Local Trails Make Ant Cooperation Possible And Might Provide Pointers For Robots

Ant trails are far more complex and sophisticated than we imagined. This is necessary for large teams to coordinate the job of getting heavy food items to the nest, new research has found. The study reveals the most efficient way of getting pieces of food too large for one ant to carry back to the nest involves the use of locally-blazed trails, rather than the perfect scent-marked paths seen elsewhere. The solutions provide a useful model for making drones collaborate.

The capacity of ants to work together to bring food back to the nest, sometimes in huge groups, can be astonishing. As a video of ants forming a daisy chain demonstrates, it is much harder than everyone just grabbing on and heaving. For creatures with such tiny brains, this coordination is remarkable.

Equally challenging is finding the way back to the nest when carrying a load too heavy for a single ant. A path that may be easily accessible to a lone ant is often too narrow or obstructed for a team working in unison. Despite the remarkable efficiency of the ant brain, there just isn’t room in there to calculate a path the whole team can follow, so Dr Ofer Feinerman of the Weizmann Institute of Science in Israel, figured there had to be another way.

In previous work on the same topic, Feinerman showed that longhorn crazy ants (Paratrechina longicornis) working as a team will change direction and accept new leadershipwhen a new ant arrives with up-to-date information about the best route.

Now in eLife, Feinerman and co-authors report that non-load-carrying longhorns lay down scent for those carrying the burden to follow. The fact ants use scent to guide other members of the colony during foraging is already well known, but this is the first demonstration that it is also used during food retrieval.

The authors invented an automated scent detecting machine so they could track what the ants smell. They reveal that longhorns lay short trails that tell the food-carrying team where to go next, rather than guide the whole way back to the nest. Feinerman calls these locally-blazed trails. The chemicals used in local-blazing evaporate more quickly than those for foraging trails, avoiding confusion for future colony members who might come upon them.

Even these trails are often ignored. In contrast to those designed to be followed by ants on their own, locally-blazed trails seem to be treated as general advice, rather than doctrine to be followed at all costs.

This sounds like a recipe for chaos, but using a mathematical model one whose predictions were verified with actual ants the authors found it represents an efficient response to the problems ants face.

The findings explain the work by a separate team, explained in this video, on how longhorn ants respond to different sorts of obstacles.

University of Colorado’s Helen McCreery demonstrates how longhorn ants respond to different sorts of obstacles.Madison Sankovitz/McCreery et al.

The ants don’t always get it right. Sometimes they find themselves lugging their food into a dead end, and have to backtrack. As the authors in the paper put it: We find that while the information conveyed by scent marks is typically precise, it is occasionally misleading.

Nevertheless, out of this mix of good and bad advice comes a system so effective it has allowed the longhorn ants to become an invasive species over much of the planet.

Given this success, we might learn a lot from the solution ants have evolved over millions of years. Besides robot coordination, the authors believe the work could inspire more efficient transport or communication networks.

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