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Related Experiment Video

Updated: May 15, 2026

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Published on: December 19, 2016

Efficient robot navigation inspired by honeybee learning flights.

Dequan Ou¹, Jesse J Hagenaars¹, Maciej R Jankowski¹

¹Micro Air Vehicle Laboratory, Department Control & Operations, Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands.

|May 13, 2026

Summary

This summary is machine-generated.

Researchers developed Bee-Nav, an efficient robot navigation system inspired by honeybees. This strategy uses a small neural network for visual homing, enabling resource-constrained robots to navigate effectively.

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Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

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Published on: December 12, 2012

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Last Updated: May 15, 2026

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

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Published on: December 19, 2016

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

Published on: December 12, 2012

Area of Science:

Robotics
Neuroethology
Computer Science

Background:

Robot navigation is computationally intensive, limiting its use in small robots.
Tiny flying insects like bees exhibit robust long-distance navigation capabilities.
Existing methods struggle with efficiency and scalability for micro-robotics.

Purpose of the Study:

To introduce Bee-Nav, a novel, efficient navigation strategy for robots.
To emulate honeybee visual learning flights for robotic navigation.
To enable resource-constrained robots to perform autonomous navigation and homing.

Main Methods:

Developed a tiny neural network for mapping omnidirectional images to a home vector.
Trained the network using path integration and visual homing principles.
Implemented and tested the strategy on small drones in simulations and real-world experiments.

Main Results:

Simulations indicated minimal training data (0.25-10%) is required for the neural network.
Small drones achieved 100% return accuracy within 0.5m for short flights (30-110m).
Drones showed 70% return accuracy for longer flights (200-600m) in windy conditions.

Conclusions:

Bee-Nav offers a computationally efficient navigation solution for resource-limited robots.
The strategy provides insights into insect navigation and the formation of cognitive maps.
This approach is vital for autonomous tasks requiring precise navigation and return-to-home capabilities.