An automatic cylinder surface roughness detection device and method based on a mobile robot.

Abstract

This invention proposes an automatic cylinder block surface roughness detection device and method based on a mobile robot, belonging to the field of precision manufacturing and intelligent inspection devices. It solves the problems of low efficiency and poor accuracy in existing manual cylinder block surface roughness inspection, and the inability of fixed automated equipment to meet the needs of flexible production with multiple varieties and small batches. The device includes a mobile cart, a collaborative robot, a roughness detector, a position detection camera, and a control system. The mobile cart has a housing, the collaborative robot is mounted on top of the housing, the roughness detector is connected to the end effector of the collaborative robot, and the position detection camera is mounted on the end effector of the collaborative robot. The control system is communicatively connected to the mobile cart, the collaborative robot, the roughness detector, and the position detection camera. It is mainly used for the automatic detection of cylinder block surface roughness.

Description

Technical Field

[0001] This invention belongs to the field of precision manufacturing and intelligent testing devices, and in particular relates to an automatic cylinder surface roughness testing device and its automatic testing method based on a mobile robot. Background Technology

[0002] As the core load-bearing component of an engine, the surface roughness of its key surfaces is a critical quality indicator that determines assembly accuracy, sealing performance, wear resistance, and overall engine lifespan. Therefore, strict testing and control are essential during the production process. Currently, in the production of precision components such as automotive engine cylinder blocks, surface roughness testing is performed on critical areas such as cylinder bores, crankshaft bores, and end faces. However, the commonly used method is manual measurement using a handheld contact roughness meter. Manual measurement relies heavily on the operator's experience, resulting in low efficiency, long testing time per piece, and difficulty matching the pace of large-scale production. Furthermore, manual operation makes it difficult to ensure that the probe maintains a constant contact pressure and perpendicular angle with the test surface of different orientations and curvatures, leading to large data dispersion and uncontrollable indication errors. For heavy cylinder blocks, manual handling and positioning are extremely labor-intensive. Large, fatigue-prone, or incorrectly detected cylinders make manual inspections unreliable. The accuracy and consistency of these inspections rely excessively on human skills, resulting in poor objectivity. To overcome these drawbacks, fixed-station automated inspection lines or specialized machines have emerged. While these machines reduce reliance on manual labor, they suffer from low flexibility. They are typically fixed at specific workstations and cannot be moved, making them unsuitable for the flexible inspection needs of workpieces moving between different positions in a multi-variety, small-batch production environment. Furthermore, their adaptability is poor. For cylinders of different models and sizes, it is often necessary to design and replace dedicated positioning and inspection fixtures, resulting in high modification costs and long cycles. The integrated inspection modules typically lack adaptive adjustment capabilities and cannot automatically optimize key inspection parameters such as contact pressure and sampling length based on the structural characteristics of different surfaces to be tested on the cylinder, affecting the stability and applicability of the inspection accuracy. Summary of the Invention

[0003] In view of this, the present invention aims to propose an automatic cylinder surface roughness detection device and its automatic detection method based on a mobile robot, so as to solve the problems of low efficiency and poor accuracy in the existing manual detection of cylinder surface roughness, and the inability of fixed automated equipment to adapt to the needs of flexible production of multiple varieties and small batches.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: an automatic cylinder surface roughness detection device based on a mobile robot, comprising a mobile trolley, a collaborative robot, a roughness detector, a position detection camera, and a control system. The mobile trolley is equipped with a housing, the collaborative robot is mounted on the top of the housing, the roughness detector is connected to the end effector of the collaborative robot, the position detection camera is mounted on the end effector of the collaborative robot, and the control system is communicatively connected to the mobile trolley, the collaborative robot, the roughness detector, and the position detection camera.

[0005] Furthermore, the mobile vehicle includes a vehicle body, drive wheels located at the bottom of the vehicle body, and a navigation module located on the vehicle body, with an onboard power supply installed inside the vehicle body.

[0006] Furthermore, the navigation module includes a lidar and an inertial measurement unit (IMU).

[0007] Furthermore, the collaborative robot is a six-degree-of-freedom robotic arm, and the end effector of the collaborative robot is equipped with a gripper, through which the roughness detector is connected to the collaborative robot.

[0008] Furthermore, the roughness tester includes a body, a retractable measuring rod disposed at the front end of the body, and a contact probe disposed at the end of the measuring rod.

[0009] Furthermore, the roughness tester also includes a probe protective sleeve fitted over the retractable probe rod.

[0010] Furthermore, the position detection camera is fixed to the end effector of the collaborative robot via a mounting bracket, and the lens of the position detection camera is positioned adjacent to the probe of the roughness detector.

[0011] Furthermore, the mobile vehicle is equipped with an on-board controller. The control system includes the on-board controller and a remote host computer, which is communicatively connected to the on-board controller.

[0012] Furthermore, a workpiece fixing plate is provided on the top of the box, and the workpiece fixing plate is located below the roughness tester.

[0013] An automatic detection method for cylinder surface roughness based on a mobile robot includes the following steps: S1: Control the mobile trolley to move to the target detection station; S2: Acquire an image of the target cylinder using the position detection camera, and identify the positioning features on the target cylinder based on the image; S3: Calculate the end-effector pose compensation amount based on the identified positioning features; S4: Control the collaborative robot to adjust the pose of the roughness detector according to the end pose compensation amount, so that the contact probe of the roughness detector is aligned with and contacts the test surface of the target cylinder. S5: Control the movement of the probe of the surface roughness tester on the surface to be measured in order to collect surface roughness data; S6: Send the collected surface roughness data to the host computer for processing and qualification determination.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention uses a collaborative robot to drive a roughness tester for measurement, completely replacing the traditional manual handheld operation. This eliminates human errors such as unstable contact pressure and measurement angle deviation caused by differences in human skills and operator fatigue. Furthermore, the position detection camera integrated at the end of the robot assists in the precise positioning and error compensation of the cylinder, ensuring that the probe contacts the surface to be measured with the precise position and posture each time. This fully automated measurement method makes the detection process highly repeatable, with small dispersion of measurement data and effective control of indication error. This significantly improves the accuracy, reliability, and consistency of the detection results, avoiding misjudgment of qualified products or the outflow of unqualified products due to detection errors. 2. The automatic cylinder surface roughness detection device of the present invention realizes full-process automation from task assignment, autonomous navigation, visual positioning, automatic measurement to data judgment. The mobile trolley can move autonomously and quickly to different workstations in the workshop, and the collaborative robot can accurately and quickly complete the path movement of the probe. The detection time of a single cylinder is significantly shortened compared with traditional manual detection, and the detection efficiency is significantly improved, which can better match the cycle time requirements of modern production lines. 3. This invention combines a mobile robot platform with a collaborative robot, giving the inspection system dual flexibility in spatial movement and end-effector operation. The mobile vehicle allows the device to move from a single workstation to multiple workstations to perform tasks according to production scheduling, adapting to dispersed and dynamic production layouts. The six-degree-of-freedom collaborative robot, combined with a roughness inspector with parameter adjustment function, can be programmed to adapt the probe to different cylinder models, different positions, and different angles of the surface to be tested, eliminating the need to customize special inspection fixtures for each product. This mobile platform, flexible operation, and adjustable parameter mode enable a single system to economically and efficiently meet the flexible production needs of multiple varieties and small batches, with convenient deployment and strong versatility. Attached Figure Description

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1This is a schematic diagram of the axonal structure of an automatic cylinder surface roughness detection device based on a mobile robot according to the present invention. Figure 2 This is a front view schematic diagram of an automatic cylinder surface roughness detection device based on a mobile robot according to the present invention. Figure 3 This is a schematic diagram of the system detection process structure of an automatic cylinder surface roughness detection device based on a mobile robot according to the present invention.

[0016] In the picture: 1. Mobile vehicle; 2. Collaborative robot; 3. Roughness detector; 4. Position detection camera. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other, and the described embodiments are only some embodiments of the present invention, not all embodiments.

[0018] Detailed implementation method: See Figure 1-3 This embodiment describes an automatic cylinder surface roughness detection device based on a mobile robot, comprising a mobile cart 1, a collaborative robot 2, a roughness detector 3, a position detection camera 4, and a control system. The mobile cart 1 is equipped with a housing, and the collaborative robot 2 is positioned on top of the housing. The roughness detector 3 is connected to the end effector of the collaborative robot 2, and the position detection camera 4 is mounted on the end effector of the collaborative robot 2. The control system is communicatively connected to the mobile cart 1, the collaborative robot 2, the roughness detector 3, and the position detection camera 4. The mobile cart 1 serves as the mobile carrier and platform for the entire detection device. The housing mainly serves to stabilize the installation and provide protection. The collaborative robot 2 is used to improve accuracy and complex motion capabilities, thereby driving the roughness detector 3 to move. This allows the roughness detector 3 to contact the surface to be measured at different positions on the cylinder at the correct angle and position, and guides the roughness detector 3 to complete the scanning motion along a set trajectory. The roughness detector 3 is the core sensor for detecting the surface roughness of the cylinder. The position detection camera 4 is a vision component that achieves high-precision positioning compensation. The control system is used to control the coordinated operation and information processing between the mobile car 1, the collaborative robot 2, the roughness detector 3, and the position detection camera 4.

[0019] This invention constructs an integrated, mobile automatic detection unit, which, through the collaborative work of a mobile vehicle 1, a collaborative robot 2, a roughness detector 3, and a position detection camera 4, achieves flexible automatic detection of the surface roughness of the cylinder block.

[0020] The mobile trolley 1 includes a body, drive wheels located at the bottom of the body, and a navigation module mounted on the body. An onboard power supply is installed inside the body. The body supports and secures other components, providing a stable and movable foundation platform for the entire testing device. The drive wheels provide driving and rotational force, enabling the mobile trolley 1 to move on the workshop floor. The navigation module enables the trolley to perform autonomous positioning, path planning, and obstacle avoidance, allowing it to autonomously and safely travel to the target workstation in a complex workshop environment according to task instructions. The onboard power supply is the energy supply unit for the mobile trolley 1, providing continuous and stable power to the entire mobile testing device.

[0021] The navigation module includes a lidar and an inertial measurement unit (IMU). The lidar is the sensor used by the navigation module to achieve environmental perception, mapping, and positioning. The mobile vehicle 1 uses the data scanned by the lidar to construct a global static map of the workshop and matches the real-time scan data with the stored global map to accurately calculate the position and heading angle of the mobile vehicle 1 in the current map. The IMU is used to measure the motion state of the mobile vehicle 1 in real time. The sensors inside the IMU directly measure the three-axis angular velocity and three-axis acceleration of the mobile vehicle 1.

[0022] The collaborative robot 2 is a six-degree-of-freedom robotic arm. The end effector of the collaborative robot 2 is equipped with a gripper. The roughness tester 3 is connected to the collaborative robot 2 through the gripper. The six-degree-of-freedom robotic arm of the collaborative robot 2 provides the entire testing system with the ability to move precisely at any position and orientation in three-dimensional space. The gripper is the mechanical interface between the collaborative robot 2 and the roughness tester 3. The gripper is usually designed as an integrated module that includes a mechanical locking mechanism and standard electrical connectors.

[0023] The roughness tester 3 includes a body, a retractable measuring rod at the front end of the body, and a contact probe at the end of the measuring rod. The body is the main structure and functional unit of the roughness tester 3. The body encapsulates core electronic components including sensors, signal processing circuits, a control unit, and a data interface, and provides an installation reference and protective housing for external components such as the retractable measuring rod. The retractable measuring rod is a transmission and buffer mechanism connecting the body and the probe. The retractable measuring rod can extend the contact probe to contact the workpiece surface. During measurement, the sensing mechanism inside the retractable measuring rod can sense and follow the microscopic contour undulations of the workpiece surface. This mechanical displacement is converted into an electrical signal. The contact probe is a component that directly contacts the surface of the workpiece being measured. The stylus at the tip of the contact probe is responsible for sensing the microscopic height changes of the surface contour. Under the precise posture control of the robot, the entire instrument is delivered to the measurement position. The retractable probe extends under controlled contact force, so that the stylus on the contact probe makes a constant force perpendicular to the workpiece surface. When the instrument moves along the set trajectory, the undulation of the surface contour causes a slight extension and retraction of the stylus and the retractable probe. This mechanical quantity is captured by the internal sensor and converted into an electrical signal characterizing the contour height. Finally, the roughness parameters such as Ra and Rz are calculated by the processor inside the instrument.

[0024] The roughness tester 3 also includes a probe protective sleeve fitted over the telescopic probe rod. The probe protective sleeve is a cylindrical mechanical structure that is fitted over the telescopic probe rod and can move with it and operate independently.

[0025] The position detection camera 4 is fixed to the end effector of the collaborative robot 2 by a mounting bracket. The lens of the position detection camera 4 is arranged adjacent to the probe of the roughness detector 3. The position detection camera 4 is directly mounted on the end effector of the collaborative robot 2, thereby establishing a direct spatial relationship chain between the collaborative robot 2, the position detection camera 4 and the workpiece. When the collaborative robot 2 moves the position detection camera 4, the changes in the workpiece image observed by the position detection camera 4 directly reflect the position changes of the end effector of the collaborative robot 2 relative to the workpiece. This allows the collaborative robot 2 to acquire images of the working scene in real time during its movement, especially for precise servo alignment or position compensation of workpieces fixed in a certain position.

[0026] The mobile vehicle 1 is also equipped with an on-board controller. The control system includes the on-board controller and a remote host computer. The host computer is communicatively connected to the on-board controller. The on-board controller is responsible for driving the underlying hardware, real-time processing of sensor data, local path planning, and executing precise motion control commands. The host computer is a remote advanced task management, human-computer interaction, and data management center. It is responsible for providing a graphical interface for operators, used for task issuance, status monitoring, data analysis, storage, and report generation.

[0027] The top of the housing is provided with a workpiece fixing plate, which is located below the roughness tester 3. The workpiece fixing plate is used to provide a stable, accurate and repeatable mounting reference surface for the workpiece to be tested.

[0028] An automatic detection method for cylinder surface roughness based on a mobile robot includes the following steps: S1: The operator enters or selects the inspection task through the host computer human-machine interface in the control system. The task information includes at least the model, batch, quantity of the cylinder to be inspected, and its target workstation number in the workshop. After the task is issued, the control system automatically retrieves the inspection parameters that match the model of the cylinder from the pre-built database. These parameters include the roughness evaluation standards of each surface to be tested, the sampling length, and the motion trajectory preset for the collaborative robot 2. Subsequently, the task instructions and parameters are sent to the on-board controller inside the mobile car 1 to control the mobile car 1 to move to the target inspection workstation. S2: After receiving the target workstation information, the onboard controller of the mobile vehicle 1 combines the environmental data collected by its navigation module to perform autonomous path planning. The mobile vehicle 1 moves by driving wheels. During the movement, the lidar continuously scans the surrounding environment and matches it with the constructed global map of the workshop to achieve accurate positioning. At the same time, it detects obstacles on the path in real time and replans the local path to avoid obstacles. The IMU unit provides the real-time acceleration and angular velocity data of the mobile vehicle 1 to assist in dead reckoning and ensure that it can maintain stable positioning even when the lidar feature points are sparse. Finally, the mobile vehicle 1 can autonomously, safely and accurately stop at the target detection workstation, and then obtain the image of the target cylinder through the position detection camera 4, and identify the positioning features on the target cylinder based on the image. S3: After the mobile trolley 1 arrives at the target workstation and stops, the control system calls the pre-stored photo-taking program. The collaborative robot 2 drives the position detection camera 4 on its end effector to move to the pre-photo-taking point. The position detection camera 4 acquires an image of the target cylinder at the workstation, obtaining a clear image containing the cylinder's positioning features. The image processing algorithm analyzes the acquired image, identifies the pixel coordinates of the preset positioning features in the actual image, and calculates the end pose compensation amount of the collaborative robot 2 based on the identified positioning features. By comparing with the standard template position, the system calculates the deviation between the target feature point and the theoretical target position in the camera coordinate system. Based on the fixed transformation relationship between the camera and the robot end effector, this two-dimensional image deviation is converted into the pose compensation amount required by the end effector of the collaborative robot 2 in three-dimensional space, including fine-tuning of position and angle. S4: Control the collaborative robot 2 to adjust the position of the roughness detector 3 according to the end-effector position compensation amount, so that the contact probe of the roughness detector 3 is aligned with and contacts the surface to be measured of the target cylinder. That is, the control system drives the collaborative robot 2, so that its end effector is superimposed with the position compensation amount calculated by step S3 on the basis of the original preset motion program, thereby accurately adjusting the spatial posture of the roughness detector 3 installed at the end. The collaborative robot 2 drives the roughness detector 3, so that the contact probe of the roughness detector 3 moves smoothly and accurately to the measurement starting point in an attitude perpendicular to the surface to be measured, and ensures that the probe is in perpendicular contact with the workpiece surface with a constant contact force. S5: Control the probe of the roughness detector 3 to move on the surface to be measured to collect surface roughness data. The parameter adjustment module inside the roughness detector 3 automatically adjusts the contact pressure, measurement speed, sampling length and other parameters to the preset values ​​issued in step S1 according to the type of the current detection surface. After the preparation is complete, the collaborative robot 2 drives the roughness detector 3 to make its probe slide and scan the surface to be measured at a uniform speed and smoothly along the preset measurement path. During this process, the micro-contour undulation of the workpiece surface causes the stylus in the probe to produce a slight displacement. This mechanical signal is converted into an electrical signal, thereby collecting the contour information of the surface in real time and calculating the roughness parameters accordingly. S6: The collected surface roughness data is sent to the host computer for processing and qualification judgment. During the measurement process, the roughness detector 3 uses its data transmission module to receive the raw contour data collected in real time or the received data after preliminary calculation. Then, it calls the built-in data processing and analysis algorithm to filter and evaluate the measurement results to obtain the final surface roughness characterization value. Subsequently, the system automatically compares the measured value with the preset qualification threshold issued from the database in step S1. Based on the comparison result, the system determines in real time whether the roughness of the surface to be tested is qualified and generates a value including the detection value, After completing the inspection of all preset surfaces at the current workstation, the vehicle controller automatically uploads the generated inspection data sheet, including the judgment result and timestamp, to the remote host computer terminal for aggregation, storage, and display. It can also be uploaded to the host computer simultaneously. Afterward, the control system controls the roughness tester 3 to lift and reset the probe, and the collaborative robot 2 returns to the safe standby posture. Finally, the mobile car 1 autonomously moves to the next target inspection workstation according to the task list, repeating steps S2 to S6, or returns to the designated standby area to wait for the next task instruction, thus completing a complete flexible automatic inspection cycle.

[0029] The specific embodiments of the present invention disclosed above are merely illustrative of the invention. These embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. An automatic cylinder surface roughness detection device based on a mobile robot, characterized in that: The system includes a mobile vehicle (1), a collaborative robot (2), a roughness tester (3), a position detection camera (4), and a control system. The mobile vehicle (1) is equipped with a housing, the collaborative robot (2) is located on the top of the housing, the roughness tester (3) is connected to the end effector of the collaborative robot (2), the position detection camera (4) is installed on the end effector of the collaborative robot (2), and the control system is communicatively connected to the mobile vehicle (1), the collaborative robot (2), the roughness tester (3), and the position detection camera (4).

2. The automatic cylinder surface roughness detection device based on a mobile robot according to claim 1, characterized in that: The mobile vehicle (1) includes a vehicle body, drive wheels located at the bottom of the vehicle body, and a navigation module located on the vehicle body. The vehicle body is equipped with an on-board power supply.

3. The automatic cylinder surface roughness detection device based on a mobile robot according to claim 2, characterized in that: The navigation module includes a lidar and an inertial measurement unit (IMU).

4. The automatic cylinder surface roughness detection device based on a mobile robot according to claim 1, characterized in that: The collaborative robot (2) is a six-degree-of-freedom robotic arm. The end effector of the collaborative robot (2) is equipped with a gripper. The roughness detector (3) is connected to the collaborative robot (2) through the gripper.

5. The automatic cylinder surface roughness detection device based on a mobile robot according to claim 1, characterized in that: The roughness tester (3) includes a body, a retractable measuring rod at the front end of the body, and a contact probe at the end of the measuring rod.

6. The automatic cylinder surface roughness detection device based on a mobile robot according to claim 5, characterized in that: The roughness tester (3) also includes a probe protective sleeve fitted over the telescopic probe rod.

7. The automatic cylinder surface roughness detection device based on a mobile robot according to claim 1, characterized in that: The position detection camera (4) is fixed to the end effector of the collaborative robot (2) by a mounting bracket, and the lens of the position detection camera (4) is arranged adjacent to the probe of the roughness detector (3).

8. The automatic cylinder surface roughness detection device based on a mobile robot according to claim 1, characterized in that: The mobile vehicle (1) is also equipped with an on-board controller. The control system includes the on-board controller and a host computer located remotely. The host computer is communicatively connected to the on-board controller.

9. The automatic cylinder surface roughness detection device based on a mobile robot according to claim 1, characterized in that: The top of the box is provided with a workpiece fixing plate, which is located below the roughness tester (3).

10. An automatic detection method for a cylinder surface roughness automatic detection device based on a mobile robot as described in claim 1, characterized in that, Includes the following steps: S1: Control the mobile trolley (1) to move to the target detection station; S2: Obtain an image of the target cylinder through the position detection camera (4), and identify the positioning features on the target cylinder based on the image; S3: Calculate the end pose compensation of the collaborative robot (2) based on the identified positioning features; S4: Control the collaborative robot (2) to adjust the position of the roughness detector (3) according to the end pose compensation amount, so that the contact probe of the roughness detector (3) is aligned with and contacts the surface to be tested of the target cylinder. S5: Control the probe of the surface roughness tester (3) to move on the surface to be measured in order to collect surface roughness data; S6: Send the collected surface roughness data to the host computer for processing and qualification determination.

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