Machine vision has transformed what's possible with industrial robotics. By giving robots the ability to "see," we enable applications that were previously impractical or impossible with fixed automation. Here's what you need to know about integrating vision with robots.
Why Add Vision?
Vision systems address fundamental limitations of traditional robotics.
Part Location Variability
Without vision, robots require parts to be presented in precisely known positions. Vision allows:
- Picking from moving conveyors
- Handling parts in random orientations
- Adapting to upstream process variation
- Reducing fixturing requirements
Part Identification
Vision enables robots to handle mixed products:
- Identifying part types for sorting
- Reading barcodes or data matrix codes
- Verifying correct parts are processed
- Tracking individual parts through processes
Quality Verification
Vision provides inspection capabilities:
- Checking assembly completeness
- Measuring critical dimensions
- Detecting surface defects
- Verifying label presence and content
2D vs. 3D Vision
Understanding when each technology applies is critical for success.
2D Vision Applications
Two-dimensional vision captures flat images like a conventional camera. Best for:
- Parts presented in a single plane
- Pattern matching and identification
- Barcode and text reading
- Surface inspection for color and defects
- Guidance for simple pick-and-place
2D vision is mature, fast, and cost-effective for appropriate applications.
3D Vision Applications
Three-dimensional vision captures depth information. Required for:
- Bin picking of randomly oriented parts
- Parts with significant height variation
- Measuring 3D features
- Depalletizing stacked items
- Robotic welding seam finding
3D vision costs more but enables applications 2D cannot address.
Implementation Considerations
Successful vision integration requires attention to several factors.
Lighting
Proper lighting is the foundation of reliable vision:
- Consistent, controllable illumination
- Elimination of ambient light variation
- Appropriate technique (diffuse, structured, backlit)
- Redundancy for critical applications
Poor lighting is the most common cause of vision system problems.
Camera Selection
Match camera capabilities to requirements:
- Resolution for required measurement precision
- Frame rate for process speed
- Sensor size for field of view
- Interface (GigE, USB3, Camera Link)
Processing Speed
Vision processing must keep pace with production:
- Image acquisition time
- Processing algorithm speed
- Communication latency to robot
- Total cycle time impact
Robot Integration
Vision and robot must work together seamlessly:
- Coordinate system calibration
- Communication protocols
- Handshaking and timing
- Error handling
Common Pitfalls
Learn from others' mistakes:
Underestimating Variation
Test with the full range of parts, lighting conditions, and environmental factors you'll encounter in production.
Insufficient Lighting Control
Ambient light changes throughout the day can cause intermittent failures that are difficult to diagnose.
Inadequate Processing Power
What works in the lab may be too slow when processing production volumes continuously.
Poor Calibration
Vision accuracy depends on proper calibration between camera and robot coordinate systems.
Getting Started
For your first vision-guided robot application:
- Start with a well-defined, moderate-complexity application
- Work with integrators experienced in both vision and robotics
- Invest in proper lighting infrastructure
- Plan for thorough testing before production
- Build in maintainability for long-term success
Vision-guided robotics opens new automation possibilities. With careful planning and implementation, these systems deliver reliable performance for demanding applications.