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Robots Learn by Checking in on Team Members


Mohamed Abdelkader is one of the researchers that developed an algorithm enabling a team of unmanned aerial vehicles to work together in real time.

Researchers at King Abdullah University of Science and Technology in Saudi Arabia have developed an algorithm that enables teamwork between unmanned aerial vehicles.

Credit: Kuat Telegenov

Engineers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia have enabled teamwork between unmanned aerial vehicles using what KAUST's Jeff Shamma calls "a distributed architecture in which the drones coordinate based on local information and peer-to-peer communications."

The team developed an algorithm designed to support an optimal level of peer-to-peer messaging and fast response times without excessive heavy computation.

"Each of our drones makes its own plan based on a forecast of optimistic views of their teammates' actions and pessimistic views of the opponent's actions," says KAUST's Mohamed Abdelkader. "Since these forecasts may be inaccurate, each drone executes only a portion of its plan, then reassesses the situation before re-planning."

The algorithm performed well in both indoor and outdoor settings under various attack scenarios, and the researchers want to facilitate drone coordination in larger outdoor areas and add adaptive machine-learning upgrades.

From KAUST Discovery
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