Intelligent car washing machine brush washing track planning method

Abstract

The application discloses a kind of intelligent car washer brush washing track planning methods, it is related to brush washing track planning technical field.The intelligent car washer brush washing track planning method, through the deviation of vehicle parking lateral offset and vehicle parking longitudinal offset angle adaptation, combined with the point-by-point collision risk verification of effective brush washing range of brush head, based on the track following deviation rate, the pertinence track adjustment of brush washing coverage overlap rate, and the verification screening of collaborative adaptation degree and track motion fluency, realize the adaptation of track and vehicle parking deviation, effectively avoid the collision risk of track and vehicle, while guarantee the smooth operation of track, solve the problem that traditional brush washing track planning is prone to appear when there is parking deviation for intelligent car washer, and the adaptability of track and vehicle parking deviation is insufficient, leading to collision risk of track and vehicle, incomplete or excessive overlap of brush washing coverage and poor track motion fluency.

Description

Technical Field

[0001] This invention relates to the field of brushing trajectory planning technology, specifically to a method for planning the brushing trajectory of an intelligent car wash machine. Background Technology

[0002] When vehicles are parked incorrectly, traditional brushing trajectory planning for intelligent car wash machines faces multiple challenges, including adaptability, collision risk, coverage effect, and smoothness. Existing planning technologies have the following shortcomings:

[0003] First, traditional methods lack adaptation to the lateral and longitudinal offset of vehicle parking, resulting in insufficient adaptability. Second, traditional solutions lack assessment of the accuracy of trajectory point alignment with the vehicle contour and the extent of intrusion, making it difficult to predict and avoid collision risks. Finally, traditional technologies do not establish a trajectory optimization mechanism based on quantitative data, resulting in highly arbitrary trajectory segment placement, making it difficult to avoid incomplete coverage or excessive overlap, leading to poor motion smoothness.

[0004] Therefore, there is an urgent need for a planning method that can quantify and adapt parking deviations, quantify and verify collision risks, optimize trajectories, and verify smoothness, in order to solve the above-mentioned technical problems of traditional solutions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for planning the brushing trajectory of an intelligent car wash machine. This method solves the problems that traditional brushing trajectory planning often fails to adapt to vehicle parking deviations when the vehicle is parked, resulting in the risk of collision between the trajectory and the vehicle, incomplete brushing coverage or excessive overlap, and poor trajectory smoothness.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for planning the brushing trajectory of an intelligent car wash machine, comprising the following steps:

[0007] Based on the vehicle parking offset and the vehicle parking offset angle, deviation adaptation is performed to obtain the initial adapted brushing trajectory.

[0008] Based on the initial adapted brushing trajectory, and combined with the effective brushing range of the brush head, the collision risk of the brush head trajectory point is checked to obtain the checked brushing trajectory.

[0009] The calibration brushing trajectory is evaluated by measuring the geometric distance between brush head trajectory points and statistically analyzing the overlap length of adjacent trajectory segments. Combined with the effective brushing range of the brush head, the trajectory following deviation rate and brushing coverage overlap rate are obtained.

[0010] Based on the trajectory following deviation rate and the brushing coverage overlap rate, the trajectory point coordinates and trajectory spacing of the calibrated brushing trajectory are adjusted to obtain alternative adjusted brushing trajectories.

[0011] Based on vehicle parking offset, vehicle parking offset angle, trajectory following deviation rate, and brushing coverage overlap rate, the collaborative adaptability and trajectory movement smoothness of the alternative adjusted brushing trajectory are verified.

[0012] The final brushed trajectory is obtained by adjusting the alternative trajectory with the minimum weighted sum of cooperative adaptation and trajectory motion smoothness.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] This invention achieves alignment between the trajectory and vehicle parking deviation by adapting the deviation between the lateral offset and longitudinal offset angle of the vehicle parking, verifying the point-to-point collision risk in conjunction with the effective brushing range, adjusting the trajectory based on the trajectory following deviation rate and brushing coverage overlap rate, and verifying and screening the degree of coordination and the smoothness of trajectory movement. This effectively avoids the risk of collision between the trajectory and the vehicle, while ensuring smooth and efficient trajectory operation. It solves the problem that traditional brushing trajectory planning is prone to insufficient adaptation to vehicle parking deviation when there is a parking deviation in intelligent car wash machines, resulting in the risk of collision between the trajectory and the vehicle, incomplete brushing coverage or excessive overlap, and poor trajectory movement smoothness. Attached Figure Description

[0015] Figure 1 This is a flowchart of the intelligent car wash machine brushing trajectory planning method of the present invention.

[0016] Figure 2 This is a flowchart of the collision risk detection method for the intelligent car wash machine brushing trajectory planning method of the present invention.

[0017] Figure 3 This is a schematic diagram of the cleaning arm structure of the intelligent car wash machine brushing trajectory planning method of the present invention.

[0018] In the diagram, 1 is the horizontal arm; 2 is the vertical arm; and 3 is the brush head. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Please refer to the accompanying drawings. Figure 1 This invention provides a technical solution: a method for planning the brushing trajectory of an intelligent car wash machine, comprising the following steps:

[0020] S1. Based on the vehicle parking offset and the vehicle parking offset angle, deviation adaptation is performed to obtain the initial adapted brushing trajectory.

[0021] Considering the uncertainty of vehicle parking and the physical limitations of the washing equipment, the specific process for obtaining the initial adapted washing trajectory is as follows:

[0022] S101. Rotate and adjust the reference trajectory using the vehicle parking offset angle as the rotation angle. The reference trajectory is a universal brushing trajectory with no parking deviation that is pre-stored in the database.

[0023] S102. If the rotation angle is greater than the maximum rotation angle, the brushing trajectory generation fails and a vehicle relocation alarm is issued. Otherwise, the translation distance is the vehicle parking offset, and all brush head trajectory points of the reference trajectory after translation and rotation are translated.

[0024] S103. If the translation distance is greater than the maximum translation distance, the brushing trajectory generation fails and a vehicle relocation alarm is issued; otherwise, a preliminary adapted brushing trajectory is obtained.

[0025] This embodiment directly adapts to vehicle parking deviations, improving the compatibility between the washing trajectory and vehicle parking deviations. At the same time, by setting a verification mechanism for the maximum rotation angle and maximum translation distance, it avoids the problem of unsuitability caused by excessive vehicle position deviations due to exceeding the physical adaptability of the washing equipment.

[0026] It should be noted that the vehicle parking offset angle is the angle between the vehicle's longitudinal axis and the parking space's longitudinal axis. For example, the rotatable angle is 0°-15°, and the maximum rotatable angle can be set to 10°.

[0027] Vehicle parking offset includes lateral offset and longitudinal offset, and the maximum translation distance includes the maximum lateral translation distance and the maximum longitudinal translation distance. The lateral offset refers to the difference between the abscissa of the vehicle's center coordinates and the abscissa of the parking space's center coordinates, and the longitudinal offset refers to the difference between the ordinate of the vehicle's center coordinates and the ordinate of the parking space's center coordinates. For example, the lateral movement distance can be 0-30cm, and the maximum lateral translation distance can be set to 25cm; the longitudinal movement distance can be 0-10cm, and the maximum longitudinal translation distance can be set to 8cm.

[0028] S2. Based on the initial adaptation of the brushing trajectory, and combined with the effective brushing range of the brush head, the collision risk of the brush head trajectory point is checked to obtain the checked brushing trajectory.

[0029] Considering the physical limitations of car wash equipment, it may not be suitable for vehicles of all widths. Also, the brush head needs to contact the side of the vehicle and apply pressure to achieve an effective wash, so the brush head will deform under pressure. However, excessive deformation cannot be tolerated, as this could damage the brush head or even the vehicle due to excessive pressure. Therefore, it is necessary to verify beforehand whether the brush head's working area poses a risk of collision with the vehicle's contours. Figure 2 As shown, the specific process is as follows:

[0030] S201. Using each brush head trajectory point in the initial adaptation brushing trajectory as the origin, construct a collision detection area with the lateral length of the effective brushing range of the brush head as the diameter. The lateral length referred to here is the natural length of the brush head when it is not in contact with the vehicle.

[0031] S202. If the collision detection area does not intersect with the vehicle outline, extend the scrubbing arm until the collision detection area intersects with the vehicle outline.

[0032] S203. If the collision detection area intersects with the vehicle outline, determine the depth of the collision detection area intruding into the vehicle outline after the brush arm reaches its maximum lateral extension.

[0033] S204. If the intrusion depth exceeds the normal brush head cleaning distance, there is a risk of collision, and an alarm requiring vehicle relocation will be issued; otherwise, the cleaning trajectory after verification will be output.

[0034] like Figure 3 As shown, one embodiment of the cleaning arm includes a horizontally extendable horizontal arm 1, a vertically extendable vertical arm 2, and a brush head 3 mounted on the vertical arm 2. When the horizontal arm 1 extends to its maximum horizontal extension, the collision detection area of ​​the brush head 3 still has an intrusion depth into the vehicle's contour that exceeds the normal brushing distance, which may cause damage to the vehicle during the car wash process. The brush head trajectory point represents the fixed, immovable connection point between the brush head and the vertical arm 2.

[0035] The normal brushing distance mentioned here refers to the lateral length of the brush head after it comes into contact with the side of the vehicle and is deformed by pressure without damaging the vehicle. In other words, it is the distance between the fixed connection point of the brush head and the vertical arm 2 and the surface of the brush head after it is deformed by pressure.

[0036] The process for determining the depth of intrusion is as follows:

[0037] A coordinate system is established along the lateral working direction of the scrubbing arm. The outer boundary of the vehicle contour is used as the baseline. The maximum radial distance of the intersection area between the collision detection area and the vehicle contour is measured from the outer boundary of the vehicle contour to the inner side of the vehicle contour. The maximum radial distance is used as the intrusion depth.

[0038] In this embodiment, the brush arm retracts until it intersects with the vehicle's side when there is no intersection, ensuring that the brush head can contact the side of the vehicle for effective cleaning. At the same time, the penetration depth is measured and verified in conjunction with the normal brush head cleaning distance. If the penetration depth is exceeded, a vehicle relocation alarm is issued, effectively avoiding the risk of collision between the trajectory and the vehicle. It also avoids damage to the brush head and the vehicle caused by excessive deformation or excessive contact pressure.

[0039] It should be noted that vehicle outlines can be obtained directly through existing means, such as through detection equipment such as visual sensors, lidar, or ultrasonic sensors. A full-range scan or image acquisition of a vehicle parked in a parking space can be performed to capture three-dimensional spatial data. Digital vehicle outline data can then be constructed through image recognition and point cloud data.

[0040] The process of determining whether the collision detection area and the vehicle outline intersect is as follows: if any point on the boundary or inside of the circular collision detection area falls on the boundary or inside of the vehicle outline, or if any point on the boundary or inside of the vehicle outline falls on the boundary or inside of the circular collision detection area, then the two are determined to intersect; otherwise, they do not intersect.

[0041] S3. After verification, the geometric distance between brush head trajectory points and the overlap length of adjacent trajectory segments are measured. Combined with the effective brush head brushing range, the trajectory following deviation rate and brushing coverage overlap rate are obtained.

[0042] Since the calibrated brushing trajectory may still have issues such as tracking deviations between the brush head trajectory points and the corresponding contact positions of the vehicle contour, as well as overlaps or omissions in the brushing coverage areas of adjacent trajectory segments, these problems cannot be accurately identified through visual observation. Therefore, it is necessary to transform these implicit trajectory adaptation defects into quantifiable indicators. Thus, to quantify the tracking accuracy of each brush head trajectory point and the corresponding contact position of the vehicle contour in the calibrated brushing trajectory, and the degree of overlap in the brushing coverage areas of adjacent trajectory segments, the process for obtaining the trajectory tracking deviation rate and brushing coverage overlap rate is as follows:

[0043] S301. Measure and verify the geometric distance from each brush head trajectory point in the brushing trajectory to the corresponding fitting position of the vehicle contour.

[0044] S302. For each group of adjacent trajectory segments, construct the brushing coverage area of ​​each trajectory segment based on the brush head trajectory points of the two trajectory segments and the longitudinal radius of the effective brushing range of the brush head. Calculate the overlap length of the brushing coverage area corresponding to each group of adjacent trajectory segments.

[0045] S303. Calculate the arithmetic mean of the distances based on the geometric distances of all brush head trajectory points, calculate the distance difference between the arithmetic mean of the distances and the normal brushing distance of the brush head, and use the ratio of the distance difference to the lateral length of the effective brushing range of the brush head as the trajectory following deviation rate.

[0046] S304. Calculate the arithmetic mean of the lengths based on the overlap lengths of all adjacent trajectory segments, and use the ratio of the arithmetic mean of the lengths to the longitudinal radius of the effective brushing range of the brush head as the brushing coverage overlap rate.

[0047] The trajectory following deviation rate reflects the deviation between the trajectory point and the vehicle contour. The closer the value is to 0, the more accurately the trajectory point matches the vehicle contour, ensuring cleaning effectiveness without damaging the brush head and the vehicle; the farther the value deviates from 0 (regardless of whether it is positive or negative), the more serious the deviation of the trajectory point from the vehicle contour, and the worse the adaptability.

[0048] The overlap rate of the brushing coverage can clearly indicate the overlap status of adjacent trajectory segments; the smaller the value, the better the coverage.

[0049] The longitudinal radius of the effective brushing range of the brush head determines the longitudinal coverage capability of the brush head in a single brushing motion. Constructing the coverage area based on this radius ensures that the coverage range of the trajectory segment is consistent with the actual working capability of the brush head, avoiding invalid trajectories. Therefore, it should be noted that the trajectory segment determination process is as follows:

[0050] Construct a brushing coverage area that matches the brush head, using the longitudinal radius of the effective brushing range of the brush head as the radius.

[0051] A brushing coverage area is pre-set at the top and bottom of the vehicle outline, so that the brushing coverage area at the top is flush with the top of the vehicle outline, and the brushing coverage area at the bottom is flush with the bottom of the vehicle outline.

[0052] Several brushing and covering areas are randomly arranged in the area not covered by the vehicle outline.

[0053] The area covered by the brushing and washing motion is called the trajectory segment, which is the area that is translated along the horizontal direction of the vehicle's outline.

[0054] The top and bottom of the vehicle outline refer to the top and bottom boundaries of the side areas of the vehicle.

[0055] Constructing the brushing coverage area based on the longitudinal radius of the brush head's effective brushing range ensures that the trajectory segment matches the actual working capacity of the brush head; while the brushing coverage area is horizontally shifted along the vehicle's outline to form the trajectory segment, allowing the trajectory segment to naturally conform to the vehicle's shape.

[0056] S4. Based on the trajectory following deviation rate and the brushing coverage overlap rate, adjust the trajectory point coordinates and trajectory spacing of the calibrated brushing trajectory to obtain alternative adjusted brushing trajectories.

[0057] Considering that the trajectory segments of the brushing trajectory after verification are randomly distributed, there may be cases where the overlap is too high or the vehicle's side outline cannot be covered. Therefore, adjustments are required, and the specific process is as follows:

[0058] S401. Based on the positive or negative or zero value of the trajectory following deviation rate, and whether the brushing coverage overlap rate is 0, and combined with the boundary distance of the coverage area of ​​adjacent trajectory segments, determine the adjustment direction of the brush head trajectory point in the horizontal and vertical directions.

[0059] S402. With minimizing the absolute values ​​of trajectory following deviation rate and brushing coverage overlap rate as the adjustment target, determine the combination of adjustment distances for brush head trajectory points in the horizontal and vertical directions to obtain several alternative adjusted brushing trajectories.

[0060] This embodiment sets minimizing the absolute values ​​of trajectory following deviation rate and brushing coverage overlap rate as the adjustment target because these two values ​​are directly related to the adaptation requirements and operational effectiveness of the intelligent car wash's brushing trajectory. Minimizing the absolute value of the trajectory following deviation rate allows the brush head trajectory points to conform to the corresponding positions of the vehicle's contours to the greatest extent possible; minimizing the absolute value of the brushing coverage overlap rate avoids excessive overlap or missed coverage between adjacent trajectory segments.

[0061] Considering that the closer the trajectory following deviation rate and the brushing coverage overlap rate are to 0, the more efficient the brushing trajectory is, and that there is a relationship between the trajectory following deviation rate and the brushing coverage overlap rate and the value of 0, the process of determining the adjustment direction of the brush head trajectory points in the horizontal and vertical directions is as follows:

[0062] S4011. If the trajectory following deviation rate is less than 0, the brush head trajectory point is adjusted laterally in a direction away from the vehicle outline. If it is equal to 0, no adjustment is made. If it is greater than 0, it is adjusted in a direction closer to the vehicle outline.

[0063] S4012. If the brushing coverage overlap rate is 0, verify whether the boundary distance between the two brushing coverage areas of adjacent trajectory segments is 0. That is, measure the straight-line distance between the nearest boundary points of the brushing coverage areas corresponding to adjacent trajectory segments along the longitudinal direction of the vehicle profile.

[0064] S4013. If the boundary distance is not 0, adjust in the direction of reducing the distance between adjacent trajectory segments; if the boundary distance is 0, do not adjust.

[0065] S4014. If the brushing coverage overlap rate is greater than 0, adjust in the direction of increasing the spacing between adjacent trajectory segments.

[0066] For trajectory following deviation rate, a value of 0 indicates the most perfect following effect. However, the brushing coverage overlap rate varies. When the value is 0 and the boundary distance between adjacent trajectory segments is 0, it indicates that the coverage connection is just right, with no omissions or overlaps. When the value is greater than 0, the larger the value, the more serious the excessive overlap between adjacent trajectory segments, which will cause waste of resources. When the value is 0 but the boundary distance is not 0, it indicates that there are omissions in brushing coverage. The closer the value is to 0 (and the boundary distance is 0), the better the coverage effect.

[0067] The process of determining the adjustment distance of the brush head trajectory point in the horizontal and vertical directions is as follows:

[0068] S4021. The absolute value of the product of the trajectory following deviation rate and the lateral length of the effective brushing range of the brush head is used as the adjustment distance of the brush head trajectory point in the lateral direction.

[0069] S4022. Keep the trajectory segments at the top and bottom of the vehicle outline unchanged, and randomly reassign several brushing coverage areas in the remaining area. Calculate the vertical distance of the brush head trajectory points based on the total number of trajectory segments, and use it as the adjustment distance of the brush head trajectory points in the longitudinal direction.

[0070] Regarding the longitudinal adjustment distance, if the brush coverage overlap rate is 0 and the boundary distance between adjacent trajectory segments is 0, the longitudinal adjustment distance is 0. If the brush coverage overlap rate is greater than 0, then one brush coverage area is reduced each time in the remaining area, that is, one trajectory segment is reduced each time, and the brush coverage overlap rate is recalculated. If the brush coverage overlap rate is still not 0, then one trajectory segment can be reduced again until the brush coverage overlap rate is 0. However, when the brush coverage overlap rate is 0, it is necessary to check whether the boundary distance is greater than 0. If it is greater than 0, it means that there is an uncovered area. At this time, one trajectory segment needs to be added, the brush coverage overlap rate is recalculated, and the longitudinal adjustment distance of the brush head trajectory point is determined according to the ratio of the distance from the top boundary to the bottom boundary of the vehicle outline to the number of trajectory segments. That is, the distance that the vertical arm 2 moves the brush head downward each time.

[0071] S5. Based on vehicle parking offset, vehicle parking offset angle, trajectory following deviation rate, and brushing coverage overlap rate, verify the cooperative adaptability and trajectory movement smoothness of the alternative adjusted brushing trajectory.

[0072] Considering the differences in trajectory following deviation rate, brushing coverage overlap rate, and spacing between the beginning and end of trajectory segments for each alternative adjusted brushing trajectory, when the brushing coverage overlap rate is closer to 0 and the boundary distance is not greater than 0, the more trajectory segments there are, the better the brushing effect. At the same time, the connection stroke between trajectory segments is also smaller, but it also means a higher brushing repetition rate. Therefore, it is necessary to select the most suitable alternative adjusted brushing trajectory. The process is as follows:

[0073] S501. The deviation adaptation coefficient is obtained by weighted summing the ratio of the vehicle parking offset to the maximum translation distance and the ratio of the vehicle parking offset angle to the maximum rotation angle. For example, the weights of the two ratios are 0.4 and 0.6, respectively.

[0074] S502. The weighted sum of the trajectory following deviation rate and the brushing coverage overlap rate is used as the trajectory adaptation coefficient, and the sum with the deviation adaptation coefficient is used to obtain the cooperative adaptation degree. For example, the weights of the trajectory following deviation rate and the brushing coverage overlap rate are 0.7 and 0.3, respectively.

[0075] S503. Calculate the distance between the first and last coordinates of adjacent trajectory segments, and use the sum of all distance values ​​as the trajectory motion smoothness.

[0076] The collaborative adaptation score integrates the deviation adaptation of vehicle parking offset and vehicle parking offset angle with the trajectory adaptation of the trajectory itself, corresponding to trajectory following deviation rate and brushing coverage overlap rate. It reflects the overall adaptation level of the trajectory in dealing with parking deviation. The smaller the value, the higher the matching degree between the vehicle parking deviation and the physical adaptation capability of the equipment, the better the fitting accuracy between the trajectory points and the vehicle outline, the fewer the overlaps or omissions of adjacent trajectory segments, and the better the overall adaptation effect. The larger the value, the more serious the trajectory itself's following deviation and coverage problems are, and the worse the overall adaptation performance.

[0077] The smoothness of trajectory movement reflects the continuity between adjacent trajectory segments, directly affecting the operating efficiency and stability of the intelligent car wash machine during washing operations. The smaller the value, the tighter the connection between adjacent trajectory segments, the shorter the travel distance during trajectory switching, the smoother the operation, and the higher the operating efficiency; the larger the value, the looser the connection between adjacent trajectory segments, the longer the trajectory switching travel distance, the possible stuttering or redundant travel during operation, the worse the smoothness, and the lower the operating efficiency.

[0078] S6. The candidate trajectory with the smallest weighted sum of cooperative adaptation and trajectory motion smoothness is adjusted and then washed as the final washed trajectory output.

[0079] The smoothness of the trajectory motion is standardized, and then weighted and summed with the cooperative adaptation to obtain a comprehensive value. The smaller the comprehensive value, the better the adaptation of the candidate adjusted washing trajectory is to the vehicle parking deviation, the more accurate the trajectory points are to fit the vehicle outline, the coverage is complete without omissions or excessive overlap, and the trajectory operation is smoother. Therefore, the candidate adjusted washing trajectory corresponding to the smallest comprehensive value is output as the final washing trajectory.

[0080] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0081] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0082] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0084] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for planning the brushing trajectory of an intelligent car wash machine, characterized in that, Includes the following steps: Based on the vehicle parking offset and the vehicle parking offset angle, deviation adaptation is performed to obtain the initial adapted brushing trajectory; Based on the initial adapted brushing trajectory, and combined with the effective brushing range of the brush head, the brush head trajectory point collision risk is checked to obtain the checked brushing trajectory. The calibration brushing trajectory is evaluated by measuring the geometric distance between brush head trajectory points and statistically analyzing the overlap length of adjacent trajectory segments. Combined with the effective brushing range of the brush head, the trajectory following deviation rate and brushing coverage overlap rate are obtained. Based on the trajectory following deviation rate and the brushing coverage overlap rate, the trajectory point coordinates and trajectory spacing of the verified brushing trajectory are adjusted to obtain alternative adjusted brushing trajectories. Based on vehicle parking offset, vehicle parking offset angle, trajectory following deviation rate, and brushing coverage overlap rate, the cooperative adaptability and trajectory movement smoothness of the alternative adjusted brushing trajectory are verified. The final brushed trajectory is obtained by adjusting the alternative trajectory with the minimum weighted sum of cooperative adaptation and trajectory motion smoothness.

2. The intelligent car wash machine brushing trajectory planning method according to claim 1, characterized in that, The process of obtaining the initial adapted brushing trajectory by performing deviation adaptation based on the vehicle parking offset and vehicle parking offset angle is as follows: The reference trajectory is adjusted by rotating around the vehicle's parking offset angle. If the rotation angle is greater than the maximum rotation angle, the brushing trajectory generation fails and a vehicle relocation alarm is issued; otherwise, the translation distance is based on the vehicle parking offset, and all brush head trajectory points of the base trajectory after translation and rotation are translated. If the translation distance is greater than the maximum translation distance, the brushing trajectory generation fails and a vehicle relocation alarm is issued; otherwise, a preliminary adapted brushing trajectory is obtained.

3. The intelligent car wash machine brushing trajectory planning method according to claim 1, characterized in that, The process of obtaining the verified brushing trajectory is as follows: Using each brush head trajectory point in the initial adaptation brushing trajectory as the origin, and the horizontal length of the effective brushing range of the brush head as the diameter, a collision detection area is constructed. If the collision detection area does not intersect with the vehicle outline, extend the scrubbing arm until the collision detection area intersects with the vehicle outline; If the collision detection area intersects with the vehicle outline, then determine the depth of the collision detection area intruding into the vehicle outline after the brush arm reaches its maximum lateral extension. If the intrusion depth exceeds the normal brushing distance of the brush head, there is a risk of collision, and an alarm requiring the vehicle to be moved will be issued; otherwise, the brushing trajectory after verification will be output.

4. The intelligent car wash machine brushing trajectory planning method according to claim 3, characterized in that, The process of determining the depth of intrusion is as follows: A coordinate system is established along the lateral working direction of the scrubbing arm. The outer boundary of the vehicle contour is used as the baseline. The maximum radial distance of the intersection area between the collision detection area and the vehicle contour is measured from the outer boundary of the vehicle contour to the inner side of the vehicle contour. The maximum radial distance is used as the intrusion depth.

5. The intelligent car wash machine brushing trajectory planning method according to claim 1, characterized in that, The process of obtaining the trajectory following deviation rate and the brushing coverage overlap rate is as follows: After each measurement and verification, the geometric distance from each brush head trajectory point in the brushing trajectory to the corresponding fitting position of the vehicle contour is measured; For each group of adjacent trajectory segments, the brushing coverage area of ​​each trajectory segment is constructed based on the brush head trajectory points of the two trajectory segments and the longitudinal radius of the effective brushing range of the brush head. The overlap length of the brushing coverage area corresponding to each group of adjacent trajectory segments is calculated. The arithmetic mean of the distances is calculated based on the geometric distances of all brush head trajectory points. The difference between the arithmetic mean of the distances and the normal brushing distance of the brush head is calculated. The ratio of the distance difference to the lateral length of the effective brushing range of the brush head is used as the trajectory following deviation rate. The arithmetic mean of the lengths is calculated based on the overlap lengths of all adjacent trajectory segments. The ratio of the arithmetic mean of the lengths to the longitudinal radius of the effective brushing range of the brush head is taken as the brushing coverage overlap rate.

6. The intelligent car wash machine brushing trajectory planning method according to claim 1, characterized in that, The process of determining the trajectory segment is as follows: Construct a brushing coverage area that matches the brush head, using the longitudinal radius of the effective brushing range of the brush head as the radius; A brushing coverage area is pre-set at the top and bottom of the vehicle outline, so that the brushing coverage area at the top is flush with the top of the vehicle outline, and the brushing coverage area at the bottom is flush with the bottom of the vehicle outline. Several brushing and washing areas are randomly arranged in the area not covered by the vehicle outline; The area covered by the brushing and washing motion is called the trajectory segment, which is the area that is translated along the horizontal direction of the vehicle's outline.

7. The intelligent car wash machine brushing trajectory planning method according to claim 1, characterized in that, The process of obtaining the alternative adjusted brushing trajectory is as follows: Based on the positive or negative or zero value of the trajectory following deviation rate, and whether the brushing coverage overlap rate is 0, combined with the boundary distance of the coverage area of ​​adjacent trajectory segments, the adjustment direction of the brush head trajectory points in the horizontal and vertical directions is determined. The adjustment distance combination of the brush head trajectory points in the horizontal and vertical directions is determined with the goal of minimizing the absolute values ​​of trajectory following deviation rate and brushing coverage overlap rate, resulting in several alternative adjusted brushing trajectories.

8. The intelligent car wash machine brushing trajectory planning method according to claim 7, characterized in that, The process of determining the adjustment direction of the brush head trajectory points in the horizontal and vertical directions is as follows: If the trajectory following deviation rate is less than 0, the brush head trajectory point is adjusted laterally in a direction away from the vehicle outline; if it is equal to 0, no adjustment is made; if it is greater than 0, it is adjusted in a direction closer to the vehicle outline. If the brushing coverage overlap rate is 0, then verify whether the boundary distance between the two brushing coverage areas of adjacent trajectory segments is 0. If the boundary distance is not 0, adjust in the direction of reducing the spacing between adjacent trajectory segments; if the boundary distance is 0, do not adjust. If the overlap rate of the brushing coverage is greater than 0, then adjust in the direction of increasing the spacing between adjacent trajectory segments.

9. The intelligent car wash machine brushing trajectory planning method according to claim 8, characterized in that, The process of determining the adjustment distance of the brush head trajectory point in the horizontal and vertical directions is as follows: The absolute value of the product of the trajectory following deviation rate and the lateral length of the effective brushing range of the brush head is used as the adjustment distance of the brush head trajectory point in the lateral direction. Keeping the trajectory segments at the top and bottom of the vehicle outline unchanged, randomly reassign and arrange several brushing coverage areas in the remaining area. Calculate the vertical distance of the brush head trajectory points based on the total number of trajectory segments, and use this distance as the longitudinal adjustment distance of the brush head trajectory points.

10. The intelligent car wash machine brushing trajectory planning method according to claim 1, characterized in that, The process of verifying the cooperative adaptability and trajectory motion smoothness of the adjusted alternative brushing trajectory is as follows: The deviation adaptation coefficient is obtained by weighted summing the ratio of vehicle parking offset to maximum translation distance and the ratio of vehicle parking offset angle to maximum rotation angle. The weighted sum of the trajectory following deviation rate and the brushing coverage overlap rate is used as the trajectory adaptation coefficient, and the sum with the deviation adaptation coefficient is used to obtain the co-fit degree. Calculate the distance between the first and last coordinates of adjacent trajectory segments, and use the sum of all distance values ​​as the trajectory motion smoothness.

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