layout_editor
Fleet size8 robots
01
Design Your Scenario
Pick a warehouse layout template and set how many robots run. No code, no terminal — just configuration.
Digital Twin for Warehouse OperationsTest every fleet decision in simulation.
Test every fleet decision in simulation.
Deploy only what the data confirms.
Model your floor, run multi-robot simulations under identical conditions, and compare traffic policies on measured throughput, block time, and charger contention — before anything moves on the real floor.
- Fleet size
- 20 robots
- Measured per run
- 9 metrics
- Traffic policies
- 3 compared
factory_twin_sim.exeLoading
Initializing viewport
GRID_LOAD: 88%AGENTS: 6 ACTIVE
Digital Twin · ViewportBuilt on
ROS 2Open-RMFNav2
Capabilities
Everything a warehouse twin should answer.
One platform from layout modeling to deployment sign-off — grounded in how your robots actually move.
01
Warehouse Modeling
Build a 2D top-down digital twin of your floor — racks, aisles, storage zones, and functional areas — as the reference for every what-if scenario.
Configurable rack, aisle, zone, and charger geometry — from a standard floor to a 120×60 m distribution centre.
02
Multi-Robot Routing & Coordination
Configure fleet size and let the engine handle routing and route reservation, so robots don't block each other at intersections.
Lane reservation with automatic yield — deadlocks are broken and resolved, not just detected.
03
Digital Twin Synchronization
Watch robot positions, status, and paths update in near real-time as the simulation runs against your chosen layout.
Position, status, and lane reservations stream as the run advances.
04
Simulation & KPI Analysis
Measure throughput, block time, charger contention, and route yields under controlled operating conditions.
9 metrics measured per run, including throughput, block time, charger wait, route yields, and deadlocks broken.
05
Scenario Comparison
Compare layout and robot-allocation options side by side and rank them by throughput once they satisfy your KPI constraints.
Ranked side by side under identical conditions — same layout, same fleet size, same run duration.
06
Deployment Review
Operation managers review simulation results and approve a layout before it is applied to the physical warehouse.
No simulation result reaches the physical floor without manager sign-off.
Workflow
From layout to sign-off in four steps.
A streamlined path from scenario design to data-backed deployment approval.
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Live product surfaces — Platooning shown on Standard Warehouse, 8 robots.
Use Cases
Decisions you can model today.
Each scenario maps to a real planning question warehouse teams face before touching the physical floor.
5 robots
8 robots
Add more robots
Will three extra robots raise throughput, or just move the queue somewhere else?
Size the fleet against measured throughput and block time instead of a spreadsheet estimate.
- Evaluate throughput impact first
- Catch congestion before it reaches the floor
- Size the fleet with data
Layout A
Layout B
Rethink the layout
Does a different rack arrangement shorten routes, or just relocate the bottleneck?
Test a new rack or aisle configuration before moving anything on the real floor.
- Simulate a new aisle or rack plan
- Compare KPIs against the current layout
- Lower operational risk
Conflict
Reserved
Prevent congestion
What happens when two robots claim the same aisle at the same moment?
Lane reservation holds one robot and reroutes the other instead of letting them meet.
- Route reservation per segment
- Deadlocks broken automatically
- Higher effective utilization
Benchmark
Measured, not assumed.
Every option is run under identical conditions and scored on the metrics that decide whether a warehouse layout actually works.
Figures below are one real run of the simulation engine — Standard Warehouse · 8 robots · 1 charger · 600s · identical task queue. Run your own on the dashboard.
Throughput
96.0/h
Missions completed per hour
CapacityCompleted missions
16
Missions finished during the run
CapacityTotal block time
297s
Traffic waiting plus charger queueing
RiskAvg charger wait
15.3s
Charger wait per completed mission
EfficiencyRoute yields
0
Times a robot retreated to clear a corridor
EfficiencyDeadlocks broken
0
Mutual blocks the traffic manager had to resolve
SafetyTraffic policy changes the answer.
The same floor, the same fleet, the same task queue — scored under each traffic policy the engine can run.
Platooning delivers 96.0/h against Exclusive lanes at 66.0/h — 45% more throughput, and total block time down from 1177s to 297s. It pays for it at the charger: avg charger wait rises from 0.1s to 15.3s. Which trade you want is exactly the decision this platform exists to inform.
Model your floor before you move it.
Load a warehouse layout, configure your fleet, and run your first simulation — no account needed to look around.
No robot hardware required. Simulation-only — deploy when you’re ready.
Try the demoHow to read this page
- Prove with metrics, not adjectives
- Every figure on this page is read from the simulation engine or from the platform's own configuration limits. Nothing here is a rounded-up estimate.
- Human-in-the-loop by design
- No simulation result reaches the physical floor without an operations manager's explicit sign-off. The constraint is the feature.
- Measured under stated conditions
- Benchmark comparisons hold the layout, fleet size, charger count and run duration constant, and the conditions are printed beside the numbers.
Benchmark figures come from a recorded run of the platform’s simulation engine on Standard Warehouse with 8 robots over 600 seconds (engine 51aaf40), regenerated with scripts/generate_benchmark_fixture.py. The platform measures throughput, missions, block time, charger wait, route yields and deadlocks — it does not currently measure robot utilization or physical collisions, and no figure here claims otherwise.