Control system and control method of self-positioning navigation object picking robot

Abstract

The invention relates to a control system and a control method for an autonomous positioning and navigation object pickup robot. A camera, a visual platform, a first driving motor and a steering engine are initialized; setting a color threshold according to the color of the target object; controlling the robot to steer to a set angle, collecting an image through a camera, and recognizing the position of a target object; when the target object is recognized, the position information of the target object is sent to the main control chip; the ultrasonic module measures the distance and sends the distance information to the main control chip; calculating coordinates of the target object, calculating moving parameters and sending the moving parameters to the driving module; and driving to move to a specified position, and triggering a pickup program to control the steering engine to pick up the target object. Through the vision module and the positioning algorithm, the object picking robot can perform accurate identification and positioning in a complex environment, and motion control of the trolley is realized in combination with the PID control algorithm, so that autonomous navigation of the object picking robot is realized, and the operation efficiency and the positioning accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the field of robot navigation technology, and more particularly to a control system and a control method for an autonomous positioning navigation object picking robot. Background Art

[0002] The positioning and picking of tiny objects are the core technologies of object-picking robots. Object-picking robots need to accurately identify tiny objects within a range of 1.5*1.5 meters in order to perform path planning and dynamic obstacle avoidance when performing tasks. In outdoor environments, the performance of cameras is not stable enough. The main problems include lighting changes, field of view limitations, and interference from dynamic objects. In contrast, OpenMV (machine vision platform) model training has better stability and accuracy in outdoor environments, strong anti-interference capabilities, and can provide high-precision distance measurement and 360-degree panoramic scanning. Therefore, OpenMV model training is widely used in small object recognition. However, model training requires a large amount of data. If the data is insufficient, the positioning accuracy will gradually decrease. Therefore, how to solve the model training problem of small ball recognition has become an important research direction.

[0003] Positioning and navigation are core technologies for object-picking robots, enabling their autonomous motion and task execution. Positioning technologies are primarily categorized as relative and absolute. Relative positioning relies on sensors such as velocity and angle sensors to measure the robot's motion, while absolute positioning relies on the OpenMV vision module to obtain the robot's absolute position within the environment. However, single relative positioning methods are susceptible to cumulative errors, resulting in reduced positioning accuracy.

[0004] Navigation technology consists of two parts: path planning and path tracking. Path planning aims to find the optimal path from a starting point to a destination in a known or unknown environment, taking into account factors such as obstacle avoidance, path smoothness, and time optimization. Path tracking involves adjusting the motion control of the object-picking robot in real time based on the planned path to ensure it stays on the planned path. The navigation system must be capable of real-time path updates and dynamic obstacle avoidance, placing higher demands on the algorithm's real-time performance and accuracy.

[0005] In summary, there is an urgent need for an object picking robot that can accurately identify and navigate to the target location in a complex environment and quickly complete the picking movement. Summary of the Invention

[0006] The technical problem to be solved by the present invention is that the recognition and navigation accuracy of existing object picking robots is low, and the picking speed is slow. In response to the above-mentioned defects of the existing technology, a control system and control method for an autonomous positioning and navigation object picking robot are provided.

[0007] The technical solution adopted by the present invention to solve its technical problem is:

[0008] A control system for an autonomous positioning, navigation and object picking robot is constructed, which includes a vision module, a drive module for mobile control and navigation, a picking device, an ultrasonic module and a main control chip; the main control chip is electrically connected to the vision module, the drive module, the picking device and the ultrasonic module respectively; the vision module includes a vision platform, a camera and a pan-tilt head, the vision platform is electrically connected to the camera and the pan-tilt head respectively, and is used for environment recognition and target detection; a positioning algorithm is provided in the vision platform; the drive module includes a motor drive circuit, a first drive motor for controlling the movement of a preset mobile device, a drive chip for driving the first drive motor and a power supply battery; the picking device includes a servo, a robotic arm and a picking frame, the servo is transmission-connected to the robotic arm, the picking frame is provided on the robotic arm and is used to pick up objects; the ultrasonic module is electrically connected to the main control chip and is used to emit ultrasonic waves to measure distance.

[0009] Furthermore, the first drive motor includes a body, a reduction gearbox and a Hall encoder. The body is transmission-connected to the reduction gearbox, and the Hall encoder is electrically connected to the body.

[0010] Furthermore, it includes a display screen, which is electrically connected to the main control chip; the pickup device also includes a buzzer for providing operation feedback and a lighting device for auxiliary lighting.

[0011] A control method for an autonomous positioning navigation object picking robot control system is constructed, comprising the following steps:

[0012] Initialize the preset camera and visual platform, and initialize the preset first drive motor and servo; wherein the initialization settings include image acquisition format and frame rate;

[0013] Set the color threshold according to the color of the target object;

[0014] Controlling the robot to turn to a set angle by the first drive motor, capturing images by the camera, and identifying the position of the target object by the visual platform;

[0015] determining whether the target object is recognized;

[0016] If yes, the position information of the target object is sent to the preset main control chip; if not, the robot is controlled to rotate and identify again;

[0017] Measure the distance through a preset ultrasonic module and send the distance information to the main control chip;

[0018] calculating coordinates of the target object in the image through a preset positioning algorithm, and calculating a movement parameter through a PID control algorithm;

[0019] sending the movement parameter to a preset driving module;

[0020] driving the robot to move to a specified position through the driving module;

[0021] judging whether the robot has reached the specified position;

[0022] if yes, triggering a picking program to control the servo to perform a picking action to pick up the target object through the master control chip; if no, returning to the identification program;

[0023] judging whether the target object has been picked up;

[0024] if yes, issuing an audible and visual prompt.

[0025] Further, the step of capturing an image through the camera comprises:

[0026] capturing an image through the camera to obtain image data;

[0027] transmitting the image data to the master control chip and processing the image data through the master control chip.

[0028] Further, the step of transmitting the image data to the master control chip and processing the image data through the master control chip comprises:

[0029] selecting an area in the image that matches the color of the target object through the color threshold;

[0030] detecting the contour and shape of the target object to determine the position of the target object.

[0031] Further, the step of calculating the coordinates of the target object in the image through a preset positioning algorithm comprises:

[0032] dynamically adjusting the color threshold through HSV color space conversion;

[0033] selecting the largest area as the target object through a connected component labeling algorithm;

[0034] obtaining the position information of the target object through Gaussian filtering and morphological opening operation.

[0035] Further, the step of calculating a movement parameter through a PID control algorithm comprises:

[0036] converting the coordinates into relative coordinates of robot movement.

[0037] The speed difference of the first drive motor is calculated using a PID control algorithm to obtain the movement parameter.

[0038] Furthermore, the step of driving the robot to move to a designated position by the driving module includes:

[0039] The robot is controlled to turn and move straight until it is at a preset distance from the target object.

[0040] Furthermore, if yes, triggering a picking program, and controlling the servo to perform a picking action to pick up the target object through the main control chip, includes:

[0041] Controlling the servo to drop to a preset picking frame and pick up the target object;

[0042] Lift up the pick frame.

[0043] The beneficial effects of the present invention are:

[0044] The object-picking robot utilizes a vision module and positioning algorithm to accurately identify and locate objects in complex environments. The PID control algorithm is combined to achieve precise motion control, enabling autonomous navigation. The robot then uses a servo to control the picking frame to pick up the target object. During the recognition process, the color threshold can be dynamically adjusted, effectively resolving issues such as unrecognized or timed-out recognition of target objects under varying lighting conditions, improving operational efficiency and positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a system block diagram of a control system for an autonomous positioning and navigation object picking robot according to one embodiment of the present invention;

[0046] Figure 2 is a block diagram of a control system of an autonomous positioning and navigation object picking robot in another embodiment of the present invention;

[0047] Figure 3 1 is a schematic diagram of the overall system architecture of a control system for an autonomous positioning, navigation, and object picking robot according to an embodiment of the present invention;

[0048] Figure 4 is a software architecture block diagram of an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of the steps of a control method for an autonomous positioning navigation object picking robot control system in one embodiment of the present invention;

[0050] Figure 6This is a method flow chart of a control method of an autonomous positioning navigation object picking robot control system in one embodiment of the present invention;

[0051] Figure 7 This is a circuit connection diagram of the main control chip and the driver chip in one embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0053] Please refer to Figure 1 The present invention proposes a control system for an autonomous positioning, navigation and object picking robot, which includes a vision module, a driving module for mobile control and navigation, a picking device, an ultrasonic module and a main control chip; the main control chip is electrically connected to the vision module, the driving module, the picking device and the ultrasonic module respectively; the vision module includes a vision platform, a camera and a pan-tilt head, the vision platform is electrically connected to the camera and the pan-tilt head respectively, and is used for environment recognition and target detection; a positioning algorithm is provided in the vision platform; the driving module includes a motor driving circuit, a first driving motor for controlling the movement of a preset mobile device, a driving chip for driving the first driving motor and a power supply battery; the picking device includes a servo, a robotic arm and a picking frame, the servo is transmission-connected to the robotic arm, the picking frame is provided on the robotic arm and is used to pick up objects; the ultrasonic module is electrically connected to the main control chip, and is used to emit ultrasonic waves to measure distance.

[0054] In this embodiment, the main control chip is electrically connected to the vision module, driver module, pickup device, and ultrasonic module, achieving integrated robot drive, perception, and control. Specifically, to provide higher data processing capabilities and implement complex navigation and environmental interaction functions, the core controller must possess high-performance processing, support multiple interfaces, and ensure stable system operation. Therefore, the STM32F407VET6 main control chip was selected as the core controller of the entire system, capable of controlling the robot's travel along a specified route.

[0055] Furthermore, the vision module includes the OpenMV visual platform, which is electrically connected to the camera and gimbal, and is used for environmental recognition and target detection. OpenMV is a MicroPython-based embedded machine vision development platform for image processing, target recognition, and artificial intelligence applications, utilizing an ARM Cortex-M series processor for data processing. By integrating OpenMV with algorithms, it enables target object recognition and navigation, improving the robot's operational efficiency and positioning accuracy in complex environments, and enabling autonomous positioning and control.

[0056] Furthermore, the drive module includes a first drive motor, an MG310 motor. The motor's speed is varied by adjusting the frequency signal of the stepper motor to achieve various speed requirements. Specifically, the first drive motor is a DC reduction motor with a Hall effect encoder. The Hall effect encoder detects changes in the magnetic field and outputs pulse signals, providing high-precision speed and position feedback. The first drive motor is capable of controlling the movement of a movable device. In one embodiment, the movable device may be a pulley or a belt.

[0057] More specifically, the driving module includes a driving chip. Figure 7 As shown, the positive and negative poles of the first drive motor are respectively connected to the output terminals of the driver chip, and the control signal output terminal of the driver chip is connected to the PWM signal output terminal of the main control chip. The PWM signal pin of the main control chip is connected to the control signal input terminal of the driver chip. At the same time, the main control chip and the driver chip are grounded. The present invention uses a pulse width modulation signal to control the speed of the first drive motor. PWM controls the output power by changing the ratio of power on and power off time, thereby adjusting the motor speed. The larger the duty cycle, the higher the motor speed.

[0058] Furthermore, the drive module includes a motor drive circuit, which uses a TB6612FNG dual-channel H-bridge driver chip, capable of supporting 1.2A continuous current output and setting the PWM frequency to 10kHz to reduce electromagnetic noise.

[0059] In a specific embodiment, if Figure 2 As shown in the figure, the system consists of a vision module, a drive module and a picking module, and is automatically controlled by the main control chip. The vision module includes OPENMV (Open Machine Vision, machine vision platform), a camera and a pan / tilt, which are responsible for environmental recognition and target positioning, and provide real-time image data for the system. The drive module includes a Tb6612 motor driver chip, a GM310 motor and a 12V battery, which is responsible for controlling the movement of the machine platform or mobile device to ensure stable operation and precise navigation. The picking module consists of an MG996R servo, a buzzer and a lighting module. The servo performs the picking action, the buzzer provides operational feedback, and the lighting module assists the vision module in working in low-light environments. Through the collaborative work between the modules, the entire process from target recognition to picking is automated, which can be applied to application scenarios such as intelligent robots.

[0060] In another specific embodiment, Figure 3As shown, the system comprises an application layer, a driver layer, and a hardware layer to implement automatic pathfinding and picking functions. The application layer includes the automatic pathfinding program and the picking program, responsible for executing core logic and task scheduling, enabling target recognition, path planning, and robotic arm control. The automatic pathfinding program is used to automatically find a path, and the picking program is used to pick up objects. The driver layer includes a camera, a motor driver chip, and a single-chip microcontroller. The motor driver chip is a TB6612. The driver layer ensures that instructions are accurately transmitted to the hardware for execution. The hardware layer includes a perception module, an execution module, and an auxiliary module. The perception module includes an OPENMV (visual platform) and an LED lighting module. OPENMV combined with LED lighting enables environmental recognition and target detection. The execution module includes a first drive motor (310 motor) and an MG996R servo. The 310 motor controls robot movement, and the MG996R servo drives the robotic arm to complete the picking action. The auxiliary module includes a buzzer and a 12V lithium battery. The buzzer provides status feedback, and the lithium battery powers the system. By coordinating the interaction between software and hardware through the driver layer, closed-loop control from environmental perception to task execution is achieved, which is suitable for meeting automated operation requirements in dynamic scenarios.

[0061] The pickup device includes a servo, which is a MG996R metal standard servo with high performance and a metal gear structure. It has the characteristics of high torque and strong durability and can be used in high-load scenarios.

[0062] Specifically, the control circuit sends a programmed PWM signal to the MG996R servo. The servo signal is generated by a timer with an angular resolution of 0.5°. When a 1.5ms pulse width is sent, the servo's output shaft remains in its neutral position (0°). A 2ms pulse width rotates the servo's output shaft 90° clockwise, while a 1ms pulse width rotates the shaft 90° counterclockwise. By alternating 2ms and 1ms pulse widths, the robotic arm can achieve 90° movements.

[0063] In one specific embodiment, the pickup device is located at the top of the robot, the vision module, the first drive motor, and the wheels are located at the front of the robot, the main control chip (STM32F407VET6) and the motor driver chip (TB6612) are located in the middle of the robot, the lithium battery is located at the top of the robot, and the display and buzzer are located at the front of the robot. The vision module, main control chip, and motor driver chip are placed on an integrated circuit board and reinforced with unified wiring and screws to ensure component stability and facilitate maintenance.

[0064] Further, the robot can be provided with a storage area for storing the articles picked up. In a specific embodiment, the article picked up by the robot is a table tennis ball, and a table tennis ball storage area is installed at the rear portion of the robot to store the table tennis balls.

[0065] It is worth mentioning that in addition to the STM32F407VET6 and ultrasonic module listed in this invention, higher-performance processors and more accurate sensors, such as ToF (Time-of-Flight) cameras or LiDAR, can also be used to improve system response speed and positioning accuracy. The robotic arm can also be multi-degree-of-freedom or multi-axis to expand the types and range of target objects it can pick up. At the same time, a wireless communication module can be added to enable remote monitoring and data acquisition.

[0066] In addition, the system can also be applied to application scenarios such as object sorting and environmental cleaning.

[0067] In one embodiment, the first driving motor includes a body, a reduction gearbox and a Hall encoder. The body is transmission-connected to the reduction gearbox, and the Hall encoder is electrically connected to the body.

[0068] Specifically, the first drive motor is an MG310 DC reduction motor and is equipped with a Hall encoder. The motor speed is fed back through the Hall encoder and combined with an incremental PID (Proportion Integral Differential) algorithm to achieve precise speed regulation; among them, the proportional coefficient Kp = 0.8, the integral time Ti = 0.1s, and the differential time Td = 0.05s.

[0069] Next, the torque is calculated based on the robot's maximum load and wheel diameter. In one embodiment, the robot's maximum load is 500g and the wheel diameter is 6cm. The required torque is calculated as:

[0070] τ=F·rη=5N·0.03m0.85≈0.176N·mτ=ηF·r=0.855N·0.03m≈0.176N·m;

[0071] The rated torque is 0.2 N·m.

[0072] Furthermore, the reduction box is provided with a plurality of gears, and the gears are made of metal and have higher torque.

[0073] In one embodiment, the system includes a display screen electrically connected to the main control chip; the pickup device also includes a buzzer for providing operation feedback and a lighting device for auxiliary lighting.

[0074] In specific implementations, a display screen, which can be an LCD, is provided. This allows for a more intuitive understanding of the interaction between task assignments, robot status information, and real-time position and posture information, resolving issues such as difficult robot operation and non-intuitive interfaces on a host computer. This enhances the versatility and customization of mobile robots, displays the robot's status or real-time information, and enhances the intuitiveness and real-time nature of interactions. The lighting device can be an LED light to provide illumination. Furthermore, a grayscale sensor can be provided to detect the intensity of incident light reflected from an object's surface, converting optical signals into electrical signals, thereby determining the grayscale value (i.e., color depth) or surface characteristics of the target object.

[0075] Specifically, the ultrasonic module is an ultrasonic sensor, which is electrically connected to the main control chip and is used to detect the position of the target object. More specifically, the ultrasonic module measures the distance by emitting ultrasonic waves and receiving their reflected waves. When the ultrasonic wave encounters the target object, a reflected wave is generated. The ultrasonic module then calculates the time difference between the transmitted wave and the reflected wave, and then combines the propagation speed of the ultrasonic wave in the air (about 340m / s) to calculate the distance between the target object and the ultrasonic module. In a specific embodiment, the target object picked up by the picking mobile robot is a ping-pong ball. The ultrasonic module receives the reflected wave after emitting the ultrasonic wave, and then calculates the time difference between the transmitted wave and the reflected wave and the propagation speed of the ultrasonic wave in the air. After calculation, the distance between the ping-pong ball and the ultrasonic module is obtained.

[0076] In one embodiment, if Figure 4 As shown in the figure, the system implements automatic ball-finding and automatic picking functions based on the vision platform (OPENMV) provided by the vision module. OPENMV provides parameters and identifies the location of small objects. After determining the landing point, it controls the first drive motor through PWM (pulse width modulation) code to drive the object-picking robot to move, such as rotating it to the right, to achieve automatic navigation. At the same time, the servo controls the picking device to complete the grasping. The entire process is divided into three modules: the automatic object-finding module (target recognition), the automatic navigation module (path planning and movement), and the automatic picking module (robotic arm control). Ultimately, a closed-loop control is formed, which can efficiently complete the positioning and recovery tasks of small objects.

[0077] Please refer to Figure 5 The present invention proposes a control method for an autonomous positioning navigation object picking robot control system, comprising the following steps:

[0078] S1, initializing the preset camera and visual platform, and also initializing the preset first drive motor and servo; wherein the initialization settings include image acquisition format and frame rate;

[0079] S2, sets the color threshold according to the color of the target object;

[0080] S3, controlling the robot to turn to a set angle through the first drive motor, collecting images through the camera, and identifying the position of the target object through the visual platform;

[0081] S4, determining whether the target object is recognized;

[0082] S5, if yes, the location information of the target object is sent to the preset main control chip; if no, the robot is controlled to rotate and identify again;

[0083] S6, measures the distance through the preset ultrasonic module and sends the distance information to the main control chip;

[0084] S7, calculating the coordinates of the target object in the image using a preset positioning algorithm, and calculating the movement parameters using a PID control algorithm;

[0085] S8, sending the movement parameters to a preset driving module;

[0086] S9, driving the robot to the designated position through the driving module;

[0087] S10, determining whether the robot has reached the designated position;

[0088] S11, if yes, trigger the picking program, and control the servo through the main control chip to perform the picking action to pick up the target object; if no, return to the recognition program;

[0089] S12, determining whether the target object is picked up;

[0090] S13, if yes, then issue an audible and visual prompt.

[0091] In this embodiment, the camera and visual platform are first initialized, including the image acquisition format, frame rate, and frame size. The first drive motor and servo are then initialized. A color threshold is then set based on the color of the target object so that OpenMV can distinguish the target object from other objects. The robot is then controlled to steer to a set angle using the first drive motor, while the camera captures images and the visual platform identifies the target object's position. During this process, the camera continuously captures images and transmits the image data to the main control chip, which processes the captured images. A determination is then made as to whether the target object is recognized. If so, the target object's location information is sent to the main control chip. During this process, the color threshold is used to filter out areas that match the target object's color. Within these filtered color areas, the target object's contour, shape, and other features are further detected to determine its location. A preset ultrasonic module is then used to measure the distance, which is then sent to the main control chip. A positioning algorithm is then used to calculate the target object's coordinates in the image, and a PID control algorithm is used to calculate movement parameters. The PID control algorithm precisely regulates the motor's speed and torque by directly controlling the magnetic pole position of the first drive motor. PID control utilizes feedback signals from the motor to monitor its status in real time, optimize the control algorithm, and improve the motor's dynamic response speed and operating efficiency. The movement parameters are then sent to the driver module, which then drives the robot to the designated location. The driver module determines whether the robot has reached the designated location. If so, the picking program is triggered, controlling the servo to pick up the target object. During this process, the PWM code controls the first drive motor to drive the robot's movement, such as rotating it to the right, while the servo controls the picking mechanism to complete the grab. The robot then determines whether the target object has been picked up. If so, an audio and visual prompt is emitted. Finally, the main control chip controls the servo to alternately perform the picking and placing actions.

[0092] Please refer to Figure 6 First, the target object is identified using OpenMV. In one specific embodiment, the target object can be a ping-pong ball, so the ping-pong ball is identified. A color threshold is then set based on the target object's color. A determination is then made as to whether the object has been identified. If so, the movement parameters are sent to the main control chip; if not, the object rotates and identification continues. After sending the movement parameters to the main control chip, a determination is made as to whether the object has reached the designated position. If so, the pickup procedure is triggered; if not, identification continues. A determination is then made as to whether the target object has been picked up. If so, an audio and visual prompt is emitted, and the program terminates.

[0093] In one embodiment, the step of capturing an image through a camera includes:

[0094] Capture an image through a camera to obtain image data;

[0095] Transmit the image data to the master chip and process the image data through the master chip.

[0096] In a specific implementation, first, the camera captures images of the surrounding environment to obtain image data, then the image data is transmitted to the master chip, and the image data is processed through the master chip.

[0097] In an embodiment, the step of transmitting the image data to the master chip and processing the image data through the master chip includes:

[0098] Screening the area in the image that meets the color of the target object through color threshold value;

[0099] Detecting the contour and shape of the target object to determine the position of the target object.

[0100] In a specific implementation, in the process of processing the image data, first, screen the area in the image that meets the target color through color threshold value, for example, if the ping-pong ball is white or orange, then screen the white area or orange area. Then, detect the contour and shape of the target object to determine the position of the target object.

[0101] In an embodiment, the step of calculating the coordinates of the target object in the image through a preset positioning algorithm includes:

[0102] Adjusting the color threshold value dynamically through HSV color space conversion;

[0103] Selecting the area with the largest area as the target object through connected component labeling algorithm;

[0104] Obtaining the position information of the target object through Gaussian filtering and morphological opening operation.

[0105] In a specific implementation, the visual positioning algorithm is optimized, first, HSV (Hue, Saturation, and Value) color space conversion is used to reduce the influence of light changes on the color threshold value. In a specific embodiment, the orange threshold value range is set to 10, 100, 50 10, 100, 50-25, 255, 255 25, 255, 255, and the white threshold value is 0, 0, 200 0, 200-180, 30, 255 180, 30, 255. Then, connected component labeling algorithm is used for multi-target recognition, and the area with the largest area is preferentially selected as the target object to avoid false detection. The connected component labeling algorithm assigns the same label to the pixel regions that are connected to each other in the image, thereby distinguishing different connected regions. Then, Gaussian filtering and morphological opening operation are performed to eliminate image noise and small interference objects. Gaussian filtering is a linear smoothing filter, which can effectively remove Gaussian noise while preserving important edge features of the image by performing convolution operation on the image using a weight template generated by a Gaussian function.

[0106] In one embodiment, the step of calculating the movement parameters using a PID control algorithm includes:

[0107] Convert the coordinates into relative coordinates for robot motion;

[0108] The speed difference of the first drive motor is calculated by a PID control algorithm to obtain a movement parameter.

[0109] In specific implementation, after calculating the coordinates of the target object in the image using a positioning algorithm, these coordinates are converted into relative coordinates for the robot's motion. The PID control algorithm then calculates the speed difference of the first drive motor to obtain the movement parameters. The PID control algorithm combines proportional, integral, and derivative control functions. It performs proportional, integral, and derivative calculations based on the input deviation value, and the results are used to control the output.

[0110] In a specific embodiment, the PID control algorithm of the present application uses an incremental algorithm:

[0111] ΔU(t)=K P ·(e(t)-e(t-1)+K i ·e(t)+K d (e(t)-2e(t-1)+e(t-2))

[0112] Among them, ΔU(t) is the control increment, K P is the proportional coefficient, which is used to adjust the response strength to the current error; K i is the integral coefficient; K d is the differential coefficient; e(t) is the error at the current moment; e(t-1) is the error at the previous moment; and e(t-2) is the error at the moment before that.

[0113] In one embodiment, the step of driving the robot to move to a specified position by a driving module includes:

[0114] Control the robot to turn and move straight until it is at a preset distance from the target object.

[0115] In a specific implementation, when the driving module drives the robot to move to a specified position, the robot is controlled to turn and move straight until it is at a preset distance from the target object. In a specific embodiment, the preset distance is 5 cm, and the robot stops moving when it is 5 cm away from the target object.

[0116] In a specific embodiment, the coordinates of the tiny object in the image are converted into relative coordinates of the robot's movement, and the speed difference of the motor is calculated through a PID control algorithm to control the robot to turn and move straight until it is 10 cm away from the tiny object.

[0117] In one embodiment, if yes, then triggering a picking program, and controlling the servo to perform a picking action to pick up the target object through the main control chip, includes:

[0118] Control the servo to drop to the preset pick-up box and pick up the target object;

[0119] Lift the pick frame.

[0120] In a specific implementation, when the picking process is executed, the steering engine is controlled to lower the picking frame and pick up the target object, and finally the picking frame is raised to store the target object in the storage area. In a specific embodiment, the storage area is in the shape of an open box, and the target object is poured into the storage area.

[0121] In a specific embodiment, the object picking robot turns at a certain angle on the spot and then continues to scan the site to find the next tiny object and repeat the above operation until all tiny objects are picked up.

[0122] In summary, the image processing function of OpenMV is used to realize the recognition and positioning of tiny objects, the motion control of the object picking robot is realized by combining the PID control algorithm, and the picking frame is controlled by the servo to complete the picking of tiny objects.

[0123] Table 1:

[0124] Function time Pick up the first tiny object 3.2s Pick up the second tiny object 5.8s Pick up the third tiny object 13.4s Pick up the fourth tiny object 19.4s Pick up the sixth tiny object 25.6s Pick up the seventh tiny object Out of bounds Pick up the eighth tiny object time out Pick up the ninth tiny object time out Pick up the tenth tiny object time out

[0125] Table 1 shows the analysis results from the object-picking robot system test. As shown in Table 1, the high DC motor frequency can easily cause the robot to collide with small objects (such as ping-pong balls), causing them to go out of bounds. The vision module also has difficulty identifying small, white objects, causing task timeouts. Therefore, the program reduces the DC motor frequency to prevent the robot from colliding with small objects, and adjusts the image or color threshold of the vision module (OpenMV) to enhance the contrast between small, white objects and the white background.

[0126] Table 2:

[0127] Function time Pick up the first tiny object 4s Pick up the second tiny object 8.6s Pick up the third tiny object 15.4s Pick up the fourth tiny object 25.8s Pick up the sixth tiny object 31.9s Pick up the seventh tiny object 40.5s Pick up the eighth tiny object 46.1s Pick up the ninth tiny object 57.2s Pick up the tenth tiny object 65.7s

[0128] Table 2 shows the analytical data obtained after adjusting the color threshold. It shows that adjusting the color threshold can avoid out-of-bounds and timeout issues. Furthermore, the object-picking robot can complete the picking of 10 small objects within 90 seconds, demonstrating a relatively fast picking speed.

[0129] In summary, the present invention realizes an intelligent picking system based on a main control chip, and realizes full-process automation of target object recognition, autonomous navigation and precise picking through the coordinated optimization of hardware, software algorithms and system integration. The system adopts modularization, including three modules: visual perception, motion control and execution, and integrates multiple sensors and PID algorithms to achieve stable performance. The dynamic threshold color recognition algorithm and connected domain analysis are applied to effectively solve the problem of target recognition under changing lighting. At the same time, the combination of encoder feedback and PID control is adopted to achieve precise motion control of the object picking robot; through the mechanical structure design of the mobile device, servo and robotic arm, it can take into account both functionality and reliability. After multiple tests and optimizations, the system can achieve a positioning error of ≤2cm and a picking success rate of ≥90% in a standard field of 1.5m×1.5m.

[0130] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0131] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A control system for an autonomous positioning and navigation object picking robot, characterized in that: It includes a vision module, a driving module for mobile control and navigation, a pickup device, an ultrasonic module and a main control chip; the main control chip is electrically connected to the vision module, the driving module, the pickup device and the ultrasonic module respectively; The visual module includes a visual platform, a camera and a pan-tilt head, wherein the visual platform is electrically connected to the camera and the pan-tilt head respectively and is used for environment recognition and target detection; The visual platform is provided with a positioning algorithm; The driving module includes a motor driving circuit, a first driving motor for controlling the movement of a preset mobile device, a driving chip for driving the first driving motor, and a power supply battery; The picking device includes a steering gear, a mechanical arm and a picking frame, wherein the steering gear is in transmission connection with the mechanical arm, and the picking frame is provided on the mechanical arm and is used to pick up items; The ultrasonic module is electrically connected to the main control chip and is used to transmit ultrasonic waves to measure distance.

2. The control system of the autonomous positioning and navigation object picking robot according to claim 1, characterized in that: The first drive motor includes a body, a reduction gearbox and a Hall encoder. The body is transmission-connected to the reduction gearbox, and the Hall encoder is electrically connected to the body.

3. The control system of the autonomous positioning and navigation object picking robot according to claim 1, characterized in that: comprising a display screen, wherein the display screen is electrically connected to the main control chip; The pickup device further includes a buzzer for providing operation feedback and a lighting device for auxiliary lighting.

4. A control method for an autonomous positioning navigation object picking robot control system, characterized in that: The following steps are involved: Initialize the preset camera and visual platform, and initialize the preset first drive motor and servo; wherein the initialization settings include image acquisition format and frame rate; Set the color threshold according to the color of the target object; Controlling the robot to turn to a set angle by the first drive motor, capturing images by the camera, and identifying the position of the target object by the visual platform; determining whether the target object is recognized; If yes, the position information of the target object is sent to the preset main control chip; if not, the robot is controlled to rotate and identify again; Measure the distance through a preset ultrasonic module and send the distance information to the main control chip; Calculating the coordinates of the target object in the image using a preset positioning algorithm, and calculating movement parameters using a PID control algorithm; Sending the movement parameters to a preset driving module; Drive the robot to a designated position by the driving module; Determining whether the robot has reached the designated position; If yes, the picking program is triggered, and the main control chip controls the servo to perform a picking action to pick up the target object; if no, the process returns to the recognition program; Determining whether the target object is picked up; If so, an audible and visual prompt will be given.

5. The control method of the autonomous positioning navigation object picking robot control system according to claim 4, characterized in that: The step of collecting images by the camera includes: capturing an image by the camera to obtain image data; The image data is transmitted to the main control chip, and the image data is processed by the main control chip.

6. The control method of the autonomous positioning navigation object picking robot control system according to claim 5, characterized in that: The step of transmitting the image data to the main control chip and processing the image data by the main control chip includes: Filtering the area in the image that matches the color of the target object using the color threshold; The contour and shape of the target object are detected to determine the position of the target object.

7. The control method of the autonomous positioning navigation object picking robot control system according to claim 4, characterized in that: The step of calculating the coordinates of the target object in the image using a preset positioning algorithm includes: Dynamically adjust the color threshold through HSV color space conversion; Through the connected domain labeling algorithm, the area with the largest area is selected as the target object; The position information of the target object is obtained through Gaussian filtering and morphological opening operation.

8. The control method of the autonomous positioning navigation object picking robot control system according to claim 4, characterized in that: The step of calculating the movement parameters by the PID control algorithm includes: Converting the coordinates into relative coordinates for robot motion; The speed difference of the first drive motor is calculated using a PID control algorithm to obtain the movement parameter.

9. The control method of the autonomous positioning navigation object picking robot control system according to claim 4, characterized in that: The step of driving the robot to move to a designated position by the driving module includes: The robot is controlled to turn and move straight until it is at a preset distance from the target object.

10. The control method of the autonomous positioning navigation object picking robot control system according to claim 4, characterized in that: If so, triggering a picking program, and controlling the servo to perform a picking action to pick up the target object through the main control chip, includes: Controlling the servo to drop to a preset picking frame and pick up the target object; Lift up the pick frame.

Images