Podcast: Ant Inspired Swarm Robots

03 Jan, 2023

In this episode, we talk about about how an ant inspired robotics platform could be the future of swarm robotics due to its simple, affordable, flexible, and scalable nature.

This podcast is sponsored by Mouser Electronics.

EPISODE NOTES

(5:30) - The Physical Intelligence of Ant And Robot Collectives

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Helping drone swarms avoid obstacles without hitting each other

Engineers at EPFL have developed a predictive control model that allows swarms of drones to fly in cluttered environments quickly and safely. It works by enabling individual drones to predict their own behavior and that of their neighbors in the swarm.

Clara Marc

fromEPFL

24 May, 2021

machine learning

- swarm robotics

- unmanned aerial vehicles (UAVs)

There is strength in numbers. That’s true not only for humans, but for drones too. By flying in a swarm, they can cover larger areas and collect a wider range of data, since each drone can be equipped with different sensors.

Preventing drones from bumping into each other

One reason why drone swarms haven’t been used more widely is the risk of gridlock within the swarm. Studies on the collective movement of animals show that each agent tends to coordinate its movements with the others, adjusting its trajectory so as to keep a safe inter-agent distance or to travel in alignment, for example.

“In a drone swarm, when one drone changes its trajectory to avoid an obstacle, its neighbors automatically synchronize their movements accordingly,” says Dario Floreano, a professor at EPFL’s School of Engineering and head of the Laboratory of Intelligent Systems (LIS). “But that often causes the swarm to slow down, generates gridlock within the swarm or even leads to collisions.”

Drones flying in a swarm © Alain Herzog / 2021 EPFL

Not just reacting, but also predicting

Enrica Soria, a PhD student at LIS, has come up with a new method for getting around that problem. She has developed a predictive control model that allows drones to not just react to others in a swarm, but also to anticipate their own movements and predict those of their neighbors. “Our model gives drones the ability to determine when a neighbor is about to slow down, meaning the slowdown has less of an effect on their own flight,” says Soria. The model works by programing in locally controlled, simple rules, such as a minimum inter-agent distance to maintain, a set velocity to keep, or a specific direction to follow. Soria’s work has just been published in Nature Machine Intelligence.

With Soria’s model, drones are much l

Image: Kirstin Petersen, assistant professor of electrical and computer engineering, and colleagues, have developed a simple model called a swarmalator, where particles self-organize to synchronize in both time and space. Credit: Noël Heaney/Cornell University

'Swarmalators' better envision synchronized microbots

Imagine a world with precision medicine, where a swarm of microrobots delivers a payload of medicine directly to ailing cells. Or one where aerial or marine drones can collectively survey an area while exchanging minimal information about their location.

Krishna Ramanujan

fromCornell University

02 Mar, 2023

microrobots

- swarm robotics

Imagine a world with precision medicine, where a swarm of microrobots delivers a payload of medicine directly to ailing cells. Or one where aerial or marine drones can collectively survey an area while exchanging minimal information about their location.

One early step towards realizing such technologies is being able to simultaneously simulate swarming behaviors and synchronized timing – behaviors found in slime molds, sperm and fireflies, for example.

In 2014, Cornell researchers first introduced a simple model of swarmalators – short for ‘swarming oscillator’ – where particles self-organize to synchronize in both time and space. In the study, "Diverse Behaviors in Non-uniform Chiral and Non-chiral Swarmalators,” which published Feb. 20 in the journal Nature Communications, they expanded this model to make it more useful for engineering microrobots, better understand existing, observed biological behaviors, and for theoreticians to experiment in this field.

“We wanted a simple mathematical model that can lay the foundation for swarmalators in general, something that captures all of the complex emergent phenomena we see in natural and engineered swarms,” said Kirstin Petersen, the paper’s senior author, assistant professor and an Aref and Manon Lahham Faculty Fellow in the Department of Electrical and Computer Engineering in Cornell Engineering.

Steven Ceron, Ph.D. ‘22, a former graduate student in Petersen’s lab, is the paper’s first author, and Kevin O’Keeffe Ph.D. ‘17, a former graduate student in applied mathematics, is a co-author.

O’Keeffe compared this model to the largest doll in a set of Russian dolls, with each smaller doll representing models capable of simulating more refined behaviors. “We’ve tried to come up with a model that is as simple as possible in the hope of capturing generic phenomena,” he said.

The researchers simplified their model to work with just four mathematical constants linked together

Podcast: Ant Inspired Swarm Robots

In this episode, we talk about about how an ant inspired robotics platform could be the future of swarm robotics due to its simple, affordable, flexible, and scalable nature.

EPISODE NOTES

Preventing drones from bumping into each other

Not just reacting, but also predicting

Reprogrammable materials selectively self-assemble

Stanford's JackRabbot 2: The polite pedestrian robot

Soft Robotics With Hod Lipson

In this episode, we talk about about how an ant inspired robotics platform could be the future of swarm robotics due to its simple, affordable, flexible, and scalable nature.

EPISODE NOTES

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