A robotic arm control system and control method for a photovoltaic panel intelligent installation robot

Abstract

The present invention provides a robotic arm control system and control method for an intelligent photovoltaic panel installation robot, relating to the technical field of photovoltaic equipment installation. The method constructs a three-dimensional point cloud map and identifies the curvature characteristics of the obstacle surface, while using a magnetic encoder to feedback the joint angle and monitoring the normal pressure and tangential friction through sensors; when the normal pressure exceeds the threshold, the inverse displacement compensation amount is calculated based on inverse dynamics, a compensation instruction is generated, and a safe approach direction is determined in combination with the curvature characteristics; the joint angular velocity and torque threshold are adjusted in real time through a nonlinear sliding mode control algorithm to generate a smooth joint target torque curve, ensuring that the robotic arm stably tracks the path and the end friction is controlled. In abnormal situations, the system triggers an alarm and freezes the robotic arm until the operator confirms the compensation strategy. The present invention improves the control accuracy and adaptability of the robotic arm, effectively avoids damage to photovoltaic panels, and improves installation efficiency and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic equipment installation, and in particular to a robotic arm control system and a control method for an intelligent photovoltaic panel installation robot. Background Art

[0002] During the intelligent installation of photovoltaic panels, the precise control and reliable operation of the robotic arm are key factors in determining installation efficiency and equipment safety. However, existing technologies face multiple challenges in practical applications. The robotic arm struggles to quickly perceive and analyze features in specialized installation environments, making it unable to dynamically adapt to continuous environmental changes, which can easily lead to trajectory deviations or mechanical collisions. The interactive control of the end effector during contact with the photovoltaic panel has significant shortcomings, as traditional methods lack the ability to adjust the contact force in a closed-loop, which can easily cause damage to the panel surface or installation failure. Summary of the Invention

[0003] To achieve the above objectives, the present invention is implemented through the following technical solutions: A robotic arm control system for a photovoltaic panel intelligent installation robot, comprising:

[0004] Sensor module: A 3D force sensor detects the normal pressure and tangential friction when the end effector contacts the photovoltaic panel; a magnetic encoder provides real-time feedback of joint angle data; and a binocular vision sensor constructs a 3D point cloud map of the photovoltaic panel installation area and identifies the curvature of the obstacle surface.

[0005] The central processing module processes the data collected by the sensor module; calculates the safe approach direction of the manipulator end effector based on the identified obstacle surface curvature; compares the normal pressure with the preset photovoltaic panel pressure threshold, and generates a reverse pressure compensation instruction when the normal pressure exceeds the threshold; based on the joint angle data, combined with the safe approach direction and reverse pressure compensation instruction, the anti-slip motion parameters of the manipulator joint are generated through the Jacobian matrix pseudo-inverse method and the dynamic damping coefficient;

[0006] Path planning module: Calculates the target motion parameters of each joint based on the anti-slip motion parameters of the robot arm joints, and corrects the joint angular velocity anti-shake amount and the dynamic suppression coefficient of the end effector tangential friction force according to the joint angle feedback to generate path planning instructions;

[0007] Drive module: A harmonic reducer and torque feedback servo motor directly connected to the robot arm joints. The harmonic reducer adjusts the output torque according to the path planning instructions to track the planned path. The torque feedback servo motor limits tangential friction based on the dynamic suppression coefficient and adjusts the motor speed according to the path trajectory. The dynamic suppression coefficient is used to construct a hysteresis range for the servo motor torque threshold, tightening the threshold during the friction rising phase and relaxing it during the friction falling phase.

[0008] Human-computer interaction module: triggers an alarm when the path planning instructions of the robot arm are abnormal, and receives the emergency reverse compensation input by the operator to override the path planning instructions.

[0009] Furthermore, the central processing module performs spatial discretization on the point cloud data based on the three-dimensional point cloud map of the photovoltaic panel installation area constructed by the binocular vision sensor, dividing the continuous surface into a dense discrete point set; for each discrete point, all point cloud data within a 5cm radius neighborhood around it are extracted to construct a local neighborhood covariance matrix, and three orthogonal eigenvectors and corresponding eigenvalues ​​are obtained through eigenvalue decomposition; the principal curvature is determined by the direction of the eigenvector corresponding to the minimum eigenvalue, which represents the maximum curvature of the local surface, and the curvature direction is defined by the first principal component eigenvector of the covariance matrix, reflecting the main extension trend of the surface; the distance between adjacent discrete points is determined by the direction of the eigenvector corresponding to the minimum eigenvalue, which represents the maximum curvature of the local surface, and the curvature direction is defined by the first principal component eigenvector of the covariance matrix, which reflects .... The curvature change rate is obtained by calculating the ratio of the difference between the principal curvatures of two points to the Euclidean distance, and then a curvature gradient field is constructed along the surface to quantify the continuous change characteristics of the curvature in space. The boundary of the curvature gradient mutation area is identified by detecting discrete point clusters in the gradient field whose change rate exceeds a preset threshold, forming the outline of the curvature discontinuity area. After establishing a local coordinate system with the current position of the end effector as the origin, the global curvature gradient field is projected into the coordinate system, and the initial approximation direction vector is decomposed into the normal component of the curvature gradient direction and the tangential component of the tangent plane direction. By calculating the curvature gradient change rate in each direction in the tangent plane, the curvature gradient with a change rate of less than 0.1 mm is screened out. -2 The tangential component of the curvature gradient is selected as the candidate safe direction; finally, the tangential path with the smallest curvature gradient direction component and the lowest rate of change is selected as the safe approach direction, so that the end effector approaches the photovoltaic panel along a trajectory with a gentle change in surface curvature.

[0010] Furthermore, after receiving the normal pressure data of the end effector detected by the three-dimensional force sensor, the central processing module calculates the deviation ΔP between the current pressure and the preset photovoltaic panel pressure threshold; when ΔP exceeds the safety threshold, the pressure difference is converted into the reverse displacement compensation ΔX of the end effector along the normal direction based on inverse dynamics, and an instruction containing the compensation direction and amplitude is generated; in order to achieve accurate mapping of the end compensation amount to the joint space, the inverse kinematics solution is performed through the pseudo-inverse method of the Jacobian matrix: based on the current joint angle θ fed back in real time by the magnetic encoder, the Jacobian matrix J of the robot arm is constructed, and J characterizes the differential sensitivity of the end posture change to the joint angle through the geometric parameters and kinematic calculations of each joint; then the pseudo-inverse matrix J of the Jacobian matrix is ​​calculated by singular value decomposition + damped , and introduce the dynamic damping coefficient λ to construct the damped pseudo-inverse , in order to suppress the numerical divergence under singular configurations, where J T is the transpose of the Jacobian matrix; by the formula , the terminal reverse displacement compensation ΔX is mapped to the joint angle increment ΔΨ, thereby establishing a dynamic correlation between the joint angle and the terminal pressure.

[0011] Furthermore, when dynamically adjusting the upper limit of joint angular velocity, a pressure-angular velocity proportional attenuation relationship is proposed, and the normal pressure change rate ΔP / Δt is calculated in real time, which is the average change of the current pressure deviation ΔP and the time window Δt; then, based on the initial angular velocity upper limit ω0 and the experimentally calibrated attenuation coefficient k, Dynamically correct the upper limit of joint angular velocity; k, the upper limit of the corrected joint angular velocity, is determined through calibration experiments. The k value is adjusted under typical pressure mutation scenarios so that the joint movement can still stably track the path after deceleration and the end friction force does not exceed the limit; at the same time, combined with the deviation between the actual joint angle and the target angle fed back by the magnetic encoder, the angular velocity anti-shake value is generated through sliding mode control to suppress high-frequency oscillation; and based on the end tangential friction force data fed back by the three-dimensional force sensor, the friction force and torque mapping relationship is constructed, and the torque threshold of the servo motor is adjusted in real time; finally, smooth anti-slip motion parameters are generated through multi-parameter collaborative optimization, so that the end friction force of the robot arm is controlled and the motion trajectory is stable during the reverse pressure compensation process.

[0012] Furthermore, the path planning module solves the target motion trajectory of each joint based on the target joint angle and angular velocity in the anti-slip motion parameters of the manipulator joint generated by the central processing module, and generates an initial joint target angle sequence and a corresponding angular velocity sequence through the inverse kinematics of the manipulator; the friction force trend relationship is designed to clarify the generation of the dynamic suppression coefficient. First, the tangential friction force data of the end effector fed back by the three-dimensional force sensor is read in real time, and the friction force change rate Δf / Δt is calculated, whose value is the current friction force f t Friction force f with the previous timestamp t-1 The difference between the two values ​​is divided by the sampling time interval Δt. If Δf / Δt exceeds the preset threshold for three consecutive times, it is determined that the friction force is in an abnormal growth trend, triggering the increase of the dynamic suppression coefficient. The update formula of the dynamic suppression coefficient C is: , where C0 is the initial suppression coefficient and α is the growth factor determined by calibration experiments. The calibration method is to adjust the α value in a typical friction force mutation scenario so that the suppression coefficient can quickly respond to the friction force exceeding the limit while avoiding the hysteresis of the robot arm movement due to excessive suppression.

[0013] Furthermore, the segmented cubic Hermite interpolation method is used in trajectory interpolation to smoothly generate the joint angle sequence; the joint angular velocity sequence after anti-shake is aligned with the target angle sequence according to the timestamp, and discrete data points (t i ,θ i ,ω i ), where t i is the timestamp, θi is the joint target angle, ω i is the angular velocity after anti-shake; a cubic polynomial function is constructed between every two adjacent data points to ensure that the function value and the first-order derivative (angular velocity) at the interpolation point are continuous and the data is monotonic to avoid overshoot; the polynomial coefficients are determined by solving the boundary condition equations, for example, in the time interval [t i ,t i+1 ], the interpolation function H(t) must satisfy H(t i )=θ i , represents time t i The target joint angle, H(t i+1 )=θ i+1 , represents time t i+1 The target joint angle, H ’ (t i )=ω i , represents time t i Angular velocity after image stabilization, H ’ (t i+1 )=ω i+1 , represents time t i+1 The angular velocity after anti-shake is obtained; the dynamic suppression coefficient C is mapped to the interpolation trajectory according to the timestamp to form a continuous path instruction containing joint angle, angular velocity and suppression coefficient, that is, the path planning instruction; in the final generated instruction, each timestamp is associated with a specific joint angle value H(t), angular velocity limit H ’ (t) and torque suppression threshold C(t), which are output to the harmonic reducer and torque feedback servo motor of the drive module. The harmonic reducer is controlled to adjust the output torque according to the interpolation trajectory. At the same time, the servo motor limits the tangential friction force in real time based on C(t) to ensure motion smoothness and terminal stability.

[0014] Furthermore, the harmonic reducer in the drive module receives the joint angle sequence and corresponding timestamp in the path planning instruction, analyzes the target joint angle value of the current timestamp in real time, combines the actual joint angle feedback from the magnetic encoder, calculates the angle deviation and determines the adjustment amount of the output torque based on the preset torque and angle deviation, and drives the harmonic reducer to output a torque matching the target angle to track the planned path; the torque feedback servo motor reads the dynamic suppression coefficient and angular velocity limit in the path planning instruction, and dynamically adjusts the motor speed according to the angular velocity limit of the current timestamp to synchronize it with the angular velocity requirement of the path trajectory; during the adjustment process, based on the dynamic suppression coefficient and the real-time detected end tangential friction force data, the torque control algorithm is used to limit the output torque of the servo motor to within the threshold range corresponding to the suppression coefficient. When the friction force exceeds the threshold, the output torque is reduced in proportion to the suppression coefficient to limit the tangential friction force.

[0015] Furthermore, after the harmonic reducer in the driving module receives the joint angle sequence and corresponding timestamp in the path planning instruction, it analyzes the target joint angle value θ at the current timestamp. target , combined with the actual joint angle θ fed back by the magnetic encoder actual , calculate the angle deviation ; Based on the preset torque and angle deviation, , where Δτ is the output torque adjustment, K p , K i , K d The control parameters of the harmonic reducer based on torque and angle calibration are: ∫Δθdt is the integral term of the angle deviation, which is used to eliminate the steady-state error; dΔθ / dt is the differential term of the angle deviation, which is used to predict the change trend; the calibration method is to apply step torque to the harmonic reducer under no-load and load conditions and record the angle response curve, and obtain the proportional coefficient K by least squares fitting. p , integral coefficient K i and differential coefficient K d , so that the linear response relationship between Δτ and Δθ; drive the harmonic reducer to output the adjusted torque τ=τ prev +Δτ, so that the actual angle tracks the target angle, where τ prev Indicates the output torque at the previous moment.

[0016] Furthermore, the torque feedback servo motor synchronously reads the dynamic suppression coefficient C and angular velocity limit in the path planning instruction, and according to the ω of the current timestamp max Adjust the motor speed to synchronize it with the trajectory angular velocity requirement; in the torque control process, the dynamic suppression coefficient C is used to constrain the proportional gain K when C increases. p new=K p / (1+β·C), where K p new represents the corrected proportional gain, β is the attenuation factor calibrated by the friction force over-limit experiment, which is used to balance the torque response speed and stability; at the same time, the end tangential friction force f based on the feedback of the three-dimensional force sensor is t , calculate the current torque threshold τ through the mapping relationship limit , specifically , where κ is the torque friction conversion coefficient, f max is the preset safety threshold, C is the dynamic suppression coefficient; if the friction force f t If the threshold is exceeded, press Limit the output torque to avoid the tangential friction force from getting out of control, where τ demand Represents the required torque calculated based on the robot arm motion requirements, τ limitIt represents the torque threshold calculated based on the dynamic suppression coefficient and the real-time detected end tangential friction data; the harmonic reducer adjusts the torque to drive the joint movement, and the servo motor dynamically adjusts the output according to the torque threshold, so that the end tangential friction is strictly controlled by the dynamic suppression coefficient while the robot arm tracks the path; the hysteresis interval of the servo motor torque threshold is constructed based on the real-time value of the dynamic suppression coefficient C. , tighten the threshold during the friction rising stage and relax the threshold during the friction falling stage, avoiding the frequent torque jumps caused by the traditional fixed threshold.

[0017] A method for controlling a robotic arm of a photovoltaic panel intelligent installation robot, comprising:

[0018] The S1 uses binocular vision sensors to scan the photovoltaic panel installation area and extract the curvature characteristics of the obstacle surface. Meanwhile, a magnetic encoder provides real-time feedback on the joint rotation angle of the robotic arm, and a three-dimensional force sensor obtains the normal pressure and tangential friction of the end effector in real time.

[0019] S2 calculates the approach path of the robot end effector along the normal direction of the photovoltaic panel surface according to the curvature characteristics of the obstacle surface and generates a safe approach angle;

[0020] S3 generates path planning instructions through dynamic pressure compensation and anti-slip motion parameters. The specific steps include:

[0021] S31: When the normal pressure exceeds the preset PV panel pressure threshold, the reverse compensation pressure is calculated according to the excess ratio, and a reverse pressure compensation instruction is generated;

[0022] S32 generates the anti-slip motion parameters of the robot arm joints based on the safe approach angle and reverse pressure compensation instructions, combined with the joint angle data of the magnetic encoder;

[0023] S33 uses a nonlinear sliding mode control algorithm to calculate the target motion parameters of the manipulator joint, including the anti-shake amount and the dynamic suppression coefficient of the tangential friction force, and generates path planning instructions;

[0024] S4 drives the torque feedback servo motor according to the path planning instructions, controls the robot arm to move along the planned path, and limits the tangential friction force to a preset safety range;

[0025] S5 When the normal pressure exceeds the limit continuously for more than the set number of times, the tangential friction force exceeds the preset safety range or the end effector posture deviation exceeds the allowable tolerance, and the deviation between the actual torque of the robot arm joint and the target torque continues to exceed the dynamic threshold, an alarm is triggered and the robot arm movement is frozen until the operator confirms the compensation strategy through the human-machine safety interaction module.

[0026] Furthermore, in the design of the nonlinear sliding mode control algorithm, based on the actual joint angle fed back by the magnetic encoder and the target angle generated by the path planning module, the system defines the tracking error of the joint angle as the difference between the actual angle and the target angle, and further calculates its time derivative as the angular velocity error; the sliding surface function is constructed by linearly combining the angle error and the angular velocity error in a specific weight ratio, where the weight coefficient of the angular velocity error is dynamically determined by the Lyapunov stability criterion, so that the motion trajectory of the system on the sliding surface can quickly converge to the equilibrium point; in order to suppress the high-frequency chattering caused by the sign function in traditional sliding mode control, the sign function is replaced by a saturation function, and its boundary value is dynamically adjusted by the anti-shake amount provided by the path planning module; when the tangential friction force of the end effector is fed back in real time through the three-dimensional force sensor and a sudden change trend is detected, the anti-shake amount is adaptively scaled according to the friction force change rate, thereby dynamically widening the boundary of the saturation function, effectively smoothing the torque output while maintaining tracking accuracy;

[0027] Furthermore, by collecting the terminal tangential friction force data in real time and calculating its rate of change, the rate of change is compared with the preset threshold to generate a nonlinear adjustment factor. The adjustment factor is inversely correlated with the sliding mode switching gain. When the friction force increases, the adjustment factor automatically attenuates the switching gain, reduces the sudden change amplitude of the control quantity, and avoids torque oscillations caused by external disturbances; the sliding surface function, convergence law and dynamic suppression coefficient are integrated, and a smooth joint target torque curve is generated through multi-variable collaborative solution; this curve is further mapped to the torque suppression threshold of the path planning instruction, driving the harmonic reducer and servo motor to strictly limit the terminal tangential friction force to a preset safety range while tracking the target angle.

[0028] The present invention provides a robotic arm control system and control method for a photovoltaic panel intelligent installation robot, which has the following beneficial effects:

[0029] 1. The present invention provides a sensor module composed of multiple sensors, which can comprehensively and real-timely obtain the status and environmental information of the robot arm, provide rich data support for precise control, and improve the control accuracy of the robot arm.

[0030] 2. Through the optimized control algorithm, the present invention can quickly and accurately process data and plan the motion path of the robotic arm, thereby achieving precise control of the robotic arm and improving the accuracy and stability of the installation.

[0031] 3. The real-time monitoring and adjustment function of the present invention enables the robotic arm to respond to various abnormal situations during the operation in a timely manner, avoiding damage to the photovoltaic panels and installation failures, and improving the reliability and success rate of the installation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] Example 1: Reference Figure 1 The embodiment of the present invention provides a robotic arm control system for a photovoltaic panel intelligent installation robot, and the specific implementation is as follows:

[0035] The system consists of a sensor module, a central processing module, a path planning module, a drive module and a human-computer interaction module. Each module works together to achieve high-precision installation of photovoltaic panels. The three-dimensional force sensor in the sensor module is installed at the contact point between the end effector of the robotic arm and the photovoltaic panel, which is used to detect normal pressure and tangential friction in real time. Its sampling frequency is 1000Hz and the measurement accuracy is ±0.5N. The magnetic encoder is integrated at each joint of the robotic arm with a resolution of 0.001 degrees, and it can provide real-time feedback on the joint rotation angle data. The binocular vision sensor is deployed at the base of the robotic arm to scan the photovoltaic panel installation area and generate a three-dimensional point cloud map. Its scanning accuracy is ±0.1mm, which can It can identify the curvature characteristics of the obstacle surface; for example, in the installation scenario, the binocular vision sensor collects point cloud data at a rate of 30 frames per second, and divides the continuous surface into discrete point sets with a spacing of 2mm through spatial discretization processing. Each discrete point extracts point cloud data within a neighborhood with a radius of 5cm, constructs a local covariance matrix and performs eigenvalue decomposition to extract the principal curvature and curvature direction; the principal curvature is determined by the direction of the eigenvector corresponding to the minimum eigenvalue, and the curvature direction is defined by the first principal component eigenvector; the curvature change rate between adjacent discrete points is calculated by calculating the ratio of the principal curvature difference between the two points to the Euclidean distance. For example, when the distance between the two points is 5mm and the principal curvature difference is 0.02mm -2 When the curvature changes by 0.004mm -2 .

[0036] After receiving the sensor data, the central processing module constructs the curvature gradient field based on the 3D point cloud map and identifies the area where the curvature gradient changes suddenly. It establishes a local coordinate system with the current position of the end effector as the origin, projects the global curvature gradient field into the coordinate system, and decomposes the initial approximation direction vector into the curvature gradient direction and the tangent plane direction components. By screening the curvature gradient change rate less than 0.1mm, the curvature gradient field is projected into the local coordinate system. -2 The optimal safe approach direction is selected for the tangent plane direction; for example, when the curvature gradient change rate of a tangent plane direction is detected to be 0.05mm -2When the normal pressure deviation ΔP=50N is reached, it is determined to be a safe path; the central processing module compares the normal pressure fed back by the three-dimensional force sensor with the preset photovoltaic panel pressure threshold, such as 300N, in real time. When the pressure exceeds the limit, the reverse displacement compensation of the end effector is calculated based on inverse dynamics; the end compensation is mapped to the joint space through the Jacobian matrix pseudo-inverse method. For example, when the normal pressure deviation ΔP=50N, the reverse displacement compensation ΔX=0.2mm. The dynamic damping coefficient λ in the Jacobian matrix pseudo-inverse calculation is set to 0.1 to ensure numerical stability under singular configurations; the joint angle increment ΔΨ is calculated by the formula Calculate, where J + damped It is the damped pseudo-inverse of the Jacobian matrix; the central processing module synchronously calculates the upper limit attenuation of the joint angular velocity. For example, when the initial angular velocity upper limit ω0 = 1.5 rad / s, when the pressure change rate ΔP / Δt = 10 N / s, the attenuation coefficient k = 0.1, and the corrected angular velocity upper limit is 0.75 rad / s to suppress the excessive end friction.

[0037] The path planning module generates the target joint angle sequence and angular velocity sequence based on the anti-slip motion parameters; the trajectory is smoothed using the piecewise cubic Hermite interpolation method. For example, in the time interval [0.5s, 1.0s], the initial angle θ0 = 30°, the target angle θ1 = 45°, the angular velocity after anti-shake ω0 = 0.8rad / s, and the interpolation function H(t) satisfies H(0.5) = 30°, H(1.0) = 45°, H ’ (0.5)=0.8rad / s, H ’ (1.0) = 0.6rad / s, generating a continuous and smooth angle curve; the dynamic suppression coefficient C is adjusted according to the rate of change of the terminal tangential friction force, for example, the initial value C0 = 0.5, when Δf / Δt> 10N / s is detected three times in a row, the growth factor α = 0.2 triggers the increment, and the updated formula C = 0.7; each timestamp in the path planning instruction is associated with the angle value, angular velocity limit and torque suppression threshold, for example, at t = 1.2s, θ = 40°, , C=0.8.

[0038] After receiving the path planning instruction, the harmonic reducer in the drive module analyzes the target joint angle θ target , combined with the actual angle θ fed back by the magnetic encoder actual Calculation deviation , adjust the output torque through the control algorithm; for example, when Δθ=0.5°, the preset control parameter K p =10Nm / deg, K i =0.5Nm / (deg·s), K d =2Nm·s / deg, calculate the torque increment , drives the harmonic reducer to output the adjusted torque; the torque feedback servo motor is based on the dynamic suppression coefficient C=0.8 and the angular velocity limit ω max =1.0rad / s adjusts the speed and is based on the torque threshold Limit the output torque; for example, when the real-time friction force f t =45N, τ limit =0.8×0.8×50=32Nm, if the required torque τ demand =40Nm, then the actual output torque τ output =min(40,32)=32Nm, ensuring that the tangential friction force is controlled; according to the real-time value of the dynamic suppression coefficient C, the hysteresis interval of the servo motor torque threshold is constructed. The threshold is tightened during the friction rising stage and relaxed during the friction falling stage to avoid the frequent torque jumps caused by the traditional fixed threshold. Through the nonlinear coupling of the dynamic suppression coefficient C and the hysteresis interval, the problems of torque jump, response lag, poor adaptability to multiple materials and high energy consumption in traditional control are solved, and high-precision, low-power robust control is achieved in the photovoltaic panel installation scenario.

[0039] The human-computer interaction module triggers an alarm under abnormal circumstances. For example, when the normal pressure exceeds the limit three times in a row or the tangential friction exceeds 50N, the robot arm movement is frozen and the operator is prompted to enter an emergency compensation amount. The operator can enter the compensation amount through the interface, such as ±10% reverse displacement, to overwrite the original path planning instructions. For example, when the end posture deviation is detected to exceed 5mm, the operator enters +8% compensation, and the system adjusts the reverse displacement from 0.2mm to 0.216mm and replans the path.

[0040] Example 2: A method for controlling the robotic arm of a photovoltaic panel intelligent installation robot. The specific implementation method is as follows:

[0041] When implementing the robotic arm control method for the photovoltaic panel intelligent installation robot, a binocular vision sensor is first used to comprehensively scan the photovoltaic panel installation area to construct a three-dimensional point cloud map of the installation area. At the same time, the magnetic encoder provides real-time feedback on the rotation angle of the robotic arm joint, and the three-dimensional force sensor continuously monitors the normal pressure and tangential friction when the end effector contacts the photovoltaic panel. Assume that during a certain installation operation, the end effector of the robotic arm approaches the surface of the photovoltaic panel. At this time, the three-dimensional force sensor detects that the normal pressure of the end effector in contact with the photovoltaic panel gradually increases. When the normal pressure reaches 120N, it exceeds the preset photovoltaic panel pressure threshold of 100N.

[0042] When faced with a situation where the threshold is exceeded, the central processing unit intervenes quickly and calculates the deviation between the normal pressure and the preset threshold, which is 20N. Based on the principle of inverse dynamics, the pressure difference is converted into the reverse displacement compensation of the end effector along the normal direction, and then a reverse pressure compensation instruction containing the compensation direction and amplitude is generated. At the same time, combined with the joint angle data fed back by the magnetic encoder, the reverse displacement compensation of the end effector is mapped to the joint space through inverse kinematics solution, and a dynamic correlation between the angle increment of each joint and the change of the normal pressure at the end is established.

[0043] Based on the curvature features of the obstacle surface extracted by the binocular vision sensor, the approach path of the robotic arm end effector along the normal direction of the photovoltaic panel surface is calculated, and a safe approach angle is generated. For example, if there is a roof with a certain inclination angle in the installation area, a safe approach angle will be calculated based on the curvature change of the roof surface, allowing the end effector to smoothly approach the photovoltaic panel along this angle.

[0044] Based on the generation of reverse pressure compensation instructions and safe approach angles, a nonlinear sliding mode control algorithm is further used to calculate the target motion parameters of the robot arm joint, including the anti-shake amount and the dynamic suppression coefficient of the tangential friction force; assuming that the anti-shake amount is set to 0.05rad / s and the initial value of the dynamic suppression coefficient is 0.8, after algorithm calculation, the updated dynamic suppression coefficient is 0.85, and finally the path planning instructions are generated.

[0045] According to the path planning instructions, the torque feedback servo motor is driven to rotate, controlling the robot arm to move along the planned path. During this process, the tangential friction force is strictly limited to a preset safety range. For example, the preset upper limit of the tangential friction force safety range is 80N. If the tangential friction force is detected to be close to or reaches this upper limit during movement, the robot arm control system will make real-time adjustments to ensure that the tangential friction force is always within the safe range.

[0046] However, various abnormal situations may be encountered in actual operation. For example, when the normal pressure exceeds the preset threshold three times in a row, or the tangential friction force exceeds the preset safety range, or the end effector posture deviation exceeds the allowable tolerance, such as the tolerance is set to 0.1mm, and the actual deviation reaches 0.15mm, or the deviation between the actual torque of the robot arm joint and the target torque continues to exceed the dynamic threshold, such as the dynamic threshold is set to 5N·m, and the actual deviation reaches 6N·m, the system will trigger an alarm and freeze the robot arm movement; at this time, the operator needs to confirm the compensation strategy through the human-machine safety interaction module, such as entering an emergency reverse compensation amount to overwrite the original path planning instructions, so that the robot arm can continue to perform photovoltaic panel installation operations safely and stably according to the new compensation strategy.

[0047] Through this control method, the robotic arm of the photovoltaic panel intelligent installation robot can achieve high-precision and high-stability installation operations in complex environments, effectively avoiding damage to the photovoltaic panels and installation failures, and improving the reliability and success rate of the installation.

[0048] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A robotic arm control system for a photovoltaic panel intelligent installation robot, characterized in that: include: Sensor module: A three-dimensional force sensor detects the normal pressure and tangential friction force when the end effector contacts the photovoltaic panel; The magnetic encoder provides real-time feedback of joint angle data; Binocular vision sensors construct a three-dimensional point cloud map of the photovoltaic panel installation area and identify the curvature of the obstacle surface; The central processing module processes the data collected by the sensor module; calculates the safe approach direction of the manipulator end effector based on the identified obstacle surface curvature; compares the normal pressure with the preset photovoltaic panel pressure threshold, and generates a reverse pressure compensation instruction when the normal pressure exceeds the threshold; based on the joint angle data, combined with the safe approach direction and reverse pressure compensation instruction, the anti-slip motion parameters of the manipulator joint are generated through the Jacobian matrix pseudo-inverse method and the dynamic damping coefficient; Path planning module: Calculates the target motion parameters of each joint based on the anti-slip motion parameters of the robot arm joints, and corrects the joint angular velocity anti-shake amount and the dynamic suppression coefficient of the end effector tangential friction force according to the joint angle feedback to generate path planning instructions; Drive module: A harmonic reducer and torque feedback servo motor directly connected to the robot arm joints. The harmonic reducer adjusts the output torque according to the path planning instructions to track the planned path. The torque feedback servo motor limits tangential friction based on the dynamic suppression coefficient and adjusts the motor speed according to the path trajectory. The dynamic suppression coefficient is used to construct a hysteresis range for the servo motor torque threshold, tightening the threshold during the friction rising phase and relaxing it during the friction falling phase. Human-computer interaction module: triggers an alarm when the path planning instructions of the robot arm are abnormal, and receives the emergency reverse compensation input by the operator to override the path planning instructions.

2. The robotic arm control system of a photovoltaic panel intelligent installation robot according to claim 1, characterized in that: The central processing module performs spatial discretization processing on the point cloud data in the binocular vision sensor, extracts the principal curvature and curvature direction of each discrete point on the obstacle surface, and calculates the curvature change rate between adjacent discrete points through the curvature gradient; The curvature gradient field of the obstacle surface is constructed according to the curvature change rate, and the boundary of the curvature gradient mutation area is identified; a local coordinate system is established with the current position of the end effector as the origin, and the curvature gradient field is projected into the local coordinate system. The initial approach direction vector is decomposed into the components of the curvature gradient direction and the tangent plane direction. The tangent plane direction with the smallest curvature gradient direction component is selected as the safe approach direction.

3. The robotic arm control system of a photovoltaic panel intelligent installation robot according to claim 1, characterized in that: The central processing module calculates the dynamic difference between the end-effector normal pressure data and a preset photovoltaic panel pressure threshold value. When the difference exceeds the threshold, the central processing module converts the pressure difference into a reverse displacement compensation amount of the end-effector in the normal direction based on inverse dynamics, and generates a reverse pressure compensation instruction including the compensation direction and amplitude. Based on the joint angle data, the end-effector normal displacement of the reverse pressure compensation instruction is mapped to each joint angle increment through the Jacobian matrix pseudo-inverse method and the dynamic damping coefficient. Combined with the tangent plane vector component of the safe approach direction, the motion constraints of the joint space are constructed. The angular velocity upper limit and torque threshold are adjusted through the real-time ratio of the joint angle change rate to the normal pressure change rate, generating anti-slip motion parameters that suppress the terminal tangential friction force, making the joint trajectory smooth and the terminal contact force strictly controlled by the photovoltaic panel pressure resistance threshold.

4. The robotic arm control system of a photovoltaic panel intelligent installation robot according to claim 3, characterized in that: The path planning module generates an initial joint target angle sequence and a corresponding angular velocity sequence based on the target joint angle and angular velocity in the anti-slip motion parameters of the manipulator joint generated by the central processing module, and inversely solves the target motion trajectory of each joint through the manipulator kinematics. The path planning module receives joint angle data fed back by the magnetic encoder in real time, calculates the real-time deviation between the current joint angle and the target angle, and dynamically adjusts the joint angular velocity anti-shake amount based on the time derivative of the deviation value, limiting the angular velocity change rate within a preset smoothing threshold, thereby forming a joint angular velocity sequence after anti-shake. The tangential friction force data of the end effector fed back by the three-dimensional force sensor is read synchronously. According to the mapping relationship between friction force and joint torque, the dynamic suppression coefficient of the end tangential friction force is adjusted with the friction force change rate as input, so that the suppression coefficient increases with the increase of friction force. The joint angular velocity sequence, dynamic suppression coefficient and target angle sequence after anti-shake are synchronized in time to generate path planning instructions, which are output to the harmonic reducer and torque feedback servo motor of the drive module to control the harmonic reducer to adjust the output torque according to the angle sequence.

5. The robotic arm control system of a photovoltaic panel intelligent installation robot according to claim 4, characterized in that: The harmonic reducer in the driving module receives the joint angle sequence and corresponding timestamp in the path planning instruction, parses the target joint angle value of the current timestamp, combines the actual joint angle feedback from the magnetic encoder, calculates the angle deviation, and determines the adjustment amount of the output torque based on the preset torque and the angle deviation, and drives the harmonic reducer to output the torque matching the target angle to track the planned path; The torque feedback servo motor reads the dynamic suppression coefficient and angular velocity limit in the path planning instruction, and dynamically adjusts the motor speed according to the angular velocity limit of the current timestamp to synchronize it with the angular velocity requirement of the path trajectory; during the adjustment process, the dynamic suppression coefficient and the real-time detected end tangential friction force data are used to limit the output torque of the servo motor to the threshold range corresponding to the suppression coefficient through the torque control algorithm. When the friction force exceeds the threshold, the output torque is reduced according to the proportion of the suppression coefficient to limit the tangential friction force.

6. A control method for a robotic arm control system of a photovoltaic panel intelligent installation robot according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1. A binocular vision sensor scans the photovoltaic panel installation area to extract the curvature characteristics of the obstacle surface. A magnetic encoder simultaneously provides real-time feedback on the robot arm's joint rotation angles, and a 3D force sensor acquires the end effector's normal pressure and tangential friction in real time. S2. Calculate the approach path of the robotic arm end effector along the normal direction of the photovoltaic panel surface based on the curvature characteristics of the obstacle surface and generate a safe approach angle; S3. Generate path planning instructions based on dynamic pressure compensation and anti-slip motion parameters. The specific steps include: S31. When the normal pressure exceeds the preset PV panel pressure threshold, the reverse compensation pressure is calculated according to the excess ratio and a reverse pressure compensation instruction is generated; S32. Based on the safety approach angle and reverse pressure compensation instruction, the anti-slip motion parameters of the manipulator joint are generated in combination with the joint angle data of the magnetic encoder; S33. Use a nonlinear sliding mode control algorithm to calculate the target motion parameters of the manipulator joint, including the anti-shake amount and the dynamic suppression coefficient of the tangential friction force, and generate path planning instructions; S4. Drive the torque feedback servo motor according to the path planning instructions, control the robot arm to move along the planned path, and limit the tangential friction force to a preset safety range; S5. When the normal pressure exceeds the limit for more than the set number of times continuously, the tangential friction exceeds the preset safety range, or the end effector posture deviation exceeds the allowable tolerance, and the deviation between the actual torque of the robot arm joint and the target torque continuously exceeds the dynamic threshold, an alarm is triggered and the robot arm movement is frozen until the operator confirms the compensation strategy through the human-machine safety interaction module.

7. The control method of the robotic arm control system of the photovoltaic panel intelligent installation robot according to claim 6, characterized in that: The nonlinear sliding mode control algorithm is based on the target joint angle sequence in the anti-slip motion parameters generated by the path planning module and the actual joint angle data fed back by the magnetic encoder. It defines the joint angle tracking error and its time derivative as state variables and constructs a nonlinear sliding mode surface function that includes the angle error and angular velocity error. Based on the sliding mode surface function and the dynamic suppression coefficient threshold of the end tangential friction force, a nonlinear reaching law is designed, and the control variable expression of the joint torque is derived using the Lyapunov stability criterion. A saturation function of the joint angle deviation is introduced into the control variable instead of the sign function, and the anti-shake amount is used as the saturation boundary value to suppress high-frequency chattering. Based on the real-time data of the end tangential friction force fed back by the three-dimensional force sensor, the dynamic suppression coefficient is nonlinearly coupled with the friction force change rate to generate an adjustment factor for the sliding mode switching gain, so that the switching gain decays with the increase of friction force, reducing the risk of torque mutation; the calculated joint torque control amount, anti-shake amount and dynamic suppression coefficient are integrated to generate a smooth joint target torque curve, which is mapped to the torque suppression threshold of the path planning instruction, driving the robot arm joint to track the target angle while limiting the tangential friction force to a preset safety range.

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