Back to Portfolio

Robotic Vision System Intelligent Automation

Advanced computer vision system for robotic pick-and-place operations with 98.8% success rate and real-time object manipulation capabilities.

Project Overview

Developed a sophisticated robotic vision system for automated pick-and-place operations in manufacturing environments. The system combines advanced computer vision, machine learning, and robotic control to enable precise object detection, classification, and manipulation with human-level accuracy.

Hardware

Universal Robots UR5e, Intel RealSense, Industrial Cameras

AI Framework

ROS2, OpenCV, PyTorch, MoveIt

Computer Vision

YOLOv7, Point Cloud Processing, 3D Reconstruction

Control Systems

PID Control, Trajectory Planning, Force Feedback

The Challenge

A leading electronics manufacturer needed to automate their assembly line with robotic systems capable of handling diverse components. Key challenges included:

  • Detecting and classifying 50+ different component types
  • Handling objects with varying shapes, sizes, and orientations
  • Achieving sub-millimeter precision in pick-and-place operations
  • Adapting to changing lighting conditions and backgrounds
  • Integrating with existing production line systems
  • Ensuring 24/7 operation with minimal maintenance

Our Solution

1. Multi-Camera Vision System

Deployed a synchronized multi-camera setup with RGB and depth cameras to capture comprehensive 3D information about objects. The system uses stereo vision and structured light for accurate depth estimation.

2. Advanced Object Detection & Classification

Developed a custom YOLOv7 model trained on 100,000+ annotated images of electronic components. The model achieves 98.8% accuracy in object detection and classification across all component types.

3. 3D Pose Estimation

Implemented advanced algorithms for 6DOF pose estimation, enabling the robot to understand object orientation and position in 3D space with millimeter-level accuracy.

4. Intelligent Grasp Planning

Created a grasp planning system that analyzes object geometry and selects optimal grasp points based on stability, accessibility, and collision avoidance.

Technical Implementation

Vision Pipeline

The vision system processes data through multiple stages:

  • Image Acquisition: Synchronized capture from multiple cameras
  • Preprocessing: Calibration, distortion correction, and enhancement
  • Object Detection: YOLO-based detection and classification
  • 3D Reconstruction: Point cloud generation and processing
  • Pose Estimation: 6DOF pose calculation for each object

Robotic Control

Advanced control algorithms ensure precise manipulation:

  • Trajectory planning with collision avoidance
  • Force feedback for delicate object handling
  • Adaptive control for varying object properties
  • Error recovery and retry mechanisms

Results & Impact

98.8%

Success Rate

0.5mm

Positioning Accuracy

2.3s

Cycle Time

50+

Component Types

Performance Metrics

  • Achieved 98.8% success rate in pick-and-place operations
  • Maintained 0.5mm positioning accuracy across all operations
  • Reduced cycle time to 2.3 seconds per component
  • Successfully handled 50+ different component types
  • Achieved 99.4% uptime with automated error recovery

System Capabilities

The robotic vision system can handle various tasks:

  • Object Detection: Identify and locate components in cluttered environments
  • Classification: Distinguish between different component types and variants
  • Pose Estimation: Determine 6DOF pose for precise manipulation
  • Grasp Planning: Select optimal grasp points for stable manipulation
  • Quality Inspection: Detect defects and quality issues during handling
  • Adaptive Behavior: Learn and adapt to new component types

System Integration

Seamlessly integrated with existing manufacturing infrastructure:

  • Production Line Integration: Direct communication with conveyor systems
  • Quality Control: Integration with inspection and testing systems
  • Data Management: Real-time data logging and analytics
  • Maintenance Systems: Predictive maintenance and health monitoring
  • Safety Systems: Integration with safety sensors and emergency stops

Applications

The system is deployed across various manufacturing applications:

  • Electronics Assembly: Component placement and soldering operations
  • Automotive Manufacturing: Parts handling and assembly
  • Pharmaceutical: Precise handling of medical devices
  • Food Processing: Packaging and quality control
  • Logistics: Warehouse automation and sorting

Future Enhancements

Planned improvements include:

  • Multi-robot coordination for complex assembly tasks
  • Advanced machine learning for continuous improvement
  • Integration with digital twin systems
  • Expansion to additional manufacturing processes
  • Enhanced human-robot collaboration capabilities