AI News, Robots Discover How Cooperative Behavior Evolved in Insects

Robots Discover How Cooperative Behavior Evolved in Insects

Insects like bees, termites, and ants have somehow figured out a whole repertoire of extraordinarily complex cooperative behaviors, which is all the more remarkable considering that their brains are the size of, uh, something very small.

Some leafcutter ants do the generalist thing, cutting the leaves and then carrying them all the way back to the nest themselves, but if the ants are working high up in a tree, the task partitioning is much more efficient because the ants can let gravity carry the leaves down for them, and they don’t have to make trips to the ground and back.

The slope was steep enough that the robots had to expend more time and energy going over it, and also steep enough that leaves placed at the top would slide down to the bottom by themselves.The simulation also provided a virtual sun that the robots could use for primitive navigation, with a virtual light sensor that they could use to either move towards the light source or away from it.

The researchers note that the driving force behind this selection for specialization was the slope: “In terms of the adaptive benefits of division of labor and the environmental conditions that select for it, our results demonstrated that task partitioning was favored only when features in the environment (in our case a slope) could be exploited to achieve more economic transport and reduce switching costs, thereby causing specialization to increase the net efficiency of the group.” This agrees neatly with the behavior observed in leafcutter ants: “In leafcutter ants, species that collect leaves from trees tend to engage in task partitioned leaf retrieval, whereas species living in more homogeneous grassland usually retrieve leaf fragments in an unpartitioned way, without first dropping the leaves, particularly at close range to the nest.

As far as the robots themselves are concerned, this projectis even cooler because it suggests a way in which swarms of real robots could turn to simulations like this toadapt their behavior to new situations and tasks, in effect speeding up evolution to come up with better versions of themselves.

Research Advance in Swarm Robotics

First of all, the cooperation of nature swarm and swarm intelligence are briefly introduced, and the special features of the swarm robotics are summarized compared to a single robot and other multi-individual systems.

Finally, as a main part of this paper, the current research on the swarm robotic algorithms are presented in detail, including cooperative control mechanisms in swarm robotics for flocking, navigating and searching applications.

<?xml version="1.0" encoding="UTF-8"?>Do Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed

In numerous ant species, pheromone trails play an essential role during foraging tasks by guiding workers toward previously discovered resources or helping them finding their way back to their nest [1].

Yet their behavior when crossing a symmetrical or an asymmetrical bifurcation was comparable to the behavior of Argentine ants in similar situations, suggesting that the individual decisions of Argentine ants at bifurcations are affected by the physical structure of the environment in a passive way (i.e., without the formation of a representation of the bifurcation prior to the decision).

Considering the poor performance of the Argentine ants' visual system [39] and the high tempo of the workers along the trail (up to 2.5 cm s−1 for an average body length of 3 mm, personal observation), it is unlikely that Argentine ants would have the time and capacity to evaluate the geometry of a bifurcation that they would cross in less than half of a second (the length of a bifurcation in [15], [16] is about 1 cm from the entrance to one of the two possible exits).

Assuming quasi-instantaneous direction changes (relative to the moving speed of the robots, rotation time is negligible here), standard diffusion theory [41] predicts a completely homogeneous distribution of the robots in the network at stationary state (reached in our system within 10 minutes, see Fig.

This study shows that the coupling of a particular geometrical configuration of trail networks and the forward oriented movement of ants reduces the chances of a bad choice and favors the selection of one of the shorter paths between the nest and the food source.

Then the passage of ants along these trails combined with their forward oriented walk would reinforce bifurcation branches that deviate from the originating direction of the ants by no more than a threshold angle (possibly 30°–40° from the originating direction of the ant, i.e.

Furthermore, at bifurcations where the branches would be very close to each other, the natural diffusion of the pheromone and its imperfect detection by ants would eventually lead to the fusion of the two branches into one trail only, thus preventing the maintenance of smaller angles between the two branches of a bifurcation.

While most studies of ant-made networks focus on the efficiency of their topological properties (see for instance [3], [47], [48]), we show here that their geometrical configurations also affect the spatial distribution of individuals, and hence the foraging efficiency of the colony [16].

In all these cases, the physical configuration of the environment (the structure of the network, the organization of the rooms or the shape of the other individuals) directly influences the collective outcome and can potentially modify the pattern of interaction and information exchange between individuals.

Ant colony optimization algorithms

In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.

The influence of pheromone evaporation in real ant systems is unclear, but it is very important in artificial systems.[4] The overall result is that when one ant finds a good (i.e., short) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leads to all the ants following a single path.

The orthogonal design method and the adaptive radius adjustment method can also be extended to other optimization algorithms for delivering wider advantages in solving practical problems.[7] It is a recursive form of ant system which divides the whole search domain into several sub-domains and solves the objective on these subdomains.[8] The results from all the subdomains are compared and the best few of them are promoted for the next level.

A performance analysis of continuous ant colony algorithm based on its various parameter suggest its sensitivity of convergence on parameter tuning.[11] An ant is a simple computational agent in the ant colony optimization algorithm.

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Ant colony optimization algorithms have been applied to many combinatorial optimization problems, ranging from quadratic assignment to protein folding or routing vehicles and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic problems, multi-targets and parallel implementations.

As example can be considare antennas RFID-tags based on ant colony algorithms (ACO).,[53] loopback and unloopback vibrators 10×10[52] The ACO algorithm is used in image processing for image edge detection and edge linking.[54][55] The graph here is the 2-D image and the ants traverse from one pixel depositing pheromone.The movement of ants from one pixel to another is directed by the local variation of the image's intensity values.

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They can be seen as probabilistic multi-agent algorithms using a probability distribution to make the transition between each iteration.[83] In their versions for combinatorial problems, they use an iterative construction of solutions.[84] According to some authors, the thing which distinguishes ACO algorithms from other relatives (such as algorithms to estimate the distribution or particle swarm optimization) is precisely their constructive aspect.

There is in practice a large number of algorithms claiming to be 'ant colonies', without always sharing the general framework of optimization by canonical ant colonies (COA).[86] In practice, the use of an exchange of information between ants via the environment (a principle called 'stigmergy') is deemed enough for an algorithm to belong to the class of ant colony algorithms.

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