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Forklift Hazard & Pedestrian Proximity Prevention in Warehouses
A 500,000 sq. ft. logistics distribution center operating 40 industrial forklifts alongside 150 warehouse order pickers in high-density rack aisles.
Runs onArjuna- reduction in forklift-pedestrian near-miss incidents
- 94%reduction in forklift-pedestrian near-miss incidents
- replaced wearable RFID tags
- 100%replaced wearable RFID tags
How the work runs
The pressure that made this worth automating, the steps the system runs, and what came out the other side.
Pressure & Trigger Points
- Blind intersections at aisle ends caused frequent near-miss incidents and dangerous forklift-pedestrian collisions.
- Wearable RFID tag proximity systems were unreliable because workers forgot to charge or wear their badges.
- Warehouse managers lacked objective data on high-risk collision blind spots across the facility.
The run · 5 operational steps
Click any step to inspect telemetry signals, model reasoning, and governance gates.
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Overhead Aisle Camera Video Stream Analysis
Arjuna processes video from overhead aisle CCTV cameras, tracking real-time 3D positions of forklifts and workers.
Input Signal:
Real-time operational telemetry & queue
Reasoning Pattern:
MCP grounded vector inference
Governance Gate:
Policy constrained with audit write-back
Verified Business Outcomes
- 94% reduction in forklift-pedestrian near-miss incidents across the distribution center.
- Zero forklift collision injuries achieved since deployment.
- Replaced wearable RFID tags with 100% camera-based, zero-maintenance pedestrian safety coverage.
Capabilities this relies on
- vision perception
- anomaly detection
- alert routing
- policy compliance
- in boundary deployment
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