The robot is a 4 DOF arm. The complete forward and inverse kinematics have been solved and are provided in the documentation.
Because we are directly driving the joints with the servo motors, we avoid using bearings. Except at the base of the robot, where we are using two radial ball bearings for smooth motion.
The motors are strong enough to handle the weight of the camera at the end of the arm. It is important to drive the motors at 7.5V, otherwise Joints 2 and 3 will tend to fail in extended positions.
At the base of the robot we use Nylon M3 threaded standoff to provide separation between the ESP32 and the servo board. As well as to mount the base joint of the motor. All screws are M3, except for those that come with the servo motors, which are M2.
At the end of the arm, we attach a USB camera and the LED light. A 3D printed diffuser is installed in front of the LED ring. This was printed with clear PETG.
The robot is a 4 DOF arm. Each joint is directly driven by a serial bus servo, Feetech SCS 225.
This servo is easy to control and because it is a serial bus servo, we can daisy-chain them together, which makes for a very clean setup (few wires).
The motors connect to the Feetech URT board, which is powered with a 7.5 V AC-to-DC power adapter.
Then the URT board is connected to an ESP32 through the TX and RX pin. We are using an ESP32 development board for easier connection
For the light source, we are using an LED ring light. These are individually addressable LEDs, which allow extensive customization.
For this project, we use an Anker 2K camera because it delivers excellent resolution and reliable autofocus. Although it is slightly expensive, we can switch to a different camera without making major system changes. However, we would need to 3D print a new mounting solution to fit the alternative camera.
In addition, we may need to fine-tune certain parameters to adjust for differences in camera settings. We clearly outline all of these steps and considerations in the documentation to ensure a smooth transition.
We use the OpenCV library to access the camera’s video feed and perform vision detection. More specifically, we use MediaPipe Hands, a machine learning (ML) solution that infers 3D hand landmarks in real time.
Next, we process the landmarks generated by MediaPipe and apply custom logic to identify specific gestures. The system then maps each detected gesture to a corresponding robot action. Additionally, we calculate the hand’s offset relative to the center of the image and use that positional data to determine how the robot should move.
The rest of the python code is essentially a state machine driven by the outputs of the vision program.
Additionally, we implement a basic PID controller to drive the robot and center the detected hand in the field of view of the camera.
Finally, we send commands over serial to the ESP32 to drive the motors and set the LED ring behavior (colors and intensity).
The Python program sends commands directly to the ESP32, including servo angles and LED settings. The ESP32 then executes these commands. Its only onboard logic is to perform a final validation of the servo instructions, ensuring that all angle values remain within the motor’s safe operating limits.
In addition, you can easily extend the system to support more advanced features. For example, the ESP32 can retrieve and report servo data such as temperature, position, current, and torque, enabling better monitoring and control of the robot’s performance.
You can find a demo of the robot in the Youtube video above.
The design files and documentation of this project can be found in the Shop page. This will include:
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