An adaptive intelligent detection control system and method for automobile mistake-proofing

Abstract

The application provides a self-adaptive intelligent detection control system and method for automobile error-proof assembly, the system comprises information acquisition, point prediction, point correction, collision detection, path planning, speed planning, motion execution, motion judgment, information detection and deviation acquisition modules, and optionally comprises point re-prediction, path re-planning, speed re-planning and motion re-execution modules. The method obtains the position information of the vehicle workpiece to be detected through sensors and global cameras, establishes a multi-coordinate system conversion relationship and performs weight fusion, predicts and corrects the target point, plans the path and speed in combination with the collision model, controls the mechanical arm to complete the detection task in the meeting, tracking or reaching mode under the non-stop line state, and determines whether the workpiece is correctly assembled through the image collected by the depth camera. The application can realize full-automatic error-proof detection under the condition of mixed production of multiple vehicle models, does not require manual intervention, and has the advantages of high detection flexibility, high precision and high efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field, in particular, to an adaptive intelligent detection control system and method for automobile error-proof assembly. BACKGROUND

[0002] With the prosperity of market economy and the improvement of living standards, people's growing demand for cars, and thus promote the vigorous development of the automobile. Flexible production of automobiles enables a production line to achieve mixed production of different models and different styles of cars, which leads to a dramatic increase in the number of materials and parts on the assembly, which poses a serious challenge to on-site workers assembly and detection. Any part of the wrong assembly will lead to reassembly, which greatly reduces production quality and production efficiency.

[0003] The current assembly error detection method mainly relies on manual detection. Due to the complexity of the types of parts and accessories, the error rate is high in the actual production process, resulting in product rework or scrap, and thus leading to low vehicle assembly quality. The detection method used in the prior art includes installing fixed cameras and using mechanical arms with cameras for detection. However, the current error-proof detection method or system is mostly complex, low in flexibility, single in detection scene, and static detection, which leads to great limitations in actual error-proof detection. SUMMARY

[0004] In view of the defects in the prior art, the purpose of the present application is to provide an adaptive intelligent detection control system and method for automobile error-proof assembly, which adopts an adaptive intelligent detection system to realize mixed detection of different vehicle models without stopping the line, and does not require manual operation during detection, automatic positioning, motion planning and intelligent identification, making the entire detection process more intelligent, flexible and flexible.

[0005] To achieve the above technical effects, the present application adopts the following technical solutions:

[0006] According to a first aspect of the present application, an adaptive intelligent detection control system for automobile error-proof assembly is provided, comprising a device module and a function module;

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

[0008] The function module comprises:

[0009] The information acquisition module is configured to convert position information of a workpiece to be detected of the vehicle into a coordinate point in a base coordinate system of the mechanical arm by detecting a relative positional relationship between components of the system and establishing a conversion coordinate system.

[0010] The point position prediction module is configured to predict a target point position of the workpiece to be detected according to the coordinate point and a current position of the end of the mechanical arm.

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

[0012] The collision detection module is configured to acquire vehicle obstacle information and establish a collision model, and divide a safety range to prevent the mechanical arm from colliding with the vehicle during movement.

[0013] The path planning module is configured to generate an obstacle avoidance path point position based on a starting point position of the vehicle and the corrected target point position, in combination with the collision detection information, plan an actual movement point position of the mechanical arm, and avoid collision with the vehicle body during movement.

[0014] The speed planning module is configured to plan a trajectory speed and an acceleration of the mechanical arm according to a production line speed and the path point position.

[0015] The movement execution module is configured to convert the trajectory point position into angle, angular velocity and angular acceleration information in a joint space, and control movement of the mechanical arm.

[0016] The movement judgment module is configured to compare an actual movement time of the mechanical arm with a theoretical movement time, and determine whether the movement is successful.

[0017] The information detection module is configured to acquire image information of the workpiece to be detected by the depth camera after the mechanical arm reaches the target point position, and determine whether the workpiece to be detected meets expectations.

[0018] The deviation acquisition module is configured to calculate a difference between an actual position of the workpiece to be detected and the target point position, and feed back the difference to the point position correction module during information detection.

[0019] In addition, the system further includes a point position re-prediction module, a path re-planning module, a speed re-planning module and a movement re-execution module, which are configured to re-track after movement failure.

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

[0021] Optionally, the point prediction module determines the motion mode as meeting motion, tracking motion or reaching motion according to the relative relationship between the end position of the mechanical arm and the position of the workpiece to be detected, and calculates the target point by a prediction formula in the meeting 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 conversion matrix between the depth camera coordinate system and the mechanical arm base coordinate system.

[0023] Optionally, the collision detection module equivalent obstacles to regular shapes and performs inflation processing, sets a boundary threshold and divides a safety range, and simultaneously combines the detection results of the sensor and the global camera to perform weight 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 the collision detection information; and the speed planning module calculates the theoretical motion time in combination with the line speed, and plans the speed and acceleration curve of the motion of the mechanical arm based on the path points.

[0025] According to a second aspect of the present application, an adaptive intelligent detection control method for automobile error-proof assembly is provided, which is implemented by using the above system and comprises the following steps:

[0026] S1, when a vehicle passes through a trigger sensor, an information acquisition module acquires the position information P1 of the workpiece to be detected in the database of the vehicle, and obtains the initial point P2 in the mechanical arm base coordinate system through coordinate system conversion;

[0027] Meanwhile, a global camera detects and acquires the coordinate point P3 of the workpiece to be detected in the global camera coordinate system, and converts the coordinate point P3 into the global point P4 in the mechanical arm base coordinate system, and the initial point P2 and the global point P4 are calculated by weight to obtain the current workpiece point P according to the following formula: P=K0·P2+K1·P4, wherein K0 and K1 are weight coefficients;

[0028] S2, a point prediction module predicts the target point of the workpiece to be detected according to the current coordinate point P of the workpiece to be detected and the current coordinate point P rob of the end of the mechanical arm, and determines the motion mode of the system and predicts the target point of the workpiece to be detected according to the following formula: wherein K2 is a motion coefficient;

[0029] S3, a point correction module corrects the target point P goal by using the deviation value fed back by the depth camera to obtain the target point after point correction;

[0030] S4, the collision detection module obtains the vehicle body bulge information from the database and boundary division, equivalent to the regular shape and do inflation processing, set threshold, get the maximum boundary threshold after the collision model, and divide the safety range, in order to reduce the error also carries out weight calculation, obtains the final collision point information of object;

[0031] S5, judge whether the target point position is beyond the reachable space of the mechanical arm, then according to the current coordinate point position and target point position of the mechanical arm, combined with the point position information of the collision object, the path point position planning is carried out;

[0032] S6, calculate the movement time of the workpiece to be detected to the target point position, combine the movement time and path point position planning information to carry out mechanical arm speed planning, and get the trajectory information;

[0033] S7, the motion execution module controls the mechanical arm to move along the planned trajectory information, and the motion judgment module judges whether the motion is successful, if successful, the picture acquisition identification and deviation information acquisition are carried out; if failed, a new target point position is predicted, and the path and speed of the mechanical arm are re planned and the motion is re executed until successful or the preset threshold number is reached;

[0034] S8, repeat the above operation process to detect the next workpiece to be detected, until the whole vehicle is detected.

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

[0036] The present application can realize mixed detection of different vehicle models without stopping the line during the mistake proofing detection of the assembled vehicle, and does not need manual operation during the detection process, automatic positioning, motion planning and intelligent identification, so that the whole detection process is more intelligent, flexible and flexible. BRIEF DESCRIPTION OF DRAWINGS

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

[0038] Figure 1 It is a device installation structure diagram of the adaptive intelligent detection control system for automobile mistake proofing;

[0039] Figure 2 It is a schematic diagram of relative position relationship of each part of the adaptive intelligent detection control system for automobile mistake proofing;

[0040] Figure 3 It is a structure block diagram of the adaptive intelligent detection control system for automobile mistake proofing;

[0041] Figure 4 It is a flow chart of the adaptive intelligent detection control method for automobile mistake proofing. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

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

[0045] Example 1

[0046] like Figure 1 As shown, this embodiment provides an adaptive intelligent detection and control system for preventing incorrect installation in automobiles, 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] Robotic arm 1 is installed at the inspection station to perform path tracking and workpiece inspection actions;

[0048] Sensor 5 is installed at the trigger point on the production line to detect whether a vehicle has entered the detection range;

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

[0050] Depth camera 2 is mounted on the end flange of the robotic arm to acquire local three-dimensional information of the workpiece to be inspected;

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

[0052] Figure 2 Fig. 1 is a schematic diagram of the relative position relationship of each part of the adaptive intelligent detection system for mistake-proof assembly of automobiles. According to the corresponding relationship between the devices, the conversion coordinate systems of each part are established. Based on the relative position, the spatial conversion relationship between the vehicle body coordinate system and the sensor coordinate system is established; based on the relative position, the spatial conversion relationship between the sensor coordinate system and the flange coordinate system at the end of the mechanical arm is established; based on the relative position, the spatial conversion relationship between the vehicle body coordinate system and the global camera coordinate system is established; based on the relative position, the spatial conversion relationship between the global camera coordinate system and the flange coordinate system at the end of the mechanical arm is established; according to kinematics, the spatial conversion relationship between the flange coordinate system at the end of the mechanical arm and the base coordinate system of the mechanical arm is established; based on the relative position, the spatial conversion relationship between the vehicle body coordinate system and the depth camera coordinate system is established; according to the kinematic parameters, the spatial conversion relationship between the depth camera coordinate system and the base coordinate system of the mechanical arm is established.

[0053] The sensor coordinate system is set as , the global camera coordinate system is set as , the flange coordinate system at the end of the mechanical arm is set as , the base coordinate system of the mechanical arm is set as , the depth camera coordinate system is set as , and the vehicle body coordinate system is set as . The conversion between multiple coordinate systems includes the conversion matrix of the vehicle body coordinate system relative to the sensor coordinate system , the conversion matrix of the vehicle body coordinate system relative to the global camera coordinate system , the conversion matrix of the global camera coordinate system relative to the flange coordinate system at the end of the mechanical arm , the conversion matrix of the sensor coordinate system relative to the flange coordinate system at the end of the mechanical arm , the conversion matrix of the vehicle body coordinate system relative to the depth camera coordinate system , the conversion matrix of the depth camera coordinate system relative to the flange at the end of the mechanical arm , and the conversion matrix of the flange coordinate system at the end of the mechanical arm relative to the base coordinate system of the mechanical arm .

[0054] Through the above-mentioned matrices, the position of any point in any coordinate system can be converted into the coordinate in the base coordinate system of the mechanical arm.

[0055] Figure 3The structure block diagram of the adaptive intelligent detection system for automobile assembly mistake-proofing loading comprises an information acquisition module, a point position prediction module, a point position 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.

[0056] The information acquisition module is configured to convert position information of a workpiece to be detected of a vehicle into a coordinate point in a base coordinate system of a mechanical arm by establishing a conversion coordinate system according to relative position relationships among components of the detection system.

[0057] The point position prediction module is configured to predict a target point position of the workpiece to be detected according to the coordinate point and a current position of the mechanical arm end.

[0058] The point position correction module is configured to correct the predicted target point position based on deviation information fed back by the depth camera to obtain a corrected target point position.

[0059] The collision detection module is configured to acquire vehicle obstacle information and establish a collision model, and divide a safety range to prevent the mechanical arm from colliding with the vehicle during motion.

[0060] The path planning module is configured to generate an obstacle avoidance path point position based on a starting point position of the vehicle and the corrected target point position in combination with collision detection information.

[0061] The speed planning module is configured to plan a trajectory speed and acceleration of the mechanical arm according to a line speed and a path point position.

[0062] The motion execution module is used to convert trajectory points into angle, angular velocity, and angular acceleration information in 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 robotic 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 used for subsequent motion after the system's motion judgment fails. Among them, the point re-prediction module is used to calculate the new target point; the path re-planning module is used to re-plan the new path points; the velocity re-planning module is used to plan the new trajectory points with velocity and acceleration information; and the motion re-execution module is used to re-control the actual movement of the robotic arm.

[0065] Example 2

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

[0067] S1 System Triggering and Initial Information Acquisition;

[0068] The system begins detection when the sensor acquires a vehicle trigger signal. The information acquisition module retrieves the vehicle and workpiece position information from the database and establishes a conversion matrix between devices. , , , , , and At this time, the database contains the position information of the workpiece to be detected. Convert to the initial coordinate position in the robot arm's base coordinate system The conversion formula is:

[0069]

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

[0071] Global camera 4 obtains the coordinates of the workpiece to be inspected in the global camera coordinate system through a detection algorithm. ,go through , and After the transformation matrix is ​​converted to points in the robot arm's base coordinate system, The conversion formula is:

[0072]

[0073]

[0074] The initial point position is obtained and the global point position After that, the information acquisition module calculates the weight to obtain the new current point position of the workpiece to be detected .

[0075]

[0076] wherein and are the weight coefficients of the initial point position and the global point position respectively. 、 and are the initial point position, the global point position and the point position after weight of the workpiece to be detected at the current time in the base coordinate of the robot arm.

[0077] It can be understood that the vehicle body moves along the production line, so the vehicle body coordinate system is a dynamic coordinate system. According to the motion 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 conversion coordinate system of the vehicle body relative to the sensor and the real-time conversion coordinate system of the vehicle body relative to the global camera at different times can be calculated:

[0079]

[0080]

[0081] Similarly, the positions of the vehicle body coordinate system at different times , after conversion in the sensor coordinate system and the global camera coordinate system, the positions in the base coordinate system of the robot arm and are obtained:

[0082]

[0083]

[0084] wherein the point position converted through the sensor coordinate system is the initial point position, and the point position converted through the global camera coordinate system is the global point position.

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

[0086]

[0087] wherein and are the weight coefficients of the initial point and the global point, respectively. , and are the initial point, the global point and the weighted point of the workpiece to be detected at the current time under the base coordinate of the robot arm.

[0088] In this embodiment, the initial point, the global point and the weighted point of the workpiece to be detected at the current time under the base coordinate of the robot arm are still , and are the initial point, the global point and the weighted point of the workpiece to be detected at the current time under the base coordinate of the robot arm.

[0089] S3 Point prediction

[0090] The point prediction module predicts the target point of the workpiece to be detected according to the current coordinate point of the workpiece to be detected and the current coordinate point of the robot arm end, and judges the motion mode of the system through the position difference between the robot arm end and the workpiece to be detected. If the position of the workpiece to be detected is in front of the position of the robot arm end, it is judged that the motion mode of the two is tracking motion, and if the position of the workpiece to be detected is behind the position of the robot arm end, it is meeting motion, and the rest is arrival. If it is meeting / tracking motion, the predicted point is calculated through the formula.

[0091]

[0092] wherein is the predicted meeting / tracking point, is the current point of the workpiece to be detected, is the current point of the robot arm, is the meeting / tracking motion coefficient

[0093] S4 Point correction

[0094] The point correction module obtains the deviation information of the deviation obtaining module to correct the predicted point. Since the first workpiece to be detected, the deviation information is 0 by default, and the target point after point correction is obtained .

[0095]

[0096]

[0097] wherein is the deviation information fed back by the depth camera, is the deviation information under the base coordinate system of the robot arm, is the target point after correction. , , for the conversion matrix.

[0098] S5 collision detection and safety range determination;

[0099] The collision detection module obtains the vehicle body bulge information from the database and boundary division, equivalent to the regular shape and do inflation processing, set threshold, get the maximum boundary threshold after the collision model, and divide the safety range, in order to reduce the error also for weight calculation, get the final collision point information .

[0100]

[0101]

[0102]

[0103] wherein is the safety point position after collision processing relative to the vehicle body coordinate system, is the initial safety point position converted by the sensor coordinate system, is the global safety point position converted by the global camera coordinate system, and are the weight coefficients of the initial safety point position and the global safety point position respectively, is the safety point position after collision detection after weight processing.

[0104] S6 path and speed planning;

[0105] The path planning module takes the above-mentioned current coordinate point of the mechanical arm as the motion starting point , and the predicted point as the end point . First, it is judged whether the end point is beyond the reachable space of the mechanical arm. If it is beyond, an error is reported and the mechanical arm path planning is terminated. If it is within the reachable range, a plurality of motion points are obtained by using interpolation algorithm, and the collision information obtained by the above-mentioned collision detection module is combined to obtain a series of path points after obstacle avoidance.

[0106] The path points are transmitted to the speed planning module, and the starting point and the end point are combined with the line speed to calculate the theoretical motion time of the workpiece to be detected. Combined with the theoretical motion time and the path planning information, the speed planning algorithm is used to plan the speed of the mechanical arm, and the trajectory point with speed and acceleration information is obtained.

[0107] S7 motion 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 them to the controller and the driver to realize the actual movement of the robot arm. At the same time, the robot arm is timed when moving to obtain the actual movement time of the robot arm .

[0109] The motion judgment module obtains the actual movement time of the robot arm , and judges with the theoretical movement time of the workpiece to be detected to obtain the movement result. If the judgment result of the motion execution module is , the tracking fails, and the re-tracking process is entered in step S9. If the theoretical movement time is greater than or equal to the actual movement time of the robot arm, the movement meets / tracking succeeds, and the waiting time is calculated according to the movement result to wait for the workpiece to be detected to reach the target point, and the calculation formula is as follows:

[0110]

[0111]

[0112] wherein, , , 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 movement speed of the production line.

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

[0114] S8 information detection and deviation acquisition;

[0115] The information detection module controls the depth camera to take pictures to obtain the picture information of the workpiece to be detected when the workpiece to be detected reaches the target point, and calls the algorithm to determine whether the workpiece to be detected meets the expected effect. If it meets, the detection is completed, and the deviation acquisition module is called to identify and obtain the picture information of the workpiece to be detected, and the detection algorithm is called to calculate the difference between the workpiece to be detected and the target point , and returns to the point correction module. If it does not meet, the next workpiece to be detected is identified.

[0116] S9 re-tracking process;

[0117] If the judgment result of the motion execution module is Tracking failed. The system will switch to re-tracking motion and enter the point re-prediction module, based on the actual movement time of the robotic arm. Theoretical movement time of the workpiece to be tested and production line movement speed Locate the current position of the workpiece to be inspected. And based on the re-tracking coefficient Re-determine new target locations .

[0118]

[0119]

[0120]

[0121] Then the path replanning module is called to update the collision information. Set the current position of the robotic arm As the starting point Re-predict target location As the new endpoint Determine the endpoint location Within the reachable space of the robotic arm, transition points are obtained using interpolation algorithms, combined with updated collision information. The re-tracking path points were obtained after a series of obstacle avoidances.

[0122] The re-tracking path points are passed to the speed replanning module, and the speed is adjusted based on the starting point. and finish line And combined with production line speed Calculate the theoretical motion time of the workpiece to be inspected. Combined with theoretical exercise time With path planning information, the robot arm's speed is replanned using a speed planning algorithm to obtain re-tracking trajectory point information with speed information.

[0123] The motion re-execution module converts the re-tracking trajectory points planned by the velocity re-planning module into angle, angular velocity, and angular acceleration information in joint space using inverse kinematic equations, and sends this information to the controller and actuator to realize the actual movement of the robotic arm. Simultaneously, timing is performed during the robotic arm's movement to obtain the actual movement time. .

[0124] actual movement time of robotic arm The data is input to the motion detection module and compared with the theoretical motion time of the workpiece to be detected. A difference comparison was performed, and the result was that the meeting was successful and The waiting time is calculated as , waiting for the workpiece to be detected to arrive at the target point.

[0125]

[0126] The waiting time After the end, the information detection module is called to control the depth camera to take pictures to obtain the picture information of the workpiece to be detected, and the detection algorithm is called to identify the type of the workpiece to be detected, to determine the matching between the workpiece to be detected and 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 picture information of the workpiece to be detected, the detection algorithm is called to synchronously calculate the difference between the workpiece to be detected and the target point , and return to the point correction module.

[0127] The above operations are repeated to identify the next workpiece to be detected, until the last workpiece to be detected of the complete vehicle is identified, and the entire process is ended.

[0128] The specific embodiments of the application are described above, and through the above description, relevant personnel can make various changes and modifications without deviating from the technical idea of the application.

Claims

1. An adaptive intelligent detection control method for automobile mistake-proofing, characterized in that, The method comprises the following steps: S1, when the vehicle passes through the trigger sensor, the information acquisition module acquires the position information P1 of the workpiece to be detected in the database, and obtains the initial point P2 in the base coordinate system of the mechanical arm through coordinate system conversion; At the same time, the global camera detects and acquires 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 base coordinate system of the mechanical arm, and the initial point P2 and the global point P4 are calculated by weight to obtain the current workpiece point P=K0·P2+K1·P4, wherein K0 and K1 are weight coefficients; S2, the point prediction module predicts the target point of the workpiece to be detected according to the current coordinate point P of the workpiece to be detected and the current coordinate point of the end of the mechanical arm judges the motion mode of the system and predicts the target point of the workpiece to be detected according to the following formula: wherein K2 is a motion coefficient. S3, the point correction module corrects the target point position by using the deviation value fed back by the depth camera to obtain the target point position after point correction ; S4, the collision detection module acquires the vehicle body expansion information from the database and performs boundary division, equivalent to a regular shape and expansion processing, sets a threshold, obtains the collision model after the maximum boundary threshold, and divides the safety range, and also performs weight calculation to reduce the error to obtain the final collision object point information; S5, whether the target point position exceeds the reachable space of the mechanical arm is judged, and then the path point position is planned according to the current coordinate point position and the target point position of the mechanical arm, combined with the collision object point information; S6, the motion time of the workpiece to be detected moving to the target point position is calculated, the motion time and the path point position planning information are combined to plan the speed of the mechanical arm, and the trajectory information is obtained; S7, the motion execution module controls the mechanical arm to move along the planned trajectory information, and the motion judgment module judges whether the motion is successful, if successful, picture acquisition, identification and deviation information acquisition are performed; if failed, a new target point position is predicted, and the path and speed of the mechanical arm are re-planned and re-executed, until successful or reaching a preset threshold number of times; S8, the above steps S1-S7 operation process is repeated to detect the next workpiece to be detected, until all the workpieces to be detected of the whole vehicle are completed.

2. An adaptive intelligent detection control system for automobile misloading prevention, for implementing the adaptive intelligent detection control method for automobile misloading prevention according to claim 1, characterized in that, The device module and the function module are included. The device module includes the following components: a mechanical arm, a depth camera, a database, a global camera, and a sensor; wherein the mechanical arm is arranged on a mechanical arm base, the depth camera is installed on the end flange of the mechanical arm, the sensor is arranged at a preset trigger point to detect the vehicle entering signal, and the global camera is installed between the sensor and the mechanical arm; the database is used to store vehicle data and workpiece position information to be detected; The function module includes: An information acquisition module is used to convert the position information of the workpiece to be detected into the coordinate point in the base coordinate system of the mechanical arm through the relative position relationship between the components of the detection system and the establishment of the conversion coordinate system; A point prediction module is used to predict the target point of the workpiece to be detected according to the coordinate point and the current position of the mechanical arm end; A 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; A collision detection module is used to acquire vehicle obstacle information and establish a collision model to divide a safety range to prevent the mechanical arm from colliding with the vehicle during movement; The path planning module is configured to generate an obstacle avoidance path point based on the starting point and the corrected target point of the vehicle, and to plan an actual movement point of the robot arm and avoid collision with the vehicle body during movement. The speed planning module is configured to plan a trajectory speed and acceleration of the robot arm based on the line speed and the path point. The movement execution module is configured to convert the trajectory point into angle, angular velocity and angular acceleration information in the joint space, and to control the movement of the robot arm. The movement judgment module is configured to compare the actual movement time of the robot arm with the theoretical movement time, and to determine whether the movement is successful. The information detection module is configured to obtain image information of the workpiece to be detected by the depth camera after the robot arm reaches the target point, and to determine whether the workpiece to be detected meets the expectation. The deviation acquisition module is configured to calculate the difference between the actual position of the workpiece to be detected and the target point, and to feed back the difference to the point correction module. In addition, the system further comprises a point re-prediction module, a path re-planning module, a speed re-planning module and a movement re-execution module for re-tracking after movement failure.

3. The self-adaptive intelligent detection control system for mistake-proofing of an automobile according to claim 2, characterized in that, The conversion coordinate system established by the information acquisition module includes a vehicle body coordinate system, a sensor coordinate system, a global camera coordinate system, a robot arm end flange coordinate system, a robot arm base coordinate system and a depth camera coordinate system, and the position mapping between different coordinate systems is realized through a preset conversion matrix.

4. The self-adaptive intelligent detection control system for mistake-proofing of an automobile according to claim 2, characterized in that, The point prediction module determines the movement mode as meeting movement, tracking movement or reaching movement based on the relative relationship between the position of the robot arm end and the position of the workpiece to be detected, and calculates the target point through a prediction formula in the meeting or tracking mode.

5. The self-adaptive intelligent detection control system for mistake-proofing of an automobile as claimed in claim 2, wherein, 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 robot arm base coordinate system.

6. The self-adaptive intelligent detection control system for mistake-proofing of an automobile according to claim 2, characterized in that, The collision detection module equivalent obstacles to regular shapes and performs inflation processing, sets a boundary threshold and divides a safety range, and simultaneously performs weight fusion based on the detection results of the sensor and the global camera to obtain a safe point after collision detection.

7. The self-adaptive intelligent detection control system for mistake-proofing of an automobile as claimed in claim 2, wherein The path planning module obtains intermediate movement points based on an interpolation algorithm, and generates an obstacle avoidance path based on the collision detection information; and the speed planning module calculates a theoretical movement time based on the line speed, and plans a speed and acceleration curve of the movement of the robot arm based on the path point.

Images