Use method and system of multi-purpose intelligent transfer robot

Abstract

The invention belongs to the technical field of robot automation and intelligent control, and discloses a using method of a multipurpose intelligent transfer robot. The carrying robot is specifically used according to the following steps of S1, system initialization and start waiting, S2, downhill control and two-dimensional code recognition, S3, side bridge alignment and article grabbing, S4, bridge getting-on and getting-off control, S5, position correction and line patrol preparation after bridge getting-off, S6, line patrol and article placement and S7, parking area returning and task ending. A traditional mechanical arm is upgraded into the sliding table mechanical arm, the grabbing stability and bearing capacity of the intelligent vehicle are remarkably improved, control code writing is simplified, the actual measurement effect is excellent, in addition, in order to achieve all-terrain carrying and optimize the motor speed of the intelligent vehicle, the motor speed is adjusted to 150 revolutions per minute, the risk that the intelligent vehicle rushes out of a bridge floor at a high speed is avoided, and the service life of the intelligent vehicle is prolonged. And the problem of insufficient low-speed torque is solved, and perfect balance between the speed and the torque is realized.

Description

Technical Field

[0001] The present invention belongs to the field of robot automation technology and intelligent control technology, and specifically relates to a method and system for using a multi-purpose intelligent handling robot. Background Art

[0002] A method and system for using a multi-purpose intelligent handling robot is an automated device that integrates advanced sensor technology, autonomous navigation technology, machine vision technology, artificial intelligence algorithm, and high-precision mechanical arms or mobile platforms and other high-tech means. It can autonomously perform a series of operations such as grabbing, handling, stacking, loading, unloading, and precise placement of objects in various complex environments according to preset task instructions or by continuously optimizing its behavior through learning. Traditional intelligent transporter manipulators usually adopt a complex multi-joint design and require 6 to 8 servos to drive, which not only increases the manufacturing cost, but also has obvious shortcomings in grabbing accuracy and load-bearing capacity, making it difficult to efficiently and stably carry heavy objects. At the same time, through the Arduino microprocessor with its powerful integration capabilities, it perfectly integrates mechanics, electronics, sensor technology, vector control technology, PID algorithm, and visual recognition technology, and realizes efficient and flexible control through C / C++ language programming. However, when it comes to uphill and downhill detection of smart cars, traditional methods mostly rely on mechanical sensors, such as mercury sensors. These sensors have a single function and can only detect tilt in a specific direction. They cannot fully and accurately reflect the three-dimensional tilt state of the car body and are insensitive to small tilt changes. This undoubtedly limits the terrain adaptability and driving accuracy of smart cars, and technological innovation is urgently needed. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for using a multi-purpose intelligent handling robot to solve the problems raised in the above-mentioned background technology.

[0004] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method and system for using a multi-purpose intelligent transport robot, wherein the specific steps for using the transport robot are as follows:

[0005] S1: System initialization and waiting for startup: After the handling robot is started, the system is initialized, including the microprocessor, camera, and sensor, and the waiting function is exited after the start button is pressed;

[0006] S2: Downhill control and QR code recognition: The car makes real-time corrections when going downhill, turns when encountering QR code recognition, ensures stable downhill and prepares for side bridge alignment;

[0007] S3: Align the side bridge and grab the object: After the handling robot aligns with the side bridge, it grabs the object according to the color of the QR code to ensure accurate positioning and grabbing;

[0008] S4: Upbridge and downbridge control: The handling robot uses up_Bridge to correct the upbridge and down_Bridge to ensure stability, both of which are controlled by sensor data;

[0009] S5: Position correction and line patrol preparation after getting off the bridge: After getting off the bridge, the correction function is executed to adjust the position and turn, and smoothly enter the line patrol channel;

[0010] S6: Line patrol and object placement: The handling robot patrols the line, accurately places objects after color recognition, uses ultrasonic positioning, and uses PID algorithm to control speed and direction;

[0011] S7: Return to parking area and end of task: The handling robot returns after patrolling the line in sequence, stops at the door accurately, and the task is completed, waiting for the next start.

[0012] Preferably, the specific steps of system initialization and waiting for startup in S1 are as follows:

[0013] Step 1: Start the handling robot system and configure the microprocessor

[0014] After the handling robot is started, the overall system startup is performed first. This step includes the configuration of the Arduino 2560 microprocessor, which is the core control unit of the handling robot and is responsible for processing various sensor data and executing control instructions.

[0015] Step 2: Camera and sensor initialization

[0016] After the microprocessor configuration is completed, the next step is to initialize the PIXY2 vector video camera and OpenMV camera. These two cameras are used to collect route information and road sign information respectively, which are important bases for the handling robot to navigate and make decisions. At the same time, the photoelectric sensor on the servo extension arm, the MCU6050 attitude sensor and the ultrasonic sensor need to be calibrated and initialized.

[0017] Step 3: Wait for the start signal and enter the main program

[0018] After completing the initialization of the camera and sensor, the program enters the Wait_start() function. The main function of this function is to wait for the start signal of the handling robot. In this function, the program will loop to determine whether the start button of the handling robot is pressed. If the button is not pressed, the program will continue to wait in this loop; when the button is pressed, it means that the user has given a start command. At this time, the program will exit the Wait_start() function and enter the main program for subsequent operations.

[0019] Preferably, the downhill control and QR code recognition in S2 refer to the fact that the transport robot will execute the start_Grade(6700) function to ensure stable driving during the downhill process. This function accurately determines the current running position of the car by comprehensively reading the inclination angle data of the car and the real-time feedback of the photoelectric sensor, and makes dynamic real-time corrections accordingly, so that the car can move smoothly along the set downhill ramp. When it is detected that the distance between the car and the preset QR code sign reaches the preset threshold, the downhill control function will automatically exit. Subsequently, the transport robot will execute the carGrade_zhuanWan() function, which is responsible for the QR code recognition task after downhill. After successful recognition, the announcement operation will be performed, and the body posture will be automatically adjusted according to the recognition result to complete a ninety-degree turn, laying a solid foundation for the subsequent side bridge alignment task.

[0020] Preferably, the specific steps of aligning the side bridge and the object grabbing in S3 are as follows:

[0021] Step 1: Align the side bridges

[0022] The handling robot executes the align_sideBridge(30,11,8) function, using the data from the ultrasonic sensor and the photoelectric sensor to accurately adjust the position of the robot. This function ensures that the robot uses a distance of 30 centimeters from the back wall as a reference point, while keeping the parallel distances between the robot body and the platform where the items are placed at 11 centimeters and 8 centimeters respectively.

[0023] Step 2: Identify the color of the QR code

[0024] After successfully aligning the side bridge, the handling robot executes the Colour_Code_read() function to start the QR code colour recognition module. This function determines the location of the object to be grabbed by identifying the colour of the QR code on the platform.

[0025] Step 3: Grab the item

[0026] According to the QR code color result recognized by the Colour_Code_read() function, the handling robot automatically moves to the corresponding object, starts the grasping mechanism, accurately grasps the specified object on the platform, and completes the grasping task.

[0027] Preferably, the specific steps of controlling the upper bridge and the lower bridge in S4 are as follows:

[0028] Step 1: Bridge control

[0029] The handling robot executes the up_Bridge(5000) function, which monitors the status of the vehicle in real time through the data of the photoelectric sensor and the MCU6050 attitude sensor. During the process of going up the bridge, the function ensures that the vehicle remains stable and automatically corrects any deviation from the track. The parameter 5000 is used as the running time reference value to help control the entire process from the beginning of the vehicle going up the bridge to the stable arrival of the flat area on the bridge.

[0030] Step 2: Lower bridge control

[0031] The transport robot executes the down_Bridge() function, which also relies on the data of the photoelectric sensor and the MCU6050 attitude sensor to monitor the attitude and position of the car in real time. During the process of going down the bridge, the function ensures that the car can remain stable to prevent loss of control due to excessive speed or improper angle.

[0032] Preferably, the position correction and line patrol preparation after getting off the bridge in S5 means that after the transport robot completes the action of getting off the bridge, it will immediately execute the car_xiaQiao_xiuZheng() function to make precise parking position correction. This function integrates multiple sensor data and advanced control algorithms to ensure that the car can accurately adjust its position to reach the predetermined parking point. Subsequently, the car will turn according to the preset instructions and smoothly enter the subsequent line patrol channel.

[0033] Preferably, the specific steps of line inspection and object placement in S6 are as follows:

[0034] Step 1: Line patrol control

[0035] The handling robot flexibly calls the xyz_xunxian series functions according to the current road conditions and uses the PIXY2 video head to collect road line information to form vector lines. It also uses the PID algorithm to accurately calculate the motor values ​​of the left and right wheels, thereby controlling the driving direction and speed of the vehicle and ensuring that the vehicle can patrol the line stably.

[0036] Step 2: Color Identification

[0037] The handling robot executes the Colour_recognize() function, starts the colour recognition module, and performs colour recognition on the grabbed objects. Based on the recognition results, the handling robot calls the corresponding object placement function.

[0038] Step 3: Place items

[0039] Based on color recognition, the handling robot uses ultrasonic sensors to accurately measure the distance and position the platform where the items are placed, and then automatically runs to the platform of the corresponding color to accurately place the grabbed items on the platform, completing the item placement task.

[0040] Preferably, the return to the parking area and the end of the task in S7 means that after completing the handling task, the handling robot will call the patrol line functions xyz_xunxian7(107) and xyz_xunxian6(15) in sequence to safely return to the parking area along the preset patrol line path. During the return process, the xyz_xunxian7(107) function ensures that the car passes through the door smoothly through the ultrasonic wave 6 setting; and the xyz_xunxian6(15) function uses the information collected by the No. 4 ultrasonic sensor and the PIXY2 video head to accurately determine whether the car has entered the parking area box. If the car successfully stops in the parking area, the program ends the current handling task and enters the standby state, waiting for the next start signal to arrive.

[0041] Preferably, the handling robot system consists of an Arduino 2560 microprocessor module, a motor drive module, an MCU6050 attitude sensor module, six ultrasonic modules, five servo modules, an audio processing module, a PIXY2 vector video camera module, an OpenMV road sign camera module, a photoelectric sensor module, and a color recognition module.

[0042] The beneficial effects of the present invention are as follows:

[0043] 1. The present invention upgrades the traditional manipulator to a sliding table manipulator (such as Figure 8 As shown in the figure, the grasping stability and load-bearing capacity of the smart car are significantly improved, while the control code writing is simplified, and the measured effect is excellent. In addition, in order to achieve all-terrain handling, the motor speed of the smart car is optimized and adjusted to 150 rpm, which not only avoids the risk of rushing out of the bridge deck at high speed, but also overcomes the problem of insufficient torque at low speed, achieving a perfect balance between speed and torque, and significantly improving the stability and adaptability of the smart car on the bridge. In order to save space and improve the aesthetics, we innovatively use the servo extension arm to detect changes in road slopes. The extension arm remains upright on flat roads and automatically unfolds when encountering uphill and downhill slopes, accurately detecting road conditions and realizing the deep integration of software and hardware. These innovations not only enhance the autonomous control capabilities of smart cars, but also set a new benchmark for the development of smart car technology.

[0044] 2. The present invention improves the accuracy of aligning the body of the handling robot with the tracks on both sides of the bridge, conducts multiple on-site code tests, and finally uses the infinite approximation principle of differential calculus to write software code, thereby achieving precise docking of the wheels with the bridge deck. If the wheels fail to accurately align with the bridge deck tracks, the smart car will continue to adjust until they are successfully docked, and then enter the bridge-going program. This improvement significantly improves the accuracy of the smart car going up the bridge. At the same time, in order to address the problem of loss of control due to excessive speed when going down the bridge (or downhill), the microprocessor interrupt technology is innovatively applied. The smart car automatically slows down when entering the downbridge (or downhill) section, and performs a braking action every 1000 milliseconds. This timed interrupt braking technology greatly enhances the stability of the smart car when going down the bridge (or downhill), effectively preventing the risk of detaching from the bridge deck or slope due to inertia, ensuring the smooth operation of the smart car in various terrains, and injecting new vitality into the development of smart car technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0046] Figure 2 This is a schematic diagram of the software operation flow of the handling robot of the present invention;

[0047] Figure 3 This is a schematic diagram of the mechanical structure of the handling robot of the present invention;

[0048] Figure 4 This is a wiring diagram of the intelligent handling circuit of the present invention;

[0049] Figure 5 This is a schematic diagram of the front side of the present invention;

[0050] Figure 6 It is a side physical schematic diagram of the present invention;

[0051] Figure 7 This is a schematic diagram of a three-dimensional training venue of the present invention;

[0052] Figure 8 This is a three-dimensional view of the slide robot of the present invention.

[0053] In the figure: 1. Photoelectric sensor 2; 2. Photoelectric sensor 1; 3. Shell board 4; 4. Ultrasonic sensor 1; 5. Shell board 3; 6. Shell board 1; 7. Shell board 2; 8. Ultrasonic sensor 4; 9. Ultrasonic sensor 3; 10. Speaker; 11. Ultrasonic sensor 5; 12. Shell board 6; 13. Control circuit; 14. Ultrasonic sensor 6; 15. Shell board 5; 16. Motor; 17. Manipulator; 18. Ultrasonic sensor 2; 19. Photoelectric sensor 5; 20. Photoelectric sensor 6; 21. Wheel; 22. Photoelectric sensor 3; 23. Photoelectric sensor 4. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] like Figures 1 to 8 As shown, the embodiment of the present invention provides a method and system for using a multi-purpose intelligent transport robot. The specific steps of using the transport robot are as follows:

[0056] S1: System initialization and waiting for startup: After the handling robot is started, the system is initialized, including the microprocessor, camera, sensor, etc., and the waiting function is exited after the start button is pressed;

[0057] S2: Downhill control and QR code recognition: The car makes real-time corrections when going downhill, turns when encountering QR code recognition, ensures stable downhill and prepares for side bridge alignment;

[0058] S3: Align the side bridge and grab the object: After the handling robot aligns with the side bridge, it grabs the object according to the color of the QR code to ensure accurate positioning and grabbing;

[0059] S4: Upbridge and downbridge control: The handling robot uses up_Bridge to correct the upbridge and down_Bridge to ensure stability, both of which are controlled by sensor data;

[0060] S5: Position correction and line patrol preparation after getting off the bridge: After getting off the bridge, the correction function is executed to adjust the position and turn, and smoothly enter the line patrol channel;

[0061] S6: Line patrol and object placement: The handling robot patrols the line, accurately places objects after color recognition, uses ultrasonic positioning, and uses PID algorithm to control speed and direction;

[0062] S7: Return to parking area and end of task: The handling robot returns after patrolling the line in sequence, stops at the door accurately, and the task is completed, waiting for the next start.

[0063] The specific steps of system initialization and waiting for startup in S1 are as follows:

[0064] Step 1: Start the handling robot system and configure the microprocessor

[0065] After the transport robot is started, the overall system startup is performed first. This step includes the configuration of the Arduino 2560 microprocessor, which is the core control unit of the transport robot and is responsible for processing various sensor data and executing control instructions. The configuration of the microprocessor may include setting the clock frequency, initializing the I / O port, configuring interrupts, etc. to ensure that the microprocessor can work normally.

[0066] Step 2: Camera and sensor initialization

[0067] After the microprocessor configuration is completed, the next step is to initialize the PIXY2 vector video camera and OpenMV camera. These two cameras are used to collect route information and road sign information, which are important bases for navigation and decision-making of the handling robot. At the same time, the photoelectric sensor on the servo extension arm, MCU6050 attitude sensor and ultrasonic sensor need to be calibrated and initialized. These sensors are used to detect the body position, attitude and surrounding environment information, respectively, to provide accurate data support for the control of the handling robot.

[0068] Step 3: Wait for the start signal and enter the main program

[0069] After completing the initialization of the camera and sensor, the program enters the Wait_start() function, which is mainly used to wait for the start signal of the handling robot. In this function, the program will loop to determine whether the start button of the handling robot is pressed. If the button is not pressed, the program will continue to wait in this loop; once the button is pressed, it means that the user has given the start instruction. At this time, the program will exit the Wait_start() function and enter the main program for subsequent operations. This step ensures that the handling robot will only start running under the explicit instructions of the user, avoiding the risk of misoperation or premature start.

[0070] The system initialization and waiting process ensures the stable operation of the handling robot: through the microprocessor configuration, camera and sensor initialization, the robot is provided with precise control and perception capabilities; the Wait_start() function effectively avoids misoperation and ensures that the robot is started only under the explicit instructions of the user. This process not only improves the safety and reliability of the robot, but also lays a solid foundation for the smooth execution of subsequent tasks, demonstrating a high level of intelligence and automation.

[0071] Among them, the downhill control and QR code recognition in S2 refer to the fact that the handling robot will execute the start_Grade(6700) function to ensure stable driving during the downhill process. This function accurately determines the current running position of the car by comprehensively reading the inclination angle data of the car and the real-time feedback of the photoelectric sensor, and makes dynamic real-time corrections accordingly, so that the car can move smoothly along the set downhill slope. When it is detected that the distance between the car and the preset QR code sign reaches the preset threshold, the downhill control function will automatically exit. Subsequently, the handling robot will execute the carGrade_zhuanWan() function, which is responsible for the QR code recognition task after downhill. After successful recognition, it will perform the announcement operation and automatically adjust the body posture according to the recognition result to complete a 90-degree turn, laying a solid foundation for the subsequent side bridge alignment task.

[0072] The downhill control and QR code recognition process brings significant advantages to the handling robot: the start_Grade function ensures that the car drives stably when going downhill, and moves closely along the set slope through real-time feedback and dynamic correction; once the QR code is detected, the downhill control automatically exits, and the carGrade_zhuanWan function is executed immediately to perform QR code recognition and a 90-degree turn, laying a solid foundation for subsequent tasks. This process not only improves work efficiency, but also ensures the stability and autonomy of the robot in complex environments.

[0073] The specific steps of aligning the side bridge and the object grabbing in S3 are as follows:

[0074] Step 1: Align the side bridges

[0075] The handling robot executes the align_sideBridge(30,11,8) function and uses the data from the ultrasonic sensor and the photoelectric sensor to accurately adjust the position of the trolley. This function ensures that the trolley uses a distance of 30 cm from the rear wall as a reference point, while maintaining a parallel distance of 11 cm and 8 cm between the body and the platform where the items are placed, thereby achieving precise alignment of the side bridge.

[0076] Step 2: Identify the color of the QR code

[0077] After successfully aligning the side bridge, the handling robot executes the Colour_Code_read() function to start the QR code colour recognition module. This function determines the location of the item to be grabbed by identifying the colour (red or orange) of the QR code on the platform.

[0078] Step 3: Grab the item

[0079] According to the QR code color result recognized by the Colour_Code_read() function, the handling robot automatically moves to the corresponding object, starts the grasping mechanism, accurately grasps the specified object on the platform, and completes the grasping task.

[0080] The process of aligning the side bridge and grabbing objects brings efficiency and precision to the handling robot: through the align_sideBridge function, the robot uses sensor data to accurately position itself to ensure alignment with the side bridge; the Colour_Code_read function quickly identifies the color of the QR code and locates and grabs the object; then, the robot automatically grabs the specified object. The whole process is smooth and accurate, which not only improves the work efficiency, but also ensures the stability and reliability of the operation, providing strong support for automated handling.

[0081] The specific steps of controlling the upper bridge and the lower bridge in S4 are as follows:

[0082] Step 1: Bridge control

[0083] The transport robot executes the up_Bridge(5000) function, which monitors the status of the vehicle in real time through the data of the photoelectric sensor and the MCU6050 attitude sensor. During the process of going up the bridge, the function ensures that the vehicle remains stable and automatically corrects any deviation from the track. The parameter 5000 is used as the running time reference value to help control the entire process from the beginning of the vehicle going up the bridge to the smooth arrival of the flat area on the bridge.

[0084] Step 2: Lower bridge control

[0085] The handling robot executes the down_Bridge() function, which also relies on the data of the photoelectric sensor and the MCU6050 attitude sensor to monitor the attitude and position of the car in real time. During the process of getting off the bridge, the function ensures that the car can remain stable to prevent loss of control due to excessive speed or improper angle. Through accurate sensor data feedback, the function realizes the smooth descent of the car.

[0086] The control process of getting on and off the bridge brings significant advantages to the handling robot: using photoelectric sensors and MCU6050 attitude sensors for real-time monitoring, ensuring that the robot remains stable during the process of getting on and off the bridge, automatically correcting deviations from the trajectory, and effectively preventing loss of control. This process not only improves the safety and stability of the robot, but also ensures the continuity and efficiency of task execution, providing strong support for automated handling in complex environments.

[0087] Among them, the position correction and line patrol preparation after getting off the bridge in S5 means that after the handling robot completes the action of getting off the bridge, it will immediately execute the car_xiaQiao_xiuZheng() function to make precise docking position corrections. This function integrates multiple sensor data and advanced control algorithms to ensure that the car can accurately adjust its position and reach the predetermined docking point. Subsequently, the car will turn according to the preset instructions and smoothly enter the subsequent line patrol channel, laying a solid foundation for the execution of the next task. This process reflects the high degree of autonomy and precise control capabilities of the handling robot.

[0088] The position correction and line inspection preparation process after getting off the bridge highlights the significant advantages of the handling robot: by executing the car_xiaQiao_xiuZheng() function, the robot can quickly adjust its position to the predetermined stop point, demonstrating excellent autonomy and precise control capabilities. Subsequently, the robot smoothly turned and entered the line inspection channel, laying a solid foundation for subsequent tasks. This process not only improves work efficiency, but also ensures the continuity and stability of task execution, providing reliable guarantees for automated handling.

[0089] The specific steps of line inspection and object placement in S6 are as follows:

[0090] Step 1: Line patrol control

[0091] The handling robot flexibly calls the xyz_xunxian series functions (such as xyz_xunxian1(), xyz_xunxian2(), xyz_xunxian3()) according to the current road conditions, uses the PIXY2 video head to collect road line information, forms vector lines, and accurately calculates the left and right wheel motor values ​​through the PID algorithm, thereby controlling the driving direction and speed of the car to ensure stable line patrol driving.

[0092] Step 2: Color Identification

[0093] The handling robot executes the Colour_recognize() function, starts the colour recognition module, and performs colour recognition on the grabbed objects. Based on the recognition result, the handling robot will call the corresponding object placement function. The line patrol and object placement process brings significant advantages to the handling robot: flexible line patrol control ensures stable driving of the robot; the colour recognition function enables the robot to accurately distinguish and handle objects of different colours; the precise distance measurement and positioning of the ultrasonic sensor ensures accurate placement of objects. This process not only improves handling efficiency, but also enhances the intelligence level of the robot, providing an efficient and reliable automation solution for the logistics and warehousing fields. Placement function (such as Place_red_Object(), Place_green_Object(), Place_blue_Object()).

[0094] Step 3: Place items

[0095] Based on color recognition, the handling robot uses ultrasonic sensors to accurately measure the distance and position the platform where the items are placed, and then automatically runs to the platform of the corresponding color to accurately place the grabbed items on the platform, completing the item placement task.

[0096] Among them, the return to the parking area and the end of the task in S7 means that after the handling robot completes the handling task, it will call the patrol line functions xyz_xunxian7(107) and xyz_xunxian6(15) in sequence to safely return to the parking area along the preset patrol line path. During the return process, the xyz_xunxian7(107) function uses the distance setting of the ultrasonic wave 6 to ensure that the car passes through the door smoothly; and the xyz_xunxian6(15) function uses the information collected by the No. 4 ultrasonic sensor and the PIXY2 video head to accurately determine whether the car has entered the parking area box. If the car successfully stops in the parking area, the program ends this handling task and enters the standby state, waiting for the next start signal to arrive.

[0097] The advantages of the process of returning the handling robot to the parking area and completing the task are: through precise line patrol functions and multi-sensor fusion technologies such as ultrasound and video cameras, the robot can safely and accurately return to the preset parking area after completing the handling. This process not only improves the handling efficiency, but also enhances the robot's stability and autonomy. After stopping, it enters the standby state, which is convenient for quick response to the next task, and improves the consistency and flexibility of the overall handling operation.

[0098] Among them, the handling robot system consists of an Arduino 2560 microprocessor module, a motor driver module, an MCU6050 attitude sensor module, six ultrasonic modules, five servo modules, an audio processing module, a PIXY2 vector video camera module, an OpenMV road sign camera module, a photoelectric sensor module, and a color recognition module.

[0099] Powerful processing capabilities: The Arduino 2560 microprocessor module provides a wealth of digital input and output ports and analog input ports, supports multiple communication interfaces, and can handle complex control logic and data transmission requirements to ensure efficient operation of the robot.

[0100] Precise motion control: The combination of the motor drive module and the MCU6050 attitude sensor module can achieve precise control of the robot's motion direction, including speed, acceleration and attitude adjustment, ensuring stability and accuracy during the handling process.

[0101] Comprehensive environmental perception: Six ultrasonic modules, PIXY2 vector video camera module, OpenMV road sign camera module and photoelectric sensor module together constitute the robot's environmental perception system, which can obtain the distance, image and light information of the surrounding environment in real time, providing a reliable basis for the robot's decision-making.

[0102] Flexible servo control: Five servo modules provide multi-angle, high-precision motion control, enabling the robot to complete various complex handling tasks such as gripping, placing, rotating, etc.

[0103] Humanized interactive design: The audio processing module can realize voice prompts and voice control functions, enhance the interactivity and ease of use of the robot, and enable users to operate the robot more conveniently.

[0104] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0105] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for using a multi-purpose intelligent handling robot, characterized in that: The specific steps of using the transport robot are as follows: S1: System initialization and waiting for startup: After the handling robot is started, the system is initialized, including the microprocessor, camera, and sensor, and the waiting function is exited after the start button is pressed; S2: Downhill control and QR code recognition: The car makes real-time corrections when going downhill, turns when encountering QR code recognition, ensures stable downhill and prepares for side bridge alignment; S3: Align the side bridge and grab the object: After the handling robot aligns with the side bridge, it grabs the object according to the color of the QR code to ensure accurate positioning and grabbing; S4: Upbridge and downbridge control: The handling robot uses up_Bridge to correct the upbridge and down_Bridge to ensure stability, both of which are controlled by sensor data; S5: Position correction and line patrol preparation after getting off the bridge: After getting off the bridge, the correction function is executed to adjust the position and turn, and smoothly enter the line patrol channel; S6: Line patrol and object placement: The handling robot patrols the line, accurately places objects after color recognition, uses ultrasonic positioning, and uses PID algorithm to control speed and direction; S7: Return to parking area and end of task: The handling robot returns after patrolling the line in sequence, stops at the door accurately, and the task is completed, waiting for the next start.

2. The method for using the multi-purpose intelligent handling robot according to claim 1, characterized in that: The specific steps of system initialization and waiting for startup in S1 are as follows: Step 1: Start the handling robot system and configure the microprocessor After the handling robot is started, the overall system startup is performed first. This step includes the configuration of the Arduino2560 microprocessor, which is the core control unit of the handling robot and is responsible for processing various sensor data and executing control instructions. Step 2: Camera and sensor initialization After the microprocessor configuration is completed, the next step is to initialize the PIXY2 vector video camera and OpenMV camera. These two cameras are used to collect route information and road sign information respectively, which are important bases for the handling robot to navigate and make decisions. At the same time, the photoelectric sensor on the servo extension arm, the MCU6050 attitude sensor and the ultrasonic sensor need to be calibrated and initialized. Step 3: Wait for the start signal and enter the main program After completing the initialization of the camera and sensor, the program enters the Wait_start() function. The main function of this function is to wait for the start signal of the handling robot. In this function, the program will loop to determine whether the start button of the handling robot is pressed. If the button is not pressed, the program will continue to wait in this loop; when the button is pressed, it means that the user has given a start command. At this time, the program will exit the Wait_start() function and enter the main program for subsequent operations.

3. The method for using the multi-purpose intelligent handling robot according to claim 1, characterized in that: The downhill control and QR code recognition in S2 refer to the fact that the handling robot will execute the start_Grade(6700) function to ensure stable driving during the downhill process. This function accurately determines the current running position of the car by comprehensively reading the inclination angle data of the car and the real-time feedback of the photoelectric sensor, and makes dynamic real-time corrections accordingly, so that the car can move smoothly along the set downhill slope. When it is detected that the distance between the car and the preset QR code sign reaches the preset threshold, the downhill control function will automatically exit. Subsequently, the handling robot will execute the carGrade_zhuanWan() function, which is responsible for the QR code recognition task after downhill. After successful recognition, the announcement operation will be performed, and the body posture will be automatically adjusted according to the recognition result to complete a 90-degree turn, laying a solid foundation for the subsequent side bridge alignment task.

4. The method for using the multi-purpose intelligent handling robot according to claim 1, characterized in that: The specific steps of aligning the side bridge and the object grabbing in S3 are as follows: Step 1: Align the side bridges The handling robot executes the align_sideBridge(30,11,8) function, using the data from the ultrasonic sensor and the photoelectric sensor to accurately adjust the position of the robot. This function ensures that the robot uses a distance of 30 centimeters from the back wall as a reference point, while keeping the parallel distances between the robot body and the platform where the items are placed at 11 centimeters and 8 centimeters respectively. Step 2: Identify the color of the QR code After successfully aligning the side bridge, the handling robot executes the Colour_Code_read() function to start the QR code colour recognition module. This function determines the location of the object to be grabbed by identifying the colour of the QR code on the platform. Step 3: Grab the item According to the QR code color result recognized by the Colour_Code_read() function, the handling robot automatically moves to the corresponding object, starts the grasping mechanism, accurately grasps the specified object on the platform, and completes the grasping task.

5. The method for using the multi-purpose intelligent handling robot according to claim 1, characterized in that: The specific steps of controlling the upper bridge and the lower bridge in S4 are as follows: Step 1: Bridge control The handling robot executes the up_Bridge(5000) function, which monitors the status of the vehicle in real time through the data of the photoelectric sensor and the MCU6050 attitude sensor. During the process of going up the bridge, the function ensures that the vehicle remains stable and automatically corrects any deviation from the track. The parameter 5000 is used as the running time reference value to help control the entire process from the beginning of the vehicle going up the bridge to the stable arrival of the flat area on the bridge. Step 2: Lower bridge control The transport robot executes the down_Bridge() function, which also relies on the data of the photoelectric sensor and the MCU6050 attitude sensor to monitor the attitude and position of the car in real time. During the process of going down the bridge, the function ensures that the car can remain stable to prevent loss of control due to excessive speed or improper angle.

6. The method for using the multi-purpose intelligent handling robot according to claim 1, characterized in that: The position correction and line patrol preparation after getting off the bridge in S5 refers to the fact that after the handling robot completes the action of getting off the bridge, it will immediately execute the car_xiaQiao_xiuZheng() function to make precise docking position corrections. This function integrates multiple sensor data and advanced control algorithms to ensure that the car can accurately adjust its position to reach the predetermined docking point. Subsequently, the car will turn according to the preset instructions and smoothly enter the subsequent line patrol channel.

7. The method for using the multi-purpose intelligent handling robot according to claim 1, characterized in that: The specific steps of line inspection and item placement in S6 are as follows: Step 1: Line patrol control The handling robot flexibly calls the xyz_xunxian series functions according to the current road conditions and uses the PIXY2 video head to collect road line information to form vector lines. It also uses the PID algorithm to accurately calculate the motor values ​​of the left and right wheels, thereby controlling the driving direction and speed of the vehicle and ensuring that the vehicle can patrol the line stably. Step 2: Color Identification The handling robot executes the Colour_recognize() function, starts the colour recognition module, and performs colour recognition on the grabbed objects. Based on the recognition results, the handling robot calls the corresponding object placement function. Step 3: Place items Based on color recognition, the handling robot uses ultrasonic sensors to accurately measure the distance and position the platform where the items are placed, and then automatically runs to the platform of the corresponding color to accurately place the grabbed items on the platform, completing the item placement task.

8. The method for using the multi-purpose intelligent handling robot according to claim 1, characterized in that: The return to the parking area and the end of the task in S7 means that after completing the handling task, the handling robot will call the patrol line functions xyz_xunxian7(107) and xyz_xunxian6(15) in sequence to safely return to the parking area along the preset patrol line path. During the return process, the xyz_xunxian7(107) function uses the distance setting of the ultrasonic wave 6 to ensure that the car passes through the door smoothly; and the xyz_xunxian6(15) function uses the information collected by the No. 4 ultrasonic sensor and the PIXY2 video head to accurately determine whether the car has entered the parking area box. If the car successfully stops in the parking area, the program ends this handling task and enters the standby state, waiting for the next start signal to arrive.

9. The multi-purpose intelligent handling robot system according to claim 1, characterized in that: The handling robot system consists of an Arduino 2560 microprocessor module, a motor driver module, an MCU6050 attitude sensor module, six ultrasonic modules, five servo modules, an audio processing module, a PIXY2 vector video camera module, an OpenMV road sign camera module, a photoelectric sensor module, and a color recognition module.

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