MIT makes tailsitter drones more manoeuvrable

Researchers at the Massachusetts Institute of Technology (MIT) have developed algorithms that improve the maneuverability and versatility of tailsitter drones. The mostly twin-engined drones, which can take off and land standing on the tail and wings, are thus able to carry out difficult flight maneuvers such as a sideways flight upside down. These complex maneuvers can be calculated in real time.

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Traditional methods of piloting tailsitter drones either simplify the system dynamics in their trajectory algorithm or use two different models, such as one for helicopter flight and one for airplane mode. However, these approaches have the disadvantage that they cannot plan and execute “aggressive” trajectories, the researchers write in the study “Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness” published in IEEE Transactions on Robotics. The flight algorithms developed by MIT scientists are different.

“We really wanted to use the full power of the system. These planes, while very small, are quite capable and capable of exciting acrobatic maneuvers. Our approach allows us to cover the entire range of flight with a single model, which means all conditions under which the vehicle can fly,” says Ezra Tal, a researcher at the Laboratory for Information and Decision Systems (LIDS) and lead author of the study.

The algorithms can make tailsitter drones autonomously perform complex flight maneuvers in dynamic environments. For example, the drones can quickly penetrate collapsed buildings and fly around obstacles while they search for injured people.

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For their approach, the researchers used a global dynamics model, a model that is valid for all conceivable flight conditions. It includes vertical takeoff, forward and sideways flight, and landing. The scientists had to take into account that the drones only have to manage a few flight paths with rapidly changing accelerations.

Flight path planning with “Differential Flatness”

In order to achieve this, algorithms must first check whether the planned flight path can be physically managed by the tailsitter drone. For example, a particularly sharp turn could not be flown because it falls below the minimum radius that the drone can handle. In addition, numerous calculations are required to control the two rear engines and the flaps in order to determine whether a flight maneuver can be carried out.

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To do this, the scientists used the “Differential Flatness” method – a possibility with which they can use mathematical functions to check whether a trajectory is feasible. This approach avoids complicated system dynamics and plans the trajectory for the drone as a mathematical curve in space.

“The test is computationally very simple, so you can actually use our algorithm to plan trajectories in real time,” says Tal.

These trajectories can be very complex. A quick change between vertical and horizontal flight is just as possible as lateral flight and overhead maneuvers.

“A lot of research teams have focused on the quadcopter, a configuration that’s very common on almost all consumer drones. The tail wing, on the other hand, is much more efficient in forward flight. I don’t think they’ve been used that much because they’re much more difficult to control,” says Sertac Karaman, professor of aerospace engineering at the Laboratory for Information and Decision Systems (LIDS). “But the autonomous technology we’ve developed suddenly makes it available for many applications, from consumer technology to large-scale industrial inspections.”

Flight tests

The researchers tested the algorithms with several Tailsitter drones in an MIT flight hall. They had the drones take off vertically and make quick changes of direction when flying steep turns. In addition, three tailsitter drones performed loops, sharp turns and flew through gates in sync with each other. Tal says these maneuvers would not have been possible without the use of differential flatness.

However, the MIT scientists have not yet reached the end of their research work. They are now planning to expand the algorithms so that fully autonomous outdoor flights can be carried out with the Tailsitter drones, even under adverse weather conditions such as strong winds.

(olb)

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