Robots in Farms: Ants' Navigational Skills Put to the Test

Sunday - October 8 2023, 05:16 UTC - 1 year ago

Robots are increasingly being used in agriculture to help with tasks such as harvesting crops, and weeding. However, robots often face the challenge of navigating through complex and changing environments. An algorithm inspired by ant navigational skills and neuromorphic computing has now been developed to tackle this problem. The algorithm can increase the navigational capabilities of robots and can also be used in self-driving cars.

Picture this: the setting sun paints a cornfield in dazzling hues of amber and gold. Thousands of corn stalks, heavy with cobs and rustling leaves, tower over everyone—kids running though corn mazes; farmers examining their crops; and robots whizzing by as they gently pluck ripe, sweet ears for the fall harvest.

Wait, robots? .

Idyllic farmlands and robots may seem a strange couple. But thanks to increasingly sophisticated software allowing robots to "see" their surroundings—a technology called computer vision—they’re rapidly integrating into our food production mainline. Robots are now performing everyday chores, such as harvesting ripe fruits or destroying crop-withering weeds.

With an ongoing shortage in farmworkers, the hope is that machines could help boost crop harvests, reliably bring fresh fruits and veggies to our dinner tables, and minimize waste.

To fulfill the vision, robot farmworkers need to be able to traverse complex and confusing farmlands. Unfortunately, these machines aren’t the best navigators. They tend to get lost, especially when faced with complex and challenging terrain. Like kids struggling in a corn maze, robots forget their location so often the symptom has a name: the kidnapped robot problem.

Anew study in Science Robotics aims to boost navigational skills in robots by giving them memory.

Led by Dr. Barbara Webb at the University of Edinburgh, the inspiration came from a surprising source—ants. These critters are remarkably good at navigating to desired destinations after just one trip. Like seasoned hikers, they also remember familiar locations, even when moving through heavy vegetation along the way.

Using images collected from a roaming robot, the team developed an algorithm based on brain processes in ants during navigation. When it was run on hardware also mimicking the brain’s computations, the new method triumphed over a state-of-the-art computer vision system in navigation tasks.

"Insect brains in particular provide a powerful combination of efficiency and effectiveness," said the team.

Solving the problem doesn’t just give wayward robotic farmhands an internal compass to help them get home. Tapping into the brain’s computation—a method called neuromorphic computing—could further finesse how robots, such as self-driving cars, interact with our world.

An Ant’s Life .

If you’ve ever wandered around dense woods or corn mazes, you’ve probably asked your friends: Where are we? .

Unlike walking along a city block—with storefronts and other buildings as landmarks—navigating a crop field is extremely difficult. A main reason is that it’s hard to tell where you are and what direction you’re facing because the surrounding environment looks so similar.

Robots face the same challenge in the wild. Currently, vision systems use multiple cameras to capture images as the robot transverses terrain, but they struggle to identify the same scene if lighting or weather conditions change. The algorithms are slow to adapt, making it difficult to guide autonomous robots in complex environments.

Here’s where ants come in.

Even with relatively limited brain resources compared to humans, ants are raring navigators. They slowlylearn their environment through a combination of available visual and odour cues, and they remember where they’ve been. As they sluggishly move around, their brain builds up a mental map of the situation—known as a cognitive map.