[0129] According to the current position, the current speed, the current posture, the obstacle position, the obstacle speed, and the safety radius, the motion adjustment parameters and the posture adjustment angle of the swimming pool robot are calculated using the following formula:

[0130]

[0131] Wherein, S represents the motion adjustment parameter, θ represents the posture adjustment angle, S0 represents the current speed vector of the current speed, Y represents the current position vector of the current position, Z represents the obstacle position vector of the obstacle position, R represents the safety radius vector of the safety radius, S Z The obstacle velocity vector represents the speed of the obstacle, and θ0 represents the attitude angle of the current attitude relative to the horizontal plane.

[0132] The current position refers to the specific position coordinates of the pool robot in three-dimensional space at a specific moment. The current speed refers to the speed of the pool robot at a specific moment, including the magnitude and direction of the speed. The current posture refers to the orientation or posture of the pool robot in three-dimensional space at a specific moment. The obstacle position refers to the specific position coordinates of the obstacles detected by the pool robot in its working environment. The obstacle speed refers to the movement speed of the obstacles detected by the pool robot in its working environment. The safety radius refers to a safety distance set by the pool robot to ensure its own safety and avoid collisions with surrounding obstacles when performing tasks.

[0133] By updating the initial underwater path based on the motion adjustment parameters and the attitude adjustment angle, the embodiment of the present invention obtains an underwater service path, which can adjust the path in real time, effectively avoid obstacles, adapt to changing underwater environments, and reduce the probability of being trapped. The underwater service path is an optimal path obtained by calculating the motion adjustment parameters and attitude adjustment angle based on the initial underwater path and combining it with real-time obstacle data, the robot's current position, speed, attitude, and other information.

[0134] S4. Based on the water application scenario in the application scenario, determine the water target position of the swimming pool robot corresponding to the water object, calculate the position distance between the initial position and the water target position, and determine the initial water path of the swimming pool robot based on the position distance.

[0135] By determining the target location of the pool robot corresponding to an aquatic object based on the aquatic application scenario, the embodiment of the present invention can identify and avoid aquatic obstacles, accurately navigate to the target area, and avoid blind coverage. The target location refers to the specific location that the pool robot needs to reach or operate when performing a water task.

[0136] As an embodiment of the present invention, the determining the above-water target position of the swimming pool robot corresponding to the above-water object based on the above-water application scenario in the application scenario includes:

[0137] Determining the water service requirements of the swimming pool robot based on the water application scenario;

[0138] collecting water object data of the water object;

[0139] identifying the object category of the water object according to the water object data;

[0140] Marking the water object based on the object category and the water service demand to obtain a water target object;

[0141] Determine the above-water target position of the above-water target object.

[0142] The water service requirements refer to specific tasks that the pool robot needs to perform in an aquatic environment, such as cleaning leaves on the water, transporting fruit, etc. The water object data refers to various information about aquatic objects collected by the pool robot through its onboard sensors on the water. The object categories refer to the classification of aquatic objects identified based on the water object data, such as aquatic garbage (leaves, plastic bottles, etc.), life-saving equipment (swimming rings, life-saving poles, etc.), swimming gear, etc. The water target objects refer to specific aquatic objects that the pool robot needs to identify, locate, approach, operate, or avoid when performing specific tasks.

[0143] Optionally, the object category can be identified by a target detection algorithm, such as YOLO, SSD, Faster R-CNN, etc.

[0144] By calculating the distance between the initial position and the above-water target position, embodiments of the present invention can plan an optimal path for the pool robot from the initial position to the above-water target position, thereby avoiding unnecessary detours and energy waste. The distance refers to the straight-line distance between the robot's current position and the target cleaning position.

[0145] Optionally, the position distance can be determined by mapping the initial position and the target position on the water into a two-dimensional coordinate system to obtain the initial position coordinates and the target position coordinates, calculating the coordinate difference between the initial position coordinates and the target position coordinates, and determining the position distance between the initial position and the target position on the water based on the coordinate difference.

[0146] By determining the initial aquatic path of the pool robot based on the position distance, embodiments of the present invention can plan the shortest or optimal path from the initial position to the target position for the robot, thereby reducing travel distance and time consumption and improving operational efficiency. The initial aquatic path refers to a path initially planned from the initial position to the target position by the pool robot at the beginning of a task, based on the calculated position distance between the initial position and the target position on the water, and taking into account the application scenario, environmental factors, and the robot's own performance (such as speed, maneuverability, etc.).

[0147] As an embodiment of the present invention, determining the initial water path of the swimming pool robot based on the position distance includes:

[0148] determining a path point sequence of the swimming pool robot according to the position distance;

[0149] Determining an on-water path set of the swimming pool robot according to the path point sequence;

[0150] determining the number of waypoints in the waypoint sequence, and analyzing a steering energy consumption coefficient of the waypoint sequence;

[0151] A path cost function of the swimming pool robot is defined according to the number of path points and the steering energy consumption coefficient, wherein the path cost function includes:

[0152]

[0153] Where D(B) represents the path cost, n represents the number of path points, and b i+1 represents the path point position vector of the i+1th path point in the path point sequence, b irepresents the path point position vector of the i-th path point in the path point sequence, μ represents the steering energy consumption coefficient, arctan represents the inverse tangent function, q i+1 Indicates the vertical coordinate of the path point position corresponding to the i+1th path point in the path point sequence, q i Indicates the vertical coordinate of the position of the path point corresponding to the i-th path point in the path point sequence, q i-1 Indicates the vertical coordinate of the position of the path point corresponding to the i-1th path point in the path point sequence, p i+1 Indicates the horizontal coordinate of the path point position corresponding to the i+1th path point in the path point sequence, p i Indicates the horizontal coordinate of the path point position corresponding to the i-th path point in the path point sequence, p i-1 Indicates the horizontal coordinate of the path point position corresponding to the i-1th path point in the path point sequence;

[0154] Calculating the path cost of the water path set according to the path cost function;

[0155] An initial water path in the water path set is screened out according to the path cost.

[0156] The path point sequence is an ordered series of path points that define the path the pool robot should follow to move from its initial position to its target position. The water path set is a set of multiple different water paths. The steering energy consumption coefficient is the ratio of the energy consumed by the pool robot during steering to the energy consumed for traveling the same distance in a straight line. The path cost function is a mathematical function used to evaluate the advantages and disadvantages of different paths. The path cost is the total cost required for the pool robot to move from its initial position to its target position along a path.

[0157] Optionally, the steering energy consumption coefficient can be analyzed by a machine learning algorithm, such as by constructing a relationship model between the steering action and energy consumption of the swimming pool robot through a machine learning algorithm, and analyzing the steering energy consumption coefficient of the path point sequence based on the relationship model.

[0158] For example, there are three paths A, B and C in the water path set. The path cost function calculates that the path costs of A, B and C are 70, 120 and 65 respectively, and path C is selected as the initial water path of the swimming pool robot.

[0159] S5. Collect the flow speed and flow direction of the water object in real time, analyze the object flow path of the water object based on the flow speed and the flow direction, and adjust the initial water path based on the object flow path to obtain the water service path.

[0160] By collecting the flow velocity and flow direction of the aquatic object in real time, embodiments of the present invention can dynamically adjust its travel path to avoid collisions with the flowing object or obstruction of its normal operation. The flow velocity refers to the speed at which the aquatic object moves through the water. The flow direction refers to the direction in which the aquatic object moves through the water.

[0161] Optionally, the flow velocity and the flow direction can be collected by sonar technology, such as transmitting sound waves to the water object in real time by sonar technology, receiving reflected sound waves from the water surface object based on the transmitted sound waves, analyzing the frequency and phase change values ​​of the reflected sound waves, and determining the flow velocity and flow direction of the water object based on the frequency and the phase change values.

[0162] By analyzing the flow path of the aquatic object based on the flow velocity and flow direction, embodiments of the present invention can help a pool robot analyze the location of obstacles, thereby planning a path in advance, avoiding collisions, and better adapting to the dynamically changing aquatic environment. The flow path of the object refers to the trajectory of the aquatic object under the influence of the water flow.

[0163] As an embodiment of the present invention, analyzing the object flow path of the aquatic object based on the flow velocity and the flow direction includes:

[0164] Analyzing the discrete temporal positions of the aquatic object at discrete time points based on the flow velocity and the flow direction;

[0165] Analyzing environmental impact factors of the aquatic object;

[0166] Analyzing the influencing parameters of the discrete time series positions according to the environmental influencing factors;

[0167] Optimizing the discrete time series position according to the influencing parameter to obtain an optimized discrete time series position;

[0168] The optimized discrete time sequence positions are subjected to curve fitting according to the time sequence to obtain the object flow path.

[0169] The discrete time-series position refers to the set of coordinates of the aquatic object at a series of specific time points. The environmental influencing factors refer to various environmental factors that can affect the flow speed, flow direction, and ultimate flow path of the aquatic object, such as wind speed, wind direction, water flow speed, and water flow direction. The influencing parameters refer to the impact of environmental influencing factors on the discrete time-series position of the pool robot. The optimized discrete time-series position refers to the position of the object after correcting and improving the original calculated position at each discrete time point.

[0170] Optionally, the discrete time series positions can be analyzed by machine learning and deep learning algorithms, such as recursive neural networks, long short-term memory networks, etc.

[0171] Optionally, the environmental influencing factors can be analyzed by a multi-factor analysis algorithm, such as multiple linear regression, logistic regression, Poisson regression, etc.

[0172] Optionally, the influencing parameters may be analyzed by a breakpoint design algorithm, such as evaluating the causal effect of the environmental influencing factors by a breakpoint design algorithm, and determining the influencing parameters of the discrete time series positions based on the causal effect.

[0173] By adjusting the initial water path based on the object's flow path, the embodiment of the present invention obtains an on-water service path that can adapt to the dynamically changing water environment, always maintain an optimal service state, and improve work efficiency. The on-water service path refers to the route that the pool robot moves when performing tasks above or near the water surface.

[0174] S6. Generate a final service path of the swimming pool robot in the application scenario according to the underwater service path and the above-water service path.

[0175] The embodiment of the present invention generates the final service path of the swimming pool robot in the application scenario based on the underwater service path and the water service path, thereby ensuring that the service work of the swimming pool robot covers every corner of the swimming pool, optimizing the robot's movement path, reducing repeated coverage and unnecessary movement, thereby improving work efficiency and saving time and energy.

[0176] Compared to the problems described in the background art, the embodiments of the present invention can more accurately perceive the swimming pool environment by collecting multimodal data of the swimming pool robot in real time, providing a data basis for subsequent dynamic planning of cleaning paths and optimizing work efficiency. Optionally, the embodiments of the present invention can help the robot better adapt to the environment and plan a safe and efficient path by constructing a three-dimensional swimming pool environment map of the swimming pool robot based on the fused data. The embodiments of the present invention can help the robot better adapt to the environment and plan a safe and efficient path by constructing a three-dimensional swimming pool environment map of the swimming pool robot based on the fused data. The embodiments of the present invention can adjust the path in real time by updating the initial underwater path based on the motion adjustment parameters and the posture adjustment angle to obtain an underwater service path, effectively avoiding obstacles, adapting to a changing underwater environment, and reducing the probability of being trapped. The embodiments of the present invention can determine the initial water path of the swimming pool robot based on the position distance, and plan the shortest or optimal path from the initial position to the target position for the robot, reducing travel distance and time, and improving work efficiency. Finally, the embodiments of the present invention can adjust the initial water path based on the object flow path to obtain an water service path, which can adapt to the dynamically changing water environment, always maintain the best service state, and improve work efficiency. Therefore, the multimodal inertial navigation method and system for the swimming pool robot provided by the embodiments of the present invention can improve the navigation accuracy of the swimming pool robot.

[0177] Example 2:

[0178] like Figure 2 FIG. 1 is a functional module diagram of a multi-modal inertial navigation system of a swimming pool robot according to the present invention.

[0179] The multimodal inertial navigation system 200 for a swimming pool robot described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the multimodal inertial navigation system for a swimming pool robot may include a multimodal data acquisition module 201, a three-dimensional map construction module 202, an underwater path optimization module 203, an above-water path construction module 204, an above-water path optimization module 205, and a final service path generation module 206. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These modules are stored in the electronic device's memory.

[0180] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0181] The multimodal data acquisition module 201 is used to identify application scenarios of the swimming pool robot, build a multi-sensor network for the swimming pool robot based on the application scenarios, and collect multimodal data of the swimming pool robot in real time based on the multi-sensor network, wherein the multimodal data includes inertial measurement data, underwater sonar positioning data, and visual sensor data;

[0182] The three-dimensional map construction module 202 is configured to fuse the multimodal data to obtain fused data, construct a three-dimensional pool environment map of the pool robot based on the fused data, determine an initial position, an underwater target position, and a path coverage range of the pool robot based on the underwater application scenario in the application scenario, and map the initial position, the underwater target position, and the path coverage range into the three-dimensional pool environment map to calculate an initial underwater path of the pool robot;

[0183] The underwater path optimization module 203 is configured to collect obstacle data of the swimming pool robot in real time based on the initial underwater path, calculate motion adjustment parameters and posture adjustment angles of the swimming pool robot based on the obstacle data, and update the initial underwater path based on the motion adjustment parameters and the posture adjustment angles to obtain an underwater service path;

[0184] The water path construction module 204 is configured to determine an on-water target position of the pool robot corresponding to an on-water object based on the on-water application scenario in the application scenario, calculate a position distance between the initial position and the on-water target position, and determine an initial on-water path of the pool robot based on the position distance;

[0185] The water path optimization module 205 is configured to collect the flow speed and flow direction of the water object in real time, analyze the object flow path of the water object based on the flow speed and flow direction, and adjust the initial water path based on the object flow path to obtain a water service path;

[0186] The final service path generating module 206 is configured to generate a final service path for the swimming pool robot in the application scenario according to the underwater service path and the above-water service path.

[0187] In detail, the modules in the multimodal inertial navigation system 200 of the swimming pool robot in the embodiment of the present invention are used in the same manner as above. Figure 1 The multimodal inertial navigation method of a swimming pool robot described in the present invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0188] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multimodal inertial navigation method for a swimming pool robot, characterized in that: The method comprises: Identify application scenarios for the pool robot, build a multi-sensor network for the pool robot based on the application scenarios, and collect multimodal data of the pool robot in real time based on the multi-sensor network, wherein the multimodal data includes inertial measurement data, underwater sonar positioning data, and visual sensor data; fusing the multimodal data to obtain fused data, constructing a three-dimensional swimming pool environment map of the swimming pool robot based on the fused data, determining an initial position, an underwater target position, and a path coverage range of the swimming pool robot based on an underwater application scenario in the application scenario, and mapping the initial position, the underwater target position, and the path coverage range to the three-dimensional swimming pool environment map to calculate an initial underwater path of the swimming pool robot; Based on the initial underwater path, collecting obstacle data of the pool robot in real time, calculating motion adjustment parameters and posture adjustment angles of the pool robot based on the obstacle data, and updating the initial underwater path based on the motion adjustment parameters and the posture adjustment angles to obtain an underwater service path; Determining, based on the aquatic application scenario in the application scenario, an aquatic target position of the pool robot corresponding to an aquatic object, calculating a position distance between the initial position and the aquatic target position, and determining an initial aquatic path of the pool robot based on the position distance; collecting the flow speed and flow direction of the aquatic object in real time, analyzing the object flow path of the aquatic object based on the flow speed and the flow direction, and adjusting the initial aquatic path based on the object flow path to obtain an aquatic service path; A final service path of the swimming pool robot in the application scenario is generated according to the underwater service path and the above-water service path.

2. The multimodal inertial navigation method for a swimming pool robot according to claim 1, wherein: The multi-sensor network of the swimming pool robot is constructed based on the application scenario, including: Based on the application scenario, configure the multi-sensor of the swimming pool robot; Determining a connection method and an installation layout between the multiple sensors, and determining a sensor network architecture of the multiple sensors based on the connection method and the installation layout; Building a communication network of the sensor network architecture; Determining sensor circuits and signal conditioning circuits of the sensor network architecture; The multi-sensor network is obtained by integrating the multi-sensor, the communication network, the sensor circuit and the signal adjustment circuit into the sensor network architecture.

3. The multimodal inertial navigation method for a swimming pool robot according to claim 1, wherein: The fusing the multimodal data to obtain fused data includes: Denoising the multimodal data to obtain denoised multi-source data; Unifying the timestamps of the denoised multi-source data, and temporally aligning the denoised multi-source data based on the timestamps to obtain aligned multi-source data; Constructing a spatial coordinate system for the aligned multi-source data, and mapping the aligned multi-source data to the spatial coordinate system to obtain registered multi-source data; A multi-source feature matrix of the registered multi-source data is extracted, and based on the multi-source feature matrix, feature fusion is performed on the registered multi-source data to obtain fused data.

4. The multimodal inertial navigation method for a swimming pool robot according to claim 1, wherein: The determining of the initial position, underwater target position, and path coverage of the swimming pool robot based on the underwater application scenario in the application scenario includes: Collecting environmental data of the underwater application scene, and analyzing the relative position of the swimming pool robot in the underwater application scene based on the environmental data; Matching the relative position with a three-dimensional pool environment map corresponding to the pool robot to obtain an initial position; Analyzing service requirements of the swimming pool robot, and identifying a target area of ​​the underwater application scenario based on the service requirements; determining an underwater target position in the target area; A target area volume of the target area is calculated, and a path coverage range of the swimming pool robot is determined according to the target area volume.

5. The multimodal inertial navigation method for a swimming pool robot according to claim 1, wherein: Mapping the initial position, the underwater target position, and the path coverage range into the three-dimensional environment map of the swimming pool to calculate the initial underwater path of the swimming pool robot includes: Dividing the three-dimensional environment map of the swimming pool into multiple map areas; Dividing the multi-map area into an initial position area, a target area, a coverage area, and a path planning area based on the initial position, the underwater target position, and the path coverage range; Calculating the robot's passability probability in the path planning area; Combining the initial location area, the target area, the coverage area, and the path planning area according to the passable probability to obtain a path combination set; Calculating the path length of each path in the path combination set; An initial underwater path in the path combination set is screened out according to the path length.

6. The multimodal inertial navigation method for a swimming pool robot according to claim 1, wherein: The calculating, based on the obstacle data, the motion adjustment parameters and the posture adjustment angle of the swimming pool robot comprises: Determine the current position, current speed, and current posture of the swimming pool robot, and extract the obstacle position and obstacle speed of the obstacle corresponding to the obstacle data; Determining a safety radius of the swimming pool robot; According to the current position, the current speed, the current posture, the obstacle position, the obstacle speed, and the safety radius, the motion adjustment parameters and the posture adjustment angle of the swimming pool robot are calculated using the following formula: Wherein, S represents the motion adjustment parameter, θ represents the posture adjustment angle, S0 represents the current speed vector of the current speed, Y represents the current position vector of the current position, Z represents the obstacle position vector of the obstacle position, R represents the safety radius vector of the safety radius, S Z The obstacle velocity vector represents the speed of the obstacle, and θ0 represents the attitude angle of the current attitude relative to the horizontal plane.

7. The multimodal inertial navigation method for a swimming pool robot according to claim 1, wherein: The determining, based on the water application scenario in the application scenario, the water target position of the swimming pool robot corresponding to the water object includes: Determining the water service requirements of the swimming pool robot based on the water application scenario; collecting water object data of the water object; identifying the object category of the water object according to the water object data; Marking the water object based on the object category and the water service demand to obtain a water target object; Determine the above-water target position of the above-water target object.

8. The multimodal inertial navigation method for a swimming pool robot according to claim 1, wherein: Determining an initial water path of the swimming pool robot based on the position distance includes: determining a path point sequence of the swimming pool robot according to the position distance; Determining an on-water path set of the swimming pool robot according to the path point sequence; determining the number of waypoints in the waypoint sequence, and analyzing a steering energy consumption coefficient of the waypoint sequence; A path cost function of the swimming pool robot is defined according to the number of path points and the steering energy consumption coefficient, wherein the path cost function includes: Where D(B) represents the path cost, n represents the number of path points, and b i+1 represents the path point position vector of the i+1th path point in the path point sequence, b i represents the path point position vector of the i-th path point in the path point sequence, μ represents the steering energy consumption coefficient, arctan represents the inverse tangent function, q i+1 Indicates the vertical coordinate of the path point position corresponding to the i+1th path point in the path point sequence, q i Indicates the vertical coordinate of the position of the path point corresponding to the i-th path point in the path point sequence, q i-1 Indicates the vertical coordinate of the position of the path point corresponding to the i-1th path point in the path point sequence, p i+1 Indicates the horizontal coordinate of the path point position corresponding to the i+1th path point in the path point sequence, p i Indicates the horizontal coordinate of the path point position corresponding to the i-th path point in the path point sequence, p i-1 Indicates the horizontal coordinate of the path point position corresponding to the i-1th path point in the path point sequence; Calculating the path cost of the water path set according to the path cost function; An initial water path in the water path set is screened out according to the path cost.

9. The multimodal inertial navigation method for a swimming pool robot according to claim 1, wherein: The analyzing the object flow path of the object on the water based on the flow velocity and the flow direction includes: Analyzing the discrete temporal positions of the aquatic object at discrete time points based on the flow velocity and the flow direction; Analyzing environmental impact factors of the aquatic object; Analyzing the influencing parameters of the discrete time series positions according to the environmental influencing factors; Optimizing the discrete time series position according to the influencing parameter to obtain an optimized discrete time series position; The optimized discrete time sequence positions are subjected to curve fitting according to the time sequence to obtain the object flow path.

10. A multimodal inertial navigation system for a swimming pool robot, characterized in that: The system comprises: A multimodal data acquisition module is used to identify application scenarios of the pool robot, build a multi-sensor network for the pool robot based on the application scenarios, and collect multimodal data of the pool robot in real time based on the multi-sensor network, wherein the multimodal data includes inertial measurement data, underwater sonar positioning data, and visual sensor data; a three-dimensional map construction module, configured to fuse the multimodal data to obtain fused data, construct a three-dimensional pool environment map of the pool robot based on the fused data, determine an initial position, an underwater target position, and a path coverage range of the pool robot based on an underwater application scenario in the application scenario, and map the initial position, the underwater target position, and the path coverage range into the three-dimensional pool environment map to calculate an initial underwater path of the pool robot; an underwater path optimization module, configured to collect obstacle data of the pool robot in real time based on the initial underwater path, calculate motion adjustment parameters and posture adjustment angles of the pool robot based on the obstacle data, and update the initial underwater path based on the motion adjustment parameters and the posture adjustment angles to obtain an underwater service path; an aquatic path construction module, configured to determine an aquatic target position of the pool robot corresponding to an aquatic object based on the aquatic application scenario in the application scenario, calculate a positional distance between the initial position and the aquatic target position, and determine an initial aquatic path of the pool robot based on the positional distance; a water path optimization module, configured to collect the flow speed and flow direction of the water object in real time, analyze the object flow path of the water object based on the flow speed and the flow direction, and adjust the initial water path based on the object flow path to obtain a water service path; The final service path generation module is used to generate a final service path of the swimming pool robot in the application scenario according to the underwater service path and the above-water service path.

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