Bow-shaped path planning method, system and equipment based on cleaning robot

Abstract

The present application relates to a bow-shaped path planning method, system and equipment based on a cleaning robot, the method comprising: dividing the swimming pool cleaning process into a pool wall cleaning stage and a pool bottom cleaning stage based on a preset time allocation strategy; constructing a hierarchical state machine framework including a pool wall state set and a pool bottom state set according to the division results of the pool wall cleaning stage and the pool bottom cleaning stage, wherein each state set is associated with a different motion control strategy; controlling the robot to clean the pool wall according to the motion control strategy of the pool wall state set; after the pool wall cleaning stage ends and the robot enters the pool bottom, switching from the pool wall state to the pool bottom state, and controlling the robot to clean the pool bottom according to the motion control strategy of the pool bottom state set; based on a preset path optimization algorithm, performing real-time evaluation on the coverage rate, overlap rate and steering energy consumption of the parallel paths of the pool bottom, and updating the robot's movement path according to the evaluation results.

Description

Technical Field

[0001] The present application relates to the technical field of cleaning robots, and in particular to a bow-shaped path planning method, system, device and storage medium based on a cleaning robot. Background Art

[0002] Currently, existing pool cleaning robots typically use random motion or pre-set paths when cleaning. However, these methods suffer from problems such as uneven path coverage, repeated cleaning, and missed sweeps, resulting in low cleaning efficiency and an inability to efficiently complete the cleaning task.

[0003] Therefore, it is necessary to provide a bow-shaped path planning method, system and device based on a cleaning robot, aiming to improve the path planning accuracy and cleaning efficiency of the cleaning robot's cleaning path. Summary of the Invention

[0004] The present application provides a bow-shaped path planning method, system, device and storage medium based on a cleaning robot to solve the problems of low cleaning efficiency and inaccurate path planning of existing cleaning robots.

[0005] In a first aspect, the present application provides a bow-shaped path planning method based on a cleaning robot, the method comprising:

[0006] Based on the preset time allocation strategy, the pool cleaning process is divided into the pool wall cleaning stage and the pool bottom cleaning stage;

[0007] Based on the division results of the pool wall cleaning stage and the pool bottom cleaning stage, a hierarchical state machine framework including a pool wall state set and a pool bottom state set is constructed, wherein each state set is associated with a different bow-shaped path motion control strategy;

[0008] Controlling the robot to clean the pool wall according to the motion control strategy of the pool wall state concentration;

[0009] After the pool wall cleaning phase is completed and the robot enters the pool bottom, the robot switches from the pool wall state to the pool bottom state, and controls the robot to clean the pool bottom according to the motion control strategy concentrated on the pool bottom state;

[0010] Based on a preset path optimization algorithm, the coverage rate, overlap rate and steering energy consumption of the parallel paths on the pool bottom are evaluated in real time, and the movement path of the robot is updated according to the evaluation results.

[0011] In some embodiments, the controlling the robot to clean the pool wall according to the motion control strategy of the pool wall state includes:

[0012] Based on the preset vertical movement step length, the robot is controlled to perform periodic bow-shaped up and down movements along the pool wall;

[0013] When the robot moves in the vertical direction and reaches the water surface, the horizontal translation direction and distance are determined according to a pre-configured target value;

[0014] After moving according to the horizontal translation direction and distance, the robot is controlled to continue to move up and down in a periodic bow shape along the pool wall.

[0015] In some embodiments, the method further comprises:

[0016] When the robot performs lateral translation, it determines whether a wall collision event occurs based on the trigger frequency and pressure threshold of the collision sensor;

[0017] In response to determining that a wall foot collision event has occurred, calculating a safe path for retreating to the pool bottom based on the robot's current position and a three-dimensional map of the pool bottom, and controlling the robot to move along the safe path;

[0018] After retreating to the bottom of the pool, determining the target detection direction according to the direction switching rules in the motion control strategy in the state machine framework, and controlling the robot to move forward a preset detection distance;

[0019] Based on the triggering state of the collision sensor during the movement, it is determined whether there is a new wall in the target detection direction. If so, the state machine is switched to the new wall climbing state. Otherwise, the robot is controlled to rotate back to the original wall direction and re-execute the motion control strategy in the pool wall cleaning stage.

[0020] In some embodiments, the method further comprises:

[0021] Obtaining the cleaning duration of the pool wall cleaning stage;

[0022] Determining whether the cleaning time of the pool wall cleaning stage reaches a preset threshold for cleaning time allocation;

[0023] If so, determining whether the robot has entered the bottom of the swimming pool based on the sinking depth data of the robot;

[0024] In response to the robot entering the bottom of the swimming pool, a state switch to the pool bottom cleaning phase is triggered.

[0025] In some embodiments, controlling the robot to clean the bottom of the swimming pool according to the motion control strategy of the pool bottom state includes:

[0026] Obtaining the current position of the robot and boundary data of the swimming pool;

[0027] determining an initial bow-shaped round-trip path of the robot based on the current position and the boundary data;

[0028] The pool bottom is cleaned based on the initial bow-shaped reciprocating path, and the spacing between adjacent parallel paths in the initial bow-shaped reciprocating path is dynamically adjusted according to the boundary signal fed back by the collision sensor.

[0029] In some embodiments, dynamically adjusting the spacing between adjacent parallel paths in the initial bow-shaped round-trip path based on a boundary signal fed back by a collision sensor includes:

[0030] In response to detecting a pool bottom boundary collision, determining a tentative movement direction after turning based on the coordinates of the collision point and the movement direction of the robot;

[0031] Calculating a tentative movement distance according to the current speed of the robot, a preset detection time, and a safety redundancy distance, and controlling the robot to move the tentative movement distance along the tentative movement direction;

[0032] Determine whether the pool bottom boundary collision is triggered again during the movement process. If so, determine that the current area is a corner, and generate a U-turn path away from the corner based on the corner coordinates. If not, use the trial movement distance as the spacing between new adjacent parallel paths.

[0033] In some embodiments, the safety margin distance is determined by:

[0034] Calculating the braking slip distance of the robot using a dynamic equation based on the robot mass, the wheel hub motor torque, and the pool bottom friction coefficient;

[0035] Calculate the delay compensation distance based on the response delay time of the ranging sensor;

[0036] The braking slip distance and the delay compensation distance are weightedly summed to obtain the safety redundancy distance.

[0037] In a second aspect, the present application provides a path planning system based on a cleaning robot, the system comprising:

[0038] A division module is used to divide the pool cleaning process into a pool wall cleaning stage and a pool bottom cleaning stage based on a preset time allocation strategy;

[0039] A state control module is used to construct a hierarchical state machine framework including a pool wall state set and a pool bottom state set according to the division result of the pool wall cleaning stage and the pool bottom cleaning stage, wherein each state set is associated with a different motion control strategy;

[0040] a first cleaning module, configured to control the robot to clean the pool wall according to a motion control strategy of the pool wall state concentration;

[0041] a second cleaning module, configured to switch from a pool wall state to a pool bottom state after the pool wall cleaning phase is completed and the robot enters the pool bottom, and control the robot to clean the pool bottom according to a motion control strategy centralized in the pool bottom state;

[0042] The path optimization module is used to evaluate the coverage rate, overlap rate and steering energy consumption of the parallel paths on the pool bottom in real time based on a preset path optimization algorithm, and update the movement path of the robot according to the evaluation results.

[0043] In a third aspect, an electronic device is provided, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0044] Memory for storing computer programs;

[0045] The processor is used to implement the steps of the bow-shaped path planning method based on the cleaning robot described in any embodiment of the first aspect when executing the program stored in the memory.

[0046] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the bow-shaped path planning method based on the cleaning robot as described in any embodiment of the first aspect are implemented.

[0047] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art: (1) It improves the safety of robot operation. By calculating the safety redundancy distance (including the braking slip distance and the delay compensation distance), the safety of the robot during the pool bottom cleaning process can be ensured. In particular, when the robot brakes, considering the pool bottom friction coefficient and the delay compensation, the robot can avoid collisions due to response delays or slippage. For example, after calculating the braking slip distance and the delay compensation, the robot can adjust the driving path to avoid collisions with the pool edge or obstacles, thereby improving the safety of the overall operation. (2) It enhances the robot's motion accuracy and control capabilities. The braking slip distance calculated based on factors such as mass, hub motor torque, and friction coefficient can accurately predict the slippage of the robot during braking. This enables the control system to more accurately adjust the robot's braking and driving, thereby ensuring precise control in complex environments. (3) By correcting the response delay time of the ranging sensor, the robot can respond to changes in the surrounding environment in a timely and effective manner, thereby improving the accuracy of movement. (4) By accurately calculating the braking slip distance and the delay compensation distance, the robot can optimize its energy use. When decelerating or braking, the system effectively calculates the required energy to avoid unnecessary energy waste while ensuring the robot's smooth movement along the pool floor. Delay compensation helps reduce instabilities caused by sensor response delays, allowing the robot to perform tasks more stably. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] Figure 1 A schematic flow chart of a bow-shaped path planning method for a cleaning robot provided in an embodiment of the present application;

[0051] Figure 2 is a flow chart of a bow-shaped path planning method based on a cleaning robot provided in other embodiments of the present application;

[0052] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] The technical solution disclosed in the present invention is mainly used for a swimming pool cleaning robot. The swimming pool cleaning robot can be used to clean the swimming pool. The swimming pool robot includes a shell, a filter device, a diversion device, a power module, a control module, a power module, etc. Among them, a water inlet and a water outlet are provided on the shell of the swimming pool robot. The filter device is arranged between the water inlet and the water outlet, and is used to filter the water flowing through. The filter device can be in the form of a filter basket or a filter element. The core component of the filter device is a filter element such as filter paper or coarse yarn. The filter device is configured as a detachable module to facilitate the cleaning of the filter screen, or the replacement of the filter screen and the replacement of the filter element.

[0055] The diversion device, consisting of a pump motor and diversion pipe, draws water in through the inlet, filters it through the filter, and discharges clean water through the outlet. The diversion mechanism includes control components such as a check valve or gravity valve. The gravity valve is primarily used to control the flow path when the pool robot is climbing walls.

[0056] The power module includes a drive motor, transmission mechanism, rollers, and tracks. In some pool cleaning robots, the pump motor and guide tube can also assist in the movement or steering of the body through fluid injection.

[0057] The control module and the power module are arranged in a sealed control box. The power module can be wired or wireless.

[0058] Figure 1 A schematic diagram of a flow chart of a bow-shaped path planning method based on a cleaning robot provided in an embodiment of the present application. In some embodiments, Figure 1 The process shown can be executed by an electronic device, such as Figure 1 As shown, the process may include the following operations:

[0059] Step 101 : Based on a preset time allocation strategy, the swimming pool cleaning process is divided into a pool wall cleaning phase and a pool bottom cleaning phase.

[0060] A preset time allocation strategy is a pre-determined plan or schedule for allocating time between wall cleaning and bottom cleaning. For example, in a large pool, more time may be allocated to wall cleaning, while less time may be allocated to bottom cleaning. In some embodiments, the preset time allocation strategy may be 40% of the time allocated to wall cleaning and 60% of the time allocated to bottom cleaning.

[0061] The pool cleaning process refers to all cleaning tasks performed by the robot, from start to finish, including cleaning of the pool walls and bottom. For example, the pool cleaning process includes sweeping stains on the pool walls, cleaning sediments on the pool bottom, and cleaning the edges where the pool walls meet the bottom.

[0062] The pool wall cleaning phase refers to the part of the cleaning task that is specifically performed on the pool walls, with the aim of removing dirt, algae and other attachments on the pool walls.

[0063] The pool bottom cleaning phase refers to the part of the cleaning task that is specifically carried out on the pool bottom, with the purpose of removing sediment, debris and stains on the pool bottom.

[0064] In some embodiments, a reasonable time allocation strategy can be preset based on the size, shape, and contamination level of the pool to ensure that both the pool wall cleaning and the pool bottom cleaning receive appropriate cleaning time. For example, the pool wall cleaning phase lasts 60% of the time, and the pool bottom cleaning phase lasts 40% of the time.

[0065] Step 102: constructing a hierarchical state machine framework including a pool wall state set and a pool bottom state set according to the division result of the pool wall cleaning stage and the pool bottom cleaning stage.

[0066] Each state set is associated with a different bow-shaped path motion control strategy.

[0067] The pool wall state set defines the different cleaning states of the robot during the pool wall cleaning process. States can include the robot's position on the pool wall, the cleaning action currently being performed, and the cleaning intensity. For example, a pool wall state set might include states such as "Start cleaning," "Move to next section of pool wall," and "Complete cleaning."

[0068] The pool bottom state set defines the different cleaning states of the robot during the pool bottom cleaning process. Similar to the pool wall state set, the pool bottom state set may also include states such as the robot's position and the task being performed. For example, the pool bottom state set may include states such as "Cleaning Pool Bottom," "Detecting Sediment," and "Cleaning Completed."

[0069] The hierarchical state machine framework is a model that divides tasks into multiple levels, each representing a set of states. The robot's control logic relies on these state sets, switching between states to perform corresponding actions. For example, the entire cleaning process can be divided into two major state hierarchies: the pool wall cleaning state level and the pool bottom cleaning state level. Each level contains specific states, such as the pool wall cleaning level, which includes the "move" state and the "clean" state.

[0070] A motion control strategy refers to a control algorithm designed to achieve a specific cleaning state. Specifically, it refers to how the robot's motion, direction, speed, and other aspects are controlled to complete the cleaning task. A motion control strategy can involve the robot following a specific trajectory along the pool wall or adaptively adjusting itself on the pool floor. A "bow-shaped path" refers to a path that, under a motion control strategy, the robot is controlled to follow an overall "bow" shape.

[0071] In some embodiments, the entire cleaning process can be broken down into multiple state sets based on the separation of the pool wall and pool bottom cleaning phases. For example, the state set for the pool wall cleaning phase includes "Start cleaning," "Clean pool wall," and "Move to next location." The state set for the pool bottom cleaning phase includes "Start cleaning," "Detect sediment," and "Clean completed."

[0072] Step 103 : Control the robot to clean the pool wall according to the motion control strategy of the pool wall state concentration.

[0073] In some embodiments, when the robot enters the pool wall cleaning phase, the pool wall state-focused control strategy instructs the robot to perform corresponding actions, such as activating cleaning equipment and adjusting the robot's motion path to adapt to the pool wall shape. For example, when the state machine switches to the "Clean Pool Wall" state, the robot automatically activates its brush head and begins moving up and down the pool wall, ensuring that no area is missed during the cleaning process.

[0074] In some embodiments, controlling the robot to clean the pool wall according to the motion control strategy of the pool wall state may include the following operations.

[0075] S10, based on a preset vertical movement step length, controlling the robot to perform periodic bow-shaped up and down movement along the pool wall.

[0076] The preset vertical movement step length refers to the pre-set vertical movement length of the robot moving up and down the pool wall to detect the pool bottom collision or reach the water surface. The preset vertical movement step length can vary depending on the height of the pool wall being cleaned.

[0077] The periodic up and down movement in the shape of a bow refers to the robot repeatedly moving vertically on the pool wall. This action is continuous and repeated throughout the entire pool wall cleaning process and is in the shape of a bow.

[0078] In some embodiments, the robot can move up and down along the pool wall according to a preset vertical movement step size (e.g., 10 cm).

[0079] S11, when the robot moves in the vertical direction and reaches the water surface, the horizontal translation direction and distance are determined according to a pre-configured target value.

[0080] The target value can be a parameter of the robot's roller brush (e.g., the brush's width). This parameter represents the width of the cleaning path, and thus, the robot's horizontal translation direction and distance can be determined based on the desired cleaning range. Alternatively, the target value can be the width of the roller. Since there is a numerical conversion relationship between the roller width and the brush's width, the target value can also be the roller's width.

[0081] An inertial measurement unit (IMU) can also be used to determine the robot's posture and adjust its direction of movement based on this information. An inertial measurement unit (IMU) is a sensor that detects and provides feedback on the robot's posture, including tilt angle and acceleration. For example, an IMU can provide feedback on the robot's pitch and roll angles, which helps determine whether the robot needs to adjust or translate.

[0082] The horizontal attitude angle refers to the horizontal angle of the robot's attitude sensor (IMU). It can be used to indicate the robot's tilt or direction change. For example, if the robot's horizontal attitude angle is 5 degrees, it means that the robot is slightly tilted 5 degrees. This information is used to adjust the robot's movement direction.

[0083] The horizontal translation direction and distance refer to the direction and specific displacement distance that the robot needs to move in the horizontal direction in order to perform accurate translation operations.

[0084] In some embodiments, the robot may also first detect the water line of the swimming pool. The specific detection method may be detection through a water pressure sensor or other methods, for example, detection through a float plus a pendulum rod, detection through image recognition after taking an image with a camera, and detection by detecting the change in speed of the pump motor when the robot surfaces, and determining the water line based on the change in speed.

[0085] After detecting the waterline, the robot can perform lateral movement cleaning.

[0086] S12, after moving according to the horizontal translation direction and distance, controlling the robot to continue to perform periodic bow-shaped up and down movement along the pool wall.

[0087] Once the robot completes its horizontal translation based on the feedback attitude angle, it can return to the cleaning trajectory of the pool wall and continue to perform periodic bow-shaped up and down movements according to the preset vertical step size to ensure that every part of the pool wall is thoroughly cleaned.

[0088] In some embodiments, when the robot can move horizontally and encounter an obstacle or a corner, it moves to the bottom of the pool and turns to a new wall, and repeats the up and down translation cleaning action.

[0089] Step 104, after the pool wall cleaning stage is completed and the robot enters the pool bottom, it switches from the pool wall state to the pool bottom state, and controls the robot to clean the pool bottom according to the motion control strategy concentrated on the pool bottom state.

[0090] Switching from the pool wall state to the pool bottom state means that after the robot completes the pool wall cleaning, it switches from the pool wall cleaning task state to the pool bottom cleaning task state.

[0091] After cleaning the pool walls and reaching the bottom, the robot automatically switches states and executes a predefined control strategy for the bottom state. For example, after reaching the bottom, the robot automatically switches to the "bottom cleaning" state, where the control strategy activates the suction device to remove sediment from the bottom.

[0092] In some embodiments, before cleaning the pool floor, the robot's state can be switched. This state switching can be based on cleaning time, cleaning completion, etc. Taking cleaning time as an example, the robot's state can be switched using the following operations as shown in the following embodiments.

[0093] S21, obtaining the cleaning time of the pool wall cleaning stage.

[0094] Cleaning time refers to the length of time from the start to the end of the pool wall cleaning phase. This time can be recorded by the robot's built-in timer or the operating system.

[0095] S22, determining whether the cleaning time of the pool wall cleaning stage reaches a preset threshold for cleaning time allocation.

[0096] The preset threshold for cleaning time allocation is the maximum allowed time for the wall cleaning task. This threshold determines the maximum amount of time the robot is allowed to perform cleaning operations during the wall cleaning phase. Once this time is exceeded, the robot needs to check whether it has completed the task or whether to move on to the next phase.

[0097] The judgment method can be to compare the current cleaning time with a preset cleaning time threshold to confirm whether it exceeds the preset threshold.

[0098] S23: If yes, determine whether the robot has entered the bottom of the swimming pool based on the sinking depth data of the robot.

[0099] Sinking depth data refers to the robot's depth during a mission, specifically its vertical distance from the pool bottom. This data can be obtained using a depth sensor (such as a pressure sensor or ultrasonic sensor). For example, if the robot is currently 10 cm from the pool bottom, the sinking depth is 10 cm. If the robot has touched the pool bottom, the sinking depth is 0.

[0100] S24, in response to the robot entering the bottom of the swimming pool, triggering a state switch to the pool bottom cleaning phase.

[0101] State switching refers to the process of a robot switching from the current task state to another task state.

[0102] When the robot detects it has entered the pool bottom area, the control system triggers a state switch, causing the robot to enter the pool bottom cleaning phase. At this point, the robot will adjust its movement mode, cleaning tools, or task parameters to begin the pool bottom cleaning operation.

[0103] In some embodiments, controlling the robot to clean the bottom of the swimming pool according to the motion control strategy of the pool bottom state may include the following operations.

[0104] S30, obtaining the current position of the robot and the boundary data of the swimming pool.

[0105] The robot's current position refers to the robot's real-time position in the pool. The real-time position can be provided by the robot's positioning system (such as GPS, ultrasonic sensors, or inertial navigation systems) and is usually expressed as the robot's coordinates or location points.

[0106] The boundary data of a swimming pool refers to the shape, size, and location of the swimming pool boundary. The boundary data of the swimming pool can be obtained through a pre-set measurement method or through sensor scanning.

[0107] S31, determining an initial bow-shaped round-trip path of the robot based on the current position and the boundary data.

[0108] The initial arc-shaped round-trip path refers to the path planned by the robot at the beginning of a pool floor cleaning task. This initial arc-shaped round-trip path can be a round-trip motion route designed based on the robot's current position and the pool's boundary data. A round-trip path typically moves back and forth, covering the entire pool floor area.

[0109] In some embodiments, an initial round-trip path can be planned based on the robot's current position and boundary data. For example, if the pool is rectangular, a round-trip route from one corner to the other can be selected. If the pool has a complex shape, a path can be designed to adapt to that shape based on the boundary data.

[0110] S32, cleaning the pool bottom based on the initial bow-shaped reciprocating path, and dynamically adjusting the spacing between adjacent parallel paths in the initial bow-shaped reciprocating path according to the boundary signal fed back by the collision sensor.

[0111] The boundary signal refers to the feedback signal detected by the collision sensor, which is used to indicate the current distance to the obstacle or whether there is an obstacle.

[0112] The spacing between adjacent parallel paths refers to the distance between the robot's round trips while cleaning the pool floor. Initial paths may be set to a fixed spacing, but during dynamic adjustments, the robot may respond to collision signals by narrowing or widening the spacing between paths to avoid collisions and ensure full coverage of the pool floor.

[0113] As the robot cleans, it monitors its surroundings in real time using collision sensors. If the sensor detects a close approach to the pool wall or other obstacles, it dynamically adjusts its path, reducing the distance between adjacent paths and ensuring the robot can avoid the obstacle and continue cleaning.

[0114] In some embodiments, dynamically adjusting the spacing between adjacent parallel paths in the initial bow-shaped round-trip path according to the boundary signal fed back by the collision sensor may include the following operations.

[0115] S321 , in response to detecting a pool bottom boundary collision, determining a tentative movement direction after turning based on the coordinates of the collision point and the movement direction of the robot.

[0116] The post-steering tentative movement direction is the robot's new movement direction after a collision, adjusted based on its current movement direction. This direction is based on the coordinates of the collision point and the robot's trajectory. For example, it could be a direct 90-degree turn.

[0117] If a collision occurs, the collision sensor can immediately detect this event and trigger a response. By calculating the coordinates of the collision point and combining it with the robot's movement direction, a suitable turning path is determined to obtain a new tentative movement direction.

[0118] S322: Calculate a tentative movement distance according to the current speed of the robot, a preset detection time, and a safety redundancy distance, and control the robot to move the tentative movement distance along the tentative movement direction.

[0119] The current speed of the robot refers to the speed at which the robot travels during the pool bottom cleaning process.

[0120] The preset detection time is the time the robot takes to make a tentative move after a collision. This time can be set by the control system to ensure that the robot can safely move a certain distance for further detection.

[0121] The safety margin distance is the additional distance set by the control system to avoid collisions and ensure safety. It is an additional buffer area to ensure that the robot does not collide again during the tentative movement.

[0122] The trial movement distance refers to the movement distance calculated by the robot after responding to a collision, which can be calculated comprehensively based on the robot's current speed, preset detection time, and safety margin distance.

[0123] In some embodiments, the safety redundancy distance is determined in the following manner.

[0124] S40 , calculating the braking slip distance of the robot using a dynamic equation according to the mass of the robot, the torque of the hub motor, and the friction coefficient of the pool bottom.

[0125] Robot mass refers to the overall mass of the robot, which affects the robot's inertia and motion response.

[0126] Hub motor torque refers to the torque output by the robot's hub motor, which is the rotational torque used to propel the robot forward or backward.

[0127] The pool bottom friction coefficient refers to the strength of the friction between the robot and the pool bottom. It is typically a unitless value that indicates the degree of friction between the contacting surfaces. A higher friction coefficient reduces the robot's ability to slide along the pool bottom; conversely, a lower friction coefficient increases the robot's ability to slide.

[0128] Braking slip distance refers to the distance the robot slides during braking due to insufficient friction between the wheels and the ground. This distance is calculated based on the robot's mass, the torque of the wheel hub motor, and the friction coefficient.

[0129] In some embodiments, a dynamic equation can be used to calculate the sliding distance of the robot when braking. The dynamic equation can be various common equations and will not be described in detail here.

[0130] S41, calculating a delay compensation distance based on a response delay time of the distance measuring sensor.

[0131] A range sensor is a sensor used to detect the distance between a robot and an obstacle or boundary. Range sensors can use lasers, ultrasonic waves, or other technologies to measure distance.

[0132] Response latency refers to the time it takes for a ranging sensor to detect an obstacle or boundary and then feed that data back to the control system. Response latency is typically determined by the speed of the hardware and signal processing.

[0133] The delay compensation distance is the calculated movement distance to compensate for the response delay of the ranging sensor. During robot movement, the delay compensation distance is used to ensure that the control system can respond to the ranging sensor data in a timely manner.

[0134] S42: Perform weighted summation on the braking slip distance and the delay compensation distance to obtain the safety redundancy distance.

[0135] The control system can perform weighted summation based on the braking slip distance and the delay compensation distance according to the set weights to obtain the safety redundancy distance.

[0136] S323, determining whether a pool bottom boundary collision is triggered again during the movement process; if so, determining that the current area is a corner, and generating a U-turn path away from the corner based on the coordinates of the corner; if not, using the tentative movement distance as the spacing between new adjacent parallel paths.

[0137] The control system determines whether the robot encounters another collision during the trial move. If so, the robot identifies its current location as a corner and generates a new turnaround path based on the corner's coordinates. If the trial move does not trigger a collision, the distance of the trial move is used as the spacing between the new adjacent parallel paths, and cleaning continues.

[0138] Step 105 : Based on a preset path optimization algorithm, the coverage rate, overlap rate, and steering energy consumption of the parallel paths on the pool bottom are evaluated in real time, and the movement path of the robot is updated according to the evaluation results.

[0139] A path optimization algorithm is used to adjust and optimize the robot's path planning in real time, aiming to improve cleaning efficiency and reduce energy consumption. For example, the algorithm can adjust the robot's path and speed based on its current cleaning progress and the cleanliness of the pool floor, avoiding repeated cleaning and reducing unnecessary turns.

[0140] Coverage rate refers to the ratio of the area covered by the robot during the pool bottom cleaning process to the total cleaning area.

[0141] The overlap rate refers to the ratio of the repeated cleaning area to the total cleaning area caused by improper path planning during the cleaning process.

[0142] Turning energy consumption refers to the energy consumed by the robot due to frequent turning during the cleaning process.

[0143] In some embodiments, a preset path optimization algorithm can be used to monitor the robot's path in real time, calculate coverage, overlap, and steering energy consumption, and adjust the robot's movement path based on this data. For example, this data can be used to calculate a path score, and the optimal path can be selected based on the calculated path score.

[0144] In some embodiments, the preset path optimization algorithm can use the following formula (1) to calculate the path score of each path.

[0145]

[0146] Among them, F is the calculated path score, α, β, and γ are the preset weight coefficients, and the constraints are satisfied. α + β + γ =1, C1 is the coverage rate, C2 is the overlap rate, and E is the cumulative steering energy consumption.

[0147] Figure 2 This is a flowchart of a bow-shaped path planning method based on a cleaning robot provided in other embodiments of the present application. Figure 2 The process shown can be executed by an electronic device, such as Figure 2 As shown, the process may include the following operations.

[0148] Step 201 : When the robot performs lateral translation, it is determined whether a wall foot collision event occurs based on the trigger frequency and pressure threshold of the collision sensor.

[0149] Collision sensors detect collisions between the robot and surrounding objects, such as the pool walls and bottom. They provide feedback signals that are triggered when a collision occurs. For example, the robot might be equipped with ultrasonic sensors or touch sensors. When it comes into contact with the corners (or base) of the pool walls, these sensors are triggered and send a collision signal.

[0150] The trigger frequency refers to the number of times the collision sensor is triggered per unit time. It can be used to determine whether the robot frequently contacts obstacles and thus infer whether a collision has occurred.

[0151] The pressure threshold is the minimum pressure value that the collision sensor can detect when the robot collides with an object. When the applied pressure exceeds this threshold, the collision sensor will trigger and report the collision event to the control system.

[0152] A wall collision event refers to an event in which the robot collides with the bottom or corner of the pool wall while performing a movement.

[0153] In some embodiments, when the robot performs lateral translation, a collision sensor can monitor in real time whether the robot collides with the base of the pool wall. If the collision sensor trigger frequency exceeds a set threshold and the applied pressure exceeds a set pressure threshold, a base collision event can be determined.

[0154] Step 202 : In response to determining that a wall foot collision event has occurred, a safe path for retreating to the pool bottom is calculated based on the current position of the robot and a three-dimensional map of the pool bottom, and the robot is controlled to move along the safe path.

[0155] The current pose refers to the current position and orientation of the robot when performing a task, including the robot's position coordinates and angles.

[0156] A 3D pool floor map describes the 3D coordinates of various areas of the pool floor and the distribution of obstacles. This map helps robots plan their paths and avoid collisions. For example, a 3D pool floor map includes data such as the depth of the pool floor and the location of obstacles, helping robots navigate efficiently within the pool floor.

[0157] A safe path is a path calculated based on the current posture and environment information to avoid obstacles and ensure the safe movement of the robot.

[0158] In some embodiments, once a wall collision occurs, the robot can calculate a safe path based on its current position and the three-dimensional map of the pool bottom, ensuring that the robot can avoid the pool wall and other obstacles and smoothly retreat to the safe area at the bottom of the pool.

[0159] Step 203 , after retreating to the bottom of the pool, determine the target detection direction according to the direction switching rule in the motion control strategy in the state machine framework, and control the robot to move forward a preset detection distance.

[0160] Direction switching rules are rules used within a state machine framework to determine when a robot switches its direction of motion based on certain conditions (such as sensor feedback) while performing a task. For example, if a robot detects an obstacle ahead while cleaning, the direction switching rules will cause it to switch direction and continue forward.

[0161] The target detection direction refers to the direction in which the robot detects targets when performing a detection task. For example, if the robot is currently facing the center of the pool, the target detection direction may be toward the pool wall.

[0162] The detection distance refers to the distance the robot moves forward, which is usually preset and used to determine the range of the robot's detection.

[0163] After retreating to the bottom of the pool, the robot can determine its target detection direction based on the motion control strategy in the state machine framework and the direction switching rules. The robot then moves forward according to the preset detection distance to continue executing the task.

[0164] Step 204: Based on the triggering state of the collision sensor during the movement, determine whether there is a new wall in the target detection direction. If so, switch the state machine to the new wall climbing state; otherwise, control the robot to rotate back to the original wall direction and re-execute the motion control strategy in the pool wall cleaning stage.

[0165] The collision sensor's trigger status refers to whether the collision sensor triggers a signal during movement. For example, if the robot touches the pool wall during movement, the collision sensor will trigger a signal to inform the robot of the collision.

[0166] The new wall climbing state means that the robot switches to a new state specifically for cleaning new walls. In this state, the robot will perform cleaning tasks along the new wall.

[0167] Returning to the original wall direction means that the robot returns and continues cleaning along the original pool wall direction if no new wall is detected.

[0168] During movement, collision sensors detect whether the robot encounters a new wall in real time. If a collision occurs and a new wall is detected, the state machine switches to the "New Wall Climbing State," and the robot begins cleaning the new wall. If no new wall is detected, the robot returns to its original pool wall and resumes its cleaning task.

[0169] Based on the same inventive concept, an embodiment of the present application further provides a path planning system based on a cleaning robot, the system comprising:

[0170] A division module is used to divide the pool cleaning process into a pool wall cleaning stage and a pool bottom cleaning stage based on a preset time allocation strategy;

[0171] A state control module is used to construct a hierarchical state machine framework including a pool wall state set and a pool bottom state set according to the division result of the pool wall cleaning stage and the pool bottom cleaning stage, wherein each state set is associated with a different motion control strategy;

[0172] a first cleaning module, configured to control the robot to clean the pool wall according to a motion control strategy of the pool wall state concentration;

[0173] a second cleaning module, configured to switch from a pool wall state to a pool bottom state after the pool wall cleaning phase is completed and the robot enters the pool bottom, and control the robot to clean the pool bottom according to a motion control strategy centralized in the pool bottom state;

[0174] The path optimization module is used to evaluate the coverage rate, overlap rate and steering energy consumption of the parallel paths on the pool bottom in real time based on a preset path optimization algorithm, and update the movement path of the robot according to the evaluation results.

[0175] like Figure 3 As shown, an embodiment of the present application provides an electronic device, including a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0176] Memory 113, for storing computer programs;

[0177] In one embodiment of the present application, the processor 111 is configured to execute a program stored in the memory 113 to implement the path planning method based on the cleaning robot provided in any of the aforementioned method embodiments, including:

[0178] Based on the preset time allocation strategy, the pool cleaning process is divided into the pool wall cleaning stage and the pool bottom cleaning stage;

[0179] Based on the division results of the pool wall cleaning stage and the pool bottom cleaning stage, a hierarchical state machine framework including a pool wall state set and a pool bottom state set is constructed, wherein each state set is associated with a different motion control strategy;

[0180] Controlling the robot to clean the pool wall according to the motion control strategy of the pool wall state concentration;

[0181] After the pool wall cleaning phase is completed and the robot enters the pool bottom, the robot switches from the pool wall state to the pool bottom state, and controls the robot to clean the pool bottom according to the motion control strategy concentrated on the pool bottom state;

[0182] Based on a preset path optimization algorithm, the coverage rate, overlap rate and steering energy consumption of the parallel paths on the pool bottom are evaluated in real time, and the movement path of the robot is updated according to the evaluation results.

[0183] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the bow-shaped path planning method based on the cleaning robot provided in any of the aforementioned method embodiments are implemented.

[0184] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0185] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A bow-shaped path planning method based on a cleaning robot, characterized in that: The method comprises: Based on the preset time allocation strategy, the pool cleaning process is divided into the pool wall cleaning stage and the pool bottom cleaning stage; Based on the division results of the pool wall cleaning stage and the pool bottom cleaning stage, a hierarchical state machine framework including a pool wall state set and a pool bottom state set is constructed, wherein each state set is associated with a different bow-shaped path motion control strategy; Controlling the robot to clean the pool wall according to the motion control strategy of the pool wall state concentration; After the pool wall cleaning phase is completed and the robot enters the pool bottom, the robot switches from the pool wall state to the pool bottom state, and controls the robot to clean the pool bottom according to the motion control strategy of the pool bottom state, including: Obtaining the current position of the robot and boundary data of the swimming pool; determining an initial bow-shaped round-trip path of the robot based on the current position and the boundary data; Cleaning the pool bottom based on the initial bow-shaped round-trip path and dynamically adjusting the spacing between adjacent parallel paths in the initial bow-shaped round-trip path according to the boundary signal fed back by the collision sensor includes: In response to detecting a pool bottom boundary collision, determining a tentative movement direction after turning based on the coordinates of the collision point and the movement direction of the robot; Calculating a tentative movement distance according to the current speed of the robot, a preset detection time, and a safety redundancy distance, and controlling the robot to move the tentative movement distance along the tentative movement direction; Determine whether a collision with the pool bottom boundary is triggered again during the movement process. If so, determine that the current area is a corner, and generate a U-turn path away from the corner based on the coordinates of the corner. If not, use the trial movement distance as the spacing between new adjacent parallel paths; Based on a preset path optimization algorithm, the coverage rate, overlap rate and steering energy consumption of the parallel paths on the pool bottom are evaluated in real time, and the movement path of the robot is updated according to the evaluation results.

2. The method according to claim 1, characterized in that The method of controlling the robot to clean the pool wall according to the motion control strategy of the pool wall state includes: Based on the preset vertical movement step length, the robot is controlled to perform periodic bow-shaped up and down movements along the pool wall; When the robot moves in the vertical direction and reaches the water surface, the horizontal translation direction and distance are determined according to a pre-configured target value; After moving according to the horizontal translation direction and distance, the robot is controlled to continue to move up and down in a periodic bow shape along the pool wall.

3. The method according to claim 2, characterized in that The method further comprises: When the robot performs lateral translation, it determines whether a wall collision event occurs based on the trigger frequency and pressure threshold of the collision sensor; In response to determining that a wall foot collision event has occurred, calculating a safe path for retreating to the pool bottom based on the robot's current position and a three-dimensional map of the pool bottom, and controlling the robot to move along the safe path; After retreating to the bottom of the pool, determining the target detection direction according to the direction switching rules in the motion control strategy in the state machine framework, and controlling the robot to move forward a preset detection distance; Based on the triggering state of the collision sensor during the movement, it is determined whether there is a new wall in the target detection direction. If so, the state machine is switched to the new wall climbing state. Otherwise, the robot is controlled to rotate back to the original wall direction and re-execute the motion control strategy in the pool wall cleaning stage.

4. The method according to claim 1, wherein The method further comprises: Obtaining the cleaning duration of the pool wall cleaning stage; Determining whether the cleaning time of the pool wall cleaning stage reaches a preset threshold for cleaning time allocation; If so, determining whether the robot has entered the bottom of the swimming pool based on the sinking depth data of the robot; In response to the robot entering the bottom of the swimming pool, a state switch to the pool bottom cleaning phase is triggered.

5. The method according to claim 1, characterized in that The safety margin distance is determined by: Calculating the braking slip distance of the robot using a dynamic equation based on the robot mass, the wheel hub motor torque, and the pool bottom friction coefficient; Calculate the delay compensation distance based on the response delay time of the ranging sensor; The braking slip distance and the delay compensation distance are weightedly summed to obtain the safety redundancy distance.

6. A path planning system based on a cleaning robot, characterized in that: The system comprises: A division module is used to divide the pool cleaning process into a pool wall cleaning stage and a pool bottom cleaning stage based on a preset time allocation strategy; A state control module is used to construct a hierarchical state machine framework including a pool wall state set and a pool bottom state set according to the division result of the pool wall cleaning stage and the pool bottom cleaning stage, wherein each state set is associated with a different motion control strategy; a first cleaning module, configured to control the robot to clean the pool wall according to a motion control strategy of the pool wall state concentration; The second cleaning module is configured to switch from the pool wall state to the pool bottom state after the pool wall cleaning phase is completed and the robot enters the pool bottom, and control the robot to clean the pool bottom according to the motion control strategy of the pool bottom state, including: Obtaining the current position of the robot and boundary data of the swimming pool; determining an initial bow-shaped round-trip path of the robot based on the current position and the boundary data; Cleaning the pool bottom based on the initial bow-shaped round-trip path and dynamically adjusting the spacing between adjacent parallel paths in the initial bow-shaped round-trip path according to the boundary signal fed back by the collision sensor includes: In response to detecting a pool bottom boundary collision, determining a tentative movement direction after turning based on the coordinates of the collision point and the movement direction of the robot; Calculating a tentative movement distance according to the current speed of the robot, a preset detection time, and a safety redundancy distance, and controlling the robot to move the tentative movement distance along the tentative movement direction; Determine whether a collision with the pool bottom boundary is triggered again during the movement process. If so, determine that the current area is a corner, and generate a U-turn path away from the corner based on the coordinates of the corner. If not, use the trial movement distance as the spacing between new adjacent parallel paths; The path optimization module is used to evaluate the coverage rate, overlap rate and steering energy consumption of the parallel paths on the pool bottom in real time based on a preset path optimization algorithm, and update the movement path of the robot according to the evaluation results.

7. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is used to implement the steps of the bow-shaped path planning method based on the cleaning robot as described in any one of claims 1 to 5 when executing the program stored in the memory.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the bow-shaped path planning method based on a cleaning robot are implemented.

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