Ant Insights Lead to Robot Navigation Breakthrough

Motion Design INSIDER

Have you ever wondered how insects are able to go so far beyond their home and still find their way home? The answer to this question is not only relevant to biology but also to making the AI for tiny, autonomous robots. TU Delft drone-researchers felt inspired by biological findings on how ants visually recognize their environment and combine that with counting their steps in order to get safely back home. They have used these insights to create an autonomous navigation strategy for tiny, lightweight robots. The strategy allows such robots to come back home after long trajectories, while requiring extremely little computation and memory (1.16 kB per 100 m). In the future, tiny autonomous robots could find a wide range of uses, from monitoring stock in warehouses to finding gas leaks in industrial sites. The researchers published their findings in Science Robotics, on July 17, 2024.

Tiny robots, from tens to a few hundred grams, have the potential to perform many interesting real-world applications. With their light weight, they are extremely safe even if they accidentally bump into someone. Since they are small, they can navigate in narrow areas. And if they can be made cheaply, they can be deployed in large numbers, so that they can quickly cover a large area, for instance in a greenhouse for early pest or disease detection. However, making such tiny robots operate by themselves is difficult, since they have extremely limited resources compared to larger ones.

A major obstacle for the use of tiny robots is that for real-world applications, they will have to be able to navigate by themselves with help from external infrastructure. They could use location estimates from GPS satellites outdoors or from wireless communication beacons indoors. GPS can only be used outdoors and can be highly inaccurate in cluttered environments such as in urban canyons. And installing and maintaining beacons in indoor spaces is quite expensive or simply not possible, for example in search-and-rescue scenarios.

The AI necessary for autonomous navigation with only onboard resources has been developed with large robots such as self-driving cars in mind. Some of these approaches rely on heavy, power-hungry sensors like LiDAR, which cannot be carried or powered by small robots. Other approaches use vision sensors, which typically attempt to create highly detailed 3D maps of the environment. However, that requires large amounts of processing and memory, which can only be provided by computers that are too large and power-hungry for tiny robots.

This is why some researchers have turned to nature for inspiration. Insects are especially interesting, as they operate over distances that could be relevant to many real-world applications while using very scarce sensing and computing resources. Insects combine keeping track of their own motion (odometry) with visually guided behaviors based on their low-resolution, but almost omnidirectional, visual system (view memory).

Whereas odometry is increasingly well understood even up to the neuronal level, the precise mechanisms underlying view memory are less well understood. Hence, multiple competing theories exist on how insects use vision for navigation. One of the earliest theories proposes a “snapshot” model, in which an insect such as an ant occasionally makes snapshots of its environment. Later, when arriving close to the location in the snapshot, it can compare its current visual percept to the snapshot and move to minimize the differences. This allows the insect to navigate, or ‘home’, to the snapshot location, removing any drift that inevitably builds up when only performing odometry.

“Snapshot-based navigation can be compared to how Hansel tried not to get lost in the fairy tale of Hansel and Gretel. When Hansel threw stones on the ground, he could get back home, however, when he threw breadcrumbs that were eaten by the birds, he got lost. In our case, the stones are the snapshots,” said Tom van Dijk, first author of the study. “As with a stone, for a snapshot to work, the robot has to be close enough to the snapshot location. If the visual surroundings get too different from that at the snapshot location, the robot might move in the wrong direction and never get back. Hence, one has to use enough snapshots — or in the case of Hansel drop a sufficient number of stones. On the other hand, dropping stones too close to each other would deplete Hansel’s stones too quickly. In the case of a robot, using too many snapshots leads to large memory consumption. Previous works in this field typically had the snapshots very close together, so that the robot could first visually home to one snapshot and then to the next.”

“The main insight underlying our strategy is that you can space snapshots much further apart, if the robot travels between snapshots based on odometry,” said Professor Guido de Croon, co-author of the article. “Homing will work as long as the robot ends up close enough to the snapshot location, i.e., as long as the robot’s odometry drift falls within the snapshot’s catchment area. This also allows the robot to travel much further.”

The proposed insect-inspired navigation strategy allowed a 56-gram Crazyflie drone, equipped with an omnidirectional camera, to cover distances of up to 100 meters with only 1.16kB. All visual processing happened on a tiny micro-controller.

“The proposed insect-inspired navigation strategy is an important step on the way to applying tiny autonomous robots in the real world,” said de Croon. “The functionality of the proposed strategy is more limited than that provided by state-of-the-art navigation methods; it does not generate a map and only allows the robot to come back to the starting point. Still, for many applications this may be more than enough. For instance, for stock tracking in warehouses or crop monitoring in greenhouses, drones could fly out, gather data and then return to the base station. They could store mission-relevant images on a small SD card for post-processing by a server.”

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