The Hungarian Academy of Science has demonstrated a method to induce real-world self-organizing flocking behavior in robot drones.
The Academy's Gabor Vasarhelyi says distributed flocking algorithms had been limited in real-world applications because their underlying theoretical models cannot account for "constrained motion and communication capabilities, delays, perturbations, or the presence of barriers."
Vasarhelyi's team developed models that included additional factors to reflect unpredictable real-world impediments, such as obstacles and boundaries encountered while moving at high velocities, as well as the sudden malfunction of sensors and short-range communication equipment.
Evolutionary optimization was applied to the flocking model, which was tested using 30 autonomous aerial drones operating in a setting filled with obstacles while their electronics were programmed to fail unexpectedly. After a few runs, the drone swarm flocked and flew without difficulty, especially at high velocities.
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