Your cat is stuck in a burning building too dangerous for rescue crews to go inside, so off go the drones instead – five little unmanned aerial models that hover and flit through fiery beams and door frames without any human control. They know to spread out to cover more ground, and know how to adjust their search patterns when the communication links with the other drones go down. Their algorithms find and retrieve your cat in what rescue crews tell you is record time.
Or that's the dream anyhow, to one day build artificially intelligent, self-organizing robot systems that can collaborate on complex tasks – or, at the very least, rescue imperiled cats. We're not there yet, but researchers have been getting closer, thanks in part to what we're learning from the collective behavior of ants.
Look back through artificial intelligence literature from the past few decades and you'll find ant-inspired algorithms are a popular topic of study. Of note, Swiss artificial intelligence researcher Marco Dorigo was the first to algorithmically model ant colony behavior in the early 1990, and Stanford University biologist Deborah Gordon published her own study on the expandable search networks of ants a few years after. Today, both have different but related ideas on how we might implement so-called ant-inspired swarm intelligence in robots – and perhaps soon, drones – outside of the lab.
Consider, for example, how ants explore and search. Ants change the way they scour for things such as food and water depending on the number of ants nearby. According to Gordon, if there is a high density of ants in an area, the ants search more thoroughly in small, random circles. If there are fewer ants, the ants adjust their paths to be straighter and longer, allowing them to cover more ground.
This is all well and good in typical ant environments – but how do the ants adapt when interference is introduced, and their communication with other ants interrupted? To find out, Gordon sent over 600 small, black pavement crawlers to the International Space Station in January, and believes that studying how they react to the unfamiliar microgravity of space could help build better robots. Her research is especially prescient in the age of the drone.
In a Stanford news release, Gordon likened the interference introduced by microgravity as "analogous to the radio disruption that robots might experience in a blazing building." Depending on how Gordon's space ants adapt, she thinks the results when applied to robotics and artificial intelligence could help us program more efficient algorithms for search and exploration – especially when our robots are faced with unfamiliar environments, and with little to no human control.