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The ant’s GPS system

A group of researchers discovered that certain arborical ant species use surprising algorithms to solve the problem of finding the shortest route.

ZOE AUTOR 102/Antonio_Cruz 26 DE MARZO DE 2023 11:00 h

The biological mechanisms used by different species of ants to orientate themselves have always intrigued scientists specialising in animal behaviour.

How do ants always manage to find the shortest route from the branch of a tree where they have been feeding to their anthill?

The GPS systems we have invented so far find the shortest route thanks to the signals sent by artificial satellites. However, these tiny insects achieve the same result without having to depend on any satellite.

How do ants manage to reduce to a minimum the energy cost ¡when seeking, finding and recovering their food supplies? How do they behave when they encounter an obstacle?

On account of the distribution of the branches and leaves of a tree, the number of turns to the left, right, upwards or downwards can be very complex.

Discovering how this mysterious mechanism works could have significant applications in biomimetics, the discipline that focuses on finding the natural solutions used by animals and applying them to human problems.

According to a recent article published in the magazine PNAS1, a group of researchers discovered that certain arborical ant species use surprising algorithms to solve the problem of finding the shortest route.

They appear to be able to do so despite having no central control, and by means of minimal computational resources which create and maintain networks of pathways over trees and bushes.

These are the so-called tortoise ants, which live in arboreal regions from southern Florida to the Bahamas, Cuba and Jamaica.

The shape of their head is irregular, with spine-like outgrowths, and they normally leap from one branch to another in search of the shortest path. They create networks of paths across tropical rainforests by means of which they connect their nests with sources of food.

These pathways – which are invisible to the human eye – remain perfectly functional for ants thanks to the volatile pheromones which they deposit along the edge of leaves or on the vertexes of branches in such a way that the bidirectional passage of the insects over the branches is not haphazard but in fact meticulously calculated so that the distance covered is as short as possible.

The researchers who have studied this behaviour believe that the ants solve the problem of finding the shortest distance by means of a series of systematic mathematical calculations.

It appears that they increase or reduce the speed of bidirectional flow, or transit, in both directions in proportion to the pheromone level conserved on each vertex of the branch. At certain points where the paths separate, the ants deposit signalling pheromones.

As these or the product of volatile chemical products, they gradually evaporate, which reduces the intensity of the smell they produce, thus providing the ant arriving at that point with information concerning the time that has lapsed since the previous ant passed that way.

A further item of information is added at this point: the number of individuals that leave the main pathway to explore new possible routes. With these ever-changing data the ants have sufficient information to discover the shortest possible path at any given moment.

It is, needless to say, an astonishingly precise biological behavioural pattern on the part of these tiny creatures, with their miniscule brains, and one which can be simulated on a computer.

What is also very curious, to my mind, is the explanation offered by the authors of the said article concerning how this behaviour could have originated.

“Evolution has given rise to natural algorithms which regulate collective behaviour in many biological systems”. But how could evolution have produced the hardware and the software needed to come up with such algorithms?

Where did the physical components come from? Where did they get the programmes and instructions required to enable everything to function properly? Could errors in the random mutations really have given rise to the solution to these problems? Is it reasonable to suppose that the ants could try out different mathematical algorithms until they hit on the right ones?

Of course engineers and software developers who study the extraordinary abilities of insects in search of possible technological applications will have to invest all their knowhow and human intelligence in order to be able to develop machines capable of imitating these ants.

How is possible not to realise that these researchers would require even more intelligence in order to create the first simple example of this kind of activity?

Are these scientists not forgetting what is most fundamental in the navigation system of these insects.

Now we know that inside the tiny brain of an ant there is a sophisticated software programme that can identify certain specific reference points, analyse vectors, incorporate a range of different routes, gather sensorial information from the solar compass, from chemical pheromones or from polarised light.

All this requires a memory capable of storing data and recovering information very quickly. Also necessary is a system for measuring the length of the distance already covered and algorithms capable of assessing the number of pheromones with a view to taking the right decisions.

There must be a control centre in each ant which can incorporate all the data in order to identify the shortest route. Such complexity raises the obvious question: how did this processing power come about?

Does it make sense to believe that blind evolution, which lacks intelligence, could have been this great programmer?

For my part, I believe – as the apostle Paul would say – that the answer lies in the “mystery, which for ages past was kept hidden in God, who created all things. (Eph. 3:9).

Garg, S., Shiragur, K., Gordon, D. M. & Charikar, M, 2023, Distributed algorithms from arboreal ants for the shortest path problem, PNAS, 120 (6) e2207959120. https://doi.org/10.1073/pnas.2207959120




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