Robots that navigate the warehouse you actually have.

Mobvynt's perception and path planning stack lets AMRs dodge a reversing forklift, reroute around a dropped pallet, and hit every pick slot on time — no pre-mapped grid required.

< 80ms obstacle detection latency
97% path success rate in live deployments
12+ tested AMR hardware configurations

Most AMR software assumes a static world.

Your WMS knows the pallet is there. Your robot's map doesn't. Every time something moves, you're one surprise away from a stall.

Traditional costmap stacks build a map of your facility once, update it slowly, and expect obstacles to stay where they were. The warehouse floor doesn't cooperate. Forklifts reverse. Pallets shift. New lanes open. The map falls behind immediately.

A stalled robot in the middle of a picking lane during a peak shift can freeze an entire operation. The root cause is almost always a map that didn't know something moved.

We model the warehouse as it is, not as it was mapped.

Three core capabilities that run in real time on your robot's compute — no cloud round-trip required.

Live Obstacle Detection

Classifies object type, trajectory, and clearance in real time. Person, forklift, pallet, cart — 8 standard classes, with safe defaults for unknowns.

Dynamic Path Re-Planning

Generates collision-free alternative paths within one planning cycle — under 120ms from detection to new route output.

Pick-Slot Timing Constraints

Re-routes while preserving delivery window commitments from your WMS. The planner threads obstacle avoidance and pick-slot arrival deadlines simultaneously — configurable risk tolerance lets you tune the trade-off.

Two-stage runtime: perception before planning.

Sensor fusion pipeline feeds the path planner, runs entirely on-robot, no cloud round-trip required. Every planning cycle is complete within 120ms of detecting an obstacle change.

Full Technical Walkthrough

Built for environments where humans and machines share the floor.

Ambient Fulfillment Centers

High human-robot co-existence, frequent pallet repositioning, seasonal surge doubles obstacle frequency. Pre-trained person and forklift detection with configurable safety margins.

Reduced lane stall incidents during peak periods — Vestara Logistics

Cold Storage (-20°C)

LiDAR performance shifts in extreme cold. Workers in bulky PPE change visual signature. Forklift battery behavior affects trajectory patterns. Cold-calibrated sensor profiles built in.

Tested down to -20°C ambient with calibrated profiles

Pharmaceutical / Cleanroom

Robots can't stop abruptly (contamination risk). Strict airlock sequencing limits path options. Constrained-corridor mode limits re-plan radius to stay within zone boundaries.

Zone boundary enforcement with soft-stop vs hard-stop configuration

DTC High-Velocity Picking

400–600 picks/hour/bot, tight pick-slot windows, high cart density. Fleet-aware re-planning shares obstacle events across robots to prevent cascade stalls.

Pick-slot arrival deadline enforcement at 400+ picks/hr
Explore All Use Cases

What Operators Say

Before Mobvynt, a misplaced pallet would stall our entire picking lane until someone updated the map. Now the robot just goes around it. We haven't had a peak-shift stoppage in four months.

Chris Mendoza
Floor Operations Lead, Vestara Logistics

Our fulfillment floor changes layout three times a year for seasonal resets. Remapping killed a full shift every time. With Mobvynt we just moved the racks and kept running. No downtime, no remap cycle.

Priya Natarajan
VP Warehouse Ops, Draxis Fulfillment

See how Mobvynt fits your AMR stack.

Send us your robot model and a 2-sentence description of your warehouse layout. We'll show you specifically what Mobvynt adds.