Learning from nature and by using a little math, it may soon be possible to create a swarm of inexpensive, unmanned drones to quickly map an offshore oil spill.
For the drones to take on the task, a mathematical formula needs to come in play.
“Nature may not proactively use mathematics, nor does it have foresight. It behaves in ways driven by feedback, implicit drive for adaptation, and a certain degree of apparent randomness,” said Souma Chowdhury, PhD, assistant professor of mechanical and aerospace engineering in the University at Buffalo’s School of Engineering and Applied Sciences. “But we can look at what kind of mathematical principles define that behavior. Once we have that, we can use it to solve very complex problems.”
Chowdhury is pioneering a way to program a team of drones to quickly map an oil spill. He worked on a study, called “A Swarm-Intelligence Approach to Oil Spill Mapping using Unmanned Aerial Vehicles,” optimized and simulated a five-drone swarm that can map a nearly one-kilometer wide spill in nine minutes.
To make that work, Chowdhury overcame the lack of communication bandwidth typical of a flying ad hoc network and the short battery life of off-the-shelf drones.
Following the principles partly inspired by the dynamics of a flock of birds, Chowdhury devised a method for the drones to quickly record whether they are over water, oil or the edge of the spill. In addition, the drones assume the space around the oil they have spotted is also oil, although that ends up recorded as less than certain. This simple information ends up shared with the other drones in the swarm, as opposed to sharing actual images or video, which would require too much bandwidth.
As the drones move from point to point over the spill, they avoid going over space that other drones have already covered. Thus, with five drones making observations every five seconds, the size of the spill can end up determined quickly. The drones also fly to their base, on a boat, when their batteries get low. The drones that replace them on the search already have the information from all the other drones, so they avoid previously mapped locations.
“The thematic focus of my lab is developing computational design approaches that take inspiration from nature,” Chowdhury said.
The low computer power — each drone can operate with a $35 Raspberry Pi computer — keeps costs low. Chowdhury’s approach accomplishes a complex task using simple agents.
“There is no need for human interaction during its entire mission,” he said. “That’s the big deal.”
Another big deal is the cost. Chowdhury’s approach assumes simple, affordable drones, which makes it accessible to many more people. “This task can be accomplished by off-the-shelf drones that cost under $1,000. All they need is to have a simple drone-mountable camera system and use our software,” he said.
Collision avoidance is another challenge for the swarm, and here too, Chowdhury is following nature’s simple rules. In recent work reported by the University of Queensland, researchers watched very carefully how parrots never crashed into each other. They observed through tunnel experiments that they always veer to the right, a simple rule that keeps every member of the flock safe.
Chowdhury’s lab is exploring how using similar principles, drones can pre-emptively turn a certain angle to the right when they sense another flying member of the swarm.
Swarming drones could end up used elsewhere, such as mapping forest fires or other natural disasters. It’s possible they could end up used to help locate people trapped after an earthquake by changing the type of cameras used.