Manual cycle counting is slow (50–100 locations/hour per counter), disruptive (requires MHE downtime in the aisle), and labor-intensive. High-bay racking is dangerous to count manually. Drone audits solve the "continuous, non-disruptive, high-throughput inventory verification" problem, scanning 600–1,000 locations/hour with computer vision and barcode reading.
An operator defines a scan mission covering a set of aisles or zones. The drone takes off, navigates autonomously using visual SLAM or fixed reference markers, and flies through each aisle capturing high-resolution images of racking faces. Computer vision models read barcodes, license plates, and product labels from the images; 3D case-counting algorithms estimate pallet quantity (Ti × Hi). The captured data is compared against the WMS ledger, and variances are flagged as exceptions. Drones can operate during off-hours or alongside active operations (depending on regulatory and safety clearance). Battery swap stations enable near-continuous operation.
Autonomous indoor drone platform (SLAM navigation or marker-based) + computer vision pipeline (barcode/OCR/case-counting models) + WMS integration layer for variance comparison + mission planning software + battery swap/charging station + image archive and search engine.
A single-source-of-truth transactional record that tracks every inventory unit's identity, quantity, location, lot, and status in real time.
Ledger data provides expected SKU and quantity per location for variance comparison.
A structured program of ongoing, partial inventory counts that continuously validates ledger accuracy without shutting down operations for a full.
Cycle count provides the conceptual framework and process that drone audits augment or replace.
Nothing downstream yet.
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