Accurate butt joint motion control method based on laser correction

Abstract

The invention relates to a precise docking motion control method based on laser correction, and belongs to the field of robot navigation and path control. Comprising the following steps: S1, calculating a curve target point position of a curve track; s2, recording odometer data of a starting point and constructing a local map by taking the starting point as an original point; s3, calculating a curve track from the starting point to the curve target point; s4, performing coordinate mapping on the current point odometer data; s5, calculating an actual look-ahead distance and obtaining a look-ahead point from the curve track; s6, calculating the linear velocity and the angular velocity of the curvilinear motion; s7, the robot moves according to the linear velocity and the angular velocity of the curvilinear motion until the robot reaches a curve target point; s8, calculating the angular velocity of the linear motion according to the position of the recharging seat; and S9, the robot moves according to the linear velocity and the angular velocity of the linear motion until the robot reaches the recharging seat. Accurate docking of the mobile charging robot can be realized without relying on map or environment modification in advance, and the movement efficiency and the control precision are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot navigation and path control, in particular to a precise docking motion control method based on laser correction. BACKGROUND

[0002] At present, with the popularization and intelligent development of electric vehicles, mobile charging robots as supporting service facilities are gradually applied to semi-structured and dynamically changing environments such as underground parking lots. Such robots usually need to complete precise docking with fixed charging stations under the condition of no fixed guide and no preset map to realize autonomous charging.

[0003] In the prior art, mobile charging robots mostly rely on a previously constructed global map or install special alignment devices in the environment to complete precise docking with the charging station, which has certain limitations in dynamic environments such as underground parking lots. The main problems are as follows: the existing scheme relies on SLAM to construct a map or sets identification marks in the environment, which has strong dependence on the environment. Such methods have certain effects in scenes with stable environmental features and unchanged layout, but in dynamic and complex environments such as underground parking lots where vehicles frequently enter and exit, light changes greatly, and temporary obstacles exist, the map is easily invalidated, the marks are easily blocked or damaged, leading to robot positioning deviation or even docking failure; traditional docking control usually adopts a step-by-step strategy of "rough attitude adjustment first, then fine alignment", and the robot needs to first search and adjust the pose multiple times to roughly align with the charging station, which takes a long time.

[0004] Therefore, there is an urgent need in the art to provide a motion control method for mobile charging robots to quickly, smoothly and accurately dock with the charging station without relying on existing maps and changing map features. SUMMARY

[0005] The technical problem solved by the present application is: in order to overcome at least one technical problem in the prior art, the present application provides a precise docking motion control method based on laser correction.

[0006] The technical scheme adopted by the present application to solve its technical problem is: The present application provides a precise docking motion control method based on laser correction, comprising: S1, acquiring the charging station position by laser radar, and calculating the curve target point position of the curve trajectory based on the charging station position and the preset straight line motion distance; S2, acquiring the starting point odometer data and constructing a local map with the starting point as the origin; S3, constructing a curve trajectory based on the starting point data and the curve target point position in the local map coordinate system; S4, obtain current point odometer data, and perform coordinate mapping on the current point odometer data to obtain current point data in a local map coordinate system; S5, based on the curve target point position and the current point data in the local map coordinate system, calculate an actual look-ahead distance, and obtain an actual look-ahead point position corresponding to the curve trajectory according to the actual look-ahead distance; S6, calculate linear velocity and angular velocity of curve motion according to the actual look-ahead point position and the current point data in the local map coordinate system; S7, the robot moves according to the linear velocity and angular velocity of curve motion until it reaches the curve target point; S8, take the linear velocity at the curve target point as the linear velocity of straight line motion, and calculate the angular velocity of straight line motion according to the return seat position; S9, the robot moves according to the linear velocity and angular velocity of straight line motion until it reaches the return seat.

[0007] Further, the start point data in the local map coordinate system includes a start point position coordinate and a start point orientation angle.

[0008] Further, the step S3 includes: S31, obtain a start point tangent vector according to the start point orientation angle, and obtain a curve target point tangent vector according to the direction in which the curve target point points to the return seat; S32, construct a curve trajectory based on the start point position coordinate, the curve target point position, the start point tangent vector, and the curve target point tangent vector.

[0009] Further, the calculation formula of the curve trajectory is as follows: ; ; ; ; ; In the formula, is a curve trajectory from the start point to the curve target point; is a progress parameter; is a start point coordinate; is a curve target point coordinate; is a start point tangent vector; is a curve target point tangent vector; , , and are base functions.

[0010] Furthermore, in step S4, the formula for calculating the coordinate mapping of the current point data is as follows: ; ; ; ; ; ; In the formula, The x-axis coordinate of the current point in the local map coordinate system; The y-coordinate of the current point in the local map coordinate system; This refers to the orientation angle of the current point in the local map coordinate system. The rotation angle; The current odometer angle; The starting point odometer angle; The x-axis coordinate of the current point on the odometer. The starting point is the x-axis coordinate of the odometer. The y-axis coordinate of the current point on the odometer; The y-axis coordinate of the starting point odometer; This is the difference between the x-axis coordinate of the current odometer point and the x-axis coordinate of the starting odometer point; This is the difference between the y-axis coordinate of the current odometer point and the y-axis coordinate of the starting odometer point.

[0011] Furthermore, step S5 includes: S51. Based on the location of the target point on the curve and the current point data in the local map coordinate system, calculate the distance between the current point and the target point on the curve; S52. Calculate the actual look-ahead distance based on the distance between the current point and the target point of the curve and the preset theoretical look-ahead distance; S53. Obtain the actual forward point position in the curve trajectory based on the actual forward distance.

[0012] Furthermore, the formula for calculating the actual look-ahead distance is as follows: LD ; In the formula, LD is the actual look-ahead distance; The preset maximum speed; This represents the distance between the current point and the target point on the curve.

[0013] Furthermore, step S6 includes: S61. Calculate the motion curvature based on the actual forward point location and the current point data in the local map coordinate system; S62. Based on the curvature of motion, calculate the linear velocity and angular velocity of the curvilinear motion.

[0014] Furthermore, the formula for calculating the linear velocity of the curvilinear motion is as follows: ; ; In the formula, The linear velocity of curvilinear motion; The maximum speed of the curvilinear motion; For the curvature of motion; The curvature coefficient; The angle between the current point's orientation and the forward-looking point; This represents the actual forward-looking distance.

[0015] Furthermore, the formula for calculating the angular velocity of the curvilinear motion is as follows: ; ; In the formula, The angular velocity of the curvilinear motion; For the curvature of motion; This is the angular error proportionality coefficient; This is for angular error; The angle between the current point's orientation and the forward-looking point; This represents the actual forward-looking distance.

[0016] Secondly, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the aforementioned laser-corrected precision docking motion control method. Thirdly, embodiments of the present invention also provide a readable storage medium, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to execute the aforementioned laser-corrected precision docking motion control method.

[0017] The beneficial effects of this invention are: 1. Precise docking of mobile charging robots can be achieved without relying on pre-existing maps or environmental modifications. The system generates local maps in real time through laser sensing and odometer data, and uses cubic Hermite splines to dynamically plan smooth curve trajectories, significantly improving adaptability and deployment efficiency in dynamic environments.

[0018] 2. The system adopts a dynamic look-ahead distance coupled with curvature and velocity algorithm, which enables the robot to automatically adjust its travel speed according to the curvature of the path. This ensures stability during high-speed operation and smoothness of path tracking, effectively improving motion efficiency and control accuracy. Attached Figure Description

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Figure 1 This is a flowchart of the laser-corrected precision docking motion control method provided in Embodiment 1 of the present invention; Figure 2 This is a motion trajectory diagram provided in Embodiment 1 of the present invention; Figure 3 A flowchart of step S3 provided in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of coordinate mapping provided in Embodiment 1 of the present invention; Figure 5 A flowchart of step S5 provided in Embodiment 1 of the present invention; Figure 6 A flowchart of step S6 provided in Embodiment 1 of the present invention; Figure 7 This is a partial block diagram of an electronic device provided in Embodiment 2 of the present invention. Detailed Implementation

[0021] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0022] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] The present invention will now be described in detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0024] For ease of understanding, the inventive concept will be described in its entirety before a detailed description of the embodiments of the present invention: This invention provides a laser-corrected precision docking motion control method, aiming to solve the problems of strong environmental dependence, long docking time, and cumbersome processes in existing motion control methods. The motion path is divided into two parts: a curved trajectory and a straight trajectory. For the curved trajectory, without a pre-defined map, the laser detects the recharge dock position and calculates the target point of the curve. A local map is constructed using odometry, and a smooth curved trajectory from the starting point to the target point is generated in real time using cubic Hermite splines. A dynamic look-ahead distance mechanism, curvature and velocity coupling control, and angle error feedback are introduced to ensure both stability during high-speed operation and smooth path tracking. For the straight trajectory, a lidar continuously scans the recharge dock position and corrects the motion angular velocity in real time, enabling fine-tuning of the robot's motion direction and preventing deviation. Ultimately, this achieves efficient, smooth, and high-precision autonomous docking of the mobile charging robot in complex environments.

[0025] Example 1 like Figure 1 As shown, this embodiment provides a precise docking motion control method based on laser correction, including: S1. The robot is equipped with an odometer and a lidar. The lidar scans to obtain the position of the charging dock, and based on the position of the charging dock and the preset straight-line movement distance, it calculates the position of the target point of the curved trajectory.

[0026] Specifically, based on the actual requirements of the system's docking accuracy, a preset straight-line distance can be set extending forward from the recharge base position along its normal direction (i.e., the direction of the docking center axis). This distance serves as an important parameter in trajectory planning, used to determine the location of the target point on the curve and the starting point of the laser correction stage. Its specific value should be determined comprehensively in conjunction with the physical structure of the recharge base and the docking strategy. For example, combining... Figure 2 As shown, when the charging interface of the return charging dock is a V-shaped guide groove, in order to provide sufficient guiding margin and compensate for the initial position deviation, the preset straight distance should be set to a larger value to ensure that the charging gun head can be smoothly and stably guided into the groove, thereby ensuring docking accuracy and success rate.

[0027] Specifically, the target point of the curve is located at the end of a preset straight line distance extending forward from the recharge seat position along its normal direction (i.e., the direction of the docking center axis), and serves as the key path point connecting the two motion stages of spline curve tracking and final straight line alignment.

[0028] S2. Obtain the starting point odometer data and construct a local map with the starting point as the origin. Construct a local map with the starting point as the origin and the direction of the starting point as the positive x-axis.

[0029] S3. Based on the starting point data and the target point position of the curve in the local map coordinate system, construct the curve trajectory using the cubic Hermite spline algorithm. The starting point data in the local map coordinate system includes the starting point position coordinates and the starting point orientation angle. The starting point data in the local map coordinate system is (0, 0, 0).

[0030] In some feasible implementations, combined with Figure 3 As shown, step S3 includes: S31. Obtain the tangent vector of the starting point based on the orientation angle of the starting point, and obtain the tangent vector of the target point of the curve based on the direction of the target point of the curve pointing back to the charging seat.

[0031] S32. Construct the curve trajectory based on the starting point's position coordinates, the target point's position, the starting point's tangent vector, and the target point's tangent vector. Simply put, based on the pose (position and orientation) of the starting point and the target point, construct a curve trajectory that smoothly transitions from the starting point to the target point.

[0032] In some feasible implementations, the formula for calculating the curve trajectory is as follows: ; ; ; ; ; In the formula, This represents the trajectory of a curve from the starting point to the target point. The progress parameter represents a normalized interpolation parameter, indicating the percentage progress from the starting point to the target point of the curve, with a value range of [0, 1]. The starting point coordinates are obtained via an odometer; The coordinates of the target point on the curve are calculated based on the position of the charging seat and the preset linear motion distance. The tangent vector of the starting point is a unit vector with the same direction as the starting point, which can be obtained from the angle of the starting point's orientation. The tangent vector to the target point of the curve is a unit vector that is the same as the direction from the target point of the curve to the recharge seat, and can be obtained based on the position of the target point of the curve and the position of the recharge seat. , , and Using basis functions is a standard technique and will not be elaborated upon here.

[0033] It's important to note that in traditional approaches, cubic Hermite spline algorithms are used to fit or interpolate known path points. The path is pre-existing, and the input consists of the positions of a series of points along the path and preset tangent directions. The goal is to make a curve pass through known points more smoothly. In contrast, this approach generates a guiding trajectory from scratch in an environment where the path is completely unknown. The input is real-time sensor data, and the goal is to generate a smooth trajectory that naturally connects the robot's current state with the docking task requirements in a map-free environment.

[0034] S4. Obtain the current point odometer data, which includes the current point position and the current point orientation angle. Then, perform coordinate mapping on the current point odometer data to obtain the current point data in the local map coordinate system.

[0035] In some feasible implementations, combined with Figure 4 As shown, the formula for calculating the coordinate mapping of the current point's odometer data is as follows: ; ; ; ; ; ; In the formula, The x-axis coordinate of the current point in the local map coordinate system; The y-coordinate of the current point in the local map coordinate system; This refers to the orientation angle of the current point in the local map coordinate system. This is the rotation angle, representing the angle at which the rotation operation is performed during coordinate mapping, used to align the coordinate system. The current odometer angle represents the orientation angle of the current point obtained through the odometer. The odometer angle at the starting point represents the orientation angle of the starting point obtained through the odometer. The x-axis coordinate of the current point is represented by the odometer reading. The x-axis coordinate of the starting point is represented by the odometer reading. The y-axis coordinate of the current point obtained from the odometer is: The y-axis coordinate of the starting point is represented by the odometer reading. This is the difference between the x-axis coordinate of the current odometer point and the x-axis coordinate of the starting odometer point; This is the difference between the y-axis coordinate of the current odometer point and the y-axis coordinate of the starting odometer point.

[0036] It should be noted that the purpose of the above coordinate mapping algorithm is to accurately transform the robot's real-time pose in the global odometry coordinate system to a local map coordinate system with the starting point as the origin. This is achieved by calculating the position difference between the current odometry and the starting odometry in the X and Y directions. and angle difference And after a rotation transformation (multiplied by) The rotation matrix (formed by the model) eliminates the influence of the global pose, thus obtaining the coordinates and angles on the local map.

[0037] S5. Based on the location of the target point on the curve and the current point data in the local map coordinate system, calculate the actual look-ahead distance, and obtain the corresponding actual look-ahead point location in the curve trajectory based on the actual look-ahead distance.

[0038] In some feasible implementations, combined with Figure 5 As shown, step S5 includes: S51. Based on the location of the target point on the curve and the current point data in the local map coordinate system, calculate the distance between the current point and the target point on the curve. That is, calculate the straight-line distance between the two points based on the coordinates of the current point and the coordinates of the target point on the curve. This is a conventional technique and will not be elaborated here.

[0039] S52. Calculate the actual look-ahead distance based on the distance between the current point and the target point of the curve and the preset theoretical look-ahead distance.

[0040] S53. Obtain the actual look-ahead point position corresponding to the curve trajectory based on the actual look-ahead distance.

[0041] Specifically, based on the current point position in the local map coordinate system and the calculated actual look-ahead distance, the look-ahead point is searched and located along the planned curve trajectory.

[0042] In some feasible implementations, the formula for calculating the actual look-ahead distance is as follows: LD ; In the formula, LD is the actual look-ahead distance; The preset maximum speed can be adjusted according to actual conditions and route. For example, in a crowded underground parking lot, for safety reasons, It can be set to a suitable value of 0.8 m / s; 0.2 is the distance between the current point and the target point of the curve; 0.2 is the preset theoretical look-ahead distance, used as the lower limit of the look-ahead distance.

[0043] It should be noted that, firstly, a theoretical look-ahead distance of 0.2 is preset as the lower limit of the look-ahead distance (implemented through the max function). When the robot's speed is very low or it has just started, The value of this value can be very small, resulting in an excessively short look-ahead distance, causing the control system to become overly sensitive and volatile. This lower limit ensures that the system maintains a stable control field of view even at low speeds. Secondly, The 0.5 value determines the sensitivity of speed to the effect of look-ahead distance, through... This ensures a positive correlation between the anticipation distance and the speed, achieving speed adaptation. The faster the speed, the greater the anticipation distance needs to be, providing sufficient time and space for smooth cornering. Furthermore, The 0.5 is used to control the convergence speed of the robot's behavior as it approaches the target point, ensuring that the look-ahead distance shortens rapidly when the robot is very close to the target point. Finally, the min function is used to set a dynamic upper limit for the look-ahead distance. When the robot is far from the target point, the look-ahead distance is usually determined by the result of the max function to ensure tracking performance; while as the robot gradually approaches the target point, the remaining distance d will decrease, making d×0.5 a smaller value, thus forcing the look-ahead distance to shrink accordingly.

[0044] S6. Based on the actual forward point location and the current point data in the local map coordinate system, calculate the linear velocity and angular velocity of the curvilinear motion.

[0045] In some feasible implementations, combined with Figure 6 As shown, step S6 includes: S61. Calculate the motion curvature based on the actual look-ahead point location and the current point data in the local map coordinate system.

[0046] S62. Based on the curvature of motion, calculate the linear velocity and angular velocity of the curvilinear motion.

[0047] In some feasible implementations, the formula for calculating the linear velocity of curvilinear motion is as follows: ; ; In the formula, The linear velocity of curvilinear motion; The preset maximum speed for curvilinear motion can be adjusted according to actual conditions and route. Motion curvature is used to describe the degree of path bending. This is the curvature coefficient, used to control the sensitivity of curvature to speed. It needs to be set according to the specific application scenario and performance requirements. The angle between the current point's orientation and the look-ahead point, i.e., the angle between the current point's orientation and the "line connecting the robot to the look-ahead point", represents the current robot's angular deviation relative to the look-ahead point; This represents the actual forward-looking distance.

[0048] It should be noted that this algorithm primarily adjusts the desired speed dynamically based on the curvature of the path, appropriately reducing speed at curves. Curvature and speed are inversely proportional; increasing curvature increases the denominator and decreases the desired speed. (Curvature coefficient) The larger the curvature, the stronger the reduction in speed due to curvature, and the more conservative the cornering behavior; curvature coefficient The smaller the curvature, the less impact it has on speed, allowing for more aggressive cornering.

[0049] In some feasible implementations, the formula for calculating the angular velocity of curvilinear motion is as follows: ; ; In the formula, The angular velocity of the curvilinear motion; For the curvature of motion; This is the angular error proportionality coefficient; This is the angle between the current point's orientation and the look-ahead point, i.e., the angle between the current point's orientation and the "line connecting the robot to the look-ahead point"; The angle error is the difference between the angle from the current point to the target point on the curve and the current point angle. It describes the deviation between the actual steering command and the theoretical orientation of the path. This represents the actual forward-looking distance.

[0050] It should be noted that this formula adjusts the system offset by adjusting the angular deviation. It is a P controller used to eliminate steady-state errors, accurately align with the target, enhance system robustness, and prevent oscillations near the target point. The larger the error, the larger the angular velocity command. This feedback adjustment is divided into two parts: when far from the target point on the curve, It does not participate in calculations and always remains at 0 in order to quickly respond to changes in the path; when approaching the target point on the curve, it activates feedback and always aligns with the direction of the target point.

[0051] S7. The robot moves along the linear velocity and angular velocity of the curve until it reaches the target point on the curve.

[0052] Specifically, the robot continuously performs motion control based on the real-time calculated linear and angular velocities of the curved path, and determines whether it has reached the target point on the curve within each control cycle. If it has reached the target point, it switches to the next stage of the straight-line alignment process; if it has not reached the target point, it continues to search for and track new look-ahead points along the curved path based on the updated robot pose and look-ahead distance, thus forming a closed-loop control until it finally reaches the target point on the curve.

[0053] S8. Take the linear velocity at the target point of the curve as the linear velocity of the linear motion, and calculate the angular velocity of the linear motion based on the position of the return seat.

[0054] Specifically, the linear velocity at the target point on the curve is the velocity of the last frame of the curved motion. During the straight-line motion, laser correction is performed using LiDAR, which continuously scans the recharge dock position coordinates and calculates the real-time angular velocity of the straight-line motion based on the recharge dock position. This allows for real-time fine-tuning of the angular velocity of the straight-line motion, preventing the robot from deviating from its course. Rescanning the recharge dock position coordinates during the straight-line motion phase is to avoid scanning data errors caused by problems such as excessive distance from the starting point or the existence of blind spots.

[0055] In some feasible implementations, the formula for calculating the angular velocity of linear motion is as follows: ; In the formula, Angular velocity for linear motion; The angle of the charging dock relative to the current point, that is, the angle between the current point and the line connecting the current point and the charging dock; The x-axis coordinate of the recharge seat in the robot coordinate system (with the robot as the origin); This is the y-axis coordinate of the recharge seat in the robot coordinate system (with the robot as the origin).

[0056] It should be noted that this algorithm uses LiDAR to obtain the coordinates of the recharge station in the robot coordinate system, and then uses the arctangent function. Calculate the direction of the line connecting the robot and the charging dock. This direction angle... Directly used as angular velocity This drives the robot to continuously adjust its orientation until it is precisely aligned with the charging dock, thereby achieving simple, efficient and precise orientation control in the final straight-line docking stage.

[0057] Example 2 Please see Figure 7 The present invention also provides an electronic device, including: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to implement the laser-corrected precision docking motion control method provided in Embodiment 1.

[0058] The memory 702 and processor 701 are connected via a bus, which may include any number of interconnecting buses and bridges, connecting various circuits of one or more processors 701 and memory 702 together. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 701 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 701.

[0059] Processor 701 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 702 can be used to store data used by processor 701 during operation.

[0060] Example 3 This invention also proposes a storage medium storing a laser-corrected precision docking motion control method. When executed, the laser-corrected precision docking motion control program implements the steps of the laser-corrected precision docking motion control method described above. Since this storage medium employs all the technical solutions of the above embodiments, it possesses at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated upon further here.

[0061] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, based on the guidance provided in this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A precise docking motion control method based on laser correction, characterized in that, include: S1. Obtain the position of the charging dock using a lidar, and calculate the position of the target point of the curved trajectory based on the position of the charging dock and the preset straight-line movement distance. S2. Obtain the starting point odometer data and construct a local map with the starting point as the origin; S3. Construct the curve trajectory based on the starting point data and the position of the curve target point in the local map coordinate system; S4. Obtain the current point odometer data and perform coordinate mapping on the current point odometer data to obtain the current point data in the local map coordinate system; S5. Based on the location of the target point on the curve and the current point data in the local map coordinate system, calculate the actual forward distance and obtain the corresponding actual forward point location in the curve trajectory according to the actual forward distance. S6. Based on the actual forward point location and the current point data in the local map coordinate system, calculate the linear velocity and angular velocity of the curvilinear motion. S7. The robot moves along the linear velocity and angular velocity of the curve until it reaches the target point on the curve. S8. Take the linear velocity at the target point of the curve as the linear velocity of the linear motion, and calculate the angular velocity of the linear motion based on the position of the return seat. S9. The robot moves along the linear velocity and angular velocity of linear motion until it reaches the charging dock.

2. The laser-corrected precision docking motion control method according to claim 1, characterized in that, The starting point data in the local map coordinate system includes the starting point's position coordinates and the starting point's orientation angle.

3. The laser-corrected precision docking motion control method according to claim 2, characterized in that, Step S3 includes: S31. Obtain the tangent vector of the starting point based on the orientation angle of the starting point, and obtain the tangent vector of the target point of the curve based on the direction of the target point of the curve pointing to the return seat. S32. Construct the curve trajectory based on the starting point coordinates, the target point position, the starting point tangent vector, and the target point tangent vector.

4. The laser-corrected precision docking motion control method according to claim 3, characterized in that, The formula for calculating the curve trajectory is as follows: ； ； ； ； ； In the formula, This represents the trajectory of a curve from the starting point to the target point. For progress parameters; The coordinates of the starting point; The coordinates of the target point on the curve; The tangent vector is the starting point. The tangent vector to the target point of the curve; , , and These are basis functions.

5. The laser-corrected precision docking motion control method according to claim 1, characterized in that, In step S4, the formula for calculating the coordinate mapping of the current point data is as follows: ； ； ； ； ； ； In the formula, The x-axis coordinate of the current point in the local map coordinate system; The y-coordinate of the current point in the local map coordinate system; This refers to the orientation angle of the current point in the local map coordinate system. The rotation angle; The current odometer angle; The starting point odometer angle; The x-axis coordinate of the current point on the odometer. The x-axis coordinate of the starting point odometer; The y-axis coordinate of the current point on the odometer; The y-axis coordinate of the starting point odometer; This is the difference between the x-axis coordinate of the current odometer point and the x-axis coordinate of the starting odometer point; This is the difference between the y-axis coordinate of the current odometer point and the y-axis coordinate of the starting odometer point.

6. The laser-corrected precision docking motion control method according to claim 1, characterized in that, Step S5 includes: S51. Based on the location of the target point on the curve and the current point data in the local map coordinate system, calculate the distance between the current point and the target point on the curve; S52. Calculate the actual look-ahead distance based on the distance between the current point and the target point of the curve and the preset theoretical look-ahead distance; S53. Obtain the actual look-ahead point position corresponding to the curve trajectory based on the actual look-ahead distance.

7. The laser-corrected precision docking motion control method according to claim 6, characterized in that, The formula for calculating the actual forward distance is as follows: LD ; In the formula, LD is the actual look-ahead distance; The preset maximum speed; This represents the distance between the current point and the target point on the curve.

8. The laser-corrected precision docking motion control method according to claim 1, characterized in that, Step S6 includes: S61. Calculate the motion curvature based on the actual forward point location and the current point data in the local map coordinate system; S62. Based on the curvature of motion, calculate the linear velocity and angular velocity of the curvilinear motion.

9. The laser-corrected precision docking motion control method according to claim 8, characterized in that, The formula for calculating the linear velocity of the curvilinear motion is as follows: ； ； In the formula, The linear velocity of curvilinear motion; The maximum speed of the curvilinear motion; For the curvature of motion; The curvature coefficient; The angle between the current point's orientation and the forward-looking point; This represents the actual forward distance.

10. The laser-corrected precision docking motion control method according to claim 8, characterized in that, The formula for calculating the angular velocity of the curvilinear motion is as follows: ； ； In the formula, The angular velocity of the curvilinear motion; For the curvature of motion; This is the angular error proportionality coefficient; This is for angular error; The angle between the current point's orientation and the forward-looking point; This represents the actual forward distance.

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