How ants and robots manage to escape from prison without a plan or scheme

How ants and robots manage to escape from prison without a plan or scheme

Credit: Unsplash / CC0 Public Domain

Individual ants are relatively simple creatures, yet an ant colony can perform really complex tasks, such as complex construction, foraging, and defense.

Recently, Harvard researchers drew inspiration from ants to design a team of relatively simple robots that can work collectively to perform complex tasks using a few basic parameters.

Research published in eLife.

“This project has continued along an abiding interest in understanding the collective dynamics of social insects such as termites and bees, and especially how these insects can manipulate the environment to create complex functional structures,” said L Mahadevan, Lola England de Valpine Professor of Applied Mathematics. , from organic and evolutionary biology, physicists, and senior author of the paper.

The research team set out to study how black carpenter ants work together to scavenge for and escape from the soft fold.

S Ganga Prasath, a postdoctoral fellow at Harvard John A. One of the lead authors of the paper.






Credit: Harvard College John A. Paulson Engineering and Applied Sciences

Ants mainly rely on their antennae to interact with the environment and other ants, a process called antennae. The researchers noticed that the ants would automatically congregate around areas where they interacted the most. Once a few ants began tunneling into the barn, others quickly joined in. Over time, the excavation of one of these sites proceeded faster than the others, and eventually the ants moved out of the enclosure.

From these observations, Mahadevan and his team identified two parameters relevant to understanding the task of digging ants; Group collaboration strength and exploration rate. Numerical simulations of mathematical models encoding these parameters showed that ants can only dig successfully when they cooperate with each other forcefully enough while digging efficiently at the same time.

Based on this understanding and building on the models, the researchers built the robotic ants, dubbed RANts, to see if they could work together to escape a similar coop. Instead of chemical pheromones, RANts use “photohormones,” fields of light left by wandering bunting that mimic pheromone fields or antennae.

RANts were only programmed by simple local rules: to follow the color gradient of the photoromon field, avoid other robots where the photoromon density was high and pick up obstacles where the photoromon density was high and drop them where the photoromon was low. These three rules enabled the Rants to quickly escape their confinement and, just as importantly, also allowed the researchers to explore areas of behavior that would be difficult to detect with real ants.

“We have shown how collaborative task achievement can emerge from simple rules and such behavioral rules can be applied to solve other problems complex problems Such as construction, search and rescue and defense.”

This approach is very resilient and robust to errors in sensing and control. It can be scaled up and applied to teams of dozens or hundreds of robots using a range of different types of communication domains. It’s also more flexible than other approaches to collaborative problem-solving — even if some individual robotic units fail, the rest of the team can complete the task.

“Our work, which combines laboratory experiments, theory, and automated simulations, highlights the role of the resilient environment as a communication channel, where self-reinforcing signals lead to the emergence of cooperation and thus to complex problem solving. Even without global representation, planning or optimization, the interplay between simple local rules at the individual level and the embodied physics of the group leads to intelligent behavior and thus potentially relevant on a larger scale,” Mahadevan said.

more information:
S Ganga Prasath et al, Dynamics of Cooperative Foraging in Ant and Robot Groups, eLife (2022). DOI: 10.7554/eLife.79638

Journal information:
eLife


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