Insect-size drones are too small to lug around complex navigation systems. To help tiny autonomous fliers find their way home, researchers are taking their cues from honeybees with a new system called Bee-Nav.
A honeybee leaving the hive first takes a short learning flight to memorize nearby landmarks, explains Guido de Croon, an artificial-intelligence and robotics researcher at the Delft University of Technology in the Netherlands. As a bee flies away, “it keeps track of the direction and speed of its movement,” de Croon says, in a process called path integration. Because path integration is prone to accumulating little measurement errors over time, the insect relies on the memorized landmarks to correct its course as it heads back home. As described in Nature, de Croon and his colleagues copied this workflow.
First, a drone performs a beelike learning flight around its starting point, using a minuscule omnidirectional camera to capture the surrounding scenery. In midflight, it uses a tiny onboard neural network to map these images to home vectors, basically invisible arrows pointing back to the launchpad. This mapping establishes a safe zone called the learned homing area. Once trained, the drone can be sent far away and begin its journey back using path integration, backtracking based on measured speed and direction. If the drone winds up anywhere inside its starting safe zone, the visual neural network then guides it the rest of the way home.
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Bee-Nav does this using an off-the-shelf Raspberry Pi 4 computer the size of a credit card that runs neural nets with 3.4 to 42.3 kilobytes of memory. For comparison, conventional mapping setups use thousands of times more. The team’s test bots homed in from a maximum of 600 meters (1,970 feet) away outdoors despite wind gusts and camera-blinding sun glare.
A honeybee-based navigation system could help miniature autonomous drones find their way home.
Jeff R Clow/Getty Images
“What I find especially exciting is how little computation is needed,” says Sarah Bergbreiter, a mechanical engineer at Carnegie Mellon University, who was not involved in the study. “For the small-scale robots that my group and others work on, this is the kind of approach that makes serious outdoor deployments plausible.”
De Croon and his team are still working to resolve a few challenges for the platform, such as navigating between multiple memorized places and dealing with starting points that do not have any landmarks.
“Platforms running Bee-Nav will also need local obstacle avoidance and planning capability if the environment is cluttered or dynamic,” says Sean Humbert, a mechanical engineer at the University of Colorado Boulder, who was not involved in the study.
But even now, de Croon says, Bee-Nav can help to make autonomous outdoor drones smaller and more power-efficient. “We could easily put it on a 50-gram, even 30-gram drone,” de Croon claims. Scaling autonomous drones further down to the size of actual bees, he notes, would require solving other fundamental problems such as the need to miniaturize batteries. “But we hope that when these problems are solved in the long term, we will have the intelligence ready to match that,” de Croon says.