1、 What is visual servoing control?

Visual servoing control is a technology that obtains image information through visual sensors and uses closed-loop feedback to control robot motion. Its core lies in introducing machine vision into closed-loop control to overcome the uncertainty problem in the underlying system model, reduce sensitivity to system calibration errors, and thus improve the control accuracy of robots.

The concept of visual servoing was proposed by Hill and Park in 1979 and gained widespread attention in the 1980s. With the development of computer technology and camera equipment, visual servoing control has evolved from the initial static "look before move" mode to a dynamic real-time feedback system, which has been widely used in industrial robots, medical surgery, autonomous driving and other fields.

Basic Principles

Visual servoing control establishes a closed-loop system of "perception decision control", which obtains environmental image information through cameras, extracts target features, and generates control signals based on feature errors to drive actuators (such as robot arms, drones) to complete target tracking, positioning, or operation tasks. This technology realizes the mapping relationship between image spatial features and robot motion space.

Main Categories

According to the representation of feedback information, visual servoing control algorithms can be divided into:

1. Position based visual servoing (PBVS): Image features are projected back into three-dimensional space through a camera model, and the position and pose of the target in the world coordinate system are calculated. After comparing with the expected position, control variables are generated

2. Image based visual servoing (IBVS): Define errors directly in the image space (such as feature point pixel coordinate deviation), and map the image errors to the robot's motion speed through the image Jacobian matrix

3. Hybrid visual servoing: Combining the advantages of PBVS and IBVS, some features use 3D position feedback, while others use image feature feedback.

Composition of visual servo control system

A complete visual servoing control system typically consists of four main parts:

Visual sensor: Camera is used to capture image information

Image processing unit: Extract key features (such as corners, edges, etc.)

Control law calculation unit: Design control law based on image error

Mechanical control unit: Convert control commands into actual mechanical motion

Implementation steps of visual servo control

The core principle of controlling servo motors in visual cameras is to obtain information such as the position and angle of the target object through a machine vision system, convert image data into mechanical motion commands, and then drive the servo motor through a controller (such as PLC, industrial computer) to achieve precise positioning or trajectory tracking. The following are the specific implementation methods and key steps:

1. Image acquisition and processing:Industrial cameras capture images of target objects and extract feature point coordinates (such as object center coordinates and angular deviations) through image processing algorithms such as edge detection and template matching. For example, in a visually guided loading and unloading scene, after the camera captures the position of the material, the software calculates the pixel coordinates (CameraPoSX, CameraPoSY).

2. Coordinate conversion and error calculation:Convert the data in the image coordinate system to a mechanical coordinate system. This requires pre calibrating camera parameters (such as pixel equivalents) and establishing a mapping relationship between image coordinates and actual physical positions. For example, pixel coordinates are converted to mechanical coordinates using the formula targetPosX=CameraPoSX × 0.1+50.0 (specific coefficients need to be adjusted according to actual calibration). After conversion, obtain the target position (Target PosX, Target PosY), compare it with the current coordinates of the robotic arm or motion platform, and calculate the position error.

3. Generate control instructions:Generate motion commands for servo motors based on error values. There are two main control methods:
Position based control (PBVS): Directly convert the target position into the target coordinates of the servo motor, and output pulses or analog signals through a controller (such as PLC or motion controller) to drive the motor to complete positioning. For example, in the automatic alignment system of the motor detection platform, after the vision system calculates the coupling deviation, the controller sends instructions to the four axis servo motor to adjust the position of the platform to achieve alignment.

4. Image based control (IBVS):Directly utilizing image feature errors (such as feature point offsets) to calculate motor speed commands through the image Jacobian matrix, achieving dynamic tracking. For example, in a robot grasping scene, if the target moves, the system calculates the image error in real time and adjusts the speed of the robotic arm to make the end effector follow the target's movement.

5. Servo system execution:The controller sends instructions to the servo driver to drive the motor to rotate. The servo system provides feedback on position or velocity through an encoder, forming a closed-loop control to ensure motion accuracy. For example, in a visual dispensing machine, after the visual system corrects the position of the workpiece, the controller outputs pulses to drive the servo motor to adjust the trajectory of the dispensing head, achieving high-precision dispensing.

6. Communication and synchronization:The visual system and servo controller need to transmit data in real-time through reliable communication (such as Ethernet, RS232, Modbus). For example, the FX3U PLC can receive the angle value of a visual camera through the Modbus RTU protocol and convert it into a pulse signal to drive the servo motor to rotate to a specified angle.

In practical applications, the system needs to consider factors such as camera calibration accuracy, mechanical transmission errors, and control cycles. For example, a servo control system based on machine vision monitors the position deviation of the motor movement in real time, separates the deviation amount, and adjusts the control parameters to improve system stability.

2、 What are the functions of micro servo motors in visual servo control

Micro servo motors play a crucial role in the field of visual cameras, and can be said to be the core power source that enables cameras to "see clearly, see accurately, and move steadily". The application of servo motors in visual cameras is mainly reflected in the high-precision motion control capability of servo motors, which enables precise positioning, stable tracking, and dynamic adjustment of the camera, thereby improving the performance and automation level of the visual system. The following are specific application directions:

1. Camera motion control in active vision system:

In active vision systems, servo motors are used to drive the camera's multi degree of freedom movements (up and down, left and right, rotation), mimicking the dynamic adjustment ability of the human eye. Real time image processing is used to obtain target position information, and servo motor closed-loop control is used to control camera motion, keeping the target at the center of the image and achieving dynamic tracking. For example, the binocular active vision system uses 5 DC servo motors to control camera motion, combined with an image acquisition card and a data acquisition card to form a closed-loop control to ensure tracking accuracy.

2. Lens autofocus and focusing:

In the field of optical lens manufacturing, servo motors drive lens movement to achieve high-precision autofocus. By switching between PP (position) mode and PV (speed) mode, the servo motor solves the problem of low-speed shaking in traditional stepper motors, improving focusing smoothness and repeatability (such as PMM28 series servo motors achieving 0.1-1mm/s shake free low-speed operation in lens focusing equipment).

3. Dynamic adjustment in visual servo control system:

In image-based visual servoing control (IBVS), the servo motor adjusts the camera pose in real-time based on image feature errors. For example, by calculating the camera speed command through the image Jacobian matrix, the servo motor drives the camera to move to reduce feature point deviation, achieving target localization or trajectory tracking. This application is commonly used in scenarios such as robotic arm control and drone obstacle avoidance.

4. Motion platform for industrial testing equipment:

The combination of servo motors and linear modules provides a high-precision motion platform for visual inspection equipment. For example, in the sorting of 3C electronic components, the servo motor linear module has a repeated positioning accuracy of ± 0.01mm, which can be used in conjunction with a visual system to quickly grasp and place small parts; In the lens dispensing machine, the servo motor drives the XYZ axis to move, achieving a dispensing accuracy of ± 0.01mm.

5. Collaborative control of multi vision systems:

In binocular or stereo vision systems, servo motors synchronously control the movement of multiple cameras to ensure coordinated viewing angles and consistent image acquisition. For example, adjusting camera spacing or angle through servo motors can optimize depth information acquisition and improve 3D reconstruction accuracy.

3、 What are the main applications of micro servo motors in visual cameras?

Based on the requirements of visual servo control, micro servo motors have the following distinct characteristics and advantages in the application of visual cameras:

Core features:

1. Ultimate high precision and closed-loop control

Closed loop feedback:Unlike ordinary open-loop motors, micro servo motors integrate high-precision encoders (such as 17 bit absolute encoders) inside. In visual applications, it's like installing "eyes" on the lens that can provide real-time feedback on position information.

Micron level positioning:It can achieve micrometer level or even finer position control. This is crucial for the autofocus of the camera, ensuring that the lens moves to the clearest imaging position, avoiding the phenomenon of "step loss", and ensuring imaging quality.

2. Compact size and high power density

Space saving:Micro servo motors are designed for compact spaces, such as motors with a diameter of only 15mm-16mm, which are widely used inside gimbals and lenses.

Lightweight:The extremely light weight (such as only 74g) makes it very suitable for use in drone gimbals, mobile phone lenses, or portable visual devices, reducing load and enhancing device flexibility.

Fast dynamic response and smooth operation

Quick response:The rotor has a small inertia and can achieve millisecond level start, stop, and steering. Can quickly complete actions when capturing moving objects or when quick switching of focal length/aperture is required.

Low speed stability:Using FOC (Field Oriented Control) algorithm, even at extremely low speeds, it runs very smoothly without shaking, which is crucial for smooth zooming and pan tilt stabilization in video shooting.

High torque density and silent operation

Powerful and lightweight:Although small in size, it can output high torque through designs such as planetary gearboxes, which is sufficient to drive the lens group or filter to switch.

Silent shooting:The running noise is extremely low (<35dB) and will not interfere with recording, making it very suitable for scenarios such as Vlogs and live broadcasts that require sound quality.

In summary, micro servo motors perfectly meet the high-performance motion control requirements of modern visual cameras through their characteristics of "small size, large torque, high precision, and low noise", and are key components for improving imaging experience and visual system stability.

Typical application scenarios:

1. PTZ stabilization:By utilizing its high response speed and precise control, it can counteract the shaking of handheld or carrier and maintain image stability.

2. Auto focus and zoom:In security monitoring and machine vision lenses, precise control of lens group displacement enables fast and noise free focusing.

3. Filter switching:In the infrared night vision function, the IR-CUT filter is switched in milliseconds to achieve seamless connection between day and night modes.

4. Aperture control:Accurately adjust the aperture size and adjust different lighting conditions.

The core advantage of these applications lies in the closed-loop control characteristics of servo motors, which can achieve micrometer level positioning accuracy, fast response, and stable operation, meeting the stringent requirements of vision systems for dynamic performance and reliability.

The deeper meaning of 'what you think is what you get' lies in its disruptive compression of research and development cycles and costs. Traditional motor prototype production involves multiple stages from design drawings to physical models, including mold development, component processing, assembly and debugging, which can take weeks or even months. 3D printing technology enables prototype iteration to be as rapid as "printing" documents, with design modifications only needing to be completed on a computer and then printed for verification, greatly reducing the development cycle. Additive Drives claims that the 3D printed motor prototype can be designed and completed within 4 weeks, and even the Hair pin winding stator sample can be manufactured in just a few days. For customized needs of small batches and multiple varieties, 3D printing does not need to bear high mold costs. The manufacturing cost per unit product can be reduced by 30% -50%, and the material utilization rate can reach over 90%, with almost no waste generated. This makes customized manufacturing economically feasible, making "on-demand production" possible and effectively solving the problem of global supply chain delays.

The potential of fully 3D printed motors goes far beyond this, and their applications have expanded to cutting-edge fields such as aerospace. An electric spray engine, which is completely manufactured by 3D printing technology, displayed by MIT, can propel small satellites by emitting liquid droplets. This innovative device is not only produced quickly and at a low cost, but can even be printed in space, which means that future space missions will no longer rely entirely on devices sent from Earth, but will be able to self repair and upgrade in orbit. In addition, the 3D printed minimalist turbojet engine independently developed by AVIC Group has successfully completed its first flight test in 2025. Some performance parameters such as fuel consumption and thrust to weight ratio have been optimized, and the number of parts has been reduced by about 60%, filling the gap in the application of domestic engine 3D printing engineering.

However, the widespread application of fully 3D printed motors still faces a series of severe challenges. In terms of material performance, although breakthroughs have been made in multi material printing technology, the printed conductive and magnetic materials still have gaps in key performance indicators such as conductivity, magnetic permeability, and mechanical strength compared to traditional high-purity copper wires and high-performance silicon steel sheets, which limits further improvement in motor power density and efficiency. In the printing process, the collaborative control of multiple materials and processes (such as melt extrusion, particle sintering, ink direct writing) is extremely complex. The curing temperature and shrinkage rate of different materials are different, which can easily lead to defects such as interlayer peeling, flexural deformation, and internal porosity during the printing process, affecting the reliability and consistency of the motor. In addition, the current multi material printing speed is relatively slow, making it difficult to meet the needs of large-scale production, and the cost of high-performance printing materials and specialized equipment is still high, limiting their popularity in price sensitive markets.

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