Autonomous Navigation: Where Precision Meets Safety

Modern greenhouses are complex, dynamic spaces. FRAVEBOT’s autonomous navigation stack integrates LiDAR, SLAM, RTK-GPS, and AI vision into a unified safety-first system. The result: sub-3 cm accuracy and a navigation platform ready for cooperative operation with other machines and human workers.

How do you navigate a robot in a modern greenhouse covering several hectares — all while working alongside dozens of other machines and human operators?

Challenge accepted.

Developing an autonomous robot that can simply drive from row to row is one thing.

Integrating it safely into a living, dynamic environment with people, carts, spraying units, and other robots — that’s a whole new level of engineering.

Our Navigation Approach

FRAVEBOT’s navigation system is built on a sensor fusion architecture, where each component contributes to a unified model of the environment. Every sensor has its own mission — together, they create a safety-first intelligence.

• SLAM (Simultaneous Localization and Mapping) – The core intelligence, continuously building a real-time map of the greenhouse and pinpointing the robot’s exact position.

  
  

• Cameras + AI – The visual cortex, powered by neural networks that differentiate between concrete paths, rail tracks, and crop rows.

  
  

• Safety LiDAR – The ever-watchful guardian, detecting all static and moving obstacles in the robot’s surroundings.

• IMU (Inertial Measurement Unit) – The inner ear, monitoring orientation, tilt, and acceleration for smooth and precise movement.

• GPS RTK – The long-range navigator, delivering centimeter-level positioning for accurate cross-facility navigation and row alignment.

• Encoders – The odometers, ensuring distance and wheel rotation data refine localization even when GPS or visual input is limited.

Why It Matters

The result is safe, efficient, and scalable automation — a navigation stack designed not just for autonomy, but for cooperation.

Field tests consistently demonstrate <3 cm row-entry deviation, proving that precision and safety can go hand in hand.

Safety is not an add-on — it’s the architecture on which true autonomy is built.