US researchers have developed and tested flight control algorithms for tailsitter drones that could enable their use for for search-and-rescue missions.
A tailsitter is a fixed-wing aircraft that takes off and lands vertically (it sits on its tail on the landing pad), and then tilts horizontally for forward flight. Faster and more efficient than quadcopter drones, these versatile aircraft can fly over a large area like an airplane but also hover like a helicopter, making them well-suited for tasks like search-and-rescue or parcel delivery.
Researchers at MIT have developed algorithms for trajectory planning and control of a tailsitter aircraft to take advantage of its maneuverability and versatility. The algorithms can execute sideways or upside-down flight and are computationally efficient enough that they can plan complex trajectories in real time.
The algorithms could enable tailsitters to autonomously perform complex moves in dynamic environments, such as flying into a collapsed building and avoiding obstacles while on a rapid search for survivors.
Typically, other methods either simplify the system dynamics in their trajectory planning algorithm or use two different models, one for helicopter mode and one for airplane mode. Neither approach can plan and execute trajectories that are as aggressive as those demonstrated by the MIT researchers.
“We wanted to really exploit all the power the system has. These aircraft, even if they are very small, are quite powerful and capable of exciting acrobatic maneuvers. With our approach, using one model, we can cover the entire flight envelope — all the conditions in which the vehicle can fly,” said Ezra Tal, a research scientist in the Laboratory for Information and Decision Systems (LIDS) and lead author of a new paper describing the work.
The researchers used their trajectory generation and control algorithms to demonstrate tailsitters that perform complex maneuvers like loops, rolls, and climbing turns, and they even showcased a drone race where three tailsitters sped through aerial gates and performed several synchronized, acrobatic maneuvers.
The design for a tailsitter was invented by Nikolai Tesla in 1928, but no one tried to build one until nearly 20 years after his patent was filed. Even today, due to the complexity of tailsitter motion, research and commercial applications have tended to focus on aircraft that are easier to control, like quadcopter drones.
Trajectory generation and control algorithms that do exist for tailsitters mostly focus on calm trajectories and slow transitions, rather than the rapid and acrobatic maneuvers these aircraft are capable of making.
The researchers knew designed trajectory planning and control algorithms specifically for agile trajectories with fast-changing accelerations in order to enable the tailsitters to reach peak performance. Using a global dynamics model that applied to all flight conditions, ranging from vertical take-off to forward and sideways flight, they leveraged a technique known as differential flatness and a mathematical function to quickly check whether a trajectory is feasible.
“That check is computationally very cheap, so that is why with our algorithm, you can actually plan trajectories in real-time,” Tal explains.
These trajectories can be very complex, rapidly transitioning between vertical and horizontal flight while incorporating sideways and inverted maneuvers, because the researchers designed their algorithm in such a way that it uniformly considers all of these diverse flight conditions.
“Many research teams focused on the quadcopter aircraft, which is very common configuration for almost all consumer drones. The tailsitters, on the other hand, are a lot more efficient in forward flight. I think they were not used as much because they are much harder to pilot,” Sertac Karaman, associate professor of aeronautics and astronautics and director of LIDS said.
“But, the kind of autonomy technology we developed suddenly makes them available in many applications, from consumer technology to large-scale industrial inspections.”
A tailsitter airshow
The researchers tested their method by planning and executing a number of challenging trajectories for tailsitters in MIT’s indoor flight test area. In one test, they demonstrate a tailsitter executing a climbing turn where the aircraft turns to the left and then rapidly accelerates and banks back to the right.
They also showcased a tailsitter “airshow” in which three synchronized tailsitters performed loops, sharp turns, and flew seamlessly through airborne gates. These maneuvers wouldn’t be possible to plan in real-time without their model’s use of differential flatness, said Tal.
“Differential flatness was developed and applied to generate smooth trajectories for basic mechanical systems, such as a motorized pendulum. Now, more than 30 years later, we’ve applied it to fixed-wing aircraft. There might be many other applications we could apply this to in the future,” said Gilhyun Ryou, a researcher on the project.
The next step for the MIT researchers is to extend their algorithm so it could be used effectively for fully autonomous outdoor flight, where winds and other environmental conditions can drastically affect the dynamics of a fixed-wing aircraft.
Giuseppe Loianno, assistant professor of electrical and computer engineering at New York University said, “This approach enables impressive agile flight maneuvers across a spectrum of demanding flight conditions in real-time, encompassing stall regimes, sideways uncoordinated flight, and even inverted flight. The versatility of the proposed approach holds promise in broadening the applicability of these platforms to various additional scenarios, including search and rescue, monitoring, and inspection presenting a compelling alternative to traditional VTOL platforms.”
This article was originally by Adam Zewe at MIT and can be found here