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

Abstract

The invention relates to an arch-shaped path planning method, system and equipment based on a cleaning robot, and the method comprises the steps: dividing a swimming pool cleaning process into a pool wall cleaning stage and a pool bottom cleaning stage based on a preset time distribution strategy; according to the division result of the pool wall cleaning stage and the pool bottom cleaning stage, a layered state machine frame containing pool wall state sets and pool bottom state sets is constructed, and each state set is associated with a different motion control strategy; controlling the robot to clean the pool wall of the swimming pool according to a motion control strategy with concentrated pool wall states; after the pool wall cleaning stage is finished and the robot enters the pool bottom of the swimming pool, the pool wall state is switched to the pool bottom state, and the robot is controlled to clean the pool bottom of the swimming pool according to a motion control strategy with concentrated pool bottom states; and on the basis of a preset path optimization algorithm, the coverage rate, the overlapping rate and the steering energy consumption of the pool bottom parallel paths are evaluated in real time, and the moving path of the robot is updated according to an evaluation result.

Description

Technical Field

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

[0002] Currently, when existing pool cleaning robots perform cleaning work, they usually adopt random movement or preset paths. However, these methods have problems such as uneven path coverage, repeated cleaning, missed cleaning, etc., resulting in low cleaning efficiency and inability to efficiently complete cleaning tasks.

[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 path of the cleaning robot. Summary of the Invention

[0004] This 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 existing in existing cleaning robots.

[0005] In a first aspect, this application provides a bow-shaped path planning method based on a cleaning robot, the method including: Dividing the pool cleaning process into a pool wall cleaning stage and a pool bottom cleaning stage based on a preset time allocation strategy; According to the division results of the pool wall cleaning stage and the pool bottom cleaning stage, constructing a hierarchical state machine framework including a pool wall state set and a pool bottom state set, wherein each state set is associated with different bow-shaped path motion control strategies; Controlling the robot to clean the pool wall of the pool according to the motion control strategy in 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 of the pool according to the motion control strategy in the pool bottom state set; Based on a preset path optimization algorithm, evaluating the coverage rate, overlap rate and turning energy consumption of the parallel paths at the pool bottom in real time, and updating the moving path of the robot according to the evaluation results.

[0006] In some embodiments, the controlling the robot to clean the pool wall of the pool according to the motion control strategy in the pool wall state set includes: Controlling the robot to perform periodic bow-shaped up and down movement along the pool wall based on a preset vertical movement step size; When the robot moves vertically to reach the water surface, determining the horizontal translation direction and distance according to a pre-configured target value; After moving according to the horizontal translation direction and distance, control the robot to continue to perform periodic zigzag up and down movement along the pool wall.

[0007] In some embodiments, the method further includes: When the robot performs lateral translation, determine whether a wall corner collision event occurs according to the trigger frequency of the collision sensor and the pressure threshold; In response to determining that a wall corner collision event occurs, calculate a safe path to retreat to the pool bottom based on the current pose of the robot and the three-dimensional map of the pool bottom, and control the robot to move along the safe path; After retreating to the pool bottom, determine a 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; Based on the trigger state of the collision sensor during the movement, determine whether there is a new wall surface in the target detection direction. If so, switch the state machine to the new wall surface climbing state; otherwise, control the robot to turn back to the original wall surface direction and re-execute the motion control strategy in the pool wall cleaning stage.

[0008] In some embodiments, the method further includes: Obtain the cleaning duration of the pool wall cleaning stage; Judge whether the cleaning duration of the pool wall cleaning stage reaches a preset threshold of the cleaning time allocation; If so, determine whether the robot enters the pool bottom based on the sinking depth data of the robot; In response to the robot entering the pool bottom, trigger a state switch to the pool bottom cleaning stage.

[0009] In some embodiments, controlling the robot to clean the bottom of the pool according to the motion control strategy in the pool bottom state set includes: Obtain the current position of the robot and the boundary data of the pool; Based on the current position and the boundary data, determine the initial zigzag round-trip path of the robot; Perform pool bottom cleaning based on the initial zigzag round-trip path, and dynamically adjust the distance between adjacent parallel paths in the initial zigzag round-trip path according to the boundary signal feedback by the collision sensor.

[0010] In some embodiments, dynamically adjusting the distance between adjacent parallel paths in the initial zigzag round-trip path according to the boundary signal feedback by the collision sensor includes: In response to detecting a pool bottom boundary collision, determine the tentative movement direction after turning based on the coordinates of the collision point and the movement direction of the robot; Calculate a tentative movement distance based on 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; Determine whether the bottom boundary collision is triggered again during the movement. If so, determine that the current area is a corner, and generate a turning path away from the corner based on the corner coordinates. If not, use the tentative movement distance as the spacing of the new adjacent parallel paths.

[0011] In some embodiments, the safety redundancy distance is determined by the following method: Calculate the braking slip distance of the robot through a dynamic equation based on the mass of the robot, the torque of the hub motor, and the bottom friction coefficient of the pool; Calculate a delay compensation distance based on the response delay time of the ranging sensor; Perform a weighted sum of the braking slip distance and the delay compensation distance to obtain the safety redundancy distance.

[0012] In a second aspect, the present application provides a path planning system based on a cleaning robot, and the system includes: A division module, configured 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, configured to construct 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 different motion control strategies; A first cleaning module, configured to control the robot to clean the pool wall of the pool according to the motion control strategy in the pool wall state set; A second cleaning module, configured to switch from the pool wall state to the pool bottom state after the pool wall cleaning stage ends and the robot enters the bottom of the pool, and control the robot to clean the bottom of the pool according to the motion control strategy in the pool bottom state set; A path optimization module, configured to perform real-time evaluation on the coverage rate, overlap rate, and turning energy consumption of the pool bottom parallel paths based on a preset path optimization algorithm, and update the movement path of the robot according to the evaluation results.

[0013] In a third aspect, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; The memory is used to store a computer program; The processor, when executing the program stored in the memory, implements the steps of the bow-shaped path planning method based on the cleaning robot according to any one of the embodiments of the first aspect.

[0014] 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 zigzag path planning method based on a cleaning robot as described in any embodiment of the first aspect are implemented.

[0015] The above technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art: (1) Improve 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. Especially when the robot brakes, considering the pool bottom friction coefficient and delay compensation, the robot can avoid collisions due to response delay or slip. For example, after calculating the braking slip distance and delay compensation, the robot can adjust its driving path to avoid collisions with the pool edge or obstacles, improving the overall operation safety. (2) Enhance the motion accuracy and control ability of the robot. The braking slip distance calculated based on factors such as mass, hub motor torque, and friction coefficient can accurately predict the sliding situation of the robot during braking. This enables the control system to more precisely adjust the braking and driving of the robot, thus ensuring precise control in complex environments. (3) By correcting through the response delay time of the ranging sensor, the robot can react to changes in the surrounding environment in a timely and effective manner, improving the motion accuracy. (4) By accurately calculating the braking slip distance and delay compensation distance, the robot can optimize its energy usage. When decelerating or braking, the system can effectively calculate the required energy to avoid unnecessary energy waste, while ensuring smooth movement of the robot on the pool bottom. Delay compensation helps reduce the instability caused by sensor response delay, enabling the robot to perform tasks more stably. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of a zigzag path planning method based on a cleaning robot provided by an embodiment of the present application; Figure 2 It is a flowchart of a zigzag path planning method based on a cleaning robot provided by other embodiments of the present application; Figure 3 Schematic structural diagram of an electronic device provided by an embodiment of the present application. Specific implementation manners

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0020] The technical solutions disclosed by the present invention are mainly used for pool cleaning robots. The pool cleaning robot can be used to clean a pool. The pool robot includes a housing, a filtering device, a guiding device, a power module, a control module, a power supply module, etc. Among them, the housing of the pool robot is provided with a water inlet and a water outlet. The filtering device is arranged between the water inlet and the water outlet and is used to filter the flowing water. The filtering device can be in the form of a filter basket or in the form of a filter element. The core components of the filtering device are filter elements such as filter paper or coarse gauze. The filtering device is set as a detachable module for cleaning the filter screen or replacing the filter screen or filter element.

[0021] The guiding device includes a pump motor and a guiding pipe, and is used to drive the water flow to be sucked from the water inlet, filtered by the filtering device, and then discharge clean water flow from the water outlet. The guiding mechanism includes control elements such as a check valve or a gravity valve. The gravity valve is mainly used to control the opening or closing of the flow channel when the pool robot climbs the wall.

[0022] The power module includes a driving motor, a transmission mechanism, rollers, and a crawler. In some pool cleaning robots, the pump motor and the guiding pipe can also assist the movement or turning of the body through fluid jetting.

[0023] The control module and the power supply module are arranged in a sealed control box. The power supply module can be in a wired or wireless manner.

[0024] Figure 1 Schematic flowchart of a bow-shaped path planning method based on a cleaning robot provided by an embodiment of the present application. In some embodiments, Figure 1 The shown process can be executed by an electronic device, such as Figure 1 shown, and this process can include the following operations: Step 101, divide the pool cleaning process into a pool wall cleaning stage and a pool bottom cleaning stage based on a preset time allocation strategy.

[0025] The preset time allocation strategy refers to a pre-set plan or scheme that stipulates how to allocate time between pool wall cleaning and pool bottom cleaning. For example, in a large swimming pool, more time may be allocated to the pool wall cleaning stage, while less time may be required for the pool bottom cleaning stage. In some embodiments, the preset time allocation strategy may be to use 40% of the time for pool wall cleaning and 60% of the time for pool bottom cleaning.

[0026] The pool cleaning process refers to all the cleaning tasks performed by the robot, from start to finish, including the cleaning work of the pool wall and the pool bottom. For example, the pool cleaning process includes sweeping the stains on the pool wall, cleaning the sediments at the pool bottom, and cleaning the edges where the pool wall and the pool bottom meet.

[0027] The pool wall cleaning stage refers to the part of the cleaning task specifically for the pool wall of the swimming pool, aiming to remove dirt, algae, and other attachments on the pool wall.

[0028] The pool bottom cleaning stage refers to the part of the cleaning task specifically for the pool bottom of the swimming pool, aiming to remove sediments, debris, and stains at the pool bottom.

[0029] In some embodiments, a reasonable time allocation strategy can be preset according to the size, shape, and pollution degree of the swimming pool to ensure that both the pool wall cleaning and the pool bottom cleaning can obtain appropriate cleaning time. For example, the pool wall cleaning stage lasts for 60% of the time, and the pool bottom cleaning stage lasts for 40% of the time.

[0030] Step 102, according to the division results of the pool wall cleaning stage and the pool bottom cleaning stage, construct a hierarchical state machine framework including a pool wall state set and a pool bottom state set.

[0031] Among them, each state set is associated with a different bow-shaped path motion control strategy.

[0032] The pool wall state set refers to the set that defines different cleaning states of the robot during the pool wall cleaning process. The states may include the position of the robot on the pool wall, the current cleaning action being performed, the cleaning intensity, etc. For example, the pool wall state set may include states such as "start cleaning", "move to the next section of the pool wall", "cleaning completed", etc.

[0033] The pool bottom state set refers to the set that defines 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 position of the robot, the task being executed, etc. For example, the pool bottom state set may include states such as "sweeping the pool bottom", "detecting sediments", "cleaning completed", etc.

[0034] The hierarchical state machine framework is a model that divides tasks into multiple levels, with each level representing a set of states. The control logic of the robot depends on these state sets to switch states and perform corresponding actions. For example, the entire cleaning process can be divided into two major state levels: the pool wall cleaning state level and the pool bottom cleaning state level. Each level contains specific states. For example, the pool wall cleaning level includes "move" state and "sweep" state.

[0035] The motion control strategy refers to the control algorithm designed to achieve a certain cleaning state, specifically referring to the control methods of the robot's motion, direction, speed, etc. to complete the cleaning task. The motion control strategy can be to let the robot clean along a certain trajectory on the pool wall or to let the robot perform adaptive adjustment on the pool bottom. The bow-shaped path refers to the path along which the robot is controlled to move under the motion control strategy, and the overall shape presents a "bow" shape.

[0036] In some embodiments, according to the division of the pool wall and pool bottom cleaning stages, the entire cleaning process can be refined into multiple state sets. For example, the state set of the pool wall cleaning stage includes "start cleaning", "sweep the pool wall", "move to the next position", etc. The state set of the pool bottom cleaning stage includes "start sweeping", "detect sediment", "sweeping completed", etc.

[0037] Step 103, according to the motion control strategy in the pool wall state set, control the robot to clean the pool wall of the swimming pool.

[0038] In some embodiments, when the robot enters the pool wall cleaning stage, according to the control strategy in the pool wall state set, instruct the robot to perform corresponding actions, such as starting the cleaning equipment and adjusting the motion path of the robot to adapt to the shape of the pool wall. For example, when the state machine switches to the "sweep the pool wall" state, the robot automatically starts the brush head and simultaneously starts to move up and down along the pool wall to ensure that no area is missed during the cleaning process.

[0039] In some embodiments, the controlling the robot to clean the pool wall of the swimming pool according to the motion control strategy in the pool wall state set may include the following operations.

[0040] S10, based on a preset vertical movement step size, control the robot to perform periodic bow-shaped up and down movement along the pool wall.

[0041] The preset vertical movement step size refers to the step size set in advance for the robot to move up and down on the pool wall to detect the collision with the pool bottom or detect reaching the water surface position. The preset vertical movement step size can vary according to the height of the pool wall of the cleaned swimming pool.

[0042] The periodic up and down movement in the shape of a bow refers to the robot repeatedly moving vertically on the pool wall. This movement is continuous and repetitive throughout the entire pool wall cleaning process and is in the shape of a bow.

[0043] In some embodiments, the robot can move up and down along the pool wall according to a preset vertical movement step length (e.g., 10 cm).

[0044] 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.

[0045] The target value may be a parameter value of the robot roller brush (for example, the width of the roller brush). Since the parameter value may characterize the coverage width of the cleaning path, the robot's horizontal translation direction and distance may be determined according to the required cleaning range. The target value may also be the width of the roller. Since there is a numerical conversion relationship between the width of the roller and the width of the roller brush, the target value may also be the width of the roller.

[0046] The robot's posture information can also be determined in conjunction with an inertial measurement unit, and the robot's movement direction can be adjusted based on the posture information. An inertial measurement unit (IMU) is a sensor that can detect and feedback the robot's posture information, including tilt angle, acceleration, etc. For example, an inertial measurement unit can feedback the robot's pitch and roll angles, which helps determine whether the robot needs to be adjusted or translated.

[0047] The horizontal attitude angle refers to the angle of the robot's attitude sensor (IMU) in the horizontal direction, which 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 by 5 degrees. This information is used to adjust the robot's moving direction.

[0048] 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.

[0049] 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 through 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 floats to the surface, and determining the water line based on the change in speed.

[0050] After detecting the waterline, the robot can perform lateral movement cleaning.

[0051] 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.

[0052] Once the robot completes the horizontal translation according to the feedback attitude angle, it can return to the cleaning trajectory of the pool wall and continue to perform periodic zigzag up-and-down movement according to the preset vertical step to ensure that every part of the pool wall is thoroughly cleaned.

[0053] In some embodiments, when the robot encounters an obstacle or a corner during horizontal movement, it moves to the bottom of the pool, turns to a new wall surface, and repeats the up-and-down translation cleaning action.

[0054] Step 104, after the pool wall cleaning stage ends and the robot enters the bottom of the swimming pool, switch from the pool wall state to the pool bottom state, and control the robot to clean the bottom of the swimming pool according to the motion control strategy in the pool bottom state set.

[0055] The pool wall state switching to the pool bottom state means that when the robot finishes cleaning the pool wall, it switches from the task state of pool wall cleaning to the task state of pool bottom cleaning.

[0056] After the pool wall cleaning is completed and the robot enters the pool bottom, the robot can automatically switch states and execute the control strategy predetermined in the pool bottom state set. For example, after the robot enters the pool bottom, it automatically switches to the "clean the pool bottom" state, and the control strategy will make the robot start the suction device to clean the sediment at the bottom of the pool.

[0057] In some embodiments, before cleaning the bottom of the swimming pool, the state of the robot can be switched first. The state switching can be based on the cleaning duration, cleaning completion degree, etc. Taking the cleaning duration as an example, the state of the robot can be switched through the operations shown in the following embodiments.

[0058] S21, obtain the cleaning duration of the pool wall cleaning stage.

[0059] The cleaning duration refers to the time length elapsed from the start to the end of the pool wall cleaning stage. This time can be obtained by recording through the built-in timer or operating system of the robot.

[0060] S22, determine whether the cleaning duration of the pool wall cleaning stage reaches the preset threshold of the cleaning time allocation.

[0061] The preset threshold of the cleaning time allocation refers to the maximum allowed time preset for the pool wall cleaning task. The threshold determines the longest time that the robot is allowed to perform the cleaning operation during the pool wall cleaning stage. Once this time is exceeded, the robot needs to check whether the task has been completed or whether it has entered the next stage.

[0062] The determination method can be to compare the current cleaning duration with the preset cleaning time threshold to confirm whether the preset threshold is exceeded.

[0063] S23. If so, based on the sinking depth data of the robot, determine whether the robot has entered the bottom of the pool.

[0064] The sinking depth data refers to the depth information of the robot during task execution, that is, the vertical distance between the robot and the bottom of the pool. This data can be obtained through depth sensors (such as pressure sensors or ultrasonic sensors). For example, if the robot is currently 10 centimeters away from the bottom of the pool, the sinking depth is 10 centimeters. If the robot has touched the bottom of the pool, the sinking depth is 0.

[0065] S24. In response to the robot entering the bottom of the pool, trigger a state transition to the bottom cleaning phase.

[0066] State transition refers to the process of the robot switching from the currently executing task state to another task state.

[0067] When the robot determines that it has entered the bottom area, the control system will trigger a state transition to enable the robot to enter the bottom cleaning phase. At this time, the robot will adjust its movement mode, cleaning tools, or task parameters, etc., and start performing the bottom cleaning operation.

[0068] In some embodiments, the controlling the robot to clean the bottom of the pool according to the motion control strategy in the bottom state set may include the following operations.

[0069] S30. Obtain the current position of the robot and the boundary data of the pool.

[0070] The current position of the robot refers to the real-time position of the robot 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 represented as the coordinates or position points of the robot.

[0071] The boundary data of the pool refers to the boundary shape, size, and position information of the pool. The boundary data of the pool can be obtained through pre-set measurement methods or by sensor scanning.

[0072] S31. Based on the current position and the boundary data, determine the initial zigzag round-trip path of the robot.

[0073] The initial zigzag round-trip path refers to the path planned by the robot at the beginning of the bottom cleaning task. The initial zigzag round-trip path can be a round-trip movement route designed according to the current position of the robot and the boundary data of the pool. The round-trip path is usually a back-and-forth movement trajectory that covers the entire bottom area of the pool.

[0074] In some embodiments, an initial round-trip path can be planned based on the current position of the robot and the boundary data. For example, if the pool is rectangular, a round-trip route from one corner to another can be selected; if the pool has a complex shape, a path adapted to the shape can be designed according to the boundary data.

[0075] S32. Based on the initial zigzag round-trip path, clean the bottom of the pool, and dynamically adjust the spacing between adjacent parallel paths in the initial zigzag round-trip path according to the boundary signal fed back by the collision sensor.

[0076] The boundary signal refers to the feedback signal detected by the collision sensor, which is used to indicate the current distance from an obstacle or the presence of an obstacle.

[0077] The spacing between adjacent parallel paths refers to the distance between the round-trip paths of the robot during bottom cleaning of the pool. The initial path may be set with a fixed spacing, but during dynamic adjustment, the robot may reduce or increase the spacing between paths according to the collision signal to avoid collisions and ensure coverage of the entire bottom area of the pool.

[0078] When the robot is performing cleaning, it monitors the surrounding environment in real time through the collision sensor. If the sensor detects that the distance to the pool wall or other obstacles is too close, the path can be dynamically adjusted to reduce the spacing between adjacent paths to ensure that the robot can avoid obstacles and continue cleaning.

[0079] In some embodiments, the dynamically adjusting the spacing between adjacent parallel paths in the initial zigzag round-trip path according to the boundary signal fed back by the collision sensor may include the following operations.

[0080] S321. In response to detecting a bottom boundary collision, based on the coordinates of the collision point and the movement direction of the robot, determine the tentative movement direction after turning.

[0081] The tentative movement direction after turning refers to the new movement direction of the robot after turning adjustment according to the current movement direction after a collision. This direction is adjusted based on the coordinates of the collision point and the movement trajectory of the robot. For example, it can be a direct 90-degree turn.

[0082] 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 the movement direction of the robot, a suitable turning path is determined to obtain the new tentative movement direction.

[0083] S322. According to the current speed of the robot, the preset detection time, and the safety redundancy distance, calculate the tentative movement distance, and control the robot to move the tentative movement distance along the tentative movement direction.

[0084] The current speed of the robot refers to the traveling speed of the robot during the bottom cleaning of the pool.

[0085] The preset detection time refers to the time used by the robot for exploratory movement after a collision. This time can be set by the control system to ensure that the robot can move a safe distance for further detection.

[0086] The safety redundancy distance refers to the additional distance set by the control system to avoid collisions and ensure safety. This is an additional buffer area to ensure that the robot does not collide again during exploratory movement.

[0087] The exploratory movement distance refers to the movement distance calculated by the robot after responding to a collision, which can be comprehensively calculated from the current speed of the robot, the preset detection time, and the safety redundancy distance.

[0088] In some embodiments, the safety redundancy distance is determined in the following manner.

[0089] S40. According to the mass of the robot, the torque of the hub motor, and the bottom friction coefficient of the pool, calculate the braking slip distance of the robot through the dynamic equation.

[0090] The mass of the robot refers to the overall mass of the robot, and the mass affects the inertia and motion response of the robot.

[0091] The torque of the hub motor refers to the torque output by the hub motor of the robot, which is the rotational torque used to push the robot forward or backward.

[0092] The bottom friction coefficient of the pool refers to the strength of the friction force between the robot and the bottom of the pool. It is usually a unitless value indicating the degree of friction between the contact surfaces. The larger the friction coefficient, the smaller the sliding of the robot on the bottom of the pool; conversely, the smaller the friction coefficient, the easier it is for the robot to slide.

[0093] The braking slip distance refers to the distance that the robot slides during braking due to insufficient friction force, resulting in the sliding between the wheels and the ground. This distance is calculated from the mass of the robot, the torque of the hub motor, and the friction coefficient.

[0094] In some embodiments, the dynamic equation can be used to calculate the slip distance of the robot during braking. The dynamic equation can be various common equations, which will not be elaborated here.

[0095] S41. Calculate the delay compensation distance based on the response delay time of the ranging sensor.

[0096] The ranging sensor is a sensor used to detect the distance between the robot and obstacles or boundaries. The ranging sensor can use laser, ultrasonic, or other technologies to measure the distance.

[0097] The response delay time refers to the time required for the ranging sensor to detect an obstacle or boundary and then feed the data back to the control system. The response delay is usually determined by the speed of the hardware and signal processing.

[0098] The delay compensation distance refers to the moving distance calculated to compensate for the response delay of the ranging sensor. During the movement of the robot, the delay compensation distance is used to ensure that the control system can respond to the data of the ranging sensor in a timely manner.

[0099] S42, perform a weighted sum of the braking skid distance and the delay compensation distance to obtain the safety redundancy distance.

[0100] The control system can perform a weighted sum of the braking skid distance and the delay compensation distance according to the set weights to obtain the safety redundancy distance.

[0101] S323, determine whether the bottom boundary collision is triggered again during the movement. If so, determine that the current area is a corner, and generate a turning path away from the corner based on the corner coordinates. If not, use the tentative movement distance as the spacing of the new adjacent parallel path.

[0102] The control system determines whether the robot collides again during the tentative movement. If so, the robot considers the current position to be a corner and generates a new turning path based on the coordinates of the corner. If the tentative movement does not trigger a collision, use the distance of the tentative movement as the spacing of the new adjacent parallel path and continue cleaning.

[0103] Step 105, based on a preset path optimization algorithm, perform real-time evaluation on the coverage rate, overlap rate, and turning energy consumption of the bottom parallel paths of the pool, and update the movement path of the robot according to the evaluation results.

[0104] The path optimization algorithm is a path planning algorithm used to adjust and optimize the path of the robot in real time, aiming to improve the cleaning efficiency and reduce energy consumption. For example, the path optimization algorithm can adjust the forward path and speed of the robot according to the current cleaning progress and the bottom cleaning situation of the pool, avoid repeated cleaning, and reduce unnecessary turning.

[0105] The coverage rate refers to the ratio of the area covered by the robot during the bottom cleaning of the pool to the total cleaning area.

[0106] The overlap rate refers to the ratio of the repeated cleaning area caused by improper path planning to the total cleaning area during the cleaning process of the robot.

[0107] The turning energy consumption refers to the energy consumed by the robot due to frequent turning during the cleaning process.

[0108] In some embodiments, a preset path optimization algorithm can be used to monitor the path of the robot in real time, calculate the coverage rate, overlap rate, and turning energy consumption, and adjust the moving path of the robot based on this data. For example, path scoring can be calculated based on this data, and the optimal path can be selected according to the size of the calculated path score.

[0109] In some embodiments, the preset path optimization algorithm can calculate the path scores of each path using the following formula (1).

[0110]

[0111] Where F is the calculated path score, and α, β, and γ are preset weight coefficients that satisfy the constraint condition α + β + γ = 1, C 1 is the coverage rate, C 2 is the overlap rate, and E is the cumulative turning energy consumption.

[0112] Figure 2 is a flowchart of a bow-shaped path planning method for a cleaning robot provided in some other embodiments of the present application. Figure 2 The shown process can be executed by an electronic device, such as Figure 2 shown, and this process can include the following operations.

[0113] Step 201, when the robot performs lateral translation, determine whether a wall-foot collision event occurs according to the trigger frequency and pressure threshold of the collision sensor.

[0114] The collision sensor is used to detect the collision of the robot with surrounding objects (such as the pool wall, pool bottom, etc.). The collision sensor can feedback a signal, and when a collision occurs, the signal will be triggered. For example, the robot can be equipped with an ultrasonic sensor or a touch sensor, and when it touches the corner (wall-foot) of the pool wall, the sensor will be triggered and send a collision signal.

[0115] The trigger frequency refers to the number of times the collision sensor is triggered per unit time, which can be used to determine whether the robot frequently touches an obstacle, and thus infer whether a collision event has occurred.

[0116] The pressure threshold refers to 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 be triggered and report the collision event to the control system.

[0117] The wall-foot collision event refers to the event that the robot collides with the bottom or corner part of the pool wall during movement.

[0118] In some embodiments, when the robot performs lateral translation, the collision sensor can continuously monitor whether the robot collides with the bottom corner of the pool wall. If the trigger frequency of the collision sensor exceeds the set threshold and the applied pressure is greater than the set pressure threshold, then it can be determined that a bottom corner collision event has occurred.

[0119] Step 202, in response to determining that a bottom corner collision event has occurred, calculate a safe path to retreat to the bottom of the pool based on the current pose of the robot and the three-dimensional map of the pool bottom, and control the robot to move along the safe path.

[0120] The current pose refers to the current position and orientation of the robot during task execution, including the position coordinates and angle of the robot.

[0121] The three-dimensional map of the pool bottom refers to a map that describes the three-dimensional coordinate information and obstacle distribution of each area of the pool bottom. The map helps the robot plan the movement path and avoid collisions. For example, the three-dimensional map of the pool bottom includes data such as the depth information of the pool bottom and the distribution positions of obstacles, which can help the robot move effectively in the pool bottom area.

[0122] The safe path refers to a path calculated based on the current pose and environmental information to avoid obstacles and ensure the safe movement of the robot.

[0123] In some embodiments, once a bottom corner collision event occurs, the robot can calculate a safe path based on its current pose and the three-dimensional map of the pool bottom to ensure that the robot can avoid the pool wall and other obstacles and smoothly retreat to a safe area at the bottom of the pool.

[0124] 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.

[0125] The direction switching rule refers to a rule in the state machine framework for the robot to switch the movement direction according to certain conditions (such as sensor feedback) during task execution. For example, if the robot detects an obstacle in front during the cleaning process, according to the direction switching rule, the robot will switch direction and continue to move forward.

[0126] The target detection direction refers to the direction of target detection when the robot performs the detection task. For example, if the robot is currently facing the center of the pool bottom, the target detection direction may be to detect along the pool wall forward.

[0127] The detection distance refers to the distance the robot moves forward, which is usually preset and used to determine the detection range of the robot.

[0128] After retreating to the bottom of the pool, the robot can determine its target detection direction according to the motion control strategy in the state machine framework and based on the direction switching rule. Then, the robot moves forward according to the preset detection distance to continue executing the task.

[0129] Step 204: Based on the triggering state of the collision sensor during the movement, determine whether there is a new wall surface in the target detection direction. If there is, switch the state machine to the new wall surface climbing state; otherwise, control the robot to turn back to the original wall surface direction and re-execute the motion control strategy in the pool wall cleaning stage.

[0130] The triggering state of the collision sensor refers to whether the collision sensor triggers a signal during the movement. For example, if the robot touches the pool wall during the movement, the collision sensor will trigger a signal to inform the robot that a collision has occurred.

[0131] The new wall surface climbing state means that the robot switches to a new state dedicated to cleaning the new wall surface. In this state, the robot will perform the cleaning task along the new wall surface.

[0132] Turning back to the original wall surface direction means that when the robot does not detect a new wall surface, it returns and continues to clean along the original pool wall direction.

[0133] During the movement, the collision sensor can detect in real time whether it touches a new wall surface. If a collision occurs and a new wall surface is detected, the state machine will switch to the "new wall surface climbing state", and the robot will start cleaning the new wall surface. If there is no new wall surface, the robot will turn back to the original pool wall direction and restart the cleaning task.

[0134] Based on the same inventive concept, the embodiment of the present application further provides a path planning system based on a cleaning robot, and the system includes: A division module, configured 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, configured to construct 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; A first cleaning module, configured to control the robot to clean the pool wall of the pool according to the motion control strategy in the pool wall state set; A second cleaning module, configured to switch from the pool wall state to the pool bottom state after the pool wall cleaning stage ends and the robot enters the bottom of the pool, and control the robot to clean the bottom of the pool according to the motion control strategy in the pool bottom state set; A path optimization module, configured to perform real-time evaluation on the coverage rate, overlap rate, and turning energy consumption of the parallel paths at the bottom of the pool based on a preset path optimization algorithm, and update the moving path of the robot according to the evaluation results.

[0135] As Figure 3 shown in the figure, 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. Among them, the processor 111, the communication interface 112, and the memory 113 complete mutual communication through the communication bus 114. The memory 113 is used to store a computer program. In an embodiment of the present application, when the processor 111 executes the program stored on the memory 113, it implements the method for path planning based on a cleaning robot provided in any of the foregoing method embodiments, including: Dividing the pool cleaning process into a pool wall cleaning stage and a pool bottom cleaning stage based on a preset time allocation strategy. According to the division results of the pool wall cleaning stage and the pool bottom cleaning stage, constructing a hierarchical state machine framework including a pool wall state set and a pool bottom state set, where each state set is associated with a different motion control strategy. Controlling the robot to clean the pool wall of the pool according to the motion control strategy in the pool wall state set. After the pool wall cleaning stage ends and the robot enters the bottom of the pool, switching from the pool wall state to the pool bottom state, and controlling the robot to clean the bottom of the pool according to the motion control strategy in the pool bottom state set. Performing real-time evaluation on the coverage rate, overlap rate, and turning energy consumption of the parallel paths at the bottom of the pool based on a preset path optimization algorithm, and updating the moving path of the robot according to the evaluation results.

[0136] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method for zigzag path planning based on a cleaning robot provided in any of the foregoing method embodiments.

[0137] It should be noted that in this text, relational terms such as "first" and "second" are only used 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0138] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest 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 swimming pool cleaning process is divided into the pool wall cleaning stage and the pool bottom cleaning stage; According to 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; According to the motion control strategy of the pool wall state concentration, the robot is controlled to clean the pool wall of the swimming pool; After the pool wall cleaning phase is finished 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; 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 up and down movement in a bow shape 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 foot 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 occurs, based on the current position of the robot and the three-dimensional map of the pool bottom, a safe path for retreating to the pool bottom is calculated, and the robot is controlled 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, characterized in that: 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 of 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 a pool bottom cleaning phase is triggered.

5. The method according to claim 4, characterized in that The method of controlling the robot to clean the bottom of the swimming pool according to the motion control strategy of the pool bottom state includes: 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; 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.

6. The method according to claim 5, characterized in that The step of 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 comprises: 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; Calculate the trial movement distance according to the current speed of the robot, the preset detection time and the safety redundancy distance, and control the robot to move the trial movement distance along the trial movement direction; 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 of new adjacent parallel paths.

7. The method according to claim 6, characterized in that The safety margin distance is determined by: According to the mass of the robot, the torque of the hub motor and the friction coefficient of the pool bottom, the braking slip distance of the robot is calculated by a dynamic equation; Calculate the delay compensation distance based on the response delay time of the distance measuring sensor; The braking slip distance and the delay compensation distance are weightedly summed to obtain the safety redundancy distance.

8. A path planning system based on a cleaning robot, characterized in that: The system comprises: A division module, used for 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; 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, used for controlling the robot to clean the pool wall according to the motion control strategy of the pool wall state concentration; A second cleaning module is used to switch from a pool wall state to a pool bottom state after the pool wall cleaning stage is completed and the robot enters the pool bottom, and control the robot to clean the pool bottom according to a motion control strategy concentrated on the pool bottom state; 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.

9. 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 through the communication bus; Memory, used to store 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 1-7 when executing the program stored in the memory.

10. 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 as described in any one of claims 1 to 7 are implemented.

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