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Podcast: How Robotic Farming Will Make Produce Better & Cheaper

In this episode, we talk about how robotic systems are being leveraged to assist farmers in streamlining their operations.

Farbod Moghaddam

fromThe Next Byte

07 Dec, 2021.

Image from Pixabay

Topic

robotics

EPISODE NOTES

(1:41) - Adaptive Swarm Robotics Could Revolutionize Agriculture 🚜🤖 (BAD)

(12:11) - Autonomous Agricultural Robots Revolutionize Organic Farming 🌾🚜 (GOOD)

Episode 47 was brought to you by Mouser Electronics, Farbod & Daniel’s favorite electronics distributor. Click here (EXTERNAL)to read about how robotic systems can be leveraged in farms to optimize their operation.

About the podcast:

Every day, some of the most innovative universities, companies, and individual technology developers share their knowledge on Wevolver. To ensure we can also provide this knowledge for the growing group of podcast listeners, we started a collaboration with two young engineers, Daniel Scott Mitchell & Farbod Moghaddam who discuss the most interesting content in this podcast series.

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AboutMouser Electronics

Mouser Electronics is a worldwide leading authorized distributor of semiconductors and electronic components for over 800 industry-leading manufacturers. We specialize in the rapid introduction of new products and technologies for design engineers and buyers. Our extensive product offering includes

Industry

Electronics

Headquarters

Mansfield, TX, USA

Founded

1964

Company Size

1001-5000

AboutThe Next Byte

The Next Byte Podcast is hosted by two young engineers - Daniel and Farbod - who select the most interesting tech/engineering content on Wevolver.com and deliver it in bite-sized episodes that are easy to understand regardless of your background. If you'd like to stay up to date with our latest ep

Industry

Entertainment

Headquarters

Washington D.C., DC, USA

Founded

2020

Image: Drones flying in a swarm © Alain Herzog / 2021 EPFL

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.

machine learning

- swarm robotics

- unmanned aerial vehicles (UAVs)

Clara Marc

fromEPFL

24 May, 2021.

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.”

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