Intelligent robot formation motion path calculation method and system for glass factories

Abstract

The present application provides an intelligent robot formation motion path calculation method and system for glass factories. The method comprises: acquiring the position of a first target to be processed, and converting the position of said first object into coordinates in a Cartesian coordinate system; defining the position state of the first target by means of a set kinematics equation, conjugate variable, angular velocity and speed to obtain a state equation of a first target object at any moment; detecting the surrounding environment of the first target object; on the basis of a detection range and distance coordinates, determining whether multiple targets can communicate or not, and using an adjacent matrix to perform recording and saving; and updating the position state of the first target by means of the kinematics equation, the conjugate variable, the angular velocity and the speed, and forming a final trajectory graph. The present application can improve the use reliability of a robot group in factories and enterprises, and is conducive to cost-saving and profit-increasing of the enterprises and making the whole factory intelligent in the future.

Description

Intelligent robot formation motion path calculation method and system for glass factories Technical Field

[0001] The present invention belongs to the technical field of cold-end lower panel shipment in glass factories, and relates to a motion path calculation method, and in particular to a motion path calculation method and system for intelligent robot formations used in glass factories. Background Art

[0002] With economic development and technological advancements, intelligent robots are increasingly thriving in various fields. Intelligent mobile robots are multifunctional, integrated systems that integrate environmental perception, dynamic decision-making and planning, behavioral control, and execution. Focusing on sensor technology, information processing, electronic engineering, computer engineering, automated control engineering, and artificial intelligence, they represent the pinnacle of mechatronics and are one of the most dynamic areas of scientific and technological development. Many countries plan to invest more in the AI ​​industry as they aim to lead global robotics in the Fourth Industrial Revolution.

[0003] Different types of robots have become popular because they can replace humans to do difficult or dangerous tasks. In addition, some types of robots, such as sweeping robots, can free up labor and save their owners a lot of time.

[0004] However, these mobile robots, especially sweeping robots, lack a static or clear trajectory. In other words, when sweeping robots begin operating, their trajectories are random. This means they cannot follow certain navigation algorithms. Especially in factory and enterprise production management, the disordered paths of robots or multiple robots can disrupt production activities on the shop floor, incurring additional production costs for companies seeking to reduce costs and increase efficiency.

[0005] Therefore, in the existing technology, due to the disorder of the mobile robot formation during operation or the inconsistent paths of multiple robots, the intelligent robots in the glass factory cannot ensure the consistency of key factors such as the direction and speed of the formation, which affects the production activities of the production workshop, increases the production costs of the enterprise, and cannot better serve the glass production workshop. As a result, the existing mobile robot formation has problems such as unreasonable operation trajectory, high collision rate, low utilization rate, high production cost, and poor stability in the process of serving glass production.

[0006] Summary of the Invention

[0007] In view of the shortcomings of the prior art mentioned above, the purpose of this application is to provide a method and system for calculating the motion path of an intelligent robot formation for a glass factory, which is used to solve the problem in the prior art that the intelligent robots in the glass factory cannot ensure the consistency of key factors such as the direction and speed of the formation due to the disorder of the mobile robot formation during operation or the inconsistency of the paths of multiple robots, thereby affecting the production activities of the production workshop, increasing the production cost of the enterprise, and failing to better serve the glass production workshop, which in turn leads to the existing mobile robot formation having problems such as unreasonable operation trajectory, high collision rate, low utilization rate, high production cost, and poor stability in the process of serving glass production.

[0008] To achieve the above-mentioned objectives and other related objectives, in the first aspect, the present application provides a method for calculating the motion path of an intelligent robot formation for a glass factory, comprising the following steps: obtaining the position of the first target robot to be processed, and converting the position of the first target robot into a coordinate position in a Cartesian coordinate system; setting the conjugate variables, angular velocity and velocity of the robot group at any time based on the robot kinematic equation, and defining them to obtain the state equation of the robot group at any time, and forming the motion trajectory of the robot; detecting the area where the first target robot is located, determining whether there are robots in the same formation within the area, and recording and saving them using an adjacency matrix; updating the position state of the first target robot based on the judgment result to form a final trajectory graph; using a Kalman filter to perform noise reduction processing on the final trajectory graph, and finally obtaining a stable and accurate robot formation motion trajectory.

[0009] In an implementation of the first aspect, the conjugate variables, angular velocity and speed of the robot group at any moment are set and defined based on the robot kinematic equation, and obtaining the state equation of the robot group at any moment includes the following steps: setting the restriction conditions of the robot formation to form the shape of the robot formation; setting the initial values ​​of the angular velocity, speed and conjugate variables, bringing them into the defined kinematic calculation equation, and limiting the restriction conditions, expressing the state of the first target robot at the current moment with a mathematical equation to obtain the state of the target at any moment; based on the robot kinematic equation, setting the position, angle and speed of the robot group at any moment through MATLAB, thereby forming the motion trajectory of the robot.

[0010] In an implementation of the first aspect, the restriction condition of the robot formation is:

[0011] in, Indicates the movement direction of the robot; Indicates the speed of the robot; x i (k) represents the position of the robot on the x-axis; y i (k) represents the position of the robot on the y-axis; The x-axis coordinate representing the final position of the robot; Indicates the y-axis coordinate of the robot's final position; X i -X j Indicates the coordinate position distance of the two robots on the x-axis; Y i -Y j Indicates the coordinate position distance of the two robots on the y-axis.

[0012] In an implementation of the first aspect, the calculation formula of the conjugate variable is:

[0013] Among them, N i (k) represents the number of robots that robot A can detect at time k; Indicates the direction of movement of the robot; x i (k) represents the position of the robot on the x-axis; y i (k) represents the position of the robot on the y-axis; v i (k) represents the speed of the robot; It means that after a robot detects other robots at time k, the robot's running angle at the next moment will become the average of the robot's angle and the other detected robots; Indicates that after a robot detects another robot at time k, the robot's position on the x-axis at the next moment will become the average of the robot itself and the other detected robots; Indicates that after a robot detects other robots at time k, the robot's position on the y-axis at the next moment will become the average of the robot and the other detected robots; It means that after a robot detects other robots at time k, the robot's running speed at the next moment will become the average of the robot itself and other detected robots.

[0014] In an implementation of the first aspect, detecting the area where the first target robot is located, determining whether there is a robot in the same formation within the area, and recording and saving it using an adjacency matrix include the following steps: setting a virtual target position; calculating a second angular velocity and a second velocity based on the virtual target position; using an adjacency matrix to detect whether there is a robot in the same formation near the intelligent robot, obtaining a detection result, and recording and saving it.

[0015] In an implementation of the first aspect, setting the virtual target position includes:

[0016] Among them, X i Indicates the horizontal coordinate position of the robot formation; Y i Indicates the vertical coordinate position of the robot formation;

[0017] The running trajectory calculation formula is:

[0018] The second angular velocity calculation formula is:

[0019] d i (t) = g i (t)-z i (t)

[0020] w i (t) = w max f(V i (t),d i (t))

[0021] Among them, d i (t) represents the distance difference between the virtual target position and the robot position; w i (t) represents the second angular velocity; v i (t) represents the second speed; w max 、V M V m All are assumed values; g i (t) represents the virtual target position.

[0022] In an implementation of the first aspect, an adjacency matrix is ​​used to detect whether there are robots in the same formation near the intelligent robot, and the detection results are obtained. The detection results are recorded and saved, including the following steps: using the adjacency matrix to compare the distance between each target robot with the detection range radius to obtain the detection result; including: when the distance between each target robot is less than the detection range radius, it can be known that they can communicate with each other, and the result is recorded and saved in the adjacency matrix; when the distance between each target robot is greater than or equal to the detection range radius, it can be known that they cannot communicate with each other; when a robot in the same formation is detected, the speed and angular velocity of the each target robot are synchronously updated so that they maintain consistent movements and directions.

[0023] In one implementation of the first aspect, updating the position state of the first target robot based on the judgment result to form a final trajectory graph includes the following steps: calculating the difference between each pair of robots and two virtual targets to obtain the same running trajectory of the two robots; calculating the next position according to a differential equation to obtain the running trajectory of the robot formation; the formula for the same running trajectory of the two robots is:

[0024] robotnextposition=robot position +speed×1

[0025] Among them, X i Indicates the horizontal coordinate position of the robot formation; Y i Indicates the vertical coordinate position of the robot formation; x i (t) represents the position of the robot on the x-axis; y i (t) represents the position of the robot on the y-axis; l represents the time interval; speed×1 represents the differential result of the robot position changing with time.

[0026] In one implementation of the first aspect, a Kalman filter is used to perform noise reduction processing on the final trajectory graph, and finally a stable and accurate robot formation motion trajectory is obtained, including the following steps: superimposing Gaussian noise on the final running trajectory, setting a new zero matrix to record the speed and direction of a robot; setting a robot coordinate matrix to obtain a state propagation and noise input function; calculating the standard covariance based on the obtained state propagation and noise input function, and finally obtaining a stable and accurate robot formation motion trajectory; the zero matrix is:

[0027] u(:,1)=u(:,1)+speed(:,1)

[0028] u(:,2)=u(:,2)+speed(:,2)

[0029] u(:,3)=u(:,3)+speed(:,3)

[0030] The calculation formula of the robot position coordinate matrix is ​​as follows:

[0031] Among them, w vt and w wt Indicates noise;

[0032] The state propagation and noise input functions are:

[0033] The standard covariance propagation equation is:

[0034] Among them, P t+1 represents the standard covariance; F t T Indicates F t The transposed matrix of Represents G t The transposed matrix of .

[0035] In the second aspect, the present application provides an intelligent robot formation motion path calculation system for a glass factory, including: a coordinate conversion module for obtaining the position of the first target robot to be processed, and converting the first target robot position into a coordinate position in a Cartesian coordinate system; a trajectory generation module for setting the conjugate variables, angular velocity and velocity of the robot group at any time based on the robot kinematic equation, and defining them, obtaining the state equation of the robot group at any time, and forming the motion trajectory of the robot; a detection module for detecting the area where the first target robot is located, judging whether there is a robot in the same formation within the area, and recording and saving it using an adjacency matrix; an update module for updating the position state of the first target robot based on the judgment result to form a final trajectory graph; a noise reduction module for using a Kalman filter to perform noise reduction processing on the final trajectory graph, and finally obtaining a stable and accurate robot formation motion trajectory.

[0036] Finally, the present application provides a motion path calculation device for an intelligent robot formation in a glass factory, comprising: a processor and a memory. The memory is configured to store a computer program; the processor is connected to the memory and configured to execute the computer program stored in the memory, thereby causing the motion path calculation device for an intelligent robot formation in a glass factory to execute the motion path calculation method for an intelligent robot formation in a glass factory.

[0037] As described above, the method and system for calculating the motion path of an intelligent robot formation in a glass factory of the present invention have the following beneficial effects:

[0038] This application provides a method for calculating the motion path of an intelligent robot formation in a glass factory. In addition to controlling algorithmic constraints such as vehicle speed, angular velocity, and the angle between the robot and the target, conjugate variables are used to form the robots into a formation to ensure the consistency of the group. A Kalman filter is used to reduce sensor errors, allowing the robots to determine whether angle and speed adjustments are needed. This increases the utilization rate of the robot group and reduces its collision rate. That is, multiple robots, random starting positions, and random target coordinates are put into a unified algorithm, making the algorithm more universal and applicable. This algorithm improves the reliability of the robot group in glass factory enterprises, facilitates cost reduction and efficiency improvement for enterprises, and facilitates the future intelligentization of the entire factory. At the same time, the robot's usage scenarios are expanded. This application can improve the rationality of the robot's operating trajectory during the glass factory's shipment process; optimize the operating trajectory, reduce the collision rate, improve utilization, reduce production costs, and improve the stability of the intelligent robot formation's operating trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] FIG1 is a flow chart showing a method for calculating a motion path of an intelligent robot formation for a glass factory according to an embodiment of the present invention.

[0040] FIG2 is a schematic diagram showing the flow of step S12 in the method for calculating a motion path of an intelligent robot formation for a glass factory according to the present invention.

[0041] FIG3 is a schematic diagram showing the flow of step S13 in the method for calculating the motion path of an intelligent robot formation for a glass factory according to the present invention.

[0042] FIG4A is a schematic diagram showing parameters of a multi-robot system with a virtual target in the method for calculating a motion path of an intelligent robot formation for a glass factory according to the present invention.

[0043] FIG4B is a schematic diagram showing an adjacency matrix in the method for calculating motion paths of intelligent robot formations used in glass factories according to the present invention.

[0044] FIG4C is a schematic diagram showing an adjacency matrix of a four-robot formation in the motion path calculation method of an intelligent robot formation for a glass factory according to the present invention.

[0045] FIG5A is a schematic diagram showing the flow of step S14 in the method for calculating a motion path of an intelligent robot formation for a glass factory according to the present invention.

[0046] FIG5B is a schematic diagram showing robot trajectories formed in parallel from different positions in the method for calculating motion paths of intelligent robot formations for a glass factory according to the present invention.

[0047] FIG6 is a schematic diagram showing the flow of step S15 in the method for calculating the motion path of an intelligent robot formation for a glass factory according to the present invention.

[0048] FIG7 is a schematic diagram showing the trajectory of a robot in the method for calculating the motion path of an intelligent robot formation for a glass factory according to the present invention without communicating with other robots.

[0049] FIG8 is a schematic diagram showing the principle structure of an embodiment of the intelligent robot formation motion path calculation system for a glass factory according to the present invention.

[0050] FIG9 is a schematic diagram showing the principle structure of an intelligent robot formation motion path calculation device for a glass factory according to an embodiment of the present invention.

[0051] Component number description

[0052] 81 Coordinate transformation module

[0053] 82 trajectory generation module

[0054] 83 Detection Module

[0055] 84 Update Module

[0056] 85 Noise Reduction Module

[0057] 91 processors

[0058] 92 Memory

[0059] Steps S11 to S15 DETAILED DESCRIPTION

[0060] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0061] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0062] The following is a detailed description of the motion path calculation method and system for an intelligent robot formation for a glass factory provided in the embodiments of the present application, in conjunction with the accompanying drawings. This application is intended to address the existing problem that, due to the disorder of the mobile robot formation during operation or the inconsistency of the paths of multiple robots, the intelligent robots in the glass factory cannot ensure the consistency of key factors such as the direction and speed of the formation, thereby affecting the production activities of the production workshop, increasing the production costs of the enterprise, and failing to better serve the glass production workshop. Furthermore, the existing mobile robot formation has problems such as unreasonable operation trajectory, high collision rate, low utilization rate, high production cost, and poor stability in the process of serving glass production.

[0063] This application uses mathematical methods to solve the problem of how to use robots to better complete shipping tasks and reduce costs and increase efficiency. For the intelligent robot formation motion path calculation solution, first, the initial position of the object to be processed is obtained, and the first target position is converted into coordinates in the Cartesian coordinate system; secondly, the conjugate variables, angular velocity, speed and virtual coordinate destination are set; the coordinates of the first target are processed through kinematic equations and conjugate variables to obtain the coordinate value, angular velocity and speed of the first target at the next moment; finally, the environmental information around the first target is obtained to determine whether there are similar targets within the detection range; if within the range, the speed and angular velocity of the two targets are updated; at the same time, the Kalman filter is used to eliminate noise errors. The present invention can improve the rationality of the robot's running trajectory during the shipping process of the glass factory.

[0064] Please refer to Figure 1, which is a flow chart of an embodiment of a method for calculating a motion path of an intelligent robot formation in a glass factory. As shown in Figure 1, this embodiment provides a method for calculating a motion path of an intelligent robot formation in a glass factory.

[0065] The method for calculating the motion path of an intelligent robot formation in a glass factory specifically includes the following steps:

[0066] S11, obtaining a first target robot position to be processed, and converting the first target robot position into a coordinate position in a Cartesian coordinate system.

[0067] The robot formation in this application is fixed, but its position is uncertain. That is, the initial position and arrival position of the formation are not fixed in the Cartesian coordinate system. Therefore, the coordinates need to be determined.

[0068] S111, obtaining the polar coordinates of the first target robot position.

[0069] In this embodiment, the latitude, longitude, angle, altitude, radius, abscissa, ordinate, etc. of the target and any other relevant polar coordinate information are obtained.

[0070] S112, converting polar coordinates into Cartesian coordinates.

[0071] In this embodiment, the conversion of polar coordinates to Cartesian coordinates requires the use of mathematical formulas. On a two-dimensional plane, the Cartesian coordinate system consists of an x-axis and a y-axis. In the polar coordinate system, the radial distance is the y-axis coordinate in the Cartesian coordinate system, and the azimuth angle is converted to the x-axis coordinate in the Cartesian coordinate system. The specific conversion formula is as follows:

[0072] x=ycos(θ)

[0073] y=ysin(θ)

[0074] Where x and y represent the x-axis and y-axis coordinates in the Cartesian coordinate system, respectively, and θ represents the azimuth angle.

[0075] In three-dimensional space, the Cartesian coordinate system consists of the x-axis, y-axis, and z-axis. In the polar coordinate system, the radial distance is the z-axis coordinate in the Cartesian coordinate system, and the azimuth and polar angle are converted to the x-axis and y-axis coordinates in the Cartesian coordinate system. The specific conversion formula is as follows:

[0076] z=z*cos(θ)

[0077] Where x, y, and z represent the x-axis, y-axis, and z-axis coordinates in the Cartesian coordinate system, respectively, and θ represents the azimuth angle. Indicates the polar angle.

[0078] These formulas can be adjusted to suit your specific needs. In practical applications, you might need to calculate in radians rather than degrees. The results are then stored for later use.

[0079] It should be noted that in some cases, the target position may not be within the range of the Cartesian coordinate system, for example, the radial distance or azimuth of the target position exceeds the range of the Cartesian coordinate system. In this case, the target position needs to be properly processed, such as mapping the target position to the boundary range of the Cartesian coordinate system.

[0080] S12, based on the robot kinematic equations, sets and defines the conjugate variables, angular velocity, and speed of the robot group at any moment, obtains the state equation of the robot group at any moment, and forms the robot's motion trajectory. Please refer to Figure 2, which shows a flow chart of S12 in the method for calculating the motion path of an intelligent robot formation for a glass factory of the present invention. As shown in Figure 2, S12 includes the following steps:

[0081] S121, setting the conjugate variables, angular velocity and velocity of the robot group at any moment based on the robot kinematic equation, and defining them to obtain the state equation of the robot group at any moment.

[0082] To ensure the realization of robot formation, it is crucial to keep the speed and direction of the robots consistent. To solve this problem, using conjugate variables may be a better approach.

[0083] In this embodiment, the initial values ​​of angular velocity, velocity, and conjugate variables are set, brought into the defined kinematic calculation equation, and the constraints are limited. The state of the first target robot at the current moment is expressed by a mathematical equation to obtain the state of the target at any moment.

[0084] Note that constraints must be set before simulation. These constraints ensure a stable robot formation. Furthermore, if the robots can detect each other, they will communicate with each other. Consequently, their trajectories may become closer or intersect, potentially leading to collisions. Therefore, it is necessary to introduce conjugate variables for the robot's orientation, position, and velocity.

[0085] According to the conjugate variable calculation formula, the conjugate variables of these robots can be calculated. In addition, in the conjugate variable equation, N i (k) is used to show the number of robots within a robot's detection range. It changes over time. Therefore, it can be used to calculate the average value of the consistent speed, which helps robots maintain the same speed and direction to avoid collisions.

[0086] Specifically, for each robot in the robot group, use Represents the conjugate variable in the angle equation.

[0087] The calculation formula of the conjugate variable is:

[0088] Among them, N i (k) represents the number of robots that robot A can detect at time k; Indicates the direction of movement of the robot; x i (k) represents the position of the robot on the x-axis; y i (k) represents the position of the robot on the y-axis; Indicates the speed of the robot; It means that after a robot detects other robots at time k, the robot's running angle at the next moment will become the average of the robot's angle and the other detected robots; Indicates that after a robot detects another robot at time k, the robot's position on the x-axis at the next moment will become the average of the robot itself and the other detected robots; Indicates that after a robot detects other robots at time k, the robot's position on the y-axis at the next moment will become the average of the robot and the other detected robots; It means that after a robot detects other robots at time k, the robot's running speed at the next moment will become the average of the robot itself and other detected robots.

[0089] The restrictions are:

[0090] in, Indicates the movement direction of the robot; Indicates the speed of the robot; x i (k) represents the position of the robot on the x-axis; y i (k) represents the position of the robot on the y-axis; The x-axis coordinate representing the final position of the robot; Indicates the y-axis coordinate of the robot's final position; X i -X j Indicates the coordinate position distance of the two robots on the x-axis; Y i -Y j Indicates the coordinate position distance of the two robots on the y-axis. It means that the final running direction of the robot is consistent with the running direction of the moving target point, so the angle is 0 degrees.

[0091] The above constraints can form a stable robot formation shape.

[0092] S122, forming a motion trajectory of the robot based on the state equation of the robot group at any moment.

[0093] In this embodiment, based on the robot kinematic equations, the position, angle, and speed of the robot group at any moment are set through MATLAB, thereby forming the robot's motion trajectory.

[0094] Specifically, using the above four equations and the robot kinematic equations, the calculation of the next position, angle, and speed of the robot group can be set in MATLAB, and the calculation results will form the trajectory of the robot.

[0095] make and The initial result is 0. After the conjugate variable result is calculated, it can be used to calculate the next position, speed, angle and other parameters of the robot group.

[0096] S13: Detect the area where the first target robot is located, determine whether there are any robots in the same formation within the area, and record and save the information using an adjacency matrix. Please refer to Figure 3, which shows a flow chart of S13 in the method for calculating the motion path of an intelligent robot formation for a glass factory according to the present invention. As shown in Figure 3, S13 includes the following steps:

[0097] S131, setting a virtual target position.

[0098] Please refer to FIG. 4A , which is a schematic diagram showing parameters of a multi-robot system with a virtual target in the method for calculating motion paths of intelligent robot formations for a glass factory according to the present invention.

[0099] When calculating the next robot's position, ensuring the correct direction of the robot's velocity presents another challenge. While there's no specific target position for the robots in this multi-robot system, achieving consistent velocity and direction is crucial for the robot swarm to form a formation. Therefore, we need to set virtual destination coordinates. These serve to guide the robots' direction.

[0100] In this embodiment, setting the virtual target position includes:

[0101] Among them, X i Indicates the horizontal coordinate position of the robot formation; Y i represents the vertical coordinate position of the robot formation; t represents time.

[0102] If the angle between the robot's orientation and the x-axis is zero at t = k, the robot's orientation will be stable and its trajectory will be a horizontal line. The key parameters used in the simulation are: the robot's initial position, initial angle, maximum velocity, angular velocity, and four initial conjugate variables.

[0103] Specifically, as shown in the figure, It's a robot R i The virtual target, V i T (t) is the velocity of the virtual target, V i (t) is the robot R i The speed, λ i (t) is the direction of robot speed and robot R i and virtual targets The angle between the lines. If λ i If the value of (t) is zero, the trajectory of the vehicle will be the same as the virtual target, which means that the running trajectory between the robot and the coordinates is consistent.

[0104] The calculation formula for the running trajectory is:

[0105] S132: Calculate a second angular velocity and a second velocity based on the virtual target position.

[0106] In this embodiment, the second angular velocity calculation formula is:

[0107] d i (t) = g i (t)-z i (t)

[0108] w i (t) = w max f(V i (t),d i (t))

[0109] Among them, d i (t) represents the distance difference between the virtual target position and the robot position; w i (t) represents the second angular velocity; v i (t) represents the second speed; w max 、V M V m All are hypothetical values.

[0110] Specifically, suppose this multi-robot formation consists of four robots. If these robots were used to form a formation, without considering overlapping nodes, the number of nodes in the formation would be four. Furthermore, communication with the robots should also be accomplished through graph functions. Therefore, we choose to use adjacency matrix functions.

[0111] S133: Use the adjacency matrix to detect whether there are robots in the same formation near the intelligent robot, and obtain a detection and judgment result. Please refer to Figure 4B, which shows a schematic diagram of the adjacency matrix in the method for calculating the motion path of intelligent robot formations in a glass factory according to the present invention.

[0112] In this embodiment, an adjacency matrix is ​​used to compare the distance between each target robot with the detection range radius to obtain a detection result. When a robot in the same formation is detected, the speed and angular velocity of each target robot are synchronously updated to ensure that the robots maintain consistent movement and direction. The judgment condition is as follows: when the distance between each target robot is less than the detection range radius, it is determined that they can communicate with each other, and the result is recorded and stored in the adjacency matrix; when the distance between each target robot is greater than or equal to the detection range radius, it is determined that they cannot communicate with each other.

[0113] Specifically, the adjacency matrix is ​​a commonly used storage representation for graphs. It uses two arrays to store information about data elements (vertices) and the relationships between data elements (edges or arcs).

[0114] For example, the diagram shows five vertices and six arrays. Imagine these five vertices as five intelligent robots, and the six arrays as how these robots communicate. For example, V1 communicates with V2 and V5. Furthermore, different robots have different detection ranges, which can lead to different situations. For example, the distance between V1 and V2 is similar to the distance between V5 and V4. However, there is no array between V5 and V4. Furthermore, the distance between V5 and V3 is longer than the distance between V5 and V3, yet V3 can communicate with V5. This is because V3 has a greater detection range than V5.

[0115] Assume that the vertex of the formation in the diagram is a robot. If a detection range is set for the robot, the specific number of neighbors of a vertex represents the specific number of robots with which robot A can communicate. This number can be denoted as Ni. If we consider that the robot's position changes over time, which may affect the results of the neighboring points of a vertex, the number Ni will also be affected by time and can be denoted as Ni(t). This number will be used in the conjugate variable formula.

[0116] Since there are four robots in this multi-robot system, this means the maximum number of communication pairs is six. In other words, robot R1 can communicate with R2, R3, and R4, forming communication pairs of (1,2), (1,3), and (1,4). Similarly, the communication pairs of R2, R3, and R4 can be recorded as (2,1), (2,3), (2,4), (3,1), (3,2), (3,4), (4,1), (4,2), and (4,3). We then delete duplicate communication pairs. The final communication pairs of these four robots are recorded as a 6*2 matrix, as shown in Table 1.

[0117] Table 1 Maximum communication pairs of 4 robots

[0118] As can be seen from the table above, these four robots can communicate with each other.

[0119] Please refer to FIG. 4C , which is a schematic diagram of an adjacency matrix of a four-robot formation in the motion path calculation method of an intelligent robot formation for a glass factory according to the present invention.

[0120] Then, the distance of each pair is calculated and compared with the radius of the detection range. If the distance of the pair is less than the radius of the detection range, the robots will communicate with each other and record the result in the adjacency matrix.

[0121] The “neighbors” function in MATLAB is used to realize communication with different robots.

[0122] According to Figure 2.3 and Table 2.1, the formation at time zero can be obtained, and the position of the robot can be uploaded over time by uploading the equation (as shown in Formula 2.12). The formula is as follows:

[0123] robotnextposition=robot position +speed×1

[0124] Here, "l" is the time interval, and "speed × 1" represents the differential of the robot's position over time. In other words, the robot's next position is equal to the sum of its current position and the time-varying variable of its position. Therefore, the formation formed by the robot swarm changes over time. Using the "neighbors" function, the total number of current communications, Ni, for each robot is counted. This is shown in Tables 2 and 3.

[0125] Table 2 Communication status of the four robots

[0126] Table 3 Ni values ​​after statistics

[0127] The values ​​of these robots at time zero can be obtained and recorded in a matrix. These values ​​are then used to calculate conjugate variables for angular velocity, the robot's x- and y-axis positions, the robot's orientation, and the angle between the site line from the virtual target to the robot's position. This ultimately enables robot communication.

[0128] S14: Based on the judgment result, the position state of the first target robot is updated to form a final trajectory graph. Please refer to Figures 5A and 5B, which respectively show a flow chart of S14 in the method for calculating the motion path of an intelligent robot formation for a glass factory according to the present invention and a schematic diagram of the robot trajectories formed in parallel from different positions in the method for calculating the motion path of an intelligent robot formation for a glass factory according to the present invention. As shown in Figures 5A and 5B, S14 includes the following steps:

[0129] In this embodiment, a virtual target is established to guide the direction of the robot formation.

[0130] Specifically, if we calculate the difference between the two virtual targets of robots Ri and Rj, we can find that when they are able to detect each other, the result is consistent with the trajectory calculated by the formula.

[0131] The formula is as follows:

[0132] Then, according to the differential equation robotnextposition=robot position The calculation of + speed × 1 can get the next position of the robot, and finally the trajectory of the robot.

[0133] S15: A Kalman filter is used to perform noise reduction on the final trajectory graph, ultimately obtaining a stable and accurate robot formation motion trajectory. Please refer to Figures 6 and 7, which respectively show a flow chart of S15 in the method for calculating the motion path of an intelligent robot formation for a glass factory according to the present invention, and a schematic diagram of the trajectory of a robot in the method for calculating the motion path of an intelligent robot formation for a glass factory according to the present invention, when the robot is not communicating with other robots. As shown in Figures 6 and 7, S15 includes the following steps:

[0134] During the formation simulation, due to the randomness of the robot group positions, there are cases where a robot cannot communicate with the other three robots during the formation process. Therefore, a Kalman filter is used to deal with this problem.

[0135] In this embodiment, Gaussian noise is superimposed on the obtained trajectory. Specifically, according to the code, a new zero matrix and robot position coordinate matrix are set, the state propagation and noise input functions are obtained, and the standard covariance propagation is calculated. Ultimately, the trajectory of a robot in a robot formation without communicating with other robots can be obtained.

[0136] Specifically, the Kalman filter parameters are initialized, including the state estimate, state estimate variance, and measurement noise covariance. The Kalman filter is used to predict the next state of the robot formation based on its current state. After obtaining new measurements, the Kalman filter is updated to obtain new measurements and predictions, which in turn update the Kalman filter's state estimate and covariance. This process is repeated until satisfactory performance is achieved. The final trajectory is then processed, using methods such as smoothing and interpolation, to make it smoother and more accurate. Finally, the processed trajectory is displayed graphically for user reference, analysis, and evaluation.

[0137] Assuming Robot 1 is not connected to the other robots, the other robots will use a Kalman filter to estimate their accurate positions. A Kalman filter assumes that the probability distribution of the current state must be a linear function of the previous state and the control variable to be executed, and then superimposed with Gaussian noise. According to the code, a new zero matrix named "u" is set to record the velocity and direction values ​​of Robot 1.

[0138] The zero matrix "u" is set to:

[0139] u(:,1)=u(:,1)+speed(:,1)

[0140] u(:,2)=u(:,2)+speed(:,2)

[0141] u(:,3)=u(:,3)+speed(:,3)

[0142] Next, set the robot position coordinate matrix. The formula is as follows:

[0143] Among them, w vt and w wt Indicates noise.

[0144] By combining the above formulas, we get the following formula:

[0145] In order to obtain the linearized system, the following assumptions are made first:

[0146] From the error state propagation equation, we can know that the state propagation and noise input function are obtained. The formula is as follows:

[0147] According to the above calculated values, the standard covariance propagation equation can be calculated, such as:

[0148] Among them, P t+1 represents the standard covariance; F t T Indicates F t The transposed matrix of Represents G t The transposed matrix of .

[0149] Finally, we can obtain a trajectory graph in which one robot does not communicate with other robots in the robot formation.

[0150] As shown in the figure, the trajectory angle of robot 1, which does not participate in communication, is calculated using the Kalman filter. The calculation results show that the final angle is approximately zero, and the trajectory of robot 1 is accurate and stable. The following table shows:

[0151] Table 4 Angle results of robot 1 under Kalman filtering

[0152] The motion path calculation method for intelligent robot formations in glass factories provided by this application can control algorithmic constraints such as vehicle speed, angular velocity, and the angle between the robot and the target. It also uses conjugate variables to form robots into formations to ensure the consistency of the group, and uses a Kalman filter to reduce sensor errors, allowing the robots to determine whether to adjust the angle and speed. This increases the utilization rate of the robot group and reduces its collision rate. That is, multiple robots, random starting positions, and random target coordinates are put into a unified algorithm, making the algorithm more universal and applicable. Through this algorithm, the reliability of the robot group used in glass factory enterprises is improved, facilitating enterprises to reduce costs and increase efficiency, as well as future factory-wide intelligence. At the same time, the robot's usage scenarios are expanded. This application can improve the rationality of the robot's operating trajectory during the glass factory's shipment process; optimize the operating trajectory, reduce the collision rate, improve utilization, reduce production costs, and improve the stability of the intelligent robot formation's operating trajectory.

[0153] The scope of protection of the method for calculating the motion path of an intelligent robot formation for a glass factory described in the embodiment of the present application is not limited to the order of execution of the steps listed in this embodiment. All solutions implemented by adding, reducing, or replacing steps in the prior art based on the principles of the present application are included in the scope of protection of the present application.

[0154] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for calculating a motion path of an intelligent robot formation for a glass factory as shown in FIG1 is implemented.

[0155] At any possible level of technical detail combination, the present application may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present application.

[0156] Computer-readable storage media can be a tangible device that can hold and store the instructions used by the instruction execution device. Computer-readable storage media can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. Computer-readable storage media used herein is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (for example, light pulses by fiber optic cables), or electrical signals transmitted by wires.

[0157] The computer-readable program described herein can be downloaded to each computing / processing device from a computer-readable storage medium, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device. The computer program instructions for performing the operation of this application can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as "C" language or similar programming languages. Computer readable program instructions can be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a computer, or entirely on a computer or server. In the case of a computer, the computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be personalized by utilizing the state information of the computer readable program instructions, and the electronic circuit can execute the computer readable program instructions, thereby realizing various aspects of the present application.

[0158] An embodiment of the present application also provides an intelligent robot formation motion path calculation system for a glass factory. The intelligent robot formation motion path calculation system for a glass factory can implement the intelligent robot formation motion path calculation method for a glass factory described in the present application. However, the implementation device of the intelligent robot formation motion path calculation method for a glass factory described in the present application includes but is not limited to the structure of the intelligent robot formation motion path calculation system for a glass factory listed in this embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the scope of protection of the present application.

[0159] The following will describe in detail the intelligent robot formation motion path calculation system for a glass factory provided by this embodiment with reference to the accompanying drawings.

[0160] This embodiment provides a motion path calculation system for an intelligent robot formation in a glass factory, including:

[0161] Please refer to Figure 8, which shows a schematic diagram of the principle structure of an embodiment of the intelligent robot formation motion path calculation system for a glass factory. As shown in Figure 8, the intelligent robot formation motion path calculation system for a glass factory includes a coordinate conversion module 81, a trajectory generation module 82, a detection module 83, an update module 84, and a noise reduction module 85.

[0162] The coordinate conversion module 81 is used to obtain a first target robot position to be processed, and convert the first target robot position into a coordinate position in a Cartesian coordinate system.

[0163] In this embodiment, the robot formation is fixed, but its position is uncertain. That is, the initial position and arrival position of the formation are not fixed in the Cartesian coordinate system. Therefore, the coordinates need to be determined.

[0164] Get the polar coordinates of the first target robot position.

[0165] In this embodiment, the latitude, longitude, angle, altitude, radius, abscissa, ordinate, etc. of the target and any other relevant polar coordinate information are obtained.

[0166] Convert polar coordinates to Cartesian coordinates.

[0167] In this embodiment, the conversion of polar coordinates to Cartesian coordinates requires the use of mathematical formulas. On a two-dimensional plane, the Cartesian coordinate system consists of an x-axis and a y-axis. In the polar coordinate system, the radial distance is the y-axis coordinate in the Cartesian coordinate system, and the azimuth angle is converted to the x-axis coordinate in the Cartesian coordinate system. The specific conversion formula is as follows:

[0168] x=ycos(θ)

[0169] y=ysin(θ)

[0170] Where x and y represent the x-axis and y-axis coordinates in the Cartesian coordinate system, respectively, and θ represents the azimuth angle.

[0171] In three-dimensional space, the Cartesian coordinate system consists of the x-axis, y-axis, and z-axis. In the polar coordinate system, the radial distance is the z-axis coordinate in the Cartesian coordinate system, and the azimuth and polar angle are converted to the x-axis and y-axis coordinates in the Cartesian coordinate system. The specific conversion formula is as follows:

[0172] z=z*cos(θ)

[0173] Where x, y, and z represent the x-axis, y-axis, and z-axis coordinates in the Cartesian coordinate system, respectively, and θ represents the azimuth angle. Indicates the polar angle.

[0174] It should be noted that in some cases, the target position may not be within the range of the Cartesian coordinate system, for example, the radial distance or azimuth of the target position exceeds the range of the Cartesian coordinate system. In this case, the target position needs to be properly processed, such as mapping the target position to the boundary range of the Cartesian coordinate system.

[0175] The trajectory generation module 82 is connected to the coordinate conversion module 81, and is used to set the conjugate variables, angular velocity and velocity of the robot group at any time based on the robot kinematic equation, and define them to obtain the state equation of the robot group at any time and form the motion trajectory of the robot.

[0176] In this embodiment, the initial values ​​of angular velocity, velocity, and conjugate variables are set, brought into the defined kinematic calculation equation, and the constraints are limited. The state of the first target robot at the current moment is expressed by a mathematical equation to obtain the state of the target at any moment.

[0177] Note that constraints must be set before simulation. These constraints ensure a stable robot formation. Furthermore, if the robots can detect each other, they will communicate with each other. Consequently, their trajectories may become closer or intersect, potentially leading to collisions. Therefore, it is necessary to introduce conjugate variables for the robot's orientation, position, and velocity.

[0178] According to the conjugate variable calculation formula, the conjugate variables of these robots can be calculated. In addition, in the conjugate variable equation, N i (k) is used to show the number of robots within a robot's detection range. It changes over time. Therefore, it can be used to calculate the average value of the consistent speed, which helps robots maintain the same speed and direction to avoid collisions.

[0179] Specifically, for each robot in the robot group, use Represents the conjugate variable in the angle equation.

[0180] The calculation formula of the conjugate variable is:

[0181] Among them, N i (k) represents the number of robots that robot A can detect at time k; Indicates the direction of movement of the robot; x i (k) represents the position of the robot on the x-axis; y i (k) represents the position of the robot on the y-axis; Indicates the speed of the robot; It means that after a robot detects other robots at time k, the robot's running angle at the next moment will become the average of the robot's angle and the other detected robots; Indicates that after a robot detects another robot at time k, the robot's position on the x-axis at the next moment will become the average of the robot itself and the other detected robots; Indicates that after a robot detects other robots at time k, the robot's position on the y-axis at the next moment will become the average of the robot and the other detected robots; It means that after a robot detects other robots at time k, the robot's running speed at the next moment will become the average of the robot itself and other detected robots.

[0182] The restrictions are:

[0183] Among them, among them, Indicates the movement direction of the robot; Indicates the speed of the robot; x i (k) represents the position of the robot on the x-axis; y i (k) represents the position of the robot on the y-axis; The x-axis coordinate representing the final position of the robot; Indicates the y-axis coordinate of the robot's final position; X i -X j Indicates the coordinate position distance of the two robots on the x-axis; Y i -Y j Indicates the coordinate position distance of the two robots on the y-axis.

[0184] The above constraints can form a stable robot formation shape.

[0185] The motion trajectory of the robot is formed based on the state equation of the robot group at any moment.

[0186] In this embodiment, based on the robot kinematic equations, the position, angle, and speed of the robot group at any moment are set through MATLAB, thereby forming the robot's motion trajectory.

[0187] The detection module 83 is used to detect the area where the first target robot is located, determine whether there is a robot in the same formation within the area, and record and save it using an adjacency matrix.

[0188] In this embodiment, a virtual target position is set.

[0189] When calculating the next robot's position, ensuring the correct direction of the robot's velocity presents another challenge. While there's no specific target position for the robots in this multi-robot system, achieving consistent velocity and direction is crucial for the robot swarm to form a formation. Therefore, we need to set virtual destination coordinates. These serve to guide the robots' direction.

[0190] In this embodiment, setting the virtual target position includes:

[0191] Among them, X i Indicates the horizontal coordinate position of the robot formation; Y i represents the vertical coordinate position of the robot formation; t represents time.

[0192] The calculation formula for the running trajectory is:

[0193] A second angular velocity and a second velocity are calculated based on the virtual target position.

[0194] In this embodiment, the second angular velocity calculation formula is:

[0195] d i (t) = g i (t)-z i (t)

[0196] w i (t) = w max f(V i (t),d i (t))

[0197] Among them, d i (t) represents the distance difference between the virtual target position and the robot position; w i (t) represents the second angular velocity; v i (t) represents the second speed; w max 、V M V m All are hypothetical values.

[0198] The adjacency matrix is ​​used to detect whether there are robots in the same formation near the intelligent robot, and the detection and judgment results are obtained.

[0199] In this embodiment, an adjacency matrix is ​​used to compare the distance between each target robot with the detection range radius to obtain a detection result. When a robot in the same formation is detected, the speed and angular velocity of each target robot are synchronously updated to ensure that the robots maintain consistent movement and direction. The judgment condition is as follows: when the distance between each target robot is less than the detection range radius, it is determined that they can communicate with each other, and the result is recorded and stored in the adjacency matrix; when the distance between each target robot is greater than or equal to the detection range radius, it is determined that they cannot communicate with each other.

[0200] Then, the distance of each pair is calculated and compared with the radius of the detection range. If the distance of the pair is less than the radius of the detection range, the robots will communicate with each other and record the result in the adjacency matrix.

[0201] The “neighbors” function in MATLAB is used to realize communication with different robots.

[0202] The values ​​of these robots at time zero can be obtained and recorded in a matrix. These values ​​are then used to calculate conjugate variables for angular velocity, the robot's x- and y-axis positions, the robot's orientation, and the angle between the site line from the virtual target to the robot's position. This ultimately enables robot communication.

[0203] The updating module 84 is used to update the position state of the first target robot based on the judgment result to form a final trajectory graph.

[0204] In this embodiment, a virtual target is established to guide the direction of the robot formation.

[0205] Specifically, if we calculate the difference between the two virtual targets of robots Ri and Rj, we can find that when they are able to detect each other, the result is consistent with the trajectory calculated by the formula.

[0206] The formula is as follows:

[0207] Then, according to the differential equation robotnextposition=robot position The calculation of + speed × 1 can get the next position of the robot, and finally the trajectory of the robot.

[0208] The noise reduction module 85 is used to perform noise reduction processing on the final trajectory graph using a Kalman filter, and ultimately obtain a stable and accurate robot formation motion trajectory.

[0209] During the formation simulation, due to the randomness of the robot group positions, there are cases where a robot cannot communicate with the other three robots during the formation process. Therefore, a Kalman filter is used to deal with this problem.

[0210] In this embodiment, Gaussian noise is superimposed on the obtained trajectory. Specifically, according to the code, a new zero matrix and robot position coordinate matrix are set, the state propagation and noise input functions are obtained, and the standard covariance propagation is calculated. Ultimately, the trajectory of a robot in a robot formation without communicating with other robots can be obtained.

[0211] Specifically, the Kalman filter parameters are initialized, including the state estimate, state estimate variance, and measurement noise covariance. The Kalman filter is used to predict the next state of the robot formation based on its current state. After obtaining new measurements, the Kalman filter is updated to obtain new measurements and predictions, which in turn update the Kalman filter's state estimate and covariance. This process is repeated until satisfactory performance is achieved. The final trajectory is then processed, using methods such as smoothing and interpolation, to make it smoother and more accurate. Finally, the processed trajectory is displayed graphically for user reference, analysis, and evaluation.

[0212] The intelligent robot formation motion path calculation system used in glass factories has expanded the robot's usage scenarios; at the same time, it has improved the reliability of the robot group used in glass factory enterprises, making it easier for enterprises to reduce costs and increase efficiency, as well as to achieve full factory intelligence in the future.

[0213] It should be understood that the division of the modules in the above system is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a single physical entity or physically separated. Furthermore, these modules may be implemented entirely in software called by a processing element, or entirely in hardware. Alternatively, some modules may be implemented in software called by a processing element, while others may be implemented in hardware. For example, module x may be a separate processing element, or integrated into a chip in the above system. Furthermore, it may be stored in the form of program code in the memory of the above system, called by a processing element in the system to perform the functions of module x. The implementation of other modules is similar. Furthermore, these modules may be fully or partially integrated or implemented independently. The processing element described herein may be an integrated circuit with signal processing capabilities. During implementation, the steps of the above method or the modules above may be performed by hardware integrated logic circuits in the processor element or by software instructions.

[0214] The above modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code through a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0215] Please refer to Figure 9, which shows a schematic diagram of the principle structure of an embodiment of the intelligent robot formation motion path calculation device for a glass factory according to the present invention. As shown in Figure 9, this embodiment provides an intelligent robot formation motion path calculation device for a glass factory. The intelligent robot formation motion path calculation device for a glass factory comprises: a processor 91 and a memory 92; the memory 92 is configured to store a computer program; the processor 91 is connected to the memory 92 and is configured to execute the computer program stored in the memory 92, thereby causing the intelligent robot formation motion path calculation device for a glass factory to perform each step of the intelligent robot formation motion path calculation method for a glass factory as described above.

[0216] Preferably, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0217] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0218] In summary, the method and system for calculating the motion path of an intelligent robot formation for a glass factory provided by this application have the following beneficial effects:

[0219] The present application provides a motion path calculation method for intelligent robot formations in glass factories. In addition to controlling algorithmic constraints such as vehicle speed, angular velocity, and the angle between the robot and the target, it uses conjugate variables to form a formation of robots to ensure the consistency of the group. It also uses a Kalman filter to reduce sensor errors, allowing the robots to determine whether to adjust the angle and speed. This increases the utilization rate of the robot group and reduces its collision rate. That is, multiple robots, random starting positions, and random target coordinates are put into a unified algorithm, making the algorithm more universal and applicable. Through this algorithm, the reliability of the robot group used in glass factory enterprises is improved, facilitating enterprises to reduce costs and increase efficiency, as well as future factory-wide intelligence. At the same time, the robot's usage scenarios are expanded. This application can improve the rationality of the robot's operating trajectory during the glass factory's shipment process; optimize the operating trajectory, reduce the collision rate, improve utilization, reduce production costs, and improve the stability of the intelligent robot formation's operating trajectory.

[0220] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. An intelligent robot formation motion path calculation method for a glass factory, characterized in that It includes the following steps: Obtain the first target robot position to be processed, and convert the first target robot position into a coordinate position in the Cartesian coordinate system; Based on the robot kinematic equation, set the conjugate variables, angular velocity and velocity of the robot group at any moment, and make definitions to obtain the state equation of the robot group at any moment, and form the motion trajectory of the robot; Detect the area where the first target robot is located, judge whether there are robots in the same formation in this area range, and record and save it using an adjacency matrix; Update the position state of the first target robot based on the judgment result to form the final trajectory graph; Use a Kalman filter to perform noise reduction processing on the final trajectory graph to finally obtain a stable and accurate robot formation motion trajectory.

2. The intelligent robot formation motion path calculation method for a glass factory according to claim 1, wherein, Based on the robot kinematic equation, setting the conjugate variables, angular velocity and velocity of the robot group at any moment, and making definitions to obtain the state equation of the robot group at any moment includes the following steps: Set the limiting conditions of the robot formation to form the shape of the robot formation; Set the initial values of the angular velocity, velocity, and conjugate variables, substitute them into the defined kinematic calculation equation, and perform the limitation of the limiting conditions, and express the state of the first target robot at the current moment with a mathematical equation to obtain the state of the target at any moment; Based on the robot kinematic equation, use MATLAB to set the position, angle and velocity of the robot group at any moment, so as to form the motion trajectory of the robot.

3. The intelligent robot formation motion path calculation method for a glass factory according to claim 2, characterized in that The limiting conditions of the robot formation are as follows: Among them, Indicates the movement direction of the robot; Represents the speed of the robot; x i (k) represents the position of the robot on the x-axis; y i (k) represents the position of the robot on the y-axis; Represents the x-axis coordinate of the final position of the robot; Represents the y-axis coordinate of the final position of the robot; X i -X j Represents the coordinate position distance between two robots on the x-axis; Y i -Y j Represents the coordinate position distance between two robots on the y-axis.

4. The intelligent robot formation movement path calculation method for a glass factory according to claim 2, wherein The calculation formula for the conjugate variable is as follows; Among them, N i (k) represents the number of robots that robot A can detect at time k; Indicates the movement direction of the robot; x i (k) indicates the position of the robot on the x-axis; y i (k) indicates the position of the robot on the y-axis; Represents the speed of the robot; It means that after the robot detects other robots at the k-th moment, the running angle of the robot in the next moment will become the average value of the robot itself and the other detected robots; It means that after the robot detects other robots at the k-th moment, the position of the robot on the x-axis in the next moment will become the average value of the robot itself and the other detected robots; It means that after the robot detects other robots at time k, the position of the robot on the y-axis in the next moment will become the average value of the robot itself and the other detected robots; It means that when the robot detects other robots at time k, the running speed of the robot in the next moment will become the average value of the body and the detected other robots.

5. The intelligent robot formation motion path calculation method for a glass factory according to claim 1, characterized in that Detect the area where the first target robot is located, judge whether there are robots in the same formation in this area range, and record and save it using an adjacency matrix includes the following steps: Set a virtual target position; Calculate the second angular velocity and the second velocity based on the virtual target position; Use an adjacency matrix to detect whether there are robots in the same formation near the intelligent robot, obtain the detection result, and record and save it.

6. The method for calculating the formation movement path of the intelligent robot for a glass factory according to claim 5, wherein Setting a virtual target position includes: Among them, X i represents the abscissa position of the robot formation; Y i represents the ordinate position of the robot formation; The running trajectory calculation formula is as follows: The calculation formula of the second angular velocity is: d i d(t) = g i d(t) - z i d(t) w i (t) = w max f(V i (t), d i (t)) Among them, d i (t) represents the distance difference between the virtual target position and the robot position; w i (t) represents the second angular velocity; v i (t) represents the second velocity; w max , V M V m are all assumed values; g i (t) represents the virtual target position.

7. The method for calculating the formation motion path of an intelligent robot for a glass factory according to claim 5, characterized in that, Use an adjacency matrix to detect whether there are robots in the same formation near the intelligent robot, obtain the detection result, and record and save it includes the following steps: Use an adjacency matrix to compare the distance between two target robots with the detection range radius to obtain the detection result; It includes: when the distance between two target robots is less than the detection range radius, it can be known that they can communicate with each other, and record the result in the adjacency matrix and save it; when the distance between two target robots is greater than or equal to the detection range radius, it can be known that they cannot communicate with each other; When detecting robots in the same formation, synchronously update the speed and angular velocity of two target robots to make them move with the same actions and directions.

8. The intelligent robot formation motion path calculation method for a glass factory according to claim 1, wherein Updating the position state of the first target robot based on the judgment result to form the final trajectory graph includes the following steps: Calculate the difference between two robots and two virtual targets to obtain the same running trajectory of the two robots; Calculate the next position according to the differential equation, and then obtain the running trajectory of the robot formation; The formula for the same running trajectory of the two robots is: robotnextposition = robot position + speed × 1 Among them, X i represents the abscissa position of the robot formation; Y i represents the ordinate position of the robot formation; x i (t) represents the position of the robot on the x-axis; y i (t) represents the position of the robot on the y-axis; l represents the time interval; speed×1 represents the differential result of the change of the robot position over time.

9. The method for calculating the formation movement path of the intelligent robot for a glass factory according to claim 1, wherein Adopt a Kalman filter to perform noise reduction processing on the final trajectory graph, and finally obtain a stable and accurate robot formation motion trajectory, including the following steps: Superimpose Gaussian noise on the final running trajectory, and set a new zero matrix to record the speed and direction of a certain robot; Set the robot coordinate matrix to obtain the state propagation and noise input functions; Calculate the standard covariance based on the obtained state propagation and noise input functions, and finally obtain a stable and accurate robot formation motion trajectory; The zero matrix is: u(:,1) = u(:,1) + speed(:,1) u(:,2) = u(:,2) + speed(:,2) u(:,3) = u(:,3) + speed(:,3) The calculation formula for the robot position coordinate matrix is as follows: where w vt and w wt represent noise; The state propagation and noise input functions are as follows: The standard covariance propagation equation is as follows: Among them, P t+1 represents the standard covariance; Denote F t as the transpose matrix of; Denote G t as the transposed matrix of 10. An intelligent robot formation motion path calculation system for a glass factory, characterized in that, Including: A coordinate conversion module, configured to obtain the position of a first target robot to be processed, and convert the position of the first target robot into a coordinate position in a Cartesian coordinate system; A trajectory generation module, configured to set conjugate variables, angular velocity, and speed of a robot group at any moment based on a robot kinematic equation, and perform definitions to obtain a state equation of the robot group at any moment, and form a motion trajectory of the robot; A detection module, configured to detect the area where the first target robot is located, and determine whether there are robots in the same formation in this area, and record and save them using an adjacency matrix; An update module, configured to update the position state of the first target robot based on the judgment result to form a final trajectory graph; A noise reduction module, configured to perform noise reduction processing on the final trajectory graph using a Kalman filter, and finally obtain a stable and accurate robot formation motion trajectory.

11. An intelligent robot formation motion path calculation device for a glass factory, characterized in that, Including: A processor and a memory; The memory is used to store a computer program; The processor is connected to the memory and is configured to execute the computer program stored in the memory, so that the intelligent robot formation motion path calculation device for a glass factory executes the intelligent robot formation motion path calculation method according to any one of claims 1 to 9.

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