Ants behavior mimicked by robots to change transportation networks (Video)

Ants behavior mimic by robots (Video)
Seeing is believing, as robot swarms mimic how ant colonies navigate complex mazes relatively mindlessly. Researchers have found ― knowledge that could help to improve designs for manmade transportation networks in the future.

Scientists are fascinated by ant colonies because they can form collectives called “superorganisms” that function as single organisms do. Investigation into how ants behave has revealed more about how such group behavior arises, and some researchers are using that knowledge to help build smarter robot swarms, said Simon Garnier, a scientist who studies animal behavior at the New Jersey Institute of Technology .
Scientists are making ants wander through mazes, just as they do with rats. These labyrinths mimic the twists and turn in ants’ journeys between their nests and places rich in food.

Insect colonies, although composed of many critters, function in a manner similar to individual organisms, according to a new study. The results suggest that these colonies act like “superorganisms,” at least in terms of their basic physiology.

The ant Messor barbarusis a major seed predator on annual grasslands of the Mediterranean area. This paper is an attempt to relate the foraging ecology of this species to resource availability and to address several predictions of optimal foraging theory under natural conditions of seed harvesting. Spatial patterns of foraging trails tended to maximize acquisition of food resources, as trails led the ants to areas where seeds were more abundant locally. Moreover, harvesting activity concentrated on highly frequented trails, on which seeds were brought into the nest in larger numbers and more efficiently, at a higher mean rate per worker.

The predictions of optimal foraging theory that ants should be more selective in both more resource-rich and more distant patches were tested in the native seed background. We confirm that selectivity of ants is positively related to trail length and thus to distance from the nest of foraged seeds. Conversely, we fail to find a consistent relationship between selectivity and density or species diversity of seed patches. We discuss how selectivity assessed at the colony level may depend on factors other than hitherto reported behavioral changes in seed choice by individual foragers.

Among the everyday challenges an ant colony faces, the exploration and the exploitation of its environment is one of the most critical. In some species food collection is achieved by thousands of workers travelling along well-defined foraging trails. These trails emerge from a succession of pheromone deposits and can result in a complex network of interconnected routes. The CouzinLab is investigating how ants manage to build such networks whose structure facilitates the navigation of workers in the foraging area of the colony. These trail networks assemble the individuals into a spatial structure that favors encounters and exchanges. As a consequence their topology as well as their geometry influence the efficiency of the food harvesting and the dynamics of information transfer within the colony. Understanding the processes that account for the emergence of such networks allow us to better grasp the way an insect colony as a whole deals with information from its environment and how it adapts to uncertain worlds.

The pheromone-laden foraging trails they leave behind are like lifelines: they direct the workers toward food hubs discovered earlier and help guide them home back to their nest.

To learn more, researchers at the New Jersey Institute of Technology (NJIT) and the Research Centre on Animal Cognition in France used miniature robots to replicate the behavior of a colony of Argentine ants on the move, reported today in the journal PLOS Computational Biology. This ant species has extremely poor eyesight and darts around at high speeds, yet it can maneuver through corridor after corridor, from home to food and vice versa.

When no obstacles are around, ants prefer to walk in a straight line without deviating from their course. People are like that too: if we were walking down a street to a restaurant that’s on the same side of the road as we are, we wouldn’t cross to the opposite sidewalk unless something was blocking our way. To imbue this sense of obstacle avoidance into the robots, researchers programmed them to avoid obstacles and follow light trails, which the researchers used as a substitute for pheromone-coated paths.

The 10 tiny robots in this study, called Alices, were then tasked to navigate a maze-like environment roughly 60 to 70 times their size, from a starting point representing a nest entrance to an end point signifying a food source. Two photoreceptors, mimicking ant antennae, detected beams of light. As the robots traveled through the maze, researchers introduced a wrench in the little machines’ plans—at random points in their journey, the robots were triggered to turn, a mechanism meant to further mimic ants’ meandering gaits as they creep along their paths. These random turns rotated at angles no greater than 30 degrees, as real ants are not very efficient at physically making U-turns.

Many insect species, including ants and bees, work together in colonies, and their cooperative behavior determines the survival of the entire group. This type of interaction has been likened to that of a single organism, with each individual in colony acting like a cell in the body, giving rise to the term “superorganism.”
However, although this phrase has been around for at least a century, it has generally been used as a metaphor, and very few, if any, studies have quantitatively compared whole colonies to individual organisms, said study researcher James Gillooly of the University of Florida.

Robot ants mimic insect behavior

12 Responses to "Ants behavior mimicked by robots to change transportation networks (Video)"

  1. Paul Berry   March 29, 2013 at 9:15 pm

    Human evolution: We stood upright, ate the Neanderthals, discovered agriculture, invented automobiles, got in and started living inside. Now we are ants. Maybe you couldn’t see this coming, I could. The only good news, it will work on Mars, so, for what it’s worth, we are making progress.

  2. Romanovski   March 29, 2013 at 8:51 pm

    Efficiency is the least of this discovery. Think of outside the box. This type of programming can be applied to AI to be used in robots or computers in learning skills and over time become extremely precise in decision making on any scale. Example: imagine AI cars that have collected data from this type of lab were the car learns every possible way to navigate the roads via the information collected through its sensors and using something to “Simplex Method” to determine best solution on the amount of variables collected. Then apply this data to actual cars on the road with a built in computer with sensors. Not only that but think of GPS data added to the equations. Think of a bigger picture of airplanes being able to make decisions on the fly like we do in different situations. These systems are able to learn from their mistakes and will just become better the more we use them over time. Think of a robot trying to learn how to walk. At first it will fall a lot, but as trial and error creates enough data to correlate the robot will be walking in no time, just like a baby learns how to walk while reaching the stage of toddler. This is awesome!!


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