Researchers Design Tiny Autonomous Drones Inspired by Insects

Researchers from TU Delft have developed an insect-inspired navigation strategy that allows tiny, lightweight robots to autonomously navigate long distances with minimal computation and memory.

Key insights from insect navigation: The researchers drew inspiration from how insects like ants visually recognize their environment and combine it with step counting (odometry) to find their way back home:

• Insects make occasional visual “snapshots” of their surroundings that they can later compare to their current view to minimize differences and navigate back to the snapshot location.
• By combining visual snapshots with odometry, insects can space out snapshots further apart and still successfully navigate home as long as their odometry drift falls within the catchment area of the next snapshot.

Enabling autonomous navigation in tiny robots: The insect-inspired approach addresses the challenges of making tiny, resource-constrained robots navigate autonomously without relying on external infrastructure:

• Tiny robots have limited sensing, computation, and memory, making traditional autonomous navigation approaches used in larger robots impractical.
• By mimicking insect strategies of combining sparse visual snapshots with odometry, the researchers enabled a 56-gram drone to navigate 100 meters with only 0.65 kilobytes of memory.
• All visual processing was performed onboard using a simple, low-power microcontroller typically found in inexpensive electronics.

Potential real-world applications: While not as full-featured as state-of-the-art navigation, the insect-inspired strategy could enable tiny robots to take on valuable real-world tasks:

• Tiny, safe, autonomous drones could monitor inventory in warehouses, detect pests and disease in greenhouses, or find gas leaks in industrial sites.
• For many applications, the ability to fly out, gather data, and return to base may be sufficient even without generating detailed maps.

Looking ahead: The researchers see their work as an important step towards practically deploying tiny autonomous robots in real-world scenarios where full mapping and navigation capabilities are not always required. By taking inspiration from nature, they were able to develop an efficient, minimal navigation strategy well-suited to the extreme size, weight, and power constraints of micro-robots. With further development, this approach could open the door to novel uses of swarms of tiny autonomous drones for sensing, monitoring, and search tasks in complex environments.