Ant insights lead to breakthrough in robot navigation

Have you ever wondered how insects can find their way even when they are far from their nest? The answer to this question has implications not only on biology, but also on creating AI for small, autonomous robots. Drone researchers from Delft University of Technology were inspired by biological discoveries about how ants visually perceive their environment and safely return to their nest in combination with step counting. They used these insights to create an autonomous navigation strategy for small, lightweight insect-inspired robots. This strategy allows the robot to follow long trajectories back to its nest, with very little computation and memory (0.65 kilobytes per 100 meters). In the future, small, autonomous robots could be used for a wide range of applications, from monitoring inventory in warehouses to finding gas leaks in industrial areas. The researchers published their findings in Science Robotics on July 17, 2024.

Defending the weak

Extremely small robots weighing tens to hundreds of grams are expected to have interesting applications in the real world. Because they are lightweight, they are extremely safe even if they accidentally bump into someone. Because they are small, they can move in tight spaces. Furthermore, if they can be manufactured cheaply, they can be deployed in large numbers to quickly cover a wide area, such as in greenhouses for the early detection of pests and diseases.

However, making such small robots move on their own is difficult, as resources are much more limited than for larger robots. A major obstacle is that the robot must be able to move on its own. For this, the robot can receive assistance from external infrastructure: position estimates can come from GPS satellites outdoors, and from radio communication beacons indoors. However, relying on such infrastructure is often undesirable: GPS is not available indoors and can be very inaccurate in cluttered environments such as urban canyons. Also, installing and maintaining beacons in indoor spaces can be very expensive or simply impossible, for example in search and rescue scenarios.

The AI ​​required for autonomous navigation with only on-board resources was developed with large robots such as self-driving cars in mind. Some approaches rely on heavy, power-hungry sensors such as LiDAR laser rangers that cannot be carried or powered by small robots. Other approaches use vision, a very power-efficient sensor that provides a wealth of information about the environment. However, these approaches typically attempt to create a highly detailed 3D map of the environment, which requires significant processing and memory that is too large for small robots and can only be provided by power-hungry computers.

Step counting and visual breadcrumbs

For this reason, some researchers have taken inspiration from nature. Insects are particularly interesting because they operate over distances that may be relevant for many real-world applications while using very limited sensor and computing resources. Biologists are gaining an understanding of the basic strategies insects use. Specifically, they combine visually guided behavior based on a low-resolution but nearly omnidirectional visual system (called “visual memory”) with tracking their own movements (called “odometry”). While odometry is better understood down to the neuronal level, the exact mechanisms underlying visual memory are still poorly understood. One of the earliest theories of how this works proposes a “snapshot” model. This model proposes that insects such as ants take snapshots of their environment from time to time. Then, when approaching the snapshot, the insect can compare its current visual perception to the snapshot and move in a way that minimizes the differences. This allows the insect to navigate, or “home”, to the location of the snapshot, eliminating the drift that inevitably accumulates when performing odometry alone.

“Snapshot-based navigation is similar to how Hansel tries to avoid getting lost in the fairy tale Hansel and Gretel. If Hans threw a stone on the ground, he would find his way home. But if he threw breadcrumbs, which the birds would eat, Hans and Gretel would get lost. In our case, the stone is the snapshot,” said Tom van Dijk, first author of the study. “Just like with the stone, for the snapshot to work, the robot needs to be close enough to the snapshot location. If the visual surroundings are too different from the snapshot location, the robot may take a wrong turn and never come back. So you need to use enough snapshots, or in Hansel's case, you need to drop enough stones. On the other hand, if you put the stones too close to each other, Hans' stones will wear out quickly. For the robot, using too many snapshots will result in increased memory consumption. Previous work in this area has typically been so close that the snapshots allowed the robot to first visually return to one snapshot and then move to the next one.”

“The main insight underlying our strategy is that if a robot navigates between snapshots based on odometry, the interval between snapshots can be much larger,” says Guido de Croon, professor of bio-inspired drones and co-author of the paper. “Homing works as long as the robot gets close enough to the snapshot location, that is, as long as the drift of the robot's odometry falls within the snapshot's capture area. This allows the robot to travel much farther, since it flies much slower when homing to a snapshot, than if it flies from one snapshot to the next based on odometry.”

This insect-inspired navigation strategy enables the 56-gram CrazyFlie drone, equipped with an omnidirectional camera, to navigate distances of up to 100 meters on just 0.65 kilobytes. All the vision processing is done by tiny computers called “microcontrollers,” which are found in many inexpensive electronic devices.

Utilizing robotics technology

“The proposal of an insect-inspired navigation strategy is an important step towards real-world applications of small autonomous robots,” says Guido de Croon. “The capabilities of the proposed strategy are more limited than those offered by state-of-the-art navigation methods: no maps are generated and the robot can only return to its starting point. Nevertheless, this may be more than sufficient for many applications. For example, for inventory tracking in a warehouse or crop monitoring in a greenhouse, a drone could take off, collect data and then return to a base station. Images relevant to the mission could also be stored on a small SD card and post-processed on a server, although this is not necessary for the navigation itself.”

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