Project Title: AI-Driven Collaborative
Assembly & Inspection Cell (Akıllı Montaj
ve Denetim Kobotu)
Overview
This project proposes a smart Human-Robot Collaboration (HRC) cell. The system features
a custom 6-axis robotic arm that works safely alongside a human operator to assemble
and inspect small, multi-part objects. This approach merges assembly tasks with
advanced vision-based quality control, eliminating the need for rigid conveyor systems
while emphasizing software architecture and AI integration.
Key Features & Requirements Met
1. 6-Axis Robot Kinematics
We will design and 3D-print a custom 6-axis robotic arm. The kinematics and motor
control will be orchestrated using ROS 2, providing an industry-standard software
foundation.
2. Collaborative Assembly
(Mensch-Roboter-Kollaboration)
The robot shares the workstation with a human operator. The human loosely places the
components on the table; the robot then takes over to visually identify the parts and
execute precision mounting in the correct sequence.
3. Dual-Camera Vision System
●​ Safety Monitoring: An overhead camera monitors the workspace.
●​ Eye-in-Hand Inspection: A camera mounted on the robot's end-effector uses
OpenCV to recognize parts during assembly.
4. Warning & Alarm Zones
Using the overhead camera and OpenCV motion tracking (or human pose estimation), we
will define three safety zones around the robot base:
●​ Zone 1 (Outer): Human proximity detected; robot speed reduces to 50%.
●​ Zone 2 (Middle): Human reaching in; robot speed reduces to 10%.
●​ Zone 3 (Inner): Human touching the workspace; state-machine triggers an
immediate emergency halt (E-Stop).
5. Actuation
The arm will be driven by the 6 Dynamixel AX-12 servo motors provided. Given their torque
limits (~1.5 N.m), the arm and the assembly objects (e.g., 3D-printed or foam parts) will be
lightweight. Two additional rotor/encoder motors will power a custom 2-finger parallel
gripper.
Advanced Capabilities: Automated Optical
Inspection (AOI)
To elevate the project to Industry 4.0 standards, the robot will perform a final inspection of
the assembled object using its eye-in-hand camera.
Dimension Measurement
By placing a printed ArUco marker (of exact known size, e.g., 50x50 mm) on the assembly
table, the camera can calculate the pixel-to-millimeter ratio. Using edge detection, the
system can output the exact physical dimensions of the finished product.
3D Model Generation (Digital Twin)
After assembly, the robot arm will perform a pre-programmed visual sweep around the
object, capturing 40-60 overlapping images. These images will be processed using
Structure from Motion (SfM) pipelines like COLMAP or Neural Radiance Fields (NeRF) to
generate a scaled 3D point cloud or mesh of the final product.
Feasibility & Value
This project avoids the mechanical complexities of conveyor belts, keeping hardware costs
minimal by utilizing provided motors. It heavily emphasizes software
engineering—specifically state-machine architecture, ROS 2 node communication, and
computer vision—making it an ideal portfolio project for automation and robotics
engineering.