Self-adaptive intelligent detection control system and method for preventing mistaken mounting of automobile

Abstract

The invention provides a self-adaptive intelligent detection control system and method for preventing mistaken installation of an automobile, and the system comprises an information obtaining module, a point location prediction module, a point location correction module, a collision detection module, a path planning module, a speed planning module, a motion execution module, a motion judgment module, an information detection module, a deviation obtaining module and the like. The system optionally comprises a point location re-prediction module, a path re-planning module, a speed re-planning module and a motion re-execution module. According to the method, position information of a to-be-detected workpiece of a vehicle is obtained through a sensor and a global camera, a multi-coordinate system conversion relation is established, weight fusion is carried out, a target point position is predicted and corrected, path and speed planning is carried out in combination with a collision model, and a mechanical arm is controlled to complete detection tasks of a meeting mode, a tracking mode or an arrival mode in a non-stop-line state. And whether the workpiece is correctly assembled or not is judged through images acquired by the depth camera. According to the invention, full-automatic error-proofing detection can be realized under the condition of multi-vehicle-type mixed production, manual intervention is not needed, and the method has the advantages of high detection flexibility, high precision and high efficiency.

Description

Technical Field

[0001] The present invention relates to a field of automobiles, and in particular to an adaptive intelligent detection control system and method for preventing mis-installation of automobiles. Background Art

[0002] With the prosperity of the market economy and the improvement of living standards, people's demand for automobiles continues to grow, which in turn promotes the rapid development of the automobile industry. Flexible automobile production enables the mixed production of different models and styles of vehicles on a single production line. This has led to a sharp increase in the number of materials and parts required for assembly, posing a severe challenge to on-site workers in assembly and inspection. Any incorrect or missing part will require reassembly, which significantly reduces production quality and efficiency.

[0003] Current methods for detecting errors and omissions in final assembly rely primarily on manual inspection. Due to the wide variety of parts, this leads to a high error rate during actual production, resulting in product rework or scrapping, and consequently, poor vehicle assembly quality. Existing detection methods include installing fixed cameras and using robotic arms with cameras. However, most current error-proofing detection methods or systems are complex, lack flexibility, and employ a single, static detection scenario, resulting in significant limitations in practical error-proofing detection. Summary of the Invention

[0004] In response to the defects in the existing technology, the purpose of the present invention is to provide an adaptive intelligent detection control system and method for preventing automobile misinstallation. The adaptive intelligent detection system can realize mixed detection of different models without stopping the line, and no manual operation is required during the detection process. Automatic positioning, motion planning and intelligent recognition are performed, making the entire detection process more intelligent, flexible and agile.

[0005] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0006] According to a first aspect of the present invention, there is provided an adaptive intelligent detection and control system for preventing mis-installation of automobiles, comprising a device module and a function module;

[0007] The equipment module includes the following components: a robotic arm, a depth camera, a database, a global camera, and a sensor; wherein the robotic arm is set on the robotic arm base, the depth camera is installed on the end flange of the robotic arm, the sensor is set at a preset trigger point for detecting vehicle entry signals, and the global camera is installed between the sensor and the robotic arm; the database is used to store vehicle model data and position information of the workpiece to be detected;

[0008] The functional modules include:

[0009] The information acquisition module is used to establish a conversion coordinate system based on the relative position relationship between the various components of the detection system, and convert the position information of the vehicle's workpiece to be detected into coordinate points in the robot arm's base coordinate system;

[0010] A point prediction module is used to predict the target point of the workpiece to be inspected based on the coordinate point and the current position of the end of the robotic arm;

[0011] The point correction module is used to correct the predicted target point based on the deviation information fed back by the depth camera to obtain the corrected target point;

[0012] The collision detection module is used to obtain vehicle obstacle information and establish a collision model, dividing the safety range to prevent the robot arm from colliding with the vehicle during movement;

[0013] The path planning module is used to generate obstacle avoidance path points based on the vehicle's starting point and the corrected target point, combined with collision detection information, to plan the actual movement points of the robotic arm and avoid collisions with the vehicle during movement;

[0014] The speed planning module is used to plan the trajectory speed and acceleration of the robot arm based on the production line speed and path points;

[0015] The motion execution module is used to convert the trajectory points into angle, angular velocity, and angular acceleration information in the joint space and control the movement of the robotic arm;

[0016] The motion judgment module is used to compare the actual motion time of the robot arm with the theoretical motion time to determine whether the motion is successful;

[0017] The information detection module is used to obtain the image information of the workpiece to be inspected through the depth camera after the robot arm reaches the target point, and determine whether it meets the expectations;

[0018] Deviation acquisition module, used to calculate the difference between the actual position of the workpiece to be detected and the target point during information detection and feed it back to the point correction module;

[0019] In addition, it also includes a point re-prediction module, a path re-planning module, a speed re-planning module and a motion re-execution module for re-tracking after motion failure.

[0020] Optionally, the conversion coordinate system established by the information acquisition module includes the vehicle body coordinate system, the sensor coordinate system, the global camera coordinate system, the robotic arm end flange coordinate system, the robotic arm base coordinate system and the depth camera coordinate system, and realizes the position mapping between different coordinate systems through a preset conversion matrix.

[0021] Optionally, the point prediction module determines whether the movement mode is an encounter movement, a tracking movement, or an arrival movement based on the relative relationship between the position of the end of the robot arm and the position of the workpiece to be detected, and calculates the target point position through a prediction formula in the encounter or tracking mode.

[0022] Optionally, the point correction module compensates the predicted point based on the three-dimensional deviation value fed back by the depth camera and in combination with the transformation matrix between the depth camera coordinate system and the robotic arm base coordinate system.

[0023] Optionally, the collision detection module equates the obstacle to a regular shape and performs expansion processing, sets a boundary threshold and divides the safety range, and combines the detection results of the sensor and the global camera for weighted fusion to obtain the safety point after collision detection.

[0024] Optionally, the path planning module obtains intermediate motion points based on an interpolation algorithm, and generates an obstacle avoidance path in combination with collision detection information; the speed planning module calculates the theoretical motion time in combination with the production line speed, and plans the speed and acceleration curves of the robotic arm movement based on the path points.

[0025] According to a second aspect of the present invention, there is provided an adaptive intelligent detection and control method for preventing mis-installation of automobiles, which is implemented using the above-mentioned system and includes the following steps:

[0026] S1. When the vehicle passes the trigger sensor, the information acquisition module obtains the position information P1 of the vehicle's workpiece to be detected in the database, and obtains the initial point P2 in the robot arm's base coordinate system through coordinate system conversion;

[0027] At the same time, the global camera detection obtains the coordinate point P3 of the workpiece to be detected in the global camera coordinate system and converts it into the global point P4 in the robot base coordinate system. The current workpiece point is obtained by weighting the initial point P2 and the global point P4 using the following formula: P=K0·P2+K1·P4, where K0 and K1 are weight coefficients;

[0028] S2, the point prediction module is based on the current coordinate point P of the workpiece to be detected and the current coordinate point P of the end of the robot arm. rob Determine the system's motion mode and predict the target position of the workpiece to be detected according to the following formula: , where K2 is the motion coefficient;

[0029] S3, the point correction module uses the deviation value fed back by the depth camera to correct the target point P goal Performing calibration to obtain the target point after point calibration;

[0030] S4. The collision detection module obtains the vehicle body expansion object information from the database and divides the boundary. The collision object is equivalent to a regular shape and expanded. After setting the threshold, the collision model after the maximum boundary threshold is obtained and the safety range is divided. To reduce the error, weight calculation is also performed to obtain the final collision object point information;

[0031] S5. Determine whether the target point exceeds the reachable space of the robot arm, and then plan the path points based on the current coordinate point of the robot arm and the target point, combined with the collision point information;

[0032] S6. Calculate the time it takes for the workpiece to be inspected to move to the target point, and perform robot arm speed planning based on the movement time and path point planning information to obtain trajectory information;

[0033] S7, the motion execution module controls the robot arm to move along the planned trajectory information, and the motion judgment module determines whether the motion is successful. If successful, image acquisition, recognition, judgment, and deviation information are obtained; if unsuccessful, a new target point is re-predicted and the path and speed of the robot arm are re-planned and the motion is re-executed until success or a preset threshold number of times is reached;

[0034] S8. Repeat the above operation process to inspect the next workpiece to be inspected until all the workpieces to be inspected on the entire vehicle are completed.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] When performing error-proofing detection on assembled vehicles, the present invention can realize mixed detection of different vehicle models without stopping the line, and no manual operation is required during the detection process. Automatic positioning, motion planning and intelligent recognition are performed, making the entire detection process more intelligent, flexible and agile. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0038] Figure 1 This is the equipment installation structure diagram for the adaptive intelligent detection and control system used to prevent automobile misinstallation;

[0039] Figure 2 A schematic diagram of the relative position relationship of various parts of the adaptive intelligent detection and control system for preventing automobile misassembly;

[0040] Figure 3 This is a structural diagram of an adaptive intelligent detection and control system for preventing incorrect installation of automobiles;

[0041] Figure 4 The present invention is a flow chart of an adaptive intelligent detection and control method for preventing automobile misassembly. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0044] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures. In addition, all directional indications in this application (such as up, down, left, right, front, back, bottom...) are only used to explain the relative position relationship, movement, etc. between the components under a specific posture (as shown in the figures). If the specific posture changes, the directional indication will also change accordingly. Furthermore, the descriptions of "first", "second", etc. in the application are for descriptive purposes only and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features.

[0045] First embodiment

[0046] like Figure 1 As shown, this embodiment provides an adaptive intelligent detection and control system for preventing automobile misassembly, including a robotic arm 1, a depth camera 2, a database 3, a global camera 4, a sensor 5, and a vehicle to be detected 6.

[0047] The robot arm 1 is installed at the inspection station and is used to perform path tracking and workpiece inspection actions;

[0048] Sensor 5 is installed at the trigger point of the production line to detect whether the vehicle enters the detection range;

[0049] The global camera 4 is installed between the sensor 5 and the robot arm 1 to obtain a wide range of position information of the workpiece to be inspected;

[0050] The depth camera 2 is installed on the end flange of the robotic arm to obtain local three-dimensional information of the workpiece to be inspected;

[0051] The database 3 is used to store the standard position coordinates of the workpiece to be inspected, the workpiece type information and the vehicle structure information of each vehicle type.

[0052] Figure 2 This is a schematic diagram showing the relative positional relationships of the various components of the adaptive intelligent detection system for preventing misassembly in automobile final assembly. Based on the corresponding relationships between the devices, the transformation coordinate systems of each component are established. The spatial transformation relationship between the vehicle coordinate system and the sensor coordinate system is established based on relative position. The spatial transformation relationship between the sensor coordinate system and the manipulator end flange coordinate system is established based on relative position. The spatial transformation relationship between the vehicle coordinate system and the global camera coordinate system is established based on relative position. The spatial transformation relationship between the global camera coordinate system and the manipulator end flange coordinate system is established based on relative position. The spatial transformation relationship between the manipulator end flange coordinate system and the manipulator base coordinate system is established based on kinematics. The spatial transformation relationship between the vehicle coordinate system and the depth camera coordinate system is established based on relative position. The spatial transformation relationship between the depth camera coordinate system and the manipulator base coordinate system is established based on kinematic parameters.

[0053] Set the sensor coordinate system to , the global camera coordinate system is , the flange coordinate system at the end of the robot arm is , the robot base coordinate system is , the depth camera coordinate system is , the vehicle coordinate system is This involves the transformation of multiple coordinate systems, including the transformation matrix of the vehicle coordinate system relative to the sensor coordinate system. , the transformation matrix of the vehicle coordinate system relative to the global camera coordinate system , the transformation matrix of the global camera coordinate system relative to the robot end flange coordinate system , the transformation matrix of the sensor coordinate system relative to the robot end flange coordinate system , the transformation matrix of the vehicle coordinate system relative to the depth camera coordinate system ,The transformation matrix of the depth camera coordinate system relative to the end flange of the robotic arm , the transformation matrix of the robot arm end flange coordinate system relative to the robot arm base coordinate system .

[0054] Through the above matrix, any point in any coordinate system can be converted into the coordinates of the robot base coordinate system.

[0055] Figure 3This is a block diagram of the adaptive intelligent detection system for preventing incorrect assembly in automobile final assembly. It includes an information acquisition module, a point prediction module, a point correction module, a collision detection module, a path planning module, a speed planning module, a motion execution module, a motion judgment module, an information detection module, and a deviation acquisition module. Optionally, it also includes a point re-prediction module, a path re-planning module, a speed re-planning module, and a motion re-execution module.

[0056] The information acquisition module is used to establish a conversion coordinate system based on the relative position relationship between the various components of the detection system, and convert the position information of the vehicle's workpiece to be detected into coordinate points in the robot arm's base coordinate system; the conversion coordinate system established by the information acquisition module includes the vehicle body coordinate system, the sensor coordinate system, the global camera coordinate system, the robot arm end flange coordinate system, the robot arm base coordinate system and the depth camera coordinate system, and realizes the position mapping between different coordinate systems through a preset conversion matrix.

[0057] The point prediction module is used to predict the target point of the workpiece to be detected based on the coordinate point and the current position of the end of the robotic arm; the point prediction module determines whether the movement mode is an encounter movement, a tracking movement or an arrival movement based on the relative relationship between the position of the end of the robotic arm and the position of the workpiece to be detected, and calculates the target point using a prediction formula in the encounter or tracking mode.

[0058] The point correction module is used to correct the predicted target point based on the deviation information fed back by the depth camera to obtain the corrected target point; the point correction module compensates the predicted point based on the three-dimensional deviation value fed back by the depth camera and the transformation matrix between the depth camera coordinate system and the robotic arm base coordinate system.

[0059] The collision detection module is used to obtain vehicle obstacle information and establish a collision model, dividing the safety range to prevent the robot arm from colliding with the vehicle during movement. The collision detection module equates the obstacle to a regular shape and performs expansion processing, sets the boundary threshold and divides the safety range. At the same time, it combines the detection results of the sensor and the global camera for weighted fusion to obtain the safe point after collision detection.

[0060] The path planning module is used to generate obstacle avoidance path points based on the vehicle's starting point and the corrected target point, combined with collision detection information; the path planning module obtains intermediate movement points based on the interpolation algorithm, and generates an obstacle avoidance path based on collision detection information; the speed planning module calculates the theoretical movement time based on the production line speed, and plans the speed and acceleration curve of the robot arm movement based on the path points.

[0061] The speed planning module is used to plan the trajectory speed and acceleration of the robot arm based on the production line speed and path points;

[0062] The motion execution module is used to convert the trajectory points into angle, angular velocity, and angular acceleration information in the joint space and control the movement of the robotic arm;

[0063] The motion judgment module is used to compare the actual motion time of the robot arm with the theoretical motion time to determine whether the motion is successful;

[0064] In addition, the point re-prediction module, path re-planning module, velocity re-planning module, and motion re-execution module are optional modules for subsequent motion after the system motion judgment fails. The point re-prediction module calculates the new target point; the path re-planning module re-plans the new path points; the velocity re-planning module plans the new trajectory points with velocity and acceleration information; and the motion re-execution module re-controls the actual motion of the robot arm.

[0065] Second embodiment

[0066] like Figure 4 As shown, this embodiment provides an adaptive intelligent detection and control method for preventing misinstallation of an automobile, which mainly includes the following steps:

[0067] S1 system triggering and initial information acquisition;

[0068] When the sensor receives the vehicle trigger signal, the system starts to detect. The information acquisition module obtains the position information of the vehicle to be detected in the database, and the information acquisition module establishes the conversion matrix between the devices. 、 、 、 、 、 and , at this time the position information of the workpiece to be detected in the database Convert to the initial coordinate point in the robot base coordinate system , the conversion formula is:

[0069]

[0070] S2 global visual information acquisition and weight fusion;

[0071] The global camera 4 obtains the coordinate points of the workpiece to be detected in the global camera coordinate system through the detection algorithm ,go through 、 and The transformation matrix is ​​converted into the point in the robot base coordinate system , the conversion formula is:

[0072]

[0073]

[0074] Get the above initial point and global points After that, the information acquisition module performs weight calculation to obtain the new current position of the workpiece to be detected. .

[0075]

[0076] in and are the weight coefficients of the initial point and the global point respectively. 、 and It is the initial position, global position and weighted position of the workpiece to be detected in the robot base coordinates at the current time.

[0077] It is understandable that the vehicle body moves along the production line, so the vehicle body coordinate system is a dynamic coordinate system. According to the movement speed of the production line, the real-time conversion coordinate system of the vehicle body at different times can be calculated. :

[0078] At this time, the real-time transformation coordinate system of the vehicle body relative to the sensor and the real-time transformation coordinate system of the vehicle body relative to the global camera at different times can be calculated:

[0079]

[0080]

[0081] Similarly, the position of the vehicle coordinate system at different times can be obtained , respectively, after the sensor coordinate system and the global camera coordinate system are converted to the position in the manipulator base coordinate system and :

[0082]

[0083]

[0084] in The point converted by the sensor coordinate system is the initial point. The point position converted by the global camera coordinate system is the global point position.

[0085] After calculating the above initial point and global point, the information acquisition module performs weight calculation to reduce the error and obtain the new current position of the workpiece to be detected. The calculation method is as follows:

[0086]

[0087] in and are the weight coefficients of the initial point and the global point respectively. 、 and It is the initial position, global position and weighted position of the workpiece to be detected in the robot arm base coordinates at the current time.

[0088] In this embodiment, 、 and It is the initial position, global position and weighted position of the workpiece to be detected in the robot base coordinates at the current time.

[0089] S3 point forecast;

[0090] The point prediction module predicts the target point of the workpiece to be inspected based on the current coordinates of the workpiece to be inspected and the current coordinates of the robot end. The difference between the robot end position and the workpiece position determines the system's motion mode. If the workpiece to be inspected is in front of the robot end, the motion mode is tracking. If the workpiece to be inspected is behind the robot end, the motion mode is meeting. Otherwise, the motion mode is arrival. If the motion mode is meeting / tracking, the predicted point position is calculated using a formula.

[0091]

[0092] in For the predicted encounter / tracking point, is the current workpiece point to be detected, is the current position of the robot arm, is the encounter / tracking motion coefficient

[0093] S4 point correction;

[0094] The point correction module obtains the deviation information of the deviation acquisition module Correct the predicted point. Since it is the first workpiece to be detected, the deviation information defaults to 0. The target point after point correction is obtained. .

[0095]

[0096]

[0097] in is the deviation information fed back by the depth camera, is the deviation information in the base coordinate system of the robot arm, is the target point after correction. 、 、 is the transformation matrix.

[0098] S5 collision detection and safety range determination;

[0099] The collision detection module obtains the vehicle body expansion object information from the database and divides the boundary. The collision object is equivalent to a regular shape and expanded. After setting the threshold, the collision model after the maximum boundary threshold is obtained and the safety range is divided. In order to reduce the error, the weight calculation is also performed to obtain the final collision object point information. .

[0100]

[0101]

[0102]

[0103] in is the safe point after collision processing relative to the vehicle coordinate system, is the initial safety point transformed by the sensor coordinate system, is the global safety point transformed by the global camera coordinate system, and are the weight coefficients of the initial safety point and the global safety point respectively, It is the safe point after collision detection with weight processing.

[0104] S6 Path and Speed ​​Planning;

[0105] The path planning module takes the current coordinate point of the above robot arm As the starting point of the movement , predicted point As the end point First, determine the end point Whether it exceeds the reachable space of the robot arm, if so, an error is reported and the robot arm path planning is terminated; if it is within the reachable range, an interpolation algorithm is used to obtain several motion points, and the collision information obtained by the above collision detection module is combined Get a series of path points after obstacle avoidance.

[0106] The path points are input into the speed planning module, and the starting point is and end point Combined with production line speed , calculate the theoretical motion time of the workpiece to be detected , combined with the theoretical movement time And path planning information, the speed planning algorithm is used to plan the speed of the robotic arm and obtain the trajectory points with speed and acceleration information.

[0107] S7 Movement Execution and Judgment;

[0108] The motion execution module converts the trajectory points planned by the speed planning module into angle, angular velocity and angular acceleration information in the joint space through the inverse kinematics equation, and sends it to the controller and driver to realize the actual movement of the robot arm. At the same time, the robot arm is timed when it moves to obtain the actual movement time of the robot arm. .

[0109] The motion judgment module obtains the actual motion time of the robotic arm , and the theoretical motion time of the workpiece to be detected Make a judgment to get the motion result. If the motion execution module judges that the result is If the tracking fails, the process goes to step S9 to re-track. If the theoretical motion time is greater than or equal to the actual motion time of the robot arm, the motion encounter / tracking is successful, and the waiting time is calculated based on the motion result. , waiting for the workpiece to be detected to reach the target point. The calculation formula is as follows:

[0110]

[0111]

[0112] in, 、 、 is the movement time of the workpiece to be detected, the movement time of the robot arm and the sleep time of the robot arm, 、 is the target position of the workpiece to be detected and the current position of the workpiece to be detected, is the production line movement speed.

[0113] If the actual movement time of the robot arm is greater than the theoretical movement time, the movement encounter / tracking fails, and the point re-prediction, path re-planning, speed re-planning and movement re-execution modules are performed before continuing the judgment.

[0114] S8 Information Detection and Deviation Acquisition;

[0115] When the workpiece to be detected reaches the target point, the information detection module controls the depth camera to take pictures to obtain the image information of the workpiece to be detected, and at the same time calls the algorithm to identify the type of the workpiece to be detected to determine whether the workpiece to be detected meets the expected effect. If it meets the requirements, the detection is completed. At the same time, the deviation acquisition module is called to identify and obtain the image information of the workpiece to be detected, and the detection algorithm is called to synchronously calculate the difference between the workpiece to be detected and the target point. , and returns it to the point calibration module. If it does not meet the requirements, it will be recorded and the next workpiece to be detected will be identified.

[0116] S9 re-tracking process;

[0117] If the motion execution module determines that the result is Tracking failed. Switch to re-tracking and enter the point re-prediction module, based on the actual movement time of the robot arm. , Theoretical movement time of the workpiece to be tested and production line speed , locate the current position of the workpiece to be detected , and according to the re-tracking coefficient Re-determine the new target point .

[0118]

[0119]

[0120]

[0121] Then call the path replanning module to update the collision information , the current position of the robot arm As a starting point , re-predict the target point As the new end point , determine the end point In the reachable space of the robot, the interpolation algorithm is used to obtain the transition point, and the updated collision information is combined Get a series of re-tracking path points after obstacle avoidance.

[0122] Re-track the path points and input them into the speed re-planning module, and then calculate the speed of the re-track path points according to the starting point. and end point Combined with production line speed , calculate the theoretical motion time of the workpiece to be detected , combined with the theoretical movement time And path planning information, the speed of the robot arm is replanned through the speed planning algorithm to obtain the re-tracking trajectory point information with speed information.

[0123] The motion re-execution module converts the re-tracking trajectory points planned by the above-mentioned speed re-planning module into angle, angular velocity and angular acceleration information in the joint space through the inverse kinematics equation, and sends it to the controller and driver to realize the actual movement of the robot arm. At the same time, the robot arm is timed when it moves to obtain the actual movement time of the robot arm. .

[0124] Actual movement time of the robotic arm The data is input into the motion judgment module and compared with the theoretical motion time of the workpiece to be detected. Compare the difference and the result is that the encounter is successful and The waiting time is calculated as , waiting for the workpiece to be inspected to reach the target point.

[0125]

[0126] Waiting time After the end, the information detection module is called to control the depth camera to take pictures to obtain the image information of the workpiece to be detected. At the same time, the detection algorithm is called to identify the type of the workpiece to be detected, determine whether the workpiece to be detected matches the expected workpiece, complete the current detection and return the result as correct. At the same time, the deviation acquisition module is called to identify and obtain the image information of the workpiece to be detected, and the detection algorithm is called to synchronously calculate the difference between the workpiece to be detected and the target point. , and returns it to the point correction module.

[0127] Repeat the above operation to identify the next workpiece to be inspected until the last workpiece to be inspected on the complete vehicle is inspected, and the entire process ends.

[0128] The above describes the specific embodiments of the present invention. Based on the above description, relevant personnel can make various changes and modifications without departing from the scope of the technical concept of this invention.

Claims

1. An adaptive intelligent detection and control system for preventing mis-installation of automobiles, characterized in that: Including equipment modules and functional modules; The equipment module includes the following components: a robotic arm, a depth camera, a database, a global camera, and a sensor; wherein the robotic arm is set on the robotic arm base, the depth camera is installed on the end flange of the robotic arm, the sensor is set at a preset trigger point for detecting vehicle entry signals, and the global camera is installed between the sensor and the robotic arm; the database is used to store vehicle model data and position information of the workpiece to be detected; The functional modules include: The information acquisition module is used to establish a conversion coordinate system based on the relative position relationship between the various components of the detection system, and convert the position information of the vehicle's workpiece to be detected into coordinate points in the robot arm's base coordinate system; A point prediction module is used to predict the target point of the workpiece to be inspected based on the coordinate point and the current position of the end of the robotic arm; The point correction module is used to correct the predicted target point based on the deviation information fed back by the depth camera to obtain the corrected target point; The collision detection module is used to obtain vehicle obstacle information and establish a collision model, dividing the safety range to prevent the robot arm from colliding with the vehicle during movement; The path planning module is used to generate obstacle avoidance path points based on the vehicle's starting point and the corrected target point, combined with collision detection information, to plan the actual movement points of the robotic arm and avoid collisions with the vehicle during movement; The speed planning module is used to plan the trajectory speed and acceleration of the robot arm based on the production line speed and path points; The motion execution module is used to convert the trajectory points into angle, angular velocity, and angular acceleration information in the joint space and control the movement of the robotic arm; The motion judgment module is used to compare the actual motion time of the robot arm with the theoretical motion time to determine whether the motion is successful; The information detection module is used to obtain the image information of the workpiece to be inspected through the depth camera after the robot arm reaches the target point, and determine whether it meets the expectations; Deviation acquisition module, used to calculate the difference between the actual position of the workpiece to be detected and the target point during information detection and feed it back to the point correction module; In addition, it also includes a point re-prediction module, a path re-planning module, a speed re-planning module and a motion re-execution module for re-tracking after motion failure.

2. The adaptive intelligent detection and control system for preventing automobile misassembly according to claim 1 is characterized in that: The conversion coordinate system established by the information acquisition module includes the vehicle body coordinate system, the sensor coordinate system, the global camera coordinate system, the manipulator end flange coordinate system, the manipulator base coordinate system and the depth camera coordinate system, and realizes the position mapping between different coordinate systems through a preset conversion matrix.

3. The adaptive intelligent detection and control system for preventing automobile misassembly according to claim 1 is characterized in that: The point prediction module determines whether the motion mode is encounter motion, tracking motion or reaching motion according to the relative relationship between the end position of the robot arm and the position of the workpiece to be detected, and calculates the target point position through the prediction formula in the encounter or tracking mode.

4. The adaptive intelligent detection and control system for preventing automobile misassembly according to claim 1 is characterized in that: The point correction module compensates the predicted point based on the three-dimensional deviation value fed back by the depth camera and the conversion matrix between the depth camera coordinate system and the robotic arm base coordinate system.

5. The adaptive intelligent detection and control system for preventing automobile misassembly according to claim 1 is characterized in that: The collision detection module converts obstacles into regular shapes and performs expansion processing, sets boundary thresholds and divides safety ranges, and simultaneously combines the detection results of sensors and global cameras for weighted fusion to obtain safety points after collision detection.

6. The adaptive intelligent detection and control system for preventing automobile misassembly according to claim 1 is characterized in that: The path planning module obtains intermediate motion points based on the interpolation algorithm and generates an obstacle avoidance path in combination with collision detection information; the speed planning module calculates the theoretical motion time in combination with the production line speed and plans the speed and acceleration curves of the robot arm movement based on the path points.

7. An adaptive intelligent detection and control method for preventing mis-installation of automobiles, implemented by the system according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. When the vehicle passes the trigger sensor, the information acquisition module obtains the position information P1 of the vehicle's workpiece to be detected in the database, and obtains the initial point P2 in the robot arm's base coordinate system through coordinate system conversion; At the same time, the global camera detection obtains the coordinate point P3 of the workpiece to be detected in the global camera coordinate system and converts it into the global point P4 in the robot base coordinate system. The current workpiece point is obtained by weighting the initial point P2 and the global point P4 using the following formula: P=K0·P2+K1·P4, where K0 and K1 are weight coefficients; S2, the point prediction module is based on the current coordinate point P of the workpiece to be detected and the current coordinate point P of the end of the robot arm. rob Determine the system's motion mode and predict the target position of the workpiece to be detected according to the following formula: , where K2 is the motion coefficient; S3, the point correction module uses the deviation value fed back by the depth camera to correct the target point P goal Performing calibration to obtain the target point after point calibration; S4. The collision detection module obtains the vehicle body expansion object information from the database and divides the boundary. The collision object is equivalent to a regular shape and expanded. After setting the threshold, the collision model after the maximum boundary threshold is obtained and the safety range is divided. To reduce the error, weight calculation is also performed to obtain the final collision object point information; S5. Determine whether the target point exceeds the reachable space of the robot arm, and then plan the path points based on the current coordinate point of the robot arm and the target point, combined with the collision point information; S6. Calculate the time it takes for the workpiece to be inspected to move to the target point, and perform robot arm speed planning based on the movement time and path point planning information to obtain trajectory information; S7, the motion execution module controls the robot arm to move along the planned trajectory information, and the motion judgment module determines whether the motion is successful. If successful, image acquisition, recognition, judgment, and deviation information are obtained; if unsuccessful, a new target point is re-predicted and the path and speed of the robot arm are re-planned and the motion is re-executed until success or a preset threshold number of times is reached; S8. Repeat the above operation process to inspect the next workpiece to be inspected until all the workpieces to be inspected on the entire vehicle are completed.

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