Case Study
Semi-Automated Heavy-Duty Liquid Cargo Handling
Modular ROS 2 control system combining Cartesian motion control and vision-based feedback to improve safety, precision, and operational efficiency for heavy-load manipulation.
At a glance
Problem & outcome
The challenge
Liquid cargo handling introduces spillage risk, low precision with manual operation, and low flexibility with fixed automation. The system needed to support semi-automated operation with stable behavior while handling heavy loads.
What we built
- Modular ROS 2 controller stack (separable pipelines)
- Reusable Cartesian controller base for rapid iteration
- Vision-based feedback loop with TF2 transforms
- Compliance layer to keep motion stable under load
Architecture overview
Two controller pipelines feed a shared compliance controller, keeping the system modular and testable.
A) Cartesian controller pipeline
B) Vision feedback pipeline
Core components
Before vs after
- Manual operation: fatigue + inconsistent precision
- Fixed automation: hard to adapt to changing conditions
- Weak feedback loops for correction and recovery
- Precise Cartesian targets with real-time error correction
- Modular pipelines: swap input source (keyboard/vision)
- Improved stability via compliance control strategy
Results & validation
Precision
High-precision pose tracking using PoseStamped targets and Cartesian error correction.
Responsiveness
Strong response to dynamic input changes; users preferred velocity-based input behavior.
Vision feedback
Accurate for nearby detections; performance impacted by distance/lighting—promising for adaptive manipulation.
Testing approach
- Target pose tracking accuracy
- Responsiveness to sudden target changes
- Stability under motion execution
- User evaluation survey + preferences
Challenges & mitigations
Vision limitations
Performance reduces with distance and adverse lighting; multi-detection ambiguity appears in cluttered scenes.
- Confidence filtering
- Depth-based 3D localization
- Frame transform validation checks
Stability under load
Heavy-load manipulation requires stable compliance behavior and predictable correction dynamics.
- Compliance controller boundary
- Error smoothing strategies
- Reusable Cartesian base for consistent behavior
Business impact
Why this matters operationally
- Reduced spillage risk through more precise, stable motion
- Less operator fatigue with semi-automated control
- Higher adaptability vs fixed automation systems
- Reusable control framework that scales to future logistics systems
Want this level of stability in your robotics stack?
If your system behaves well in demos but degrades in real conditions, we can help diagnose failure modes and ship fixes.
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