TU Delft Aerospace Engineering

🐝 researchers Guido De Croon, Dequan Ou and Christophe De Wagter have developed “Bee-Nav”, a honeybee-inspired navigation strategy that enables even very small robots to travel long distances and reliably return home using a neural memory of just 42 kilobytes. In a new environment, the robot first performs a short learning flight near home, just as honeybees do. After that, it can travel away for hundreds of meters and still find its way back. The results of their research have just been published in .

“We were fascinated by the fact that honeybees can fly far away from home along winding paths, yet return almost straight back,” says Professor of Bio-inspired AI for drones Guido De Croon. “Biologists have shown that bees rely on odometry for the return journey, and use visual memory more as they get closer to home. But exactly what and how they learn for their visual memory is still not fully understood. That was the gap we needed to bridge to create a practical navigation strategy for robots.”

In Bee-Nav, the robot also first makes a short learning flight near home. During that flight, it collects panoramic images of the environment. A small neural network then learns to process those images for estimating the direction and distance back home.

“Like an insect, the robot may not always know exactly where home is,” says PhD candidate Dequan Ou. “Home may be too small to see, or hidden behind some trees. So we trained the neural network using odometry estimates of the direction and distance home, even though these become less accurate over time. The key question was whether that would still be enough for the robot to learn to return home.”

Turns out, it was. Odometry drift did not prevent successful visual homing. Using a neural network of just 3.4 kilobytes, the robot interpreted panoramic images of its surroundings and estimated which way to move and how far it still was from home. The estimated distance allowed the robot to move faster when farther away and slower as it approached home. In all flights, the robot successfully returned home.

Read more about their research on our website!

Congratulations to graduated student Giulia Leto for receiving the “2025 Modeling and Simulation Technologies Best Paper Award” from the American Institute of Aeronautics and Astronautics () 🚀👏

The paper, titled “Effects of Turbulence Intensity and Variability on Biodynamic Feedthrough Modeling in Touchscreen Dragging Tasks”, was presented at the AIAA SciTech Forum 2025 in Orlando.

The research explores how turbulence affects human interaction with touchscreen systems, an increasingly relevant topic as digital interfaces become more common in aviation and vehicles.

Daan Pool supervised her work during her MSc. Today, Giulia is continuing her journey at TU Delft as a Ph.D. candidate in Air Traffic Management, where she focuses on air traffic control and the application of AI to future aviation systems.