Robotics

Warehouse floors are too chaotic to test autonomous systems

Shifting inventory, varied lighting, congested aisles, and human co-workers create a challenging environment for physical AI to operate. Empower your team with deterministic sensor simulation and real-fidelity digital twins to deploy faster and safer.

Software augmented testing for robotic autonomy.

Real-fidelity digital twins and deterministic sensor simulation to build, test, and validate perception systems for autonomous material handling in environments that mirror the chaos of real operations.

Challenges with Warehouse Perception Development

Safe, Reliable Perception

Edge Cases — Fallen pallets, unexpected workers, reflective surfaces, and transparent packaging create perception challenges rarely seen in controlled tests.

Observability — Model changes can introduce regressions in narrow-aisle navigation or pick accuracy.

Safety — Collisions with workers, racking, or inventory cause injuries and operational downtime.

Solution — Continuously evaluate perception against standard and edge-case warehouse scenarios in a simulation that mirrors real facility complexity.

Time and Cost

Capturing data in active warehouses disrupts operations and throughput.

Warehouse layouts change frequently and data becomes stale fast.

Labeling pallets, conveyors, shelving, and workers across sensor modalities is labor-intensive.

Solution — Generate the scenarios, test suites, and datasets you need in days, without disrupting operations.

Scalability Across Facilities and Equipment

Different warehouse layouts, racking systems, and inventory types require separate data campaigns.

New facilities mean new floor plans, lighting conditions, and workflow patterns.

Different robotics platforms carry different sensor suites.

Solution — Simulate new inventory configurations, and robot platforms by updating code, not recapturing data.

PD Solutions

PD Replica + PD Sim for warehouse robotics and material handling autonomy.

Evaluate

Open-loop and closed-loop testing integration for warehouse autonomy stacks. Regression testing for navigation, obstacle avoidance, and pallet/item detection. Perception unit testing across lighting conditions, clutter levels, and traffic density. Near-validation testing in real-world scanned facilities (PD Replica).

Analyze

Benchmark against pre-defined or custom warehouse scenarios and robot configurations. Evaluate performance across peak-traffic, night-shift, and maintenance scenarios. Explore sensor placement and field-of-view optimization for aisle widths and ceiling heights.

Train

Scaled data generation for pallet detection, barcode/label reading, and obstacle avoidance. Generate datasets across infinite variations of inventory arrangements, packaging, and lighting. Simulate in real-world scanned warehouses (PD Replica) for domain-specific training.

Benefits

Addressing Industry Challenges

Test without disrupting operations

No need to shut down aisles or pause picking to collect data. Simulate your warehouse floor in full fidelity and run thousands of test scenarios while operations continue uninterrupted.

Scale across facilities and robot fleets

Change facility layouts, racking configurations, or robot platforms through configuration. Onboard a new warehouse site in days instead of months of on-site data collection.

Protect workers in shared spaces

Exhaustively test perception against worker interaction scenarios — crossing paths, bending, reaching, operating equipment — before deploying robots alongside people.

Schedule a Demo

See how Parallel Domain can accelerate your warehouse autonomy development. Fill out the form and our team will be in touch.