Five years at the top

How Landroid learns
Landroid’s vision AI is trained the same way people learn to recognize the world — by observing it. Real-world images, accumulated across millions of mowing hours, refine the neural network leading to better detection, better navigation, and better decisions.

Why real-world data matters
Newly designed robotic mowers rely on stock photos, staged scenes, or synthetic images to train their AI. But real lawns aren’t staged. Light changes, shadows drift, textures vary, and objects appear unexpectedly — the kind of natural complexity no simulator can truly capture.

Experience you can trust
Because Landroid was the first vision-driven mower on the market, it has years of real-world data others simply don’t have. It’s experience that will take them millions of real-world mowing hours to match.

How Vision Cloud maps your lawn automatically
Vision Cloud maps your lawn the moment you drop it in — no driving, tracing, or setup required. Here’s what happens:

1. Stereo-vision AI explores the perimeter
Vision Cloud’s stereo cameras use depth perception to understand the shape of your lawn. As it travels the edges, the mower visually identifies borders and garden features in real time. Powered by Worx Vision AI, trained on millions of real-world mowing hours across gardens worldwide, it recognizes environments the way humans do.

2. RTK pins every position precisely
While the mower moves, RTK continuously records centimeter-accurate coordinates. This anchors the exploration to real-world GPS space, ensuring long-term positional accuracy.

3. V-SLAM captures visual landmarks
At the same time, V-SLAM identifies distinctive visual landmarks — patterns, edges, textures, and objects that can be recognized again later.

The result
A precise 3D model of your lawn, automatically created:

• edges and borders are clearly defined
• features and transitions are understood
• the map is anchored to real-world coordinates

All of this happens straight out of the box — no steering, no joystick, no app-driving skills required.

How RTK Cloud works — and why it matters

Most RTK mowers rely on a local base station. A fixed antenna installed at home sends correction data to the mower. It works, but it comes with trade-offs: extra hardware, an AC power connection, and careful positioning. The antenna needs a clear view of the sky — not too close to the house, not under trees. In some gardens, the only viable spot is on the roof, which may require professional installation.

Cloud-based RTK correction services
Some robotic mowing solutions use cloud-delivered RTK correction services, which provide positioning data via subscription and remove the need for a local base station. Because these services are designed to support a wide range of GNSS applications, rather than robotic mowing specifically, accuracy and consistency can vary, particularly in residential environments where signal conditions are more complex and dynamic.

RTK Cloud takes a different approach
RTK Cloud is built specifically for autonomous mowing. Worx operates a dedicated network of professionally installed reference stations, optimized to support robotic operation in real-world residential conditions. Instead of installing hardware at home, the mower receives real-time correction data from the cloud, tailored to its precise location. The result: centimeter-level accuracy, no on-site antennas, no poles, no cables — just precise positioning, delivered automatically to every mower.

Reliable navigation, even when GPS isn’t perfect

RTK delivers exceptional accuracy — but like all satellite systems, signal quality can vary near trees, buildings, walls, pergolas, or dense hedges. Vision Cloud is designed for exactly these real-world conditions.

1. Visual landmarks act as reference points
Vision Cloud memorizes visual landmarks across your garden. When satellite reception weakens, the mower recognizes these landmarks to instantly understand where it is.

2. Dead-reckoning, refined by vision
Between satellite updates, Vision Cloud tracks its movement using:

• inertial sensors (IMU) for motion and orientation
• wheel encoders for distance and direction

Unlike basic dead-reckoning systems, Vision Cloud constantly cross-checks movement against what it sees, dramatically reducing drift.

3. Seamless sensor fusion
RTK, stereo vision, landmarks, IMU, and odometry are continuously merged into one coherent position estimate.

The result

• stable navigation under trees and near buildings
• no wandering or cumulative drift
• consistent mowing accuracy across the entire lawn

Even when satellites don’t cooperate, Vision Cloud does.

Why mow after sunset?

Mowing after sunset is recommended during hot summer periods for several reasons:

1. Less stress on the grass
2. Cutting during peak heat weakens turf. Cooler evening temperatures help grass recover more easily.
3. Reduced moisture loss
4. Freshly cut blades lose water faster in sunlight. Evening mowing helps retain moisture longer.
5. Healthier lawn growth
6. Grass heals overnight, aided by lower temperatures and relaxed stomata that improve water uptake.
7. Fewer weeds
8. A less-stressed lawn is more resistant to weed invasion.