CN117732827A - Battery shell cleaning line feeding and discharging control system and method based on robot - Google Patents
Battery shell cleaning line feeding and discharging control system and method based on robot Download PDFInfo
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Abstract
The invention discloses a robot-based battery shell cleaning line feeding and discharging control system and a robot-based battery shell cleaning line feeding and discharging control method.
Description
Technical Field
The invention relates to the technical field of control systems, in particular to a battery shell cleaning line feeding and discharging control system and method based on a robot.
Background
The invention provides a system and a method for controlling the unloading of a battery shell cleaning line, which are convenient for subsequent equipment to directly process the cleaned battery shell, but when the grabbing process of the robot is based on a visual analysis result, the change of environmental factors can influence the grabbing of targets in the analysis visual analysis, and the requirements of different types of battery shells on the length, the movement range and the speed conditions of the mechanical arm of the robot are different, and when the types and the environmental factors of the battery shells are changed, the actual running errors exist among different movement mechanical arms, so as to improve the accuracy and the adaptability of the robot when grabbing the battery shells, optimize the control strategy of the robot for grabbing the battery shells.
Disclosure of Invention
Aiming at the situation, in order to overcome the defects of the prior art, the invention aims to provide a battery shell cleaning line feeding and discharging control system and method based on a robot, a decision optimization module analyzes a motion path of a gripable point under the influence of an obstacle, and performs optimization search on different gripable point positions to obtain an optimal gripable point combination, so that when the type of a battery shell to be cleaned and scene data are changed, the mechanical arm gripping process of the robot is optimized continuously, and the accuracy of the feeding and discharging control on the battery shell cleaning line is improved.
A battery shell cleaning line feeding and discharging control system based on a robot comprises a sensor module, an identification module, a decision optimization module, a grabbing module, an adjusting module and a transmission module;
the sensor module is used for acquiring sensing information of the environment and comprises an inclination sensor and a pressure sensor, and the pressure sensor is used for detecting position information of the battery shell;
the identification module is used for obtaining an initial image of the battery shell to be cleaned, determining the position and the gesture of the category of the battery shell through gesture evaluation, performing image analysis on image data of the battery shell to be cleaned by using a CNN image analysis algorithm, determining a grabbing target and a target gesture through target segmentation and target detection in an image analysis technology, performing data conversion on the grabbing target by using the target gesture of the battery shell to obtain corresponding data, determining all grabbing points of the battery shell target according to the image depth characteristics obtained by image analysis, and obtaining a grabbing point set, wherein the grabbing point set changes when the category of the battery shell changes;
the method comprises the steps that sensing information obtained by a sensor module is utilized to analyze grabbing control, when an obstacle is grabbed, a decision optimization module optimizes a motion path of a mechanical arm according to collected data, the position of a robot is fixed, grabbing space parameters of the mechanical arm of the robot are determined, the decision optimization module performs optimization analysis on the motion path from a mechanical gripper to a grabable point to obtain an optimal motion path, different grabable point obstacles are different in shielding degree, the optimal motion path from the mechanical gripper to different grabable points and the motion grabbing time are different, and then a path coverage value is determined for the relation between the optimal motion paths of different grabable points;
the method comprises the steps of extracting characteristic values of bearing values, grabbing time, path coverage values and position parameters corresponding to the grabable points from different bearing conditions of different positions of a battery shell, marking the grabable points in a coordinate system in the form of the characteristic vectors, calculating Euclidean distances among the different grabable points, and finally establishing an optimization objective function based on the Euclidean distances to optimize to obtain a grabable point combination, wherein the bearing values, grabbing time, path coverage values and the characteristic values of the battery shell parameters corresponding to the grabable points are different;
searching and optimally analyzing all the obtained grippable point combinations to obtain optimal grippable point positions by using a neural network algorithm;
the grabbing module grabs the battery case according to the optimal grabbing point position, and obtains an adjustment angle according to comparison calculation of the actual grabbing point position and the standard fixed point position of the transmission module;
the adjusting module adjusts the angle of the battery shell according to the adjusting angle.
Further, in the control process of feeding and discharging of the battery shell cleaning line, parameters of shapes, colors, weights and sizes of different types of battery shells are different, when the type of the battery shell to be cleaned is changed, the number of the battery shells to be cleaned for single grabbing is changed, the decision optimization module establishes an optimization objective function according to Euclidean distance, and when single grabbing is performed, the number of grabbing points for grabbing the battery shell to be cleaned by the mechanical grabbing hand is different.
Further, when the environment scene where the battery shell to be cleaned is located and the type of the battery shell are changed, analysis errors of image data collected by the identification module are changed, when obstacles in the environment scene are blocked, pixels of grabbing points in an image analysis result are blurred, high-definition image data of the battery shell to be cleaned, which are prestored in a database, are called, image features of the high-definition image and the image in an actual scene are respectively extracted, the image features of the battery shell to be cleaned are compared by using an image analysis algorithm, the image features on the battery shell to be cleaned are extracted, the overlapping features of the high-definition image and the actual image are obtained, and then image analysis is carried out on the overlapping features of the battery shell to be cleaned.
Further, when the position of the robot is fixed, the amplitude parameters of the movement of the mechanical arm are fixed, the degrees of freedom of the movement of the mechanical arm are different in the grabbing process of battery cases at different positions, the node number of the mechanical arm is recorded as N, the movement adjacent error from the mechanical arm to each grabbing point is calculated to be different, when the obstacle is blocked in the grabbing process, the grabbing action is limited, and the critical threshold value of the movement path is analyzed by using a boundary analysis method.
Further, the identification module further comprises identification of the transmission module and the battery shell cleaning and blanking process, the identification module determines whether the battery shell is cleaned on the transmission module through analysis of collected image data, the sensor module is also arranged on the transmission module, and the position of the battery shell is determined through pressure sensing data of the pressure sensor.
Further, the transmission module comprises a motor, a speed controller, a sensor, an actuator and a motion controller, the transmission device moves according to the set parameters under the drive of the motor, the sensor on the transmission module is used for detecting the position, the speed, the acceleration and other parameters of the transmission module, the actuator is a device for receiving control signals and driving the conveyor belt,
further, the adjusting module comprises an angle adjusting module, the angle adjusting module performs angle overturning on the battery shell in the process of feeding and discharging of the battery shell cleaning line, the adjusting angle is obtained by comparing the standard angle of the battery shell with the grabbing angle of the robot, and when scene data of the battery shell to be cleaned are changed, the adjusting module adjusts the image acquisition range by adjusting the angle of the image acquisition equipment.
The invention also provides a battery shell cleaning line feeding and discharging control method based on the robot, which comprises the following steps:
s1, carrying out identification analysis on an initial image of a battery shell to be cleaned to obtain a graspable point set of the battery shell;
s2, calculating the degrees of freedom of different motion nodes of the mechanical arm, and performing path optimization on the running paths from the mechanical gripper to different grippable points to obtain an optimal motion path;
s3, analyzing different simulated motion paths of all the grippable points, setting target grippable points, calculating the number of grippable points on the optimal motion path of the target grippable points, marking the number as a path coverage value, and marking the simulated motion time from the mechanical gripper to the target grippable points as the gripping time;
s4, obtaining characteristic values when the grippable points are gripped through image analysis and optimal path optimization, determining characteristic vectors corresponding to different points according to the characteristic values of the grippable points, expressing the vectors in a coordinate system, calculating the distances between the different grippable points through a Euclidean distance calculation method, establishing a target optimization function based on the distances, and clustering the grippable points to obtain a grippable point combination;
s5, combining characteristic data of the battery case to form a new training data set by combining different grippable points, training the training data set by using a neural network algorithm, and obtaining an optimal grippable point combination through data search;
s6, the grabbing module controls the mechanical arm to grab the battery shell.
Further, the optimal path analysis in S2 and S3 is based on the planning of the motion path of the mechanical gripper when the environmental scene where the battery case is located changes, and the obstacle appears in the environmental scene, so that the battery case is shielded, and the motion process of the mechanical gripper bypasses the obstacle to obtain the optimal motion path by utilizing the motion path planning.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages:
according to the invention, the identification module in the control system analyzes and determines the grippable points of the battery shell through the acquired image information of the battery shell to be cleaned, when scene data in an image acquired by the identification module changes, an obstacle shields the battery shell to be cleaned, the identification module invokes a high-definition image of the battery shell to be cleaned, extracts image features of the high-definition image and an actually acquired image, acquires the overlapping part of the high-definition image and the actually acquired image by utilizing an image analysis technology, determines the grippable point set of the battery shell to be cleaned, and further analyzes the grippable points of the overlapping part acquired by the decision optimization module, so that the problem of uncertainty of the grippable points caused by environmental scene change is solved, and the error of the robot arm during gripping is greatly reduced.
Drawings
FIG. 1 is a method analysis diagram of the present invention;
FIG. 2 is a flow chart of the overall module in the present invention.
Detailed Description
The foregoing and other features, aspects and advantages of the present invention will become more apparent from the following detailed description of the embodiments with reference to the accompanying drawings 1 through 2. The embodiments of the present application and features of the embodiments may be combined with each other, and terms used in the specification are meanings commonly understood by those skilled in the art of the present invention.
The method comprises the steps that the feeding and discharging control of a battery shell cleaning line is finished by a robot or a robotic arm, the robot grabs the battery shell to be cleaned to move and overturn at a corresponding angle, the given cleaning procedure of the cleaning line is guaranteed to finish cleaning the battery shell to be cleaned, after the battery shell passes through the cleaning line, the cleaned battery shell is overturned at the corresponding angle and then is placed into a discharging conveyor belt during discharging, direct processing of the cleaned battery shell by subsequent equipment is facilitated, after the robotic arm or the robotic arm is in place, the gesture and the height of the robotic arm are adjusted to achieve verification of the initial position of the robotic arm, stability and precision of the grabbing process of the robotic arm are guaranteed, and the robotic arm grabs the battery shell to clean according to a motion cleaning path in a set operation procedure;
in the cleaning process, the robot needs to continuously adjust the posture and the position of the robot so as to ensure the best cleaning effect; when the sensor detects surrounding obstacles, the mechanical arm sets and automatically avoids the obstacles according to a program, so that the safety of the mechanical arm and the smooth proceeding of a cleaning process are ensured, but in the actual working process, due to the change of an environmental scene, the deviation of an image and sensing data of a battery shell is caused, and the mechanical arm avoids the obstacles and also generates corresponding deviation on the basis of data analysis, so that the grabbing accuracy of the mechanical arm is improved. Therefore, the embodiment of the invention provides a battery shell cleaning line feeding and discharging control system based on a robot, which comprises a control module, and a sensor module, an identification module, a decision optimization module, a grabbing module, an adjusting module and a transmission module which are connected with the control module; the control module is used for controlling, communication processing and data processing of each module, the system obtains the environment sensing parameters of the mechanical arm of the robot and the battery shell to be cleaned through the sensor module, the robot analyzes the sensing information by utilizing the computer vision and natural language processing technology, the structure and the composition of the environment are understood, and then the mechanical arm interacts with the environment according to the understanding of the environment to execute the action;
the sensor module is used for acquiring sensing information of the environment, the sensor module comprises an inclination sensor and a pressure sensor, the pressure sensor is used for detecting position information of the battery shell, and when the scene of the running environment changes in a complex manner, errors generated by sensor data comprise measurement errors, installation errors and dynamic errors;
the identification module comprises a feeding identification module, a discharging identification module and a cleaning identification module, wherein the images acquired in the identification module comprise a to-be-cleaned battery shell image, a cleaned battery shell image and a battery shell image in the cleaning process, and the images are subjected to image processing to acquire state information of the battery shell;
the identification module is used for obtaining an initial image of the battery shell to be cleaned, determining the position and the gesture of the category of the battery shell through gesture evaluation, performing image analysis on image data of the battery shell to be cleaned by using a CNN image analysis algorithm, determining a grabbing target and a target gesture through target segmentation and target detection in an image analysis technology, performing data conversion on the grabbing target by using the target gesture of the battery shell to obtain corresponding data, determining all grabbing points of the battery shell target according to the image depth characteristics obtained by image analysis, and obtaining a grabbing point set, wherein the grabbing point set changes when the category of the battery shell changes;
the method comprises the steps that sensing information obtained by a sensor module is utilized to analyze grabbing control, when an obstacle is grabbed, a decision optimization module optimizes a motion path of a mechanical arm according to collected data, the position of a robot is fixed, grabbing space parameters of the mechanical arm of the robot are determined, the decision optimization module performs optimization analysis on the motion path from a mechanical gripper to a grabable point to obtain an optimal motion path, different grabable point obstacles are different in shielding degree, the optimal motion path from the mechanical gripper to different grabable points and the motion grabbing time are different, and then a path coverage value is determined for the relation between the optimal motion paths of different grabable points;
the method comprises the steps of extracting characteristic values of bearing values, grabbing time, path coverage values and position parameters corresponding to the grabable points from different bearing conditions of different positions of a battery shell, marking the grabable points in a coordinate system in the form of the characteristic vectors, calculating Euclidean distances among the different grabable points, and finally establishing an optimization objective function based on the Euclidean distances to optimize to obtain a grabable point combination, wherein the bearing values, grabbing time, path coverage values and the characteristic values of the battery shell parameters corresponding to the grabable points are different;
searching and optimally analyzing all the obtained grippable point combinations to obtain optimal grippable point positions by using a neural network algorithm;
further, in the embodiment of the invention, the battery shell cleaning line feeding and discharging control system based on the robot further comprises a communication module, which is used for realizing data transmission and communication management among all modules in the system, transmitting and receiving instructions through the communication module, a communication interface of the communication module is used for connecting physical borrowing interfaces among corresponding devices of all modules,
the grabbing module grabs the battery shells according to the optimal grabbing point positions, and obtains an adjustment angle through comparison calculation between the actual grabbing point positions and the standard fixed point positions of the transmission module;
the adjustment module is used for adjusting the overturning angle of the robot mechanical arm for grabbing the battery shell, is also used for adjusting the collection angle of the image equipment collected by the identification module, is used for adjusting the angle of the currently grabbed battery shell to be cleaned, the angle of the battery shell borne in the transmission module is kept consistent and recorded as a standard placement angle, and after the angle of the mechanical arm for grabbing the battery shell is adjusted to the standard placement angle, the battery shell is fixed and the transmission module is used for driving the battery shell cleaning line to move.
The decision optimization module in the embodiment performs the quasi-analysis on the grabbing motion paths of the grabbing points of the battery case to be cleaned, when the obstacle is blocked, the moving paths of the grabbing battery case are blocked, the decision optimization module performs the path optimization analysis on the grabbing motion paths of all the grabbing points to obtain the optimal motion paths of different grabbing points, and then analyzes the different optimal motion paths to obtain the path coverage value, so that the grabbing paths are optimal under the influence of the fault object through path optimization, the grabbing time is reduced, and the working efficiency of feeding and discharging of the cleaning line is improved.
In the control process of the feeding and discharging of the battery shell cleaning line, the shape, the color, the weight and the size of different types of battery shells are different, the battery shells can be accurately grabbed and positioned to be more challenging due to the irregularity of the battery shells, the bearing capacity of different positions of the battery shells is different, the risk of damaging the battery shells exists in the mechanical arm grabbing process, when the type of the battery shells to be cleaned is changed, the number of the battery shells to be cleaned which are grabbed once is changed, when the battery shell volume is changed, the optimal grabbing point position is changed, the length and the grabbing force of the mechanical arm are changed, the grabbing target of the mechanical arm can be a single battery shell or a grabbing target formed by a plurality of parts, the decision optimization module establishes an optimized target function according to the Euclidean distance, the grabbing points to be cleaned of the mechanical arm are different in number according to the parameter and the grabbing speed requirement of the battery shells, when the grabbing target is formed by a plurality of individuals, the target function comprises a plurality of individual influencing variables, and the grabbing points can be classified according to the target function.
When the environment scene where the robot is to be cleaned and the type of the battery shell are changed, the analysis error of the image data collected by the identification module is changed, the temperature, the humidity and the air pressure in the environment factors can all influence the grabbing precision of the robot, and when the temperature change is too high, the size of the robot or the battery shell is changed, so that the grabbing precision is influenced; when an obstacle in an environment scene where the battery shell is located is shielded, pixels of a grabbing point in an image analysis result are blurred, high-definition image data of the battery shell to be cleaned, which are prestored in a database, are called, image features of the high-definition image and the image in an actual scene are respectively extracted, the image features of the battery shell to be cleaned are compared by using an image analysis algorithm, the image features on the battery shell to be cleaned are extracted, the overlapping features of the high-definition image and the actual image are obtained, and then image analysis is carried out on the overlapping features of the battery shell to be cleaned.
When the position of the robot is fixed, the amplitude parameters of the movement of the mechanical arm of the robot are fixed, the degrees of freedom of the movement of the mechanical arm are different in the process of grabbing battery shells at different positions, the node number of the mechanical arm is recorded as N, the movement adjacency error from the mechanical arm to each grabbing point mechanical arm is calculated to be different, when the obstacle is blocked in the grabbing process, the grabbing action is limited, the critical threshold value of the movement path is analyzed by utilizing a boundary analysis method, and in the analysis process, the practical requirements of the battery shell cleaning loading and unloading control are modified and optimized to introduce constraint conditions and an optimization control algorithm so as to realize better performance and precision.
Mechanical arm precision: the accuracy of the robotic arm is one of the important factors affecting the errors of the gripping process. The manufacturing accuracy, the installation accuracy, the abrasion in the use process and the like of the mechanical arm can influence the positioning accuracy and the repetition accuracy.
The identification module further comprises identification of the transmission module and the battery shell cleaning and blanking process, the identification module determines whether the transmission module is provided with a cleaning battery shell or not through analysis of collected image data, the sensor module is also provided with the transmission module, and the position of the battery shell is determined through pressure sensing data of the pressure sensor.
The adjusting module comprises an angle adjusting module, the angle adjusting module performs angle overturning on the battery shell in the process of feeding and discharging of the battery shell cleaning line, the adjusting angle is obtained by comparing the standard angle of the battery shell with the grabbing angle of the robot, and when scene data of the battery shell to be cleaned are changed, the adjusting module adjusts the image acquisition range by adjusting the angle of the image acquisition equipment.
The embodiment of the invention also provides a battery shell cleaning line feeding and discharging control method based on the robot, which comprises the following steps:
s1, carrying out identification analysis on an initial image of a battery shell to be cleaned to obtain a graspable point set of the battery shell;
s2, calculating the degrees of freedom of different motion nodes of the mechanical arm, optimizing the running paths from the mechanical gripper to different grippable points to obtain an optimal motion path, wherein the robot is possibly limited by the length, the motion range and the speed conditions of the mechanical arm of the robot in the process of gripping and cleaning due to the fact that the shape and the size of the battery case are different, the mechanical gripper moves to the battery case to grip under the driving of the mechanical arm, and algorithm analysis for avoiding obstacles of the robot is combined in the analysis process of the optimal motion path;
s3, analyzing different simulated motion paths of all the grippable points, setting target grippable points, calculating the number of grippable points on the optimal motion path of the target grippable points, marking the number as a path coverage value, and marking the simulated motion time from the mechanical gripper to the target grippable points as the gripping time;
s4, obtaining characteristic values when the grippable points are gripped through image analysis and optimal path optimization, determining characteristic vectors corresponding to different points according to the characteristic values of the grippable points, expressing the vectors in a coordinate system, calculating the distances between the different grippable points through a Euclidean distance calculation method, establishing a target optimization function based on the distances, and clustering the grippable points to obtain a grippable point combination;
s5, combining characteristic data of the battery case to form a new training data set by combining different grippable points, training the training data set by using a neural network algorithm, and obtaining an optimal grippable point combination through data search;
s6, the grabbing module is used for controlling the mechanical arm to grab the battery shell, and after receiving a control instruction of the robot, the grabbing module drives the mechanical arm to move according to the control instruction so as to grab the battery shell along an optimal running track of track analysis.
The optimal path analysis in the steps S2 and S3 is based on the planning of the movement path of the mechanical gripper when the environmental scene where the battery case is located is changed, and the obstacle appears in the environmental scene, so that the battery case is shielded, and the movement process of the mechanical gripper bypasses the obstacle to obtain the optimal movement path by utilizing the movement path planning.
The system comprises a control module, a sensor module, an identification module, a decision optimization module, a grabbing module, an adjusting module, a transmission module and a communication module, wherein the sensor module, the identification module, the decision optimization module, the grabbing module, the adjusting module, the transmission module and the communication module are connected with the control module, the sensor module is used for collecting sensor parameters in the up-down process of a battery shell, the identification module is used for acquiring image data of the battery shell, extracting image features of the image data, acquiring the overlapping part of a high-definition image and an actual image by utilizing an image analysis technology, determining a grabbing point set of the battery shell to be cleaned, carrying out path optimization analysis on grabbing motion paths of all grabbing points by the decision optimization module to obtain optimal motion paths of different grabbing points, calculating the relation between the optimal motion paths of the different grabbing points to obtain path coverage values, taking the path coverage value as one of characteristics of the grabbing points, extracting characteristic parameters of the grabbing points to obtain corresponding characteristic vectors, mapping the characteristic vectors into a coordinate system for analysis, calculating Euclidean distance of the different grabbing points, constructing an optimization objective function based on the Euclidean distance, combining the grabbing points, carrying out combination on the battery shell to be cleaned by utilizing the decision optimization module to obtain the optimal motion parameters, and carrying out combination and the grabbing calculation on the optimal motion parameters according to the grabbing point set, and the optimal combination calculation to obtain the optimal motion parameters, and the combination and the optimal motion parameters, thereby obtaining the grabbing requirements by the optimal combination and the actual conditions by the battery shell to clean the battery shell, the grabbing interference caused by environmental scene change is avoided, and the working efficiency of feeding and discharging of the cleaning line is improved.
In the control method provided by the embodiment, the decision optimization module is utilized to analyze the number of the coincident points of the optimal motion path to obtain the path coverage value, the path coverage value is used as one of the characteristics of the grippable points to perform characteristic analysis on a point set consisting of different grippable points, the grippable points are classified according to the grippable requirements to obtain a grippable combined data set, and the neural network algorithm is utilized to train the data set to obtain the optimal grippable combination, so that the mechanical arm grips the optimal position of the battery shell according to the optimal motion track, grips the battery shell according to the gripping requirements, the speed of the charging and discharging process of the battery shell is ensured, the battery shell is not damaged in the gripping process, and the gripping precision is improved.
While the invention has been described in detail in connection with specific embodiments, it will be readily understood by those skilled in the art that the scope of the invention is not limited to such specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
Claims (9)
1. The battery shell cleaning line feeding and discharging control system based on the robot is characterized by comprising an identification module and a decision optimization module;
the recognition module is used for obtaining an initial image of the battery shell to be cleaned, determining the position and the gesture of the category of the battery shell through gesture evaluation, performing image analysis on image data of the battery shell to be cleaned by using a CNN image analysis algorithm, determining a grabbing target and a target gesture through target segmentation and target detection in an image analysis technology, performing data conversion on the grabbing target by using the target gesture of the battery shell to obtain corresponding data, and determining all grabbing points of the battery shell target according to image depth characteristics obtained through image analysis to obtain a grabbing point set;
the decision optimization module optimizes the motion path of the mechanical arm according to the acquired data, performs optimization analysis on the motion path from the mechanical gripper to the grippable point to obtain an optimal motion path, and then determines a path coverage value for the relationship between the optimal motion paths of different grippable points;
extracting characteristic values of the grippable points corresponding to battery shell parameters to obtain characteristic vectors of the grippable points, marking the grippable points in a coordinate system in the form of the characteristic vectors, calculating Euclidean distances between different grippable points, and establishing an optimization objective function based on the Euclidean distances to optimize the grippable points to obtain a grippable point combination; and searching and optimally analyzing the grippable point combination by using a neural network algorithm to obtain an optimal grippable point.
2. The battery shell cleaning line loading and unloading control system based on the robot, which is characterized by further comprising a sensor module, a grabbing module, an adjusting module and a transmission module;
the sensor module is used for acquiring sensing information of the environment and comprises an inclination sensor and a pressure sensor, and the pressure sensor is used for detecting position information of the battery shell;
the grabbing module grabs the battery case according to the optimal grabbing point position, and obtains an adjustment angle according to comparison calculation of the actual grabbing point position and the standard fixed point position of the transmission module;
the adjusting module adjusts the angle of the battery shell according to the adjusting angle.
3. The robot-based battery shell cleaning line feeding and discharging control system according to claim 2 is characterized in that in the control process of the battery shell cleaning line feeding and discharging, parameters of shapes, colors, weights and sizes of different types of battery shells are different, when the type of the battery shell to be cleaned is changed, the number of the battery shells to be cleaned for single grabbing is changed, the decision optimization module establishes an optimized objective function according to Euclidean distance, and when the battery shells are grabbed for single grabbing, the number of grabbed points of the battery shells to be cleaned by the mechanical grabbing hand is different.
4. The robot-based battery shell cleaning line feeding and discharging control system is characterized in that when the environment scene where the battery shell is to be cleaned and the type of the battery shell are changed, the analysis error of the image data collected by the identification module is changed, when the obstacle in the environment scene is blocked, the pixels of the grabbing points in the image analysis result are blurred, the high-definition image data of the battery shell to be cleaned, which are prestored in the database, are called, the image features of the high-definition image and the image features of the image in the actual scene are respectively extracted, the image features of the battery shell to be cleaned are compared by using an image analysis algorithm, the image features on the battery shell to be cleaned are extracted, the overlapping features of the high-definition image and the actual image are obtained, and then the image analysis is carried out on the overlapping features of the battery shell to be cleaned.
5. The battery shell cleaning line feeding and discharging control system based on the robot is characterized in that when the position of the robot is fixed, the amplitude parameters of the movement of the mechanical arm are fixed, the degree of freedom of the movement of the mechanical arm is different in the battery shell grabbing process of different positions, the node number of the mechanical arm is recorded as N, the movement adjacent error of the mechanical arm to each grabbing point mechanical arm is calculated, when the obstacle blocking exists in the grabbing process, the grabbing action is limited, and the critical threshold value of the movement path is analyzed by using a boundary analysis method.
6. The robot-based battery case cleaning line loading and unloading control system according to claim 1, wherein the identification module further comprises identification of a transmission module and a battery case cleaning and unloading process, the identification module determines whether a battery case is cleaned on the transmission module through analysis of collected image data, the sensor module is also present on the transmission module, and the position of the battery case is determined through pressure sensing data of a pressure sensor.
7. The robot-based battery shell cleaning line feeding and discharging control system according to claim 2, wherein the adjusting module comprises an angle adjusting module, the angle adjusting module performs angle overturning on the battery shell in the process of feeding and discharging of the battery shell cleaning line, the adjusting angle is obtained by comparing a standard angle of the battery shell with a grabbing angle of a robot, and when scene data of the battery shell to be cleaned is changed, the adjusting module adjusts an image acquisition range by adjusting the angle of the image acquisition device.
8. The battery shell cleaning line feeding and discharging control method based on the robot is characterized by comprising the following steps of:
s1, carrying out identification analysis on an initial image of a battery shell to be cleaned to obtain a graspable point set of the battery shell;
s2, calculating the degrees of freedom of different motion nodes of the mechanical arm, and performing path optimization on the running paths from the mechanical gripper to different grippable points to obtain an optimal motion path;
s3, analyzing different simulated motion paths of all the grippable points, setting target grippable points, calculating the number of grippable points on the optimal motion path of the target grippable points, marking the number as a path coverage value, and marking the simulated motion time from the mechanical gripper to the target grippable points as the gripping time;
s4, obtaining characteristic values when the grippable points are gripped through image analysis and optimal path optimization, determining characteristic vectors corresponding to different points according to the characteristic values of the grippable points, expressing the characteristic vectors in a coordinate system, calculating the distances between the different grippable points through a Euclidean distance calculation method, establishing a target optimization function based on the distances, and clustering the grippable points to obtain a grippable point combination;
s5, combining characteristic data of the battery case to form a new training data set by combining different grippable points, training the training data set by using a neural network algorithm, and obtaining an optimal grippable point combination through data search;
s6, the grabbing module controls the mechanical arm to grab the battery shell.
9. The method for controlling feeding and discharging of the battery shell cleaning line based on the robot according to claim 8, wherein the optimal path analysis in S2 and S3 is based on planning of a movement path of the mechanical gripper when an environment scene where the battery shell is located is changed, and an obstacle appears in the environment scene, so that shielding is formed for the battery shell, and the movement process of the mechanical gripper bypasses the obstacle to obtain the optimal movement path by utilizing the movement path planning.
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