Vision-Based Robotic Object Detection and Manipulation System
A vision-based robotic system for automated object detection and manipulation using a FANUC industrial robot. The system integrates computer vision with robotic control to enable precise pick-and-place operations, automated quality control, and industrial automation tasks.
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Project Documentation
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This project implements a complete vision-based robotic system for automated object detection and manipulation using a FANUC industrial robot. The system integrates advanced computer vision algorithms with precise robotic control to enable automated pick-and-place operations, quality control, and industrial automation. The implementation features camera calibration, multiple object detection pipelines, real-time pose estimation, and seamless robot integration through Ethernet/IP protocol.
Approaches
Advanced Computer Vision Pipeline
Implemented camera calibration (intrinsic and extrinsic) for accurate measurements, real-time object detection and classification, and multiple detection pipelines including marker-based detection using ArUco markers, shape-based template matching for pipes and tapes, and contour analysis with feature extraction.
Intelligent Object Recognition
Developed shape matching algorithms for robust object identification, Principal Component Analysis (PCA) for feature extraction and orientation detection, Iterative Closest Point (ICP) algorithm for point cloud registration, and template-based matching with configurable similarity thresholds.
Precise Robotic Control
Integrated full FANUC robot control via Ethernet/IP protocol with Cartesian and joint-space position control, real-time position and status monitoring, speed control and motion planning with safety features, and coordinate transformation between camera and robot frames.
Camera-to-Robot Calibration
Implemented camera-to-robot calibration for accurate coordinate transformation, ensuring precise alignment between vision system and robotic manipulator.
Results
- Successfully implemented complete vision-based robotic system with real-time processing
- Achieved accurate object detection and pose estimation for multiple object types
- Developed reliable camera calibration and coordinate transformation system
- Created efficient robotic control interface with safety monitoring
- Implemented modular architecture for easy extension and customization
Technical Details
- Used Python 3.11 with OpenCV for computer vision and image processing
- Implemented Principal Component Analysis (PCA) for orientation detection
- Developed Iterative Closest Point (ICP) algorithm for point cloud alignment
- Created template matching and contour analysis for object recognition
- Integrated camera calibration techniques (Zhang's method)
- Implemented homogeneous transformation matrices for coordinate systems
- Used PyComm3 for Ethernet/IP communication with FANUC robots
- Developed real-time image processing pipeline with optimized performance