Teaching drones to land themselves on moving targets like Delivery trucks or Navy Ships
by noreply@blogger.com (brian wang) from NextBigFuture.com on (#2DRCM)
Researchers at the University of Cincinnati's College of Engineering and Applied Science are using artificial intelligence, called fuzzy logic, to get drones to navigate and land themselves on moving platforms. This holds promise for commercial uses such as delivering packages.
Drones have problems in navigating their ever-changing airspace.
This problem is compounded when the drone tries to land on a moving platform such as a delivery van or even a U.S. Navy warship pitching in high seas.
"It has to land within a designated area with a small margin of error," Kumar said. "Landing a drone on a moving platform is a very difficult problem scientifically and from an engineering perspective."
To address this challenge, UC researchers applied a concept called fuzzy logic, the kind of reasoning people employ subconsciously every day.
While scientists are concerned with precision and accuracy in all they do, most people get through their day by making inferences and generalities, or by using fuzzy logic. Instead of seeing the world in black and white, fuzzy logic allows for nuance or degrees of truth.
Fuzzy logic helps the drone make good navigational decisions amid a sea of statistical noise, he said. It's called "genetic-fuzzy" because the system evolves over time and continuously discards the lesser solutions.
Stockton, Kumar and Cohen successfully employed fuzzy logic in a simulation to show it is an ideal system for navigating under dynamic conditions. Stockton, an engineering master's student who was lead author on the paper, is putting fuzzy logic to the test in experiments to land quadcopters on robots mounted with landing pads at UC's UAV Multi-Agent System Research (MASTER) Lab.
"This landing project is a real-world problem. A delivery vehicle could have a companion drone make deliveries and land itself," Stockton said.
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Drones have problems in navigating their ever-changing airspace.
This problem is compounded when the drone tries to land on a moving platform such as a delivery van or even a U.S. Navy warship pitching in high seas.
"It has to land within a designated area with a small margin of error," Kumar said. "Landing a drone on a moving platform is a very difficult problem scientifically and from an engineering perspective."
To address this challenge, UC researchers applied a concept called fuzzy logic, the kind of reasoning people employ subconsciously every day.
While scientists are concerned with precision and accuracy in all they do, most people get through their day by making inferences and generalities, or by using fuzzy logic. Instead of seeing the world in black and white, fuzzy logic allows for nuance or degrees of truth.
Fuzzy logic helps the drone make good navigational decisions amid a sea of statistical noise, he said. It's called "genetic-fuzzy" because the system evolves over time and continuously discards the lesser solutions.
Stockton, Kumar and Cohen successfully employed fuzzy logic in a simulation to show it is an ideal system for navigating under dynamic conditions. Stockton, an engineering master's student who was lead author on the paper, is putting fuzzy logic to the test in experiments to land quadcopters on robots mounted with landing pads at UC's UAV Multi-Agent System Research (MASTER) Lab.
"This landing project is a real-world problem. A delivery vehicle could have a companion drone make deliveries and land itself," Stockton said.
Read more