stub “Dumb Robots” Accomplish Tasks by Leveraging Physical Characteristics  - Unite.AI
Connect with us


“Dumb Robots” Accomplish Tasks by Leveraging Physical Characteristics 

Updated on
Image: Georgia Tech College of Sciences

In order for swarms of robots to act collectively, researchers must choreograph their interactions by relying on advanced algorithms and components. However, if the robots are simple with a lack of advanced programming, coordinated behavior can rarely be achieved. 

Dana Randall, ADVANCE Professor of Computing and Daniel Goldman, Dunn Family Professor, have led a team of researchers at Georgia Institute of Technology to address this issue. The team set out to demonstrate how simple robots can still accomplish tasks that go beyond the capabilities of one. 

The research was published in the journal Science Advances on April 23. 

Dumb Robots Achieve Complex Tasks

Termed “dumb robots,” the team used what were basically mobile granular particles, and these are what they set out to prove can achieve complex tasks. The researchers reported that they were able to remove all sensors, communication, memory and computation from the robots, and they leveraged the robots’ physical characteristics to complete a set of tasks. According to the team, this trait is called “task embodiment.”

The BOBbots, which stands for “behaving, organizing, buzzing bots,” were named after Bob Behringer, a pioneer in granular physics. 

The robots are “about as dumb as they get,” Randall says. “Their cylindrical chassis have vibrating brushes underneath and loose magnets on their periphery, causing them to spend more time at locations with more neighbors.”

Simple Robots, Smart Algorithms: Meet the BOBbots

Computer Simulations

Along with the experimental platform, the team also relied on precise computer simulations led by Shengkai Li, a Georgia Tech physics student. These simulations helped study various aspects of the system that were not able to be looked at in the lab. 

The BOBbots are extremely simple, but the researchers still demonstrated that when the robots move together and bump into each other, “compact aggregates form that are capable of collectively clearing debris that is too heavy for one alone to move,” Goldman explains.  “While most people build increasingly complex and expensive robots to guarantee coordination, we wanted to see what complex tasks could be accomplished with very simple robots.”

The team’s work was inspired by a theoretical model of particles moving around on a chess board, and in order to study a mathematical model of the BOBbots, a theoretical abstraction called a self-organizing particle system was developed. By pulling from probability theory, statistical physics, and stochastic algorithms, the team was able to prove that as the magnetic interactions increase, the theoretical model goes through a phase change. It quickly changes from dispersed to aggregating, forming compact clusters similar to systems like water and ice.

Randall is also a professor of computer science and adjunct professor of mathematics at Georgia Tech. 

“The rigorous analysis not only showed us how to build the BOBbots, but also revealed an inherent robustness of our algorithm that allowed some of the robots to be faulty or unpredictable,” Randall says.

Alex McFarland is a Brazil-based writer who covers the latest developments in artificial intelligence. He has worked with top AI companies and publications across the globe.