📋 Case Study
Vision-Guided Palletizing Robot for Mixed-SKU E-Commerce Fulfillment
Unstructured tote input with random part orientation, variable box dimensions, and reflective surfaces causing glare
🏗️ Project Overview
Automated distribution center serving Amazon Prime logistics hub
🎯 Challenge
Unstructured tote input with random part orientation, variable box dimensions, and reflective surfaces causing glare
🔧 Design Approach
Dual-camera stereo setup with polarized dome lighting; custom CNN-based pose estimator trained on synthetic data; dynamic pallet layer planning with real-time collision-free path regeneration
📐 Design Diagram
AI-generated project design illustration
📐 Key Calculations
FOV Coverage per Camera
2 × WD × tan(θ/2)
Result: 1240 mm × 980 mm
Ensures full tote coverage at 1.2 m working distance
Minimum Contrast Ratio
(L_max − L_min)/(L_max + L_min)
Result: 0.72
Guarantees reliable edge detection on glossy cartons
📊 Results
99.8% placement accuracy across 212 SKU types; 22% increase in palletizing throughput; zero manual intervention for 14+ hours per shift💡 Lessons Learned
- •Synthetic data augmentation is essential for rare SKU variants
- •Polarized lighting eliminated >90% specular artifacts
- •Dynamic layer planning reduced pallet instability by 40%
✅ Key Takeaways
- 1Synthetic data augmentation is essential for rare SKU variants
- 2Polarized lighting eliminated >90% specular artifacts
- 3Dynamic layer planning reduced pallet instability by 40%
📐 Prerequisites
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🔗 Engineering Applications
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