Mechanical arm control method and system for coating of insulation coating of automotive traction battery

Abstract

This invention discloses a robotic arm control method and system for coating insulating coatings on automotive power batteries, belonging to the field of battery insulating coating coating control technology. By introducing a real-time coating thickness feedback mechanism, the coating process achieves "execution and perception simultaneously," enabling real-time monitoring of the actual deposition effect of the coating and avoiding the problem of thickness deviations not being detected in time in traditional systems. Particularly in complex edges, grooves, or protrusion structures of the power battery casing, the system can identify insufficient deposition caused by structural interference or posture deviations and provide accurate thickness data support. A path adaptive correction mechanism based on trajectory offset is constructed, converting thickness deviations into spatial correction values ​​and further synchronously adjusting the spraying speed. This system can achieve local fine-tuning of the spraying path and rhythm without pausing or manual intervention, effectively improving coating consistency and coverage integrity in corner transition areas.

Description

Technical Field

[0001] This invention relates to the field of battery insulating coating control technology, specifically to a robotic arm control method and system for coating insulating coatings on automotive power batteries. Background Technology

[0002] With the development of the new energy vehicle industry, power battery components generally need to be coated with an insulating protective coating before leaving the factory to improve electrical safety, corrosion resistance, and assembly reliability. On modern production lines, this coating process is usually completed by industrial spraying robotic arms. The spraying path is preset in the control system, and the actuator automatically completes the deposition of the insulating coating on the surface of each battery module.

[0003] However, existing power battery coating systems typically rely on static trajectory planning to complete the coating task. This means the robotic arm operates according to a preset path and a uniform speed strategy, lacking the ability to provide real-time feedback and adjustment based on the coating thickness. This approach has two significant drawbacks: First, for complex areas such as casing corners, structural bosses, and seam transitions, the coating angle and posture are difficult to match perfectly, easily leading to coating blind spots or insufficient coverage. Second, if environmental factors cause fluctuations in coating thickness, the system cannot autonomously identify and compensate, easily resulting in quality issues such as thinner edges and excessively thick areas, thus affecting the consistency of the battery module, the mounting effect, and long-term electrical performance.

[0004] The root cause of this situation is that the current system lacks the ability to dynamically adjust the trajectory based on real-time feedback thickness data. The robotic arm trajectory and spraying speed are determined before the task begins, and cannot respond to the coating status during execution. When there is a deviation between the actual deposition path of the spray gun and the workpiece surface, or when the spraying thickness fluctuates, the system cannot identify local anomalies or correct them on the spot, resulting in rigid execution of the spraying action. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a robotic arm control method and system for coating insulating coatings on automotive power batteries, thus solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: a robotic arm control system for coating insulating coatings on automotive power batteries, comprising a workpiece surface pre-mapping module, a coating thickness real-time feedback module, a trajectory deviation analysis module, a motion trajectory correction module, a spraying speed control module, and a result verification and stability evaluation module; the workpiece surface pre-mapping module establishes an initial spraying trajectory plan based on the three-dimensional geometric model of the power battery casing; The real-time coating thickness feedback module is based on spray trajectory planning. It collects the optical interference displacement difference Δλ and the incident angle rs through low-angle laser interference and calculates the coating thickness Dd. The trajectory deviation analysis module compares the obtained coating thickness Dd with the standard thickness Dref to obtain the trajectory offset ΔP; The motion trajectory correction module converts the trajectory offset ΔP into a trajectory correction value, obtains the corrected new trajectory coordinates (Xn, Yn), and corrects the spatial path of the robotic arm's end effector. After the spraying speed control module corrects the robotic arm, it simultaneously adjusts the spraying speed to obtain the corrected spraying speed Vadj. After obtaining the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the result verification and stability evaluation module calculates the thickness stability index Suni in real time.

[0007] Preferably, the workpiece surface pre-mapping module includes a geometric feature extraction unit and a trajectory path generation and attitude preset unit; The geometric feature extraction unit performs surface meshing on the 3D model of the power battery shell imported from CAD and visual recognition models, and extracts the geometric feature parameters of key structural points, including projected area As, boundary slope Ks, and degree of concavity and convexity deformation Kg. The projected area As is obtained as follows: In the 3D model, each surface unit has a normal vector. The normal vector is multiplied by the unit vector of the spray gun's main axis to obtain the cosine value of the angle. In fact, it is to find the "effective sprayed area ratio" of the surface in the spraying direction. The larger the dot product, the more fully the coating is deposited on the surface that is facing the spraying direction. The smaller the dot product result, the more likely the surface is skewed, and the coating may not be deposited evenly. In order to take only the "magnitude" and not the direction, the absolute value or vector magnitude of the dot product result is then taken; Then multiply the cosine of the included angle by the original area of ​​the grid cell to obtain the projected area As; The boundary slope Ks is obtained as follows: In the three-dimensional surface model of the power battery casing, each surface point has its spatial coordinates denoted as (x, y, z). Then, the spatial coordinates (x, y, z) are analyzed, and the derivatives of z with respect to x and z with respect to y are calculated respectively. These two results tell us whether the surface is inclined or flat in the x and y directions, and the absolute values ​​are taken. The direction of the surface inclination is ignored, and only the degree of inclination is considered. This ensures that the numerical value can accurately reflect the local inclination regardless of the orientation of the surface. Finally, the two absolute values ​​are added together to obtain the boundary slope Ks. The method for obtaining the degree of concavity / convexity deformation Kg is as follows: Take any specific mesh cell, and find a ring of adjacent mesh cells around the mesh cell as the center; then calculate the degree of difference between the normal vector of the central mesh and the normal vector of each neighboring mesh, and obtain the magnitude of all difference vectors; finally, add up the magnitudes of all difference vectors to get the total deviation, and divide it by the total number of all adjacent mesh cells to obtain the degree of concavity / convexity deformation Kg.

[0008] Preferably, the trajectory path generation and attitude preset unit constructs a spraying path point set in each target area based on the obtained projected area As, boundary slope Ks, and degree of concavity and convexity deformation Kg, and simultaneously generates the spray gun attitude angle pj corresponding to each path point. Plan the path points of the spraying path point concentration; The path points are based on surface feature points, and the trajectory points are automatically densified in the high-slope area of ​​the boundary to obtain the local path point spacing ΔS. The local path point spacing ΔS is obtained using the following formula: ; In the formula, ΔSbase represents the basic trajectory spacing, exp represents the exponential function, and s1 represents the slope adjustment coefficient; The spray gun attitude angle pj is obtained using the following formula: ; In the formula, arccos represents the inverse cosine function, pni represents the normal vector of the i-th grid cell, and pdsp represents the spray gun spraying direction vector; The initial spraying trajectory plan is obtained by fitting the acquired projected area As, boundary slope Ks, degree of concavity and convexity deformation Kg, local path point spacing ΔS, and spray gun attitude angle pj.

[0009] Preferably, the real-time coating thickness feedback module includes an interference signal acquisition and incident characteristic identification unit and a thickness calculation and error correction unit; The interference signal acquisition and incident characteristic identification unit uses an angled laser interference device fixed behind and to the side of the area through which the spray gun trajectory passes to quickly scan the current spraying area and acquire the optical interference displacement difference Δλ and the incident angle rs; The optical interference displacement difference Δλ is obtained by: forming interference fringes between the coating surface and the substrate by incident low-angle laser; the change of the fringes corresponds to the optical path difference, which in turn reflects the change of the optical thickness of the coating; the laser wavelength is the equipment calibration value, and the fringe movement distance is extracted by the image displacement processing algorithm; The incident angle rs is obtained by using an embedded inclinometer or a scanning mirror angle feedback mechanism; The thickness calculation and error correction unit inputs the obtained optical interference displacement difference Δλ and incident angle rs into the mathematical model, and calculates the coating thickness Dd by combining the refractive index zn of the coating. The coating thickness Dd is obtained using the following formula: ; In the formula, Dd(t) represents the coating thickness at time t, Δλ(t) represents the optical interference displacement difference at time t, cos represents the cosine function, and rs(t) represents the incident angle at time t.

[0010] Preferably, the trajectory deviation analysis module includes a thickness deviation ratio calculation unit and a trajectory correction factor generation unit; The thickness deviation ratio calculation unit performs difference analysis on the collected coating thickness Dd and the set standard thickness Dref, and normalizes it into a relative deviation rate Rd. The relative deviation rate Rd is obtained using the following formula: ; In the formula, ln represents the logarithmic function, and Rd(t) represents the relative deviation rate at time t.

[0011] Preferably, the trajectory correction factor generation unit combines the relative deviation rate Rd with the projected area As to obtain the trajectory offset ΔP, and compares it with the preset offset threshold Tp to determine the painting status of the robotic arm. The trajectory offset ΔP is calculated by multiplying the relative deviation rate Rd by the projected area As, and then dividing by the length of the current trajectory segment. The larger the projected area, the more important the area is, and the greater the impact of missed coating; therefore, compensation offset should be increased. The shorter the trajectory segment, the denser the local path, requiring fine-tuning compensation to avoid over-spraying; The coating condition is obtained using the following formula: When the trajectory offset ΔP < offset threshold Tp × 0.5, it indicates that the spraying is qualified and there is no deviation. Continue to execute the original trajectory without intervention. When the offset threshold Tp×0.5≤trajectory offset ΔP≤offset threshold Tp, it indicates a slight deviation that needs adjustment. Secondary spraying should not be triggered. Adjust the spray gun attitude angle pj and reduce the spraying speed. When the offset threshold Tp is less than the trajectory offset ΔP, it indicates a severe deviation, requiring additional spraying, and is marked as a respray point.

[0012] Preferably, the motion trajectory correction module includes a trajectory direction angle calculation unit and a coordinate correction execution unit; The trajectory direction angle calculation unit calculates the spraying direction angle Qo based on the displacement between the historical point and the target point of the current trajectory segment of the robotic arm; the spraying direction angle represents the spatial direction of the current spray gun movement, which is a prerequisite for projecting the trajectory offset ΔP onto the actual spatial coordinate system. The spraying direction angle Qo is obtained using the following formula: ; In the formula, arctan represents the arctangent function, (Xt, Yt) represents the coordinates of the trajectory point at time t, and (Xt-1, Yt-1) represents the coordinates of the trajectory point at time t-1. The coordinate correction execution unit decomposes the trajectory offset ΔP in a two-dimensional coordinate system based on the obtained spraying direction angle Qo and trajectory offset ΔP to obtain the corrected new trajectory coordinates (Xn, Yn). The new trajectory coordinates (Xn, Yn) are obtained using the following formula: Xn=Xt+ΔP(t)×cos(Qo(t)); Yn=Yt+ΔP(t)×cos(Qo(t)); In the formula, cos represents the cosine function.

[0013] Preferably, after updating the trajectory coordinates of the robotic arm's movement, the spraying speed control module adjusts the spraying speed to obtain the corrected spraying speed Vadj; The coating thickness Dd(t) at receiving time t is compared with the standard thickness Dref, and the relative thickness deviation xDd is calculated. The relative thickness deviation xDd is obtained by dividing the absolute value of the difference between the standard thickness Dref and the coating thickness Dd(t) at time t by the standard thickness Dref; The relative thickness deviation xDd is combined with the base spraying speed Vo to calculate and obtain the corrected spraying speed Vadj; The corrected spraying speed Vadj is obtained using the following formula: Vadj = Vo × exp(-xDd); In the formula, exp represents an exponential function.

[0014] Preferably, the result verification and stability evaluation module includes a periodic thickness sampling and recording unit and a thickness stability index calculation unit; After executing the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the periodic thickness sampling and recording unit collects the actual thickness value Dd(tx) within a fixed period T in real time and caches it to form a thickness time-series sample sequence. ; The thickness stability index calculation unit receives the thickness time-series sample sequence. The thickness stability index Suni is obtained by using the normalized deviation integral method to calculate the cumulative average of the thickness offset within a fixed period T, and the state of the coating is determined. The thickness stability index Suni is obtained using the following formula: ; In the formula, Suni(t) represents the thickness stability index at time t, and d represents the integral sign; The coating condition is obtained by matching in the following way: When the thickness stability index Suni < 0.05, it indicates that the coating thickness does not change and the deposition state is stable; When 0.05 ≤ thickness stability index Suni ≤ 0.1, it indicates that the coating thickness is uneven, which prolongs the dwell time of the next segment path; When 0.1 < the thickness stability index Suni, it indicates that the coating thickness is abnormal and triggers compensation strategies, including local area respraying, local path segment reconstruction, and fine adjustment of the spraying direction angle.

[0015] A robotic arm control method for applying insulating coatings to automotive power batteries includes the following steps: Step 1: The workpiece surface pre-mapping module establishes the initial spraying trajectory plan based on the three-dimensional geometric model of the power battery casing; Step 2: The real-time coating thickness feedback module, based on the spraying trajectory planning, collects the optical interference displacement difference Δλ and the incident angle rs through low-angle laser interference, and calculates the coating thickness Dd. Step 3: The trajectory deviation analysis module compares the obtained coating thickness Dd with the standard thickness Dref to obtain the trajectory offset ΔP. Step 4: The motion trajectory correction module converts the trajectory offset ΔP into a trajectory correction value, obtains the corrected new trajectory coordinates (Xn, Yn), and corrects the spatial path of the robotic arm's end effector. Step 5: After the spraying speed control module corrects the robotic arm, it simultaneously adjusts the spraying speed to obtain the corrected spraying speed Vadj. Step Six: Result Verification and Stability Evaluation Module. After obtaining the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the module calculates the thickness stability index Suni in real time.

[0016] This invention provides a robotic arm control method and system for coating insulating coatings on automotive power batteries, which has the following advantages: (1) During system operation, by introducing a real-time coating thickness feedback mechanism, the coating process is "executed and perceived simultaneously", which can monitor the actual deposition effect of the coating in real time and avoid the problem that thickness deviation cannot be detected in time in traditional systems. Especially in the complex edge, groove or boss structure area of ​​the power battery shell, the system can identify the insufficient deposition caused by structural interference or posture deviation and provide accurate thickness data support.

[0017] An adaptive path correction mechanism based on trajectory offset ΔP was constructed. By converting thickness deviation into spatial correction and further simultaneously adjusting the spraying speed, a dynamic and flexible control strategy was achieved. Compared with the rigid spraying method of traditional robotic arms with "fixed trajectory + constant speed", this system can achieve local fine-tuning of the spraying path and rhythm without pausing or manual intervention, effectively improving the coating consistency and coverage integrity in the corner transition area.

[0018] (2) By using the geometric feature extraction unit and the trajectory path generation and attitude preset unit, the system's ability to perceive complex geometric structures and its ability to adaptively generate paths are effectively enhanced, significantly improving the accuracy of path preparation before spraying the power battery casing. The geometric feature extraction unit can quantitatively analyze the minute geometric changes of the power battery casing by meshing the three-dimensional model, and construct three-dimensional structural factors including local projected area, boundary slope changes, and the degree of concavity and convexity deformation. These factors comprehensively characterize the key structural areas on the workpiece surface that may lead to uneven thickness during the spraying process. Through the accurate extraction of these structural parameters, the system no longer relies on manual experience or rule templates, and can automatically identify potentially difficult-to-spray areas such as complex transition surfaces, edge height difference areas, and deposition dead corners, thus improving the accuracy of spraying preparation.

[0019] (3) Through a structured hierarchical judgment system, the system can not only respond and intervene at the initial stage of deviation to prevent further spread of deviation, but also adaptively adjust the spray gun attitude angle and spraying speed according to the trajectory offset level, avoiding unnecessary repeated spraying and process waste. The overall process has stronger self-judgment ability and local response strategy adaptation ability, which greatly improves the robustness and uniformity guarantee ability of the spraying control system under different geometric contours and actual error disturbances. Thus, it realizes intelligent closed-loop feedback control of the whole process from "thickness data acquisition → deviation quantitative judgment → precise attitude and speed linkage control", effectively making up for the technical shortcomings of traditional spraying system such as feedback lag, trajectory rigidity and slow response, and ensuring that the quality of the insulating coating on the surface of the battery shell is more stable, the coverage is more uniform and the process control is more flexible.

[0020] (4) By setting up a motion trajectory correction module and a spraying speed control module, a two-way control path with offset direction decoupling and dynamic speed adjustment as the core is constructed, forming a complete closed loop of "trajectory correction - speed matching - error suppression". The motion trajectory correction module compares the spatial displacement of historical trajectory points and target spraying points through the trajectory direction angle calculation unit, and calculates the spraying direction angle in real time to ensure that subsequent trajectory correction operations can be decoupled according to the actual spatial path direction, effectively avoiding trajectory jumps or spray gun pose errors caused by blind linear correction; the coordinate correction execution unit decomposes the offset into vectors based on the obtained direction angle, and updates the trajectory coordinates on the XY axis respectively, ensuring the continuity and smoothness of the correction path in the spatial direction, thereby improving the motion stability of the end effector and significantly reducing the phenomenon of repeated, overlapping or dry spraying of the corner coating caused by spraying trajectory offset. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of the control system for the robotic arm used in the coating of insulating coatings for automotive power batteries according to the present invention. Figure 2 This is a schematic diagram of the steps of the robotic arm control method for coating insulating coatings on automotive power batteries according to the present invention; Figure 3 This is a schematic diagram illustrating the process of obtaining the thickness stability index according to the present invention. Figure 4 This is a line graph of the thickness stability index of the present invention. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example 1

[0023] This invention provides a robotic arm control system for coating insulating coatings on automotive power batteries. Please refer to [link to relevant documentation]. Figures 1 to 4 It includes a workpiece surface pre-mapping module, a coating thickness real-time feedback module, a trajectory deviation analysis module, a motion trajectory correction module, a spraying speed control module, and a result verification and stability evaluation module; the workpiece surface pre-mapping module establishes an initial spraying trajectory plan based on the three-dimensional geometric model of the power battery shell; The real-time coating thickness feedback module is based on spray trajectory planning. It collects the optical interference displacement difference Δλ and the incident angle rs through low-angle laser interference and calculates the coating thickness Dd. The trajectory deviation analysis module compares the obtained coating thickness Dd with the standard thickness Dref to obtain the trajectory offset ΔP; The motion trajectory correction module converts the trajectory offset ΔP into a trajectory correction value, obtains the corrected new trajectory coordinates (Xn, Yn), and corrects the spatial path of the robotic arm's end effector. After the spraying speed control module corrects the robotic arm, it simultaneously adjusts the spraying speed to obtain the corrected spraying speed Vadj. After obtaining the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the result verification and stability evaluation module calculates the thickness stability index Suni in real time.

[0024] In this embodiment, by introducing a real-time coating thickness feedback mechanism, the coating process is "executed and perceived simultaneously," enabling real-time monitoring of the actual deposition effect of the coating and avoiding the problem of thickness deviations not being detected in a timely manner in traditional systems. Especially in complex edge, groove, or protrusion structures of the power battery casing, the system can identify insufficient deposition caused by structural interference or posture deviations and provide accurate thickness data support.

[0025] An adaptive path correction mechanism based on trajectory offset ΔP was constructed. By converting thickness deviation into spatial correction and further simultaneously adjusting the spraying speed, a dynamic and flexible control strategy was achieved. Compared with the rigid spraying method of traditional robotic arms with "fixed trajectory + constant speed", this system can achieve local fine-tuning of the spraying path and rhythm without pausing or manual intervention, effectively improving the coating consistency and coverage integrity in the corner transition area.

[0026] Through result verification and thickness stability assessment mechanisms, the system automatically evaluates the stability of the deposition state after each spraying action, forming a three-in-one quality closed-loop control chain of "trajectory-thickness-speed". If the system detects that the coating thickness fluctuation exceeds the preset stability range, it can also trigger compensation actions or path re-optimization to further improve film uniformity and finished product consistency, reduce rework rate and defect rate, and ensure the long-term reliability and process consistency of the power battery insulation layer. Example 2

[0027] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 and Figure 3 Specifically: the workpiece surface pre-mapping module includes a geometric feature extraction unit and a trajectory path generation and attitude preset unit; The geometric feature extraction unit performs surface meshing on the 3D model of the power battery shell imported from CAD and visual recognition models, and extracts the geometric feature parameters of key structural points, including projected area As, boundary slope Ks, and degree of concavity and convexity deformation Kg. The projected area As is obtained as follows: In the 3D model, each surface unit has a normal vector. The normal vector is multiplied by the unit vector of the spray gun's main axis to obtain the cosine value of the angle. Then multiply the cosine of the included angle by the original area of ​​the grid cell to obtain the projected area As; The boundary slope Ks is obtained as follows: In the three-dimensional surface model of the power battery casing, each surface point has its spatial coordinate value denoted as (x, y, z). Then, the spatial coordinate value denoted as (x, y, z) is analyzed, and the derivative of z with respect to x and the derivative of z with respect to y are calculated respectively. The absolute values ​​are then taken, and the two absolute values ​​are added together to obtain the boundary slope Ks. The method for obtaining the degree of concavity / convexity deformation Kg is as follows: Take any specific mesh cell, and find a ring of adjacent mesh cells around the mesh cell as the center; then calculate the degree of difference between the normal vector of the central mesh and the normal vector of each neighboring mesh, and obtain the magnitude of all difference vectors; finally, add up the magnitudes of all difference vectors to get the total deviation, and divide it by the total number of all adjacent mesh cells to obtain the degree of concavity / convexity deformation Kg.

[0028] The trajectory path generation and attitude preset unit constructs a spraying path point set in each target area based on the obtained projected area As, boundary slope Ks, and degree of concavity and convexity deformation Kg, and simultaneously generates the spray gun attitude angle pj corresponding to each path point. Plan the path points of the spraying path point concentration; The path points are based on surface feature points, and the trajectory points are automatically densified in the high-slope area of ​​the boundary to obtain the local path point spacing ΔS. The local path point spacing ΔS is obtained using the following formula: ; In the formula, ΔSbase represents the basic trajectory spacing, exp represents the exponential function, and s1 represents the slope adjustment coefficient; The spray gun attitude angle pj is obtained using the following formula: ; In the formula, arccos represents the inverse cosine function, pni represents the normal vector of the i-th grid cell, and pdsp represents the spray gun spraying direction vector; The initial spraying trajectory plan is obtained by fitting the acquired projected area As, boundary slope Ks, degree of concavity and convexity deformation Kg, local path point spacing ΔS, and spray gun attitude angle pj.

[0029] In this embodiment, the geometric feature extraction unit and the trajectory path generation and attitude preset unit effectively enhance the system's ability to perceive complex geometric structures and its ability to adaptively generate paths, significantly improving the accuracy of path preparation before spraying the power battery casing. The geometric feature extraction unit, by meshing the 3D model, can quantitatively analyze the minute geometric changes of the power battery casing, constructing 3D structural factors including local projected area, boundary slope changes, and the degree of concavity and convexity deformation. These factors comprehensively characterize the key structural areas on the workpiece surface that may lead to uneven thickness during the spraying process. Through the precise extraction of these structural parameters, the system no longer relies on manual experience or rule templates, and can automatically identify potentially difficult-to-spray areas such as complex transition surfaces, edge height difference areas, and deposition dead corners, improving the accuracy of spraying preparation.

[0030] The trajectory path generation and attitude preset unit, based on the aforementioned characteristic parameters, establishes a trajectory point planning strategy according to the structural response density distribution. This strategy dynamically adjusts the path point spacing based on local boundary abrupt changes, ensuring a denser distribution of path points in areas such as edges and corners, resulting in more comprehensive deposition coverage. Simultaneously, based on the angle between the grid normal and the spray gun direction, this unit automatically calculates the optimal attitude angle of the spray gun at each path point, achieving precise control of the spraying direction and effectively preventing problems such as side spraying, missed spraying, or edge material buildup caused by spray gun deflection.

[0031] This embodiment realizes an integrated path pre-mapping process from "geometric perception → parameterized extraction → path adaptive generation → spray gun posture matching". It can make intelligent responses according to the microstructure changes on the battery shell surface, which significantly improves the adaptability of the coating path to the actual geometry. It provides a precise and highly matched starting path foundation for subsequent thickness feedback control and fine control, thereby improving the coating consistency in complex areas, reducing the occurrence rate of process defects in corner areas, and enhancing the stability and automation level of the whole machine coating system. Example 3

[0032] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically: the real-time coating thickness feedback module includes an interference signal acquisition and incident characteristic identification unit and a thickness calculation and error correction unit; The interference signal acquisition and incident characteristic identification unit uses an angled laser interference device fixed behind and to the side of the area through which the spray gun trajectory passes to quickly scan the current spraying area and acquire the optical interference displacement difference Δλ and the incident angle rs; The optical interference displacement difference Δλ is obtained by: forming interference fringes between the coating surface and the substrate by incident low-angle laser; the change of the fringes corresponds to the optical path difference, which in turn reflects the change of the optical thickness of the coating; the laser wavelength is the equipment calibration value, and the fringe movement distance is extracted by the image displacement processing algorithm; The incident angle rs is obtained by using an embedded inclinometer or a scanning mirror angle feedback mechanism; The thickness calculation and error correction unit inputs the obtained optical interference displacement difference Δλ and incident angle rs into the mathematical model, and calculates the coating thickness Dd by combining the refractive index zn of the coating. The coating thickness Dd is obtained using the following formula: ; In the formula, Dd(t) represents the coating thickness at time t, Δλ(t) represents the optical interference displacement difference at time t, cos represents the cosine function, and rs(t) represents the incident angle at time t.

[0033] The trajectory deviation analysis module includes a thickness deviation ratio calculation unit and a trajectory correction factor generation unit; The thickness deviation ratio calculation unit performs difference analysis on the collected coating thickness Dd and the set standard thickness Dref, and normalizes it into a relative deviation rate Rd. The relative deviation rate Rd is obtained using the following formula: ; In the formula, ln represents the logarithmic function, and Rd(t) represents the relative deviation rate at time t.

[0034] The trajectory correction factor generation unit combines the relative deviation rate Rd with the projected area As to obtain the trajectory offset ΔP, and compares it with the preset offset threshold Tp to determine the painting status of the robotic arm. The trajectory offset ΔP is obtained by multiplying the relative deviation rate Rd by the projected area As, and then dividing by the length of the current trajectory segment. The coating condition is obtained using the following formula: When the trajectory offset ΔP < offset threshold Tp × 0.5, it indicates that the spraying is qualified and there is no deviation. Continue to execute the original trajectory without intervention. When the offset threshold Tp×0.5≤trajectory offset ΔP≤offset threshold Tp, it indicates a slight deviation that needs adjustment. Secondary spraying should not be triggered. Adjust the spray gun attitude angle pj and reduce the spraying speed. When the offset threshold Tp is less than the trajectory offset ΔP, it indicates a severe deviation, requiring additional spraying, and is marked as a respray point.

[0035] In this embodiment, the thickness monitoring accuracy and trajectory adaptive control capability during the spraying process are further enhanced. By setting up a real-time coating thickness feedback module and a trajectory deviation analysis module, a closed-loop linkage is realized from laser interference signal acquisition and dynamic identification of the incident angle to thickness quantification analysis and deviation judgment, which greatly improves the system's timeliness and response accuracy in dealing with spraying anomalies such as thickening at corners, local material accumulation, and excessively thin edges.

[0036] The real-time coating thickness feedback module, through an interference signal acquisition and incident characteristic recognition unit, utilizes angled laser interferometers positioned on the sides and rear of the spray gun to perform non-contact scanning of the sprayed area. This not only rapidly acquires the optical interference displacement difference during coating thickness formation but also simultaneously obtains changes in the incident angle, eliminating the impact of incident angle disturbances on interference accuracy. Combined with an inclinometer or mirror feedback mechanism, this method ensures stronger geometric adaptability and environmental robustness in the real-time coating thickness sampling process, making it suitable for diverse curved surface structures on complex battery casings and significantly improving the accuracy of the feedback signal.

[0037] The thickness deviation ratio calculation unit normalizes the actual thickness and target thickness and introduces a logarithmic function to enhance the resolution of numerical differences, establishing a relative deviation rate index to quantify the real-time deviation of coating accuracy. Subsequently, the trajectory correction factor generation unit combines this deviation rate with the projected area parameter to construct a trajectory offset ΔP related to the actual geometric structure. Finally, based on the graded threshold setting, the current spraying state is determined, and three response strategies are classified: "normal tracking," "slight deviation adjustment," and "heavy deviation respray."

[0038] Through a structured, graded judgment system, the system can not only intervene at the initial stage of deviation to prevent further spread, but also adaptively adjust the spray gun attitude angle and spraying speed according to the trajectory deviation level, avoiding unnecessary repeated spraying and process waste. The overall process has stronger self-judgment capabilities and local response strategy adaptability, greatly improving the robustness and uniformity of the spraying control system under different geometric contours and actual error disturbances. This achieves intelligent closed-loop feedback control throughout the entire process, from "thickness data acquisition → quantitative deviation judgment → precise attitude and speed linkage control," effectively making up for the technical shortcomings of traditional spraying systems such as feedback lag, trajectory rigidity, and slow response, ensuring more stable quality, more uniform coverage, and more flexible process control of the insulating coating on the battery casing surface. Example 4

[0039] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 3 Specifically: the motion trajectory correction module includes a trajectory direction angle calculation unit and a coordinate correction execution unit; The trajectory direction angle calculation unit calculates the spraying direction angle Qo based on the displacement between the historical point and the target point of the current trajectory segment of the robotic arm; The spraying direction angle Qo is obtained using the following formula: ; In the formula, arctan represents the arctangent function, (Xt, Yt) represents the coordinates of the trajectory point at time t, and (Xt-1, Yt-1) represents the coordinates of the trajectory point at time t-1. The coordinate correction execution unit decomposes the trajectory offset ΔP in a two-dimensional coordinate system based on the obtained spraying direction angle Qo and trajectory offset ΔP to obtain the corrected new trajectory coordinates (Xn, Yn). The new trajectory coordinates (Xn, Yn) are obtained using the following formula: Xn=Xt+ΔP(t)×cos(Qo(t)); Yn=Yt+ΔP(t)×cos(Qo(t)); In the formula, cos represents the cosine function.

[0040] After updating the trajectory coordinates of the robotic arm's movement, the spraying speed control module adjusts the spraying speed to obtain the corrected spraying speed Vadj. The coating thickness Dd(t) at receiving time t is compared with the standard thickness Dref, and the relative thickness deviation xDd is calculated. The relative thickness deviation xDd is obtained by dividing the absolute value of the difference between the standard thickness Dref and the coating thickness Dd(t) at time t by the standard thickness Dref; The relative thickness deviation xDd is combined with the base spraying speed Vo to calculate and obtain the corrected spraying speed Vadj; The corrected spraying speed Vadj is obtained using the following formula: Vadj = Vo × exp(-xDd); In the formula, exp represents an exponential function.

[0041] In this embodiment, by setting up a motion trajectory correction module and a spraying speed control module, a bidirectional control path with offset direction decoupling and dynamic speed adjustment as the core is constructed, forming a complete closed loop of "trajectory correction - speed matching - error suppression". The motion trajectory correction module, through the trajectory direction angle calculation unit, compares the spatial displacement of historical trajectory points and target spraying points, and calculates the spraying direction angle in real time. This ensures that subsequent trajectory correction operations can be decoupled according to the actual spatial path direction, effectively avoiding trajectory jumps or spray gun pose errors caused by blind linear correction. Based on the obtained direction angle, the coordinate correction execution unit performs vector decomposition of the offset and updates the trajectory coordinates on the X and Y axes respectively, ensuring the continuity and smoothness of the correction path in the spatial direction. This improves the motion stability of the end effector and significantly reduces phenomena such as repeated, overlapping, or dry spraying of the edge coating caused by spraying trajectory offset.

[0042] While correcting the trajectory, the spraying speed control module performs real-time evaluation of the thickness error at various moments during the robotic arm's spraying process. Based on the dynamic functional relationship between the deviation value and the base speed, it constructs an adaptive speed adjustment model. When the deviation value increases, the system automatically reduces the spraying speed to extend the spraying dwell time and promote regional coating compensation; when the deviation decreases, the speed gradually recovers to prevent coating accumulation. This method does not rely on a fixed speed setting but instead constructs a continuous and self-inhibiting speed adjustment model, enabling the system to adaptively adjust the material release rhythm under different error levels, improving coating uniformity and surface consistency.

[0043] This embodiment, through dual decoupling control of trajectory spatial direction and motion speed, not only enhances the automatic compensation capability of the spraying path for offset error, but also realizes flexible rhythm control based on thickness dynamic feedback at the speed level. It effectively solves the technical problems of insufficient trajectory rigidity, lag in path recovery, and single speed response in the traditional robotic arm spraying process, further improving the intelligent level of coating stability control and providing key technical support for high uniformity insulating coating of complex workpiece structures. Example 5

[0044] This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 3 and Figure 4 Specifically: the result verification and stability evaluation module includes a periodic thickness sampling and recording unit and a thickness stability index calculation unit; After executing the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the periodic thickness sampling and recording unit collects the actual thickness value Dd(tx) within a fixed period T in real time and caches it to form a thickness time-series sample sequence. ; The thickness stability index calculation unit receives the thickness time-series sample sequence. The thickness stability index Suni is obtained by using the normalized deviation integral method to calculate the cumulative average of the thickness offset within a fixed period T, and the state of the coating is determined. The thickness stability index Suni is obtained using the following formula: ; In the formula, Suni(t) represents the thickness stability index at time t, and d represents the integral sign; Specific examples: Table 1: Calculation table of coating thickness stability index; Sample number Thickness sequence Dd(tx) (μm) Thickness stability index Suni Sample 1 [19.8, 20.1, 20.3, 19.7, 20.2] 0.011 Sample 2 [19.4, 20.5, 20.0, 19.8, 20.1] 0.014 Sample 3 [20.3, 20.4, 20.2, 19.9, 20.0] 0.010 Sample 4 [21.0, 19.0, 20.0, 19.5, 20.5] 0.030 Sample 5 [18.5, 21.0, 20.8, 19.2, 19.7] 0.044 The coating condition is obtained by matching in the following way: When the thickness stability index Suni < 0.05, it indicates that the coating thickness does not change and the deposition state is stable; When 0.05 ≤ thickness stability index Suni ≤ 0.1, it indicates that the coating thickness is uneven, which prolongs the dwell time of the next segment path; When 0.1 < thickness stability index Suni, it indicates that the coating thickness is abnormal, triggering a compensation strategy.

[0045] In this embodiment, by setting up a result verification and stability evaluation module, end-point feedback control and adaptive optimization closed-loop of the coating formation process are realized, effectively improving the coating deposition uniformity and control accuracy. After performing trajectory correction and spraying speed adjustment, this module relies on the periodic thickness sampling and recording unit to continuously sample the coating thickness of the new trajectory segment in real time within a fixed period T, constructing a time-series sample sequence of thickness changes. This ensures the continuous capture of thickness trends and time-domain stability analysis, providing data support for subsequent judgments.

[0046] The thickness stability index calculation unit introduces a normalized deviation integral method to accumulate the thickness fluctuation range within the cycle, thereby obtaining a thickness stability index that reflects the stability trend of the deposition process. This index can accurately characterize the fluctuation state of the coating formed after the spraying path is executed, and automatically assess whether there are thickness uniformity anomalies in the current spraying section based on the set multi-level judgment intervals. If the fluctuation amplitude is in a low range, the system maintains the current rhythm; if the fluctuation is close to the threshold, the path dwell time of the next section is adjusted in advance to achieve early compensation; and if the fluctuation is severely exceeded, respraying or trajectory fine-tuning is immediately triggered to avoid subsequent error amplification.

[0047] Through a chain-like judgment process of "real-time recording - normalization analysis - threshold matching - dynamic response", the system can complete a complete closed-loop assessment of thickness stability in each cycle and autonomously control the subsequent spraying strategy based on the feedback results. It does not rely on manual judgment by operators and effectively solves the problems of traditional spraying systems, such as the inability to perceive deposition uniformity in real time, slow response to thickness changes in corners or complex structural areas, and inability to intervene in advance. Example 6

[0048] For the robotic arm control method used for coating insulating coatings on automotive power batteries, please refer to... Figure 2 Specifically, it includes the following steps: Step 1: The workpiece surface pre-mapping module establishes the initial spraying trajectory plan based on the three-dimensional geometric model of the power battery casing; Step 2: The real-time coating thickness feedback module, based on the spraying trajectory planning, collects the optical interference displacement difference Δλ and the incident angle rs through low-angle laser interference, and calculates the coating thickness Dd. Step 3: The trajectory deviation analysis module compares the obtained coating thickness Dd with the standard thickness Dref to obtain the trajectory offset ΔP. Step 4: The motion trajectory correction module converts the trajectory offset ΔP into a trajectory correction value, obtains the corrected new trajectory coordinates (Xn, Yn), and corrects the spatial path of the robotic arm's end effector. Step 5: After the spraying speed control module corrects the robotic arm, it simultaneously adjusts the spraying speed to obtain the corrected spraying speed Vadj. Step Six: Result Verification and Stability Evaluation Module. After obtaining the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the module calculates the thickness stability index Suni in real time.

[0049] In this embodiment, a spraying control process with "feedback-correction-regulation-evaluation" as its core closed loop is constructed through six consecutive steps, fully demonstrating a data-driven adaptive optimization strategy. In step one, the structural parameters of the three-dimensional model of the power battery casing are extracted to construct an initial trajectory plan, achieving basic adaptability to complex casing surfaces. Steps two and three establish a high-precision response channel from real-time feedback of coating thickness to quantification of trajectory deviation, ensuring that the spraying effect in any area can be quantitatively evaluated. Steps four and five convert the deviation feedback into execution strategies for trajectory correction and spraying speed regulation, completing real-time correction of spraying behavior and synchronous updating of control commands.

[0050] In step six, a thickness stability index is introduced as a final evaluation indicator. Periodic sample sequence analysis is used to determine the stability of the spraying state, enabling refined monitoring of deposition consistency. This provides a basis for further dynamic scheduling and parameter adjustment of the system, demonstrating significant advantages in closed-loop process management. The overall method not only effectively improves the uniformity of coverage in edge areas, avoiding localized overspray or underspray, but also significantly improves the process consistency and product qualification rate of the insulating coating, exhibiting good engineering practicality and promotional value.

[0051] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A robotic arm control system for coating insulating coatings on automotive power batteries, characterized in that: It includes a workpiece surface pre-mapping module, a coating thickness real-time feedback module, a trajectory deviation analysis module, a motion trajectory correction module, a spraying speed control module, and a result verification and stability evaluation module; The workpiece surface pre-mapping module establishes an initial spraying trajectory plan based on the three-dimensional geometric model of the power battery casing; The real-time coating thickness feedback module is based on spray trajectory planning. It collects the optical interference displacement difference Δλ and the incident angle rs through low-angle laser interference and calculates the coating thickness Dd. The trajectory deviation analysis module compares the obtained coating thickness Dd with the standard thickness Dref to obtain the trajectory offset ΔP; The motion trajectory correction module converts the trajectory offset ΔP into a trajectory correction value, obtains the corrected new trajectory coordinates (Xn, Yn), and corrects the spatial path of the robotic arm's end effector. After the spraying speed control module corrects the robotic arm, it simultaneously adjusts the spraying speed to obtain the corrected spraying speed Vadj. After obtaining the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the result verification and stability evaluation module calculates the thickness stability index Suni in real time.

2. The robotic arm control system for coating insulating coatings on automotive power batteries according to claim 1, characterized in that: The workpiece surface pre-mapping module includes a geometric feature extraction unit and a trajectory path generation and attitude preset unit; The geometric feature extraction unit performs surface meshing on the 3D model of the power battery shell imported from CAD and visual recognition models, and extracts the geometric feature parameters of key structural points, including projected area As, boundary slope Ks, and degree of concavity and convexity deformation Kg. The projected area As is obtained as follows: In the 3D model, each surface unit has a normal vector. The normal vector is multiplied by the unit vector of the spray gun's main axis to obtain the cosine value of the angle. Then multiply the cosine of the included angle by the original area of ​​the grid cell to obtain the projected area As; The boundary slope Ks is obtained as follows: In the three-dimensional surface model of the power battery casing, each surface point has its spatial coordinate value denoted as (x, y, z). Then, the spatial coordinate value denoted as (x, y, z) is analyzed, and the derivative of z with respect to x and the derivative of z with respect to y are calculated respectively. The absolute values ​​are then taken, and the two absolute values ​​are added together to obtain the boundary slope Ks. The method for obtaining the degree of concavity / convexity deformation Kg is as follows: Take any specific mesh cell, and find a ring of adjacent mesh cells around the mesh cell as the center; then calculate the degree of difference between the normal vector of the central mesh and the normal vector of each neighboring mesh, and obtain the magnitude of all difference vectors; finally, add up the magnitudes of all difference vectors to get the total deviation, and divide it by the total number of all adjacent mesh cells to obtain the degree of concavity / convexity deformation Kg.

3. The robotic arm control system for coating insulating coatings on automotive power batteries according to claim 2, characterized in that: The trajectory path generation and attitude preset unit constructs a spraying path point set in each target area based on the obtained projected area As, boundary slope Ks, and degree of concavity and convexity deformation Kg, and simultaneously generates the spray gun attitude angle pj corresponding to each path point. Plan the path points of the spraying path point concentration; The path points are based on surface feature points, and the trajectory points are automatically densified in the high-slope area of ​​the boundary to obtain the local path point spacing ΔS. The local path point spacing ΔS is obtained using the following formula: ； In the formula, ΔSbase represents the basic trajectory spacing, exp represents the exponential function, and s1 represents the slope adjustment coefficient; The spray gun attitude angle pj is obtained using the following formula: ； In the formula, arccos represents the inverse cosine function, pni represents the normal vector of the i-th grid cell, and pdsp represents the spray gun spraying direction vector; The initial spraying trajectory plan is obtained by fitting the acquired projected area As, boundary slope Ks, degree of concavity and convexity deformation Kg, local path point spacing ΔS, and spray gun attitude angle pj.

4. The robotic arm control system for coating insulating coatings on automotive power batteries according to claim 3, characterized in that: The real-time coating thickness feedback module includes an interference signal acquisition and incident characteristic identification unit and a thickness calculation and error correction unit; The interference signal acquisition and incident characteristic identification unit uses an angled laser interference device fixed behind and to the side of the area through which the spray gun trajectory passes to quickly scan the current spraying area and acquire the optical interference displacement difference Δλ and the incident angle rs; The optical interference displacement difference Δλ is obtained by: forming interference fringes between the coating surface and the substrate by incident low-angle laser; the change of the fringes corresponds to the optical path difference, which in turn reflects the change of the optical thickness of the coating; the laser wavelength is the equipment calibration value, and the fringe movement distance is extracted by the image displacement processing algorithm; The incident angle rs is obtained by using an embedded inclinometer or a scanning mirror angle feedback mechanism; The thickness calculation and error correction unit inputs the obtained optical interference displacement difference Δλ and incident angle rs into the mathematical model, and calculates the coating thickness Dd by combining the refractive index zn of the coating. The coating thickness Dd is obtained using the following formula: ； In the formula, Dd(t) represents the coating thickness at time t, Δλ(t) represents the optical interference displacement difference at time t, cos represents the cosine function, and rs(t) represents the incident angle at time t.

5. The robotic arm control system for coating insulating coatings on automotive power batteries according to claim 4, characterized in that: The trajectory deviation analysis module includes a thickness deviation ratio calculation unit and a trajectory correction factor generation unit; The thickness deviation ratio calculation unit performs difference analysis on the collected coating thickness Dd and the set standard thickness Dref, and normalizes it into a relative deviation rate Rd. The relative deviation rate Rd is obtained using the following formula: ； In the formula, ln represents the logarithmic function, and Rd(t) represents the relative deviation rate at time t.

6. The robotic arm control system for coating insulating coatings on automotive power batteries according to claim 5, characterized in that: The trajectory correction factor generation unit combines the relative deviation rate Rd with the projected area As to obtain the trajectory offset ΔP, and compares it with the preset offset threshold Tp to determine the painting status of the robotic arm. The trajectory offset ΔP is obtained by multiplying the relative deviation rate Rd by the projected area As, and then dividing by the length of the current trajectory segment. The coating condition is obtained using the following formula: When the trajectory offset ΔP < offset threshold Tp × 0.5, it indicates that the spraying is qualified and there is no deviation. Continue to execute the original trajectory without intervention. When the offset threshold Tp×0.5≤trajectory offset ΔP≤offset threshold Tp, it indicates a slight deviation that needs adjustment. Secondary spraying should not be triggered. Adjust the spray gun attitude angle pj and reduce the spraying speed. When the offset threshold Tp is less than the trajectory offset ΔP, it indicates a severe deviation, requiring additional spraying, and is marked as a respray point.

7. The robotic arm control system for coating insulating coatings on automotive power batteries according to claim 6, characterized in that: The motion trajectory correction module includes a trajectory direction angle calculation unit and a coordinate correction execution unit; The trajectory direction angle calculation unit calculates the spraying direction angle Qo based on the displacement between the historical point and the target point of the current trajectory segment of the robotic arm; The spraying direction angle Qo is obtained using the following formula: ； In the formula, arctan represents the arctangent function, (Xt, Yt) represents the coordinates of the trajectory point at time t, and (Xt-1, Yt-1) represents the coordinates of the trajectory point at time t-1. The coordinate correction execution unit decomposes the trajectory offset ΔP in a two-dimensional coordinate system based on the obtained spraying direction angle Qo and trajectory offset ΔP to obtain the corrected new trajectory coordinates (Xn, Yn). The new trajectory coordinates (Xn, Yn) are obtained using the following formula: Xn=Xt+ΔP(t)×cos(Qo(t)); Yn=Yt+ΔP(t)×cos(Qo(t)); In the formula, cos represents the cosine function.

8. The robotic arm control system for coating insulating coatings on automotive power batteries according to claim 7, characterized in that: After updating the trajectory coordinates of the robotic arm's movement, the spraying speed control module adjusts the spraying speed to obtain the corrected spraying speed Vadj. The coating thickness Dd(t) at receiving time t is compared with the standard thickness Dref, and the relative thickness deviation xDd is calculated. The relative thickness deviation xDd is obtained by dividing the absolute value of the difference between the standard thickness Dref and the coating thickness Dd(t) at time t by the standard thickness Dref; The relative thickness deviation xDd is combined with the base spraying speed Vo to calculate and obtain the corrected spraying speed Vadj; The corrected spraying speed Vadj is obtained using the following formula: Vadj = Vo × exp(-xDd); In the formula, exp represents an exponential function.

9. The robotic arm control system for coating insulating coatings on automotive power batteries according to claim 8, characterized in that: The result verification and stability evaluation module includes a periodic thickness sampling and recording unit and a thickness stability index calculation unit. After executing the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the periodic thickness sampling and recording unit collects the actual thickness value Dd(tx) within a fixed period T in real time and caches it to form a thickness time-series sample sequence. ; The thickness stability index calculation unit receives the thickness time-series sample sequence. The thickness stability index Suni is obtained by using the normalized deviation integral method to calculate the cumulative average of the thickness offset within a fixed period T, and the state of the coating is determined. The thickness stability index Suni is obtained using the following formula: ； In the formula, Suni(t) represents the thickness stability index at time t, and d represents the integral sign; The coating condition is obtained by matching in the following way: When the thickness stability index Suni < 0.05, it indicates that the coating thickness does not change and the deposition state is stable; When 0.05 ≤ thickness stability index Suni ≤ 0.1, it indicates that the coating thickness is uneven, which prolongs the dwell time of the next segment path; When 0.1 < thickness stability index Suni, it indicates that the coating thickness is abnormal, triggering a compensation strategy.

10. A robotic arm control method for coating insulating coatings on automotive power batteries, applied to the robotic arm control system for coating insulating coatings on automotive power batteries as described in any one of claims 1 to 9, characterized in that: Includes the following steps: Step 1: The workpiece surface pre-mapping module establishes the initial spraying trajectory plan based on the three-dimensional geometric model of the power battery casing; Step 2: The real-time coating thickness feedback module, based on the spraying trajectory planning, collects the optical interference displacement difference Δλ and the incident angle rs through low-angle laser interference, and calculates the coating thickness Dd. Step 3: The trajectory deviation analysis module compares the obtained coating thickness Dd with the standard thickness Dref to obtain the trajectory offset ΔP. Step 4: The motion trajectory correction module converts the trajectory offset ΔP into a trajectory correction value, obtains the corrected new trajectory coordinates (Xn, Yn), and corrects the spatial path of the robotic arm's end effector. Step 5: After the spraying speed control module corrects the robotic arm, it simultaneously adjusts the spraying speed to obtain the corrected spraying speed Vadj. Step Six: Result Verification and Stability Evaluation Module. After obtaining the new trajectory coordinates (Xn, Yn) and correcting the spraying speed Vadj, the module calculates the thickness stability index Suni in real time.

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