Application evidence · Dallas Roboter

Four applications.
Three kinds of proof.

Every application here is shown the same way: what the robot sees, how it gets installed, and the proposal that scoped it. Filmed on real floors, not rendered.

See the Applications Simulate a Deployment
60–1,500 kg payloads ±10 mm repeatability No floor markers 2–4 week install
How every application is shown

Same claim.
Three ways to check it.

A performance claim is worth what you can verify about it. So each application on this site carries the same three pieces of evidence — and each one can be inspected without a sales call.

01 · Vision

What the robot sees

SLAM maps, ToF point clouds and LiDAR safety zones — the raw perception layer, recorded from the robot. Including where it stops working.

Open the vision page →
02 · Installation

How it goes live

Survey, mapping, mission build, safety test. The same five steps on every deployment, with the time each one actually takes.

Open the install page →
03 · Proposal

What the numbers say

A deployment simulator you can run yourself: demand in, fleet size, cycle time and install window out — with every assumption printed.

Open the simulator →
Application examples

Four loops.
Every one closed.

These are the four workflows that pay for themselves first — chosen because the loop is measurable, not because the robot looks good doing it.

A Dallas Roboter platform AMR carrying a pallet of cartons down a warehouse aisle with racking on both sides Application 01

Line feeding

Deliver the right carton to the right station before the line needs it. The robot takes the replenishment run, the operator stays at the station.

Example Vision Simulate
A Dallas Roboter AMR transporting a loaded pallet across a production floor Application 02

Pallet transport

Move pallets between zones so a forklift does not have to. The most common pallet task is horizontal, and that is exactly what the PX33 is built for.

Example Vision Simulate
An AMR arriving at a picking station carrying a shelving unit of goods Application 03

Goods to person

Stop walking to the rack. The rack comes to the picker, which means the same headcount covers more lines per shift.

Example Vision Simulate
A compact AMR carrying a tote between storage and a packing bench Application 04

Tote to person

For storage that has to be dense and picking that has to be fast. Small bodies, tight aisles, high move counts.

Example Vision Simulate
The evidence standard

What we show.
What we won't.

The robotics industry runs on renders and adjectives. This site is built on the opposite rule, so it is worth stating the rule plainly.

What you will find here

  • Recorded perception. Point clouds and maps captured from the robot, labelled with what each colour means.
  • Install timings. The five steps, what happens in each, and who does it.
  • A simulator with its assumptions on screen. Every formula printed, every constant named.
  • The limits. Glass, mesh, crowds and dirty floors are on the vision page with what happens to them.

What we will not do

  • Show a render and caption it as a deployment.
  • Quote a fleet size we have not simulated or surveyed.
  • Call a single-site pilot a rollout.
  • Promise a cycle time the floor cannot physically hold.
8AMR models, one navigation stack
60–1,500kg payload range
±10mm repeatable positioning
2–4weeks from survey to live

Start with the loop that hurts.

Pick one workflow. We will map it, show you what the robot sees, and put a number on the fleet before anyone signs anything.

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