Same software, four different floors.
Mobvynt runs in ambient fulfillment, cold storage, pharmaceutical, and high-velocity DTC environments. Here's what changes across each — and what stays the same.
Ambient Fulfillment Centers
Large ambient temperature distribution centers, 200,000+ sq ft, with high human-robot co-existence. Forklifts and workers share aisle space continuously. Seasonal peak surge doubles obstacle frequency — the same floor that ran fine in September runs at 2× obstacle density in November.
What Mobvynt adds: pre-trained person and forklift detection with configurable safety margins by zone, surge-mode parameter for peak density, and fleet-aware re-planning that prevents cascade stalls across multiple robots in the same aisle.
Vestara Logistics reduced lane stall incidents during peak periods after deploying Mobvynt on their existing AMR fleet without hardware changes.
Cold Storage (−20°C)
Frozen warehouses at −20°C to 0°C introduce three specific problems for AMR perception. First, LiDAR performance changes — fog from warm objects entering the cold zone creates point cloud noise artifacts that confuse standard obstacle models. Second, workers wear bulky PPE that changes their visual signature substantially versus ambient environments. Third, forklift battery behavior changes at extreme cold, affecting speed and trajectory patterns.
What Mobvynt adds: cold-calibrated sensor profiles that account for atmospheric interference, a bulky-PPE person model variant with larger bounding volumes, and a slower-speed forklift trajectory model that reflects how electric forklifts actually move in sub-zero conditions.
Pharmaceutical / Cleanroom
Cleanroom Grade C/D environments have strict zone boundaries and gowning areas that create bottleneck chokepoints. The robotics challenge is unusual: robots in cleanrooms often can't stop abruptly because vibration and sudden motion changes create contamination risk. Strict airlock sequencing further limits path options — you can't re-route outside zone boundaries.
What Mobvynt adds: constrained-corridor mode that limits re-plan radius to stay within zone boundaries, soft-stop vs hard-stop configuration (so the planner knows when to decelerate smoothly versus when it can stop quickly), and zone boundary enforcement built into the path planner so the robot never plans across a gowning area boundary.
DTC High-Velocity Picking
DTC fulfillment runs at 400–600 picks/hour/bot with tight pick-slot windows and high cart density. The problem at scale: at peak, aisle density means every path re-route risks cascade stalls. When robot A slows for an obstacle, robot B behind it also slows, and if robot B re-routes, it may block robot C's intended path.
What Mobvynt adds: fleet-aware re-planning through MV Bridge, which shares obstacle event data across robots in real time. Robot A's obstacle detection informs robots B and C before they reach the same spot. Timing-window constraint adherence keeps pick-slot arrival deadlines even when re-routing.
Your environment not listed?
These four are our most common deployments. If your warehouse type is different, tell us — we've probably seen something close and can tell you exactly what Mobvynt adds.