A control system for pipeline inspection robot

Abstract

The present invention relates to the field of pipeline detection technology, in particular to a control system of a pipeline detection robot, comprising a control terminal and a robot execution system connected in communication, the robot execution system comprising a body control part, a head control part, and an LED control part, the control terminal comprising a display module, a motion control module, and a video processing module; the control terminal adopts an MVVM architecture mode to control and interact with the robot execution system, the MVVM architecture mode comprises a view layer, a view model layer, and a model layer, the user's operation in the view layer will be converted into an instruction through the view model layer, and sent to the model layer for transmitting the instruction to each part of the robot execution system, and the video processing module comprises a video image acquisition function, an image compression encoding function, a network transmission function, and a local storage function. The present invention not only solves the problems existing in the prior art, but also achieves remarkable results in improving detection efficiency, enhancing system intelligence and flexibility.

Description

Technical Field

[0001] The invention relates to the technical field of pipeline detection, and in particular to a control system of a pipeline detection robot. Background Art

[0002] With the continuous expansion of urban scale, the inspection and maintenance of underground pipeline networks are becoming increasingly important. As an important inspection equipment, pipeline inspection robots are widely used in many fields such as petroleum, chemical, electric power, and municipal administration to detect corrosion, cracks, foreign objects, etc. inside pipelines. Traditional pipeline inspection methods rely on manual inspection, which is inefficient and costly, and cannot meet the needs of modern industry. In recent years, with the development of robotics and image processing technology, pipeline inspection robots have gradually become an important means to replace manual inspection. However, although the existing pipeline inspection robots have improved the inspection efficiency to a certain extent, they still have the following problems: (1) Due to the limitation of hardware performance or development cost, it is difficult to realize an intuitive and convenient human-computer interaction interface, or professional personnel are required to operate, which limits its application scope, resulting in complex control interface and inconvenient operation; (2) Due to limited processing power and encoding and decoding algorithms, it is difficult to process and transmit high-definition video streams in real time, especially in wireless communication environments, where the delay problem is more prominent, resulting in large image transmission delays and affecting real-time performance; (3) Due to defects in the hardware platform or software system, or limited system resources, it is difficult to cope with complex working conditions and long-term operation challenges, resulting in poor system stability and easy crash. Summary of the invention

[0003] In order to solve the above technical problems, the present invention specifically adopts the following technical solutions.

[0004] A control system for a pipeline inspection robot is designed, including a control terminal and a robot execution system. The control terminal and the robot execution system are connected to each other by wireless or wired communication. The robot execution system includes a body control part, a front control part, and an LED control part. The body control part is used to control the power supply of a rear camera, the astigmatism lamp control, the lifting mechanism, and the travel motor. The front control part includes a control part for controlling the universal adjustment and pitch angle adjustment of the front camera. The LED control part is used to control the power supply and lighting of the front camera.

[0005] The control terminal includes a display module, a motion control module, and a video processing module; the control terminal uses the MVVM architecture model to control and interact with the robot execution system. The MVVM architecture model includes a view layer, a view model layer, and a model layer. The user's operation in the view layer will be converted into instructions through the view model layer and sent to the model layer for transmitting the instructions to various parts of the robot execution system. The view layer is used for human-computer interface interaction and display, and the model layer is used to abstract and manage the status information and operation instructions of the body control part, the front control part, and the LED control part. The view model layer is used to subscribe to the data changes of the status information in the model layer and display it through the display module of the view layer;

[0006] The video processing module includes video image acquisition function, image compression encoding function, network transmission function and local storage function.

[0007] Preferably, the view layer View is used for human-machine interface interaction and display, including displaying real-time video images captured by the camera, robot status information, control buttons, virtual joysticks, and receiving user touch operations.

[0008] Preferably, the view model layer ViewModel processes business logic and data conversion, binds data to the view layer View, subscribes to status data changes in the model layer Model, and reflects its data changes to the view layer View, and at the same time converts user operations received by the view layer View into corresponding control instructions and sends them to the model layer Model.

[0009] Preferably, the model layer Model manages the underlying data and communications, is connected with the body control part, the front control part and the LED control part of the robot execution system, and is responsible for communicating with the robot execution system, sending control instructions and receiving status data.

[0010] Preferably, the view model layer obtains the status data of the robot through the interface provided by the model layer.

[0011] Preferably, the video image acquisition function realizes image acquisition by calling the Camera API of the control terminal system or using RTSP streaming technology.

[0012] Preferably, the model layer communicates with the robot execution system via Bluetooth, Wi-Fi, or serial port.

[0013] Preferably, the image compression encoding function adopts the H.265 / HEVC standard.

[0014] Preferably, the H.265 / HEVC encoding mainly includes code stream parsing, entropy coding, prediction, transformation and quantization, and loop filtering.

[0015] Preferably, the network transmission function uses TCP or UDP protocol to transmit the compressed and encoded video data to the control terminal or cloud server via Wi-Fi or 4G / 5G network.

[0016] The beneficial effects of the present invention are:

[0017] 1. The present invention adopts MVVM architecture design to realize efficient data interaction and control between the control terminal and the robot execution system. The view layer View is responsible for user interface display and interaction, the view model layer ViewModel processes business logic and data conversion, and the model layer Model manages the underlying data and communication, making the system highly intelligent and flexible.

[0018] 2. The present invention realizes the forward, backward and steering control of the robot through touch gestures, and the user can easily control the movement of the robot inside the pipeline. The body control part, the front control part and the LED control part work together to realize the all-round control of the robot, ensuring the flexibility and stability of the robot in a complex pipeline environment.

[0019] 3. The present invention adopts the H.265 / HEVC coding standard to achieve efficient video compression and transmission, ensuring the quality and real-time performance of video images. The video processing module supports functions such as video image acquisition, image compression coding, network transmission and local storage, allowing users to view the high-definition video images inside the pipeline in real time and perform subsequent analysis and processing.

[0020] 4. The present invention periodically obtains key operating data from various parts of the robot execution system through wired or wireless communication, including battery voltage, current, motor speed, camera status, lighting status, sensor data and communication status, etc., to ensure stable operation of the system.

[0021] 5. The present invention has fault diagnosis and recovery functions. When a fault is detected, corresponding recovery measures can be automatically taken, which reduces the frequency of manual intervention and improves the reliability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a control principle block diagram of the present invention; DETAILED DESCRIPTION

[0023] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0024] Example 1

[0025] A control system for a pipeline inspection robot, such as Figure 1 As shown, it includes a control terminal and a robot execution system. The control terminal and the robot execution system are connected by wireless or wired communication. The robot execution system includes a body control part, a front control part, and an LED control part. The body control part is used to control the power supply of the rear camera, the astigmatism lamp control, the lifting mechanism and the travel motor. The front control part includes a part for controlling the universal adjustment and pitch angle adjustment of the front camera. The LED control part is used to control the power supply and lighting of the front camera.

[0026] The control terminal includes a display module, a motion control module, and a video processing module; the motion control module realizes the forward, backward, and steering control of the robot through touch gestures. The control terminal can use the Android system, and the video processing module includes the following functions:

[0027] (1) Video image acquisition function

[0028] The video image acquisition function is implemented by calling the Android system's Camera API or using technologies such as RTSP streaming. The front and rear cameras of the robot execution system are used as video sources. The control terminal accesses the above cameras through the network (such as the WI-FI hotspot established by the robot execution system or in the same local area network as the mobile phone). The RTSP protocol can be used to push the camera's video stream to the control terminal to receive and parse the RTSP stream.

[0029] (2) Image compression encoding function

[0030] The image compression coding function is implemented using the H.265 / HEVC standard. H.265 is an efficient video coding standard that can reduce the video size by about 50% with the same image quality compared to H.264. The H.265C coding process mainly includes the following processes:

[0031] a) Code stream analysis

[0032] The H.265 bitstream consists of a series of NAL units (Network Abstraction Layer Units). Each NAL unit contains a header and a payload. The header indicates the type of the NAL unit (such as video parameter set VPS, sequence parameter set SPS, picture parameter set PPS, coding segment, etc.) and time level information.

[0033] The video parameter set (VPS) provides high-level information about the entire video sequence, such as maximum resolution, frame rate, and temporal hierarchy.

[0034] The sequence parameter set (SPS) provides information about the encoded video sequence, such as picture size, chroma format, bit depth, profile, and level.

[0035] The picture parameter set (PPS) provides information about one or more coded pictures, such as entropy coding mode, quantization parameter (QP) range, and deblocking filter control information.

[0036] An access unit contains a collection of all NAL units required to decode a single picture.

[0037] Decoding order and display order H.265 allows decoding order and display order to be different. Decoding order is the order in which the decoder processes the images, while display order is the order in which the images are displayed.

[0038] b) Entropy Coding

[0039] H.265 uses context-adaptive binary arithmetic coding (CABAC) or context-adaptive variable length coding (CAVLC) for entropy coding. CABAC has higher compression efficiency than CAVLC, but has higher computational complexity. The encoder selects an appropriate coding algorithm based on the context model and encodes the syntax elements into a binary code stream.

[0040] c) Prediction

[0041] Intra prediction uses the coded pixels in the same frame to predict the pixel value of the current block. H.265 supports more sophisticated intra prediction modes, including 33 angular predictions, DC prediction, and planar prediction.

[0042] Inter Prediction uses pixels in a reference frame (previously encoded frame) to predict the pixel values ​​of the current block. H.265 supports more advanced motion compensation techniques, including:

[0043] Merge Mode derives the motion information of the current block from the motion information of adjacent blocks.

[0044] Advanced Motion Vector Prediction (AMVP) uses multiple candidate motion vectors for prediction and selects the best candidate motion vector.

[0045] Motion Vector Scaling: When the temporal distance between the current image and the reference image is different, the motion vector is scaled.

[0046] Affine Motion Compensation supports more complex motion models such as rotation, scaling, and shearing.

[0047] Bi-directional Prediction uses reference frames from the past and the future for prediction.

[0048] d) Transform and Quantization:

[0049] Transform transforms the residual block (the difference between the original block and the predicted block) from the spatial domain to the frequency domain. H.265 supports multiple transform sizes, including 4x4, 8x8, 16x16 and 32x32, as well as discrete sine transform (DST) for 4x4 luminance blocks.

[0050] Quantization reduces the precision of transform coefficients to achieve compression. The quantization parameter (QP) controls the degree of quantization. The larger the QP, the coarser the quantization, the higher the compression rate, but the lower the image quality.

[0051] e) In-loop Filtering

[0052] H.265 uses a deblocking filter and a sample adaptive offset (SAO) filter to reduce coding noise and improve image quality.

[0053] (3) Network transmission function

[0054] The compressed and encoded video data is transmitted to the control terminal or cloud server via Wi-Fi or 4G / 5G network using TCP or UDP protocol. For example, the compressed video data can be packaged and transmitted using RTP / RTCP protocol to ensure real-time performance and reliability.

[0055] (4) Local storage function

[0056] The compressed and encoded video data can be stored in a local SD card or SSD hard disk for subsequent viewing and analysis. For example, the video data can be segmented and stored by time or event, making it easier for users to search and playback.

[0057] When the user starts the pipeline inspection task, the front camera starts to collect video images inside the pipeline and uses the V4L2 framework to read the raw video data into the memory. Then, the video processing module calls the H.265 encoder to compress and encode the raw video data, divides the encoded data into multiple data packets, and sends the data packets to the Android control terminal via Wi-Fi through the TCP / UDP protocol. At the same time, the video processing module can also store the encoded video data in the local SD card. After receiving the data packet, the video processing module of the Android control terminal decodes it and displays the video image inside the pipeline in real time on the display module. The user can control the movement of the robot through touch gestures, such as sliding forward to move forward and sliding left to turn left. At the same time, the user can also control the direction and pitch angle of the camera, as well as the switch and brightness of the light by clicking the button.

[0058] The video processing module supports functions such as video image acquisition, image compression encoding, network transmission and local storage, allowing users to view high-definition video images inside the pipeline in real time and perform subsequent analysis and processing.

[0059] The control terminal uses the MVVM architecture model to control and interact with the robot execution system. The MVVM architecture model includes the view layer View, the view model layer ViewModel, and the model layer Model.

[0060] The body control part, front control part, and LED control part in the robot execution system are respectively used as control objects, and their status information (such as camera opening, voltage, current, motor speed, light brightness, etc.) and operation instructions are abstractly managed through the model layer Model. The view model layer ViewModel subscribes to the changes of these status data in the model layer Model and reflects these changes to the display module of the view layer View, such as displaying the camera picture, power indicator, light status, etc.

[0061] The user's operations in the view layer (such as clicking buttons, sliding sliders, and touch gestures) will be converted into instructions through the view model layer and sent to the model layer for passing the instructions to various parts of the robot's execution system, thereby realizing the control of the robot. For example, when the user slides forward on the touch screen, the view layer View passes this gesture to the view model layer ViewModel, which parses it into a "forward" instruction and sends it to the model layer Model. The model layer Model then sends this instruction to the body control part through wireless or wired communication to control the forward rotation of the walking motor, thereby realizing the robot's forward movement.

[0062] The view layer is used for human-machine interface interaction and display. In the Android system, it is usually composed of Activity, Fragment, and various UI components (such as TextView, ImageView, Button, SeekBar, etc.). In the control system of the pipeline inspection robot, the View layer is responsible for displaying the real-time video images collected by the camera, robot status information (battery level, signal strength, etc.), control buttons, virtual joysticks, etc., and receiving user touch operations.

[0063] The view model layer ViewModel is used to process business logic and data conversion, and bind the data to the view layer View. The view model layer ViewModel is connected to the body control part, the head control part and the LED control part of the robot execution system. It obtains the robot status data through the interface provided by the model layer Model, such as: whether the camera is turned on, voltage, current, motor speed, light brightness, etc., and performs necessary processing and conversion (for example: converting the voltage value into a percentage of power, decoding the original video data collected by the camera and converting it into an image format that can be displayed by the view layer View), and then binds these data to the UI component of the view layer View to achieve real-time display. At the same time, the view model layer ViewModel is also responsible for converting the user operations received by the view layer View (such as clicking a button, sliding a slider, and touching gestures) into corresponding control instructions, and sends them to the robot execution system through the interface provided by the model layer Model. For example, the view model layer ViewModel can subscribe to the switch status of the rear camera of the model layer Model, and when the status changes, update the corresponding icon on the view layer View; when the user clicks the "turn on the headlights" button, the view model layer ViewModel will convert this operation into a "turn on the headlights" instruction and send it to the LED control part through the model layer Model.

[0064] The Model layer is used to manage underlying data and communication. The Model layer is connected to the body control part, the front control part and the LED control part of the robot execution system. It is responsible for communicating with the robot execution system, sending control instructions (such as: moving forward, backward, turning, adjusting the camera angle, turning on and off the lights, etc.) and receiving status data (such as: whether the camera is turned on, voltage, current, motor speed, light brightness, etc.). The Model layer can communicate with the robot execution system through Bluetooth, Wi-Fi, serial port, etc. For example, the Model layer can maintain a data warehouse to store various status information of the robot, and provide an interface for the View Model layer to access and modify; the Model layer can also encapsulate the details of communication with the robot execution system, such as protocol parsing, data packaging, etc., to provide a concise interface for the upper layer.

[0065] The present invention also has system status detection and fault recovery functions, and supports data cloud storage and remote access functions.

[0066] Among them, the system status monitoring is realized through the model layer Model. The model layer Model periodically obtains key operation data from the body control part, the head control part, and the LED control part of the robot execution system through wired or wired communication, including but not limited to the following operation data status:

[0067] (1) Battery voltage and current: used to monitor battery power and system power consumption. When the voltage is lower than the set threshold, it is judged as low power.

[0068] (2) Motor speed and current: used to monitor the working status of the travel motor. When the current is too large or the speed is abnormal, it may indicate that the motor is overloaded or faulty.

[0069] (3) Camera status: used to monitor whether the camera is working properly.

[0070] (4) Light status: used to monitor whether the light is working properly.

[0071] (5) Sensor data: Data from sensors such as gyroscopes and encoders, which are used to determine the robot's posture and motion status.

[0072] (6) Communication status: used to monitor whether the communication link between the control terminal and the robot execution system is normal. If no response is received from the robot execution system within a certain period of time, it is judged as a communication failure.

[0073] Fault determination is based on the above monitoring data and preset rules and thresholds to determine whether the system has faults, such as the following faults:

[0074] (1) Low battery: When the battery voltage is lower than the set threshold, it is judged as low battery.

[0075] (2) Motor failure: When the motor current continuously exceeds the set threshold, or the speed deviates too much from the control command, it is judged as a motor failure.

[0076] (3) Camera failure: When the camera image data cannot be obtained or the image quality is abnormal, it is judged as a camera failure.

[0077] (4) Communication failure: When the control terminal does not receive a response from the robot execution system within the set time, or the communication packet loss rate is too high, it is judged as a communication failure.

[0078] Fault recovery is to take appropriate recovery measures based on the fault type.

[0079] If the battery is low, the view layer will pop up a warning prompt to remind the user to charge in time.

[0080] When a motor fails, the View layer pops up a warning prompt, stops sending control instructions to the faulty motor, and tries to restart the motor. If the fault still exists, manual intervention is required.

[0081] Camera failure: The View layer pops up a warning prompt, try restarting the camera. If the failure persists, manual intervention is required.

[0082] Communication failure: Try to reestablish the communication connection. If the connection still cannot be restored, the robot will be controlled to stop moving, an alarm will be sounded, and human intervention will be awaited.

[0083] In summary, the present invention periodically obtains key operating data from various parts of the robot execution system through wired or wireless communication, including battery voltage, current, motor speed, camera status, lighting status, sensor data and communication status, etc., to ensure the stable operation of the system; when a fault is detected, it can automatically take corresponding recovery measures, reducing the frequency of manual intervention and improving the reliability and safety of the system.

[0084] The workflow of the present invention is as follows:

[0085] S1: When the user performs operations (such as touch gestures) on the view layer of the control terminal, these operations are passed to the view model layer ViewModel;

[0086] S2: The view model layer ViewModel converts the user's operation into corresponding control instructions and sends them to the model layer Model;

[0087] S3: The model layer Model sends the control instructions to the corresponding parts of the robot execution system (body control part, head control part, LED control part) through the communication interface (Bluetooth, Wi-Fi, serial port, etc.);

[0088] S4: The robot execution system executes actions according to the received instructions and feeds back its status information (such as camera opening status, voltage, current, motor speed, light brightness, etc.) to the model layer Model of the control terminal;

[0089] S5: The model layer receives status information and updates the internal data warehouse;

[0090] S6: The view model layer ViewModel subscribes to the changes in these status information. When there is an update, it will reflect these changes to the view layer View, such as updating the displayed power indicator or light status.

[0091] S7: If the video processing function is enabled, the camera of the robot execution system collects video images, which are compressed and encoded, and then sent to the control terminal through the network transmission function for the user to view in real time.

[0092] S8: When encountering a fault, the system will trigger the fault diagnosis mechanism and try to recover automatically. If it fails, it will prompt the user to intervene manually.

[0093] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A control system for a pipeline inspection robot, characterized in that: It includes a control terminal and a robot execution system, wherein the control terminal and the robot execution system are connected to each other by wireless or wired communication, and the robot execution system includes a body control part, a front control part, and an LED control part. The body control part is used to control the power supply of the rear camera, the astigmatism lamp control, the lifting mechanism, and the travel motor. The front control part includes a part for controlling the universal adjustment and pitch angle adjustment of the front camera, and the LED control part is used to control the power supply and lighting of the front camera. The control terminal includes a display module, a motion control module, and a video processing module; the control terminal uses the MVVM architecture model to control and interact with the robot execution system. The MVVM architecture model includes a view layer, a view model layer, and a model layer. The user's operation in the view layer will be converted into instructions through the view model layer and sent to the model layer for transmitting the instructions to various parts of the robot execution system. The view layer is used for human-computer interface interaction and display, and the model layer is used to abstract and manage the status information and operation instructions of the body control part, the front control part, and the LED control part. The view model layer is used to subscribe to the data changes of the status information in the model layer and display it through the display module of the view layer; The video processing module includes video image acquisition function, image compression encoding function, network transmission function and local storage function.

2. The control system of the pipeline inspection robot according to claim 1, characterized in that: The view layer View is used for human-computer interface interaction and display, including displaying real-time video images captured by the camera, robot status information, control buttons, virtual joysticks, and receiving user touch operations.

3. The control system of the pipeline inspection robot according to claim 1, characterized in that: The view model layer ViewModel processes business logic and data conversion, binds data to the view layer View, subscribes to status data changes in the model layer Model, and reflects its data changes to the view layer View, and at the same time converts user operations received by the view layer View into corresponding control instructions and sends them to the model layer Model.

4. The control system of the pipeline inspection robot according to claim 1, characterized in that: The model layer manages the underlying data and communication, is connected with the body control part, the front control part and the LED control part of the robot execution system, and is responsible for communicating with the robot execution system, sending control instructions and receiving status data.

5. The control system of the pipeline inspection robot according to claim 1, characterized in that: The view model layer obtains the status data of the robot through the interface provided by the model layer.

6. The control system of the pipeline inspection robot according to claim 1, characterized in that: The video image acquisition function realizes image acquisition by calling the Camera API of the control terminal system or using RTSP streaming technology.

7. The control system of the pipeline inspection robot according to claim 1 or 4, characterized in that: The model layer communicates with the robot execution system via Bluetooth, Wi-Fi, and serial port.

8. The control system of the pipeline inspection robot according to claim 1, characterized in that: The image compression encoding function adopts the H.265 / HEVC standard.

9. The control system of the pipeline inspection robot according to claim 8, characterized in that: The H.265 / HEVC encoding mainly includes code stream parsing, entropy coding, prediction, transformation and quantization, and loop filtering.

10. The control system of the pipeline inspection robot according to claim 2, characterized in that: The network transmission function uses TCP or UDP protocol to transmit the compressed and encoded video data to the control terminal or cloud server via Wi-Fi or 4G / 5G network.

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