A high-precision, anti-disturbance SCARA robot control method for precision assembly

Abstract

This invention relates to the field of robot control technology, and particularly to a high-precision, disturbance-resistant SCARA robot control method for precision assembly. The method includes: using an ant colony algorithm to find the robot arm's working path; arranging the working paths in descending order of scores; using the optimal working path as the robot arm's preferred target path; controlling the robot arm to execute along the preferred target path; and, if a deviation occurs during execution, using a fitness function to determine whether to switch to another working path. This method, applied to SCARA robots for precision assembly, enables the robot system to have high anti-interference capabilities, a high degree of system automation, and reduced human intervention, while simultaneously considering global path optimization and local dynamic adjustment, making it suitable for precision assembly in complex environments and dynamic tasks.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and in particular to a high-precision, disturbance-resistant SCARA robot control method for precision assembly. Background Technology

[0002] With the continuous improvement of industrial automation, robots are being used more and more widely in the field of precision assembly. SCARA (Selective Compliance Assembly Robot Arm) robots are widely used in high-precision assembly tasks due to their simple structure, flexible movement, and high efficiency. However, due to the complex disturbance factors in the working environment (such as external forces, vibrations, temperature changes, etc.), SCARA robots are easily affected by disturbances when performing precision assembly tasks, resulting in decreased positional accuracy and unstable assembly quality.

[0003] Existing SCARA robot control systems mostly employ traditional PID control methods, but their slow response to disturbances makes them unsuitable for high-precision assembly. Therefore, improving the disturbance immunity of SCARA robots and ensuring their high-precision operation in disturbed environments has become a current technological challenge. Summary of the Invention

[0004] This invention discloses a high-precision, disturbance-resistant SCARA robot control method for precision assembly, the specific method of which is as follows:

[0005] Using the ant colony algorithm to find the working path of the robotic arm;

[0006] Calculate the score for each work path using a scoring function, and arrange the work paths in descending order of score;

[0007] The optimal working path is used as the preferred target path for the robotic arm;

[0008] Control the robotic arm to execute the preferred target path;

[0009] If a deviation occurs during execution, the fitness function is used to determine whether to switch to another working path.

[0010] Furthermore, the ant colony algorithm is used to find the working path of the robotic arm. The specific method is as follows:

[0011] The workspace of the robotic arm's end effector is calculated, and then the workspace is divided into grids.

[0012] Initialize the data and number of path nodes for the ants in the ant colony;

[0013] Ants calculate the next possible path node to visit based on pheromone concentration;

[0014] Use a roulette wheel to select the next node to be visited;

[0015] Once all the ants have completed one round trip, the pheromone concentration is updated.

[0016] Until the maximum number of iterations is reached, store all paths as working paths.

[0017] Furthermore, the scoring function is calculated using the following formula:

[0018] Score = ω1·L + ω2·T + ω3·S

[0019] In the formula, L represents the total path length, T represents the number of turns in the path, S represents the curvature, i.e. the smoothness of the path, and ω1, ω2, and ω3 represent adjustable weights.

[0020] Furthermore, the robotic arm is controlled using the following methods:

[0021] Construct a dynamic model of the robotic arm;

[0022] Design an adaptive robust controller;

[0023] The robotic arm is controlled by an adaptive robust controller.

[0024] Furthermore, the dynamic model of the robotic arm is constructed, and the specific method is as follows:

[0025] The Lagrangian form of the dynamics of a mechanical system is as follows:

[0026]

[0027] Where q represents angular displacement. Represents angular velocity. M(q) represents angular acceleration, and M(q) is the inertia matrix. Let G(q) be the Coriolis force and centrifugal force matrix, G(q) be the gravity vector, σ(t) be the system uncertainty parameter, and τ(t) be the control input vector.

[0028] For the three-phase current decoupling of the joint motors of SCARA robots:

[0029]

[0030] Among them, u d u q i represents the stator voltage along the d-axis and q-axis, respectively. d i q These represent the stator currents along the d-axis and q-axis, respectively, with R representing the stator resistance and L... d L q ψ represents the inductance along the d-axis and q-axis, respectively. fRepresents flux linkage, n p Representing the extreme logarithms, τ, B, J, T lp These represent the electromagnetic torque, damping coefficient, moment of inertia, and load torque of the motor, respectively.

[0031] Furthermore, the expression for electromagnetic torque can be given as:

[0032]

[0033] In vector control, make i d =0, we can get:

[0034]

[0035] because

[0036]

[0037] We can obtain:

[0038]

[0039] Rewrite in Lagrange's general form:

[0040]

[0041] In the formula, matrix F is the load torque T mentioned above. lp .

[0042] Furthermore, an adaptive robust controller is designed, and the specific method is as follows:

[0043] The trajectory of the work path is represented in the form of first-order and second-order constraint equations:

[0044]

[0045] In the formula, A is the identity matrix, and matrix b is the equivalent matrix after the differential transformation of matrix c;

[0046] The designed adaptive robust controller includes:

[0047] The nominal controller τ0 is a controller under ideal constraints;

[0048] A robust controller τ1 is used to handle modeling errors and initial condition deviations.

[0049] The adaptive controller τ2 introduces adaptive parameters to adjust the controller parameters in real time.

[0050] The specific formula is as follows:

[0051]

[0052] in, κ represents the known part of the system, P is a positive constant, ∏(·) is a known function containing the adaptive parameter α, and the function ∏(·) can be linearly decomposed.

[0053] When there are no external disturbances and system uncertainties, take That is, the controller is τ = τ0 + τ1;

[0054] When the initial conditions are unbiased and the model error can be ignored, i.e. At this point, the controller is τ = τ0.

[0055] Furthermore, the fitness function is used to determine whether to switch to another working path. The specific method is as follows:

[0056] Define the fitness function as follows:

[0057]

[0058]

[0059] Where, F current Indicates the fitness of the current path. Indicates fitness of other paths; d current This represents the Euclidean distance between the current position and the nearest point on the current target path. Indicates the deviation of the current location from other paths;

[0060] Define the path switching conditions as follows:

[0061] when When the time is right, switch to path i; where Δ is the threshold to avoid frequent switching;

[0062] The controller performs trajectory tracking based on the currently selected path. Every set time interval, it evaluates the deviation and calculates the fitness, checks whether the switching conditions are met, and if the conditions are met, it updates the current path and replans the control input; otherwise, it continues to execute along the current path.

[0063] Due to the adoption of the above technical solutions, the present invention has the following beneficial effects:

[0064] 1. Traditional path planning methods often focus on a single path. This method utilizes the ant colony algorithm to generate multiple paths, sorts them using a scoring function, finds the optimal path, and retains the next best path as a backup. Multi-path generation improves the system's robustness and flexibility, allowing the selection of the most suitable path in different scenarios and avoiding the limitations of single-path planning.

[0065] 2. The fitness function evaluates the current path's performance in real time and dynamically switches to a better path based on deviations, ensuring the continuity and accuracy of path execution. In actual operation, if the environment changes or execution errors occur, the system can automatically adjust to a suboptimal path, enhancing the adaptability and fault tolerance of path execution.

[0066] 3. The designed adaptive robust controller not only improves the accuracy of path execution but also provides robust compensation for model uncertainties and external disturbances. It can ensure the stability and reliability of the end effector in complex environments, and can quickly adjust to the new path state even if disturbances occur during path switching.

[0067] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0068] The accompanying drawings of this invention are described below.

[0069] Figure 1 This is a schematic diagram of the ant colony algorithm process.

[0070] Figure 2 This is a schematic diagram of the path switching process.

[0071] Figure 3 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0072] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0073] A high-precision, disturbance-resistant SCARA robot control method for precision assembly, such as Figure 3 As shown, the specific steps are as follows:

[0074] S1. Use the ant colony algorithm to find the working path of the robotic arm.

[0075] Specifically, the ant colony algorithm is used to find the working path of the robotic arm, such as... Figure 1 As shown, the steps are as follows:

[0076] First, the workspace of the SCARA robot's end effector is calculated, and then the workspace is divided into grids. This results in a weighted graph with N nodes, G = (N, A), where N represents the set of nodes N = 1, 2, 3…n, and A represents the edge, A = (i, j), i, j ∈ N.

[0077] Set the number of ants in the ant colony to m, the number of path nodes to n, and the mutual distance between node i and node j to (d ij ) n×n (i, j = 1, 2, 3…n). At time t, the pheromone concentration on the connection path between node i and node j is τ ij (t). At the initial moment, the pheromone concentrations between each pair of nodes are the same.

[0078] Ant k (k = 1, 2, 3…m) determines its next visited node according to the pheromone concentration on the connection path between each pair of nodes. The probability that ant k transfers from node i to node j at time t can be calculated by the following formula:

[0079]

[0080] where η ij (t) is the heuristic function, representing the expected degree for an ant to transfer from node i to node j. allow k (k = 1, 2, 3…m) is the set of nodes to be visited by ant k. α and β respectively represent the importance factor of the pheromone and the importance factor of the heuristic function.

[0081] Use the roulette wheel method to select the next node to be visited.

[0082] First, calculate the cumulative probability q j :

[0083]

[0084] The superscript l represents the number of alternative paths for the next step. Subsequently, randomly generate a r (0 < r < 1), and compare the sizes of all q j with r. The smallest q j greater than r is the node that the k-th ant will choose for the next step when on the i-th path.

[0085] r = rand(0, 1)

[0086] j = index{min[q j > r]}

[0087] When all ants complete a tour, the pheromone concentration on the path is:

[0088] τ ij (t + 1) = (1 - ρ)τ ij (t) + Δτ ij

[0089]

[0090] Where ρ (0 < ρ < 1) represents the degree of pheromone volatilization. Δτ represents the pheromone concentration released by the k-th ant on the path connecting node i and node j. ij Let L represent the sum of pheromone concentrations released by all ants along the path connecting node i and node j, where Q (Q>0) is the pheromone constant. k Let be the total length of the path traversed by the k-th ant.

[0091] S2. Calculate the score for each work path using the scoring function, and arrange the work paths in descending order of score.

[0092] Sort all paths using a scoring function:

[0093] Score = ω1·L + ω2·T + ω3·S

[0094] L represents the total path length, T represents the number of turns in the path, S represents the smoothness of the path, i.e., curvature; ω1, ω2, and ω3 represent adjustable weights.

[0095] After sorting, store the top N paths by rating and sort them sequentially.

[0096] S3. Use the optimal working path as the preferred target path for the robotic arm.

[0097] S4. Control the robotic arm to execute the preferred target path.

[0098] Specifically, the steps to control the robotic arm are as follows:

[0099] S41. Establish the dynamic model of the SCARA robot.

[0100] The Lagrangian form of the dynamics of a general mechanical system is as follows:

[0101]

[0102] Where q represents angular displacement. Represents angular velocity. M(q) represents angular acceleration, and M(q) is the inertia matrix. Let G(q) be the Coriolis force and centrifugal force matrix, G(q) be the gravity vector, σ(t) be the system uncertainty parameter, and τ(t) be the control input vector.

[0103] After decoupling the three-phase current of the joint motors of the SCARA robot, we can obtain...

[0104]

[0105] Among them, u d u qi represents the stator voltage along the d-axis and q-axis, respectively. d i q These represent the stator currents along the d-axis and q-axis, respectively, with R representing the stator resistance and L... d L q ψ represents the inductance along the d-axis and q-axis, respectively. f Represents flux linkage, n p Representing the extreme logarithms, τ, B, J, T lp These represent the electromagnetic torque, damping coefficient, moment of inertia, and load torque of the motor, respectively.

[0106] Furthermore, the expression for electromagnetic torque can be given as:

[0107]

[0108] In vector control, we use zero D-axis current, i.e. d =0, which simplifies the system.

[0109] get:

[0110]

[0111] From the third term in equation (1)

[0112]

[0113] We can obtain:

[0114]

[0115] Write equation (4) in Lagrange's general form:

[0116]

[0117] The third term in this matrix F is the load torque T mentioned above. lp .

[0118] S42, Adaptive Robust Controller Design.

[0119] The planned trajectory is represented in the form of first-order and second-order constraint equations:

[0120]

[0121] A is the identity matrix, and matrix b is the equivalent matrix after differential transformation of matrix c.

[0122] This constraint equation enables motion control of nonlinear systems.

[0123] The designed controller consists of three parts: a nominal controller τ0, a robust controller τ1, and an adaptive controller τ2. The nominal controller τ0 is the controller under ideal constraints. The robust controller τ1 is used to deal with modeling errors and initial condition deviations. The adaptive controller τ2 introduces adaptive parameters to adjust the controller parameters in real time, gradually reducing the impact of external disturbances on the system.

[0124]

[0125] in, Let κ represent the known part of the system, P be a positive constant, P be a positive definite matrix, and Π(·) be a known function containing the adaptive parameter α, where Π(·) can be linearly decomposed. When there are no external disturbances or system uncertainties, we take... That is, the controller is τ = τ0 + τ1. Furthermore, when the initial conditions have no deviation and the model error can be ignored, i.e. At this point, the controller is τ = τ0.

[0126] This adaptive robust controller can effectively handle the interference of uncertainty to the system, thereby improving the system's anti-interference capability.

[0127] S5. If a deviation occurs during execution, the fitness function is used to determine whether to switch to another working path.

[0128] Specifically, such as Figure 2 As shown, the path switching strategy is as follows:

[0129] The fitness function is defined as follows:

[0130]

[0131] F current Indicates the fitness of the current path. Indicates the fitness of other paths. d current This represents the Euclidean distance between the current position and the nearest point on the current target path. This indicates the deviation of the current location from other paths.

[0132] Path switching conditions: when When the time is right, switch to path i. Δ is a threshold to avoid frequent switching.

[0133] The controller performs trajectory tracking based on the currently selected path, evaluates the deviation and calculates the fitness every 100ms, checks whether the switching conditions are met, updates the current path and replans the control input if the conditions are met, otherwise it continues to execute along the current path.

[0134] In summary, this method, applied to SCARA robots for precision assembly, enables the robot system to have high anti-interference capabilities, a high degree of system automation, and reduced human intervention. It also takes into account both global path optimization and local dynamic adjustment, making it suitable for precision assembly in complex environments and dynamic tasks.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A high-precision, disturbance-resistant SCARA robot control method for precision assembly, characterized in that, The specific method is as follows: Using the ant colony algorithm to find the working path of the robotic arm; Calculate the score for each work path using a scoring function, and arrange the work paths in descending order of score; The optimal working path is used as the preferred target path for the robotic arm; Control the robotic arm to execute the preferred target path; If a deviation occurs during execution, the fitness function is used to determine whether to switch to another working path. The specific method for controlling the robotic arm is as follows: Construct a dynamic model of the robotic arm; Design an adaptive robust controller; The robotic arm is controlled by an adaptive robust controller; The specific method for designing an adaptive robust controller is as follows: The trajectory of the work path is represented in the form of first-order and second-order constraint equations: In the formula, A is the identity matrix, matrix b is the equivalent matrix after the differential transformation of matrix c, and q represents the angular displacement. Represents angular velocity. Represents angular acceleration; The designed adaptive robust controller includes: The nominal controller τ0 is a controller under ideal constraints; A robust controller τ1 is used to handle modeling errors and initial condition deviations. The adaptive controller τ2 introduces adaptive parameters to adjust the controller parameters in real time. The specific formula is as follows: in, Let P represent the known part of the system, k be a positive constant, P be a positive definite matrix, Π(·) be a known function containing the adaptive parameter α, and the function Π(·) can be linearly decomposed, where τ, B, J, and T are given. lp These represent the electromagnetic torque, damping coefficient, moment of inertia, and load torque of the motor, respectively. When there are no external disturbances and system uncertainties, take That is, the controller is τ = τ0 + τ1; When the initial conditions are unbiased and the model error can be ignored, i.e. At this point, the controller is τ = τ0.

2. The high-precision, disturbance-resistant SCARA robot control method for precision assembly as described in claim 1, characterized in that, The ant colony algorithm is used to find the working path of the robotic arm. The specific method is as follows: The workspace of the robotic arm's end effector is calculated, and then the workspace is divided into grids. Initialize the data and number of path nodes for the ants in the ant colony; Ants calculate the next possible path node to visit based on pheromone concentration; Use a roulette wheel to select the next node to be visited; Once all the ants have completed one round trip, the pheromone concentration is updated. Until the maximum number of iterations is reached, store all paths as working paths.

3. The high-precision, disturbance-resistant SCARA robot control method for precision assembly as described in claim 2, characterized in that, The scoring function is calculated using the following formula: Score = ω1·L + ω2·T + ω3·S In the formula, L represents the total path length, T represents the number of turns in the path, S represents the curvature, i.e. the smoothness of the path, and ω1, ω2, and ω3 represent adjustable weights.

4. The high-precision, disturbance-resistant SCARA robot control method for precision assembly as described in claim 1, characterized in that, The dynamic model of the robotic arm is constructed using the following method: The Lagrangian form of the dynamics of a mechanical system is as follows: Where q represents angular displacement. Represents angular velocity. M(q) represents angular acceleration, and M(q) is the inertia matrix. Let G(q) be the Coriolis force and centrifugal force matrix, G(q) be the gravity vector, σ(t) be the system uncertainty parameter, and τ(t) be the control input vector. For the three-phase current decoupling of the joint motors of SCARA robots: Among them, u d u q i represents the stator voltage along the d-axis and q-axis, respectively. d 、i q These represent the stator currents along the d-axis and q-axis, respectively, with R representing the stator resistance and L... d L q ψ represents the inductance along the d-axis and q-axis, respectively. f Represents flux linkage, n p Representing the extreme logarithms, τ, B, J, T lp These represent the electromagnetic torque, damping coefficient, moment of inertia, and load torque of the motor, respectively. Furthermore, the expression for electromagnetic torque can be given as: In vector control, make i d =0, we can get: because We can obtain: Rewrite in Lagrange's general form: In the formula, matrix F is the load torque T mentioned above. lp .

5. The high-precision, disturbance-resistant SCARA robot control method for precision assembly as described in claim 1, characterized in that, The fitness function is used to determine whether to switch to another working path. The specific method is as follows: Define the fitness function as follows: In the formula, F current Indicates the fitness of the current path. Indicates fitness of other paths; d current This represents the Euclidean distance between the current position and the nearest point on the current target path. Indicates the deviation of the current location from other paths; Define the path switching conditions as follows: when When the time is right, switch to path i; where Δ is the threshold to avoid frequent switching; The controller performs trajectory tracking based on the currently selected path. Every set time interval, it evaluates the deviation and calculates the fitness, checks whether the switching conditions are met, and if the conditions are met, it updates the current path and replans the control input; otherwise, it continues to execute along the current path.

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