How MIT researchers made tailsitter drones fly like acrobats

A tailsitter is a special kind of fixed-wing aircraft that sits on its tail when it is on the ground and then tilts horizontally for forward flight.
Rizwan Choudhury
MIT researchers's Tailsitter drone.
MIT researchers's Tailsitter drone.

MIT 

In the ever-evolving world of aerial technology, MIT's researchers have given wings to the brilliance of aircraft design with their new algorithms for tailsitter drones. This breathtaking technology is enabling these aircraft to execute astounding acrobatics and challenging maneuvers, paving the way for futuristic applications in search-and-rescue, parcel delivery, and more.

Tailsitter Drones

A tailsitter is a strikingly unique fixed-wing aircraft that lands and takes off vertically, sitting on its tail during the process. It subsequently tilts horizontally for forward flight. Faster, more efficient, and undeniably more versatile than standard quadcopter drones, tailsitters are opening new horizons in aviation.

But how do you control such a versatile and agile aircraft? How do you plan its trajectory and make it execute complex maneuvers? This is the challenge that MIT researchers have tackled in their new paper. They have developed new algorithms that can generate and execute trajectories for tailsitters that are fast, smooth, and acrobatic.

MIT's algorithmic mastery

MIT’s Laboratory for Information and Decision Systems (LIDS) has pioneered new algorithms for tailsitters that unlock unparalleled maneuverability and versatility. The project, supported in part by the U.S. Army Research Office, shows they can plan intricate trajectories in real-time and execute maneuvers like upside-down or sideways flights, something not witnessed before.

Traditional methods typically struggle to offer such aggressive trajectories, limiting the drones ,by simplifying system dynamics or employing different helicopter and airplane models.

But MIT's team went a step further. “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,” states Ezra Tal, a lead researcher in the project in a press release.

The researchers utilized their algorithms to exhibit tailsitters that can perform climbing turns, loops, rolls, and even a thrilling drone race. Three tailsitters zoomed through aerial gates and conducted synchronized acrobatic maneuvers in an airshow that won't be forgotten soon.

Historical perspective

The concept of a tailsitter has its roots back to 1928, credited to the inventive mind of Nikolai Tesla. Yet, it's only now that researchers are uncovering the complexity of tailsitter motion, pushing beyond the more accessible quadcopter drones.

Previously, the algorithms for tailsitters have focused on calm trajectories. MIT's research, however, has targeted rapid, agile maneuvers, setting new standards in aviation technology.

Technological innovation

One of the critical steps in planning a trajectory is to check whether the aircraft can fly it. For example, maybe the aircraft has a minimum turning radius that prevents it from making a sharp corner. Since tailsitters are complex systems with flaps and rotors and exhibit such complicated aerial motions, it usually takes a lot of calculations to check whether a trajectory is feasible. This slows down traditional planning algorithms.

By employing a global dynamics model, MIT’s researchers planned trajectories for all flight conditions, from vertical take-off to sideways flight. They utilized a technical property known as differential flatness to ensure efficiency. This technique allows them to use a simple mathematical function to check whether a trajectory is feasible quickly. Their approach avoids many complicated system dynamics and plans a trajectory for the tailsitter as a mathematical curve through space. The algorithm then uses differential flatness to rapidly check that trajectory's feasibility.

“In trajectory planning, the key step is to ensure the aircraft can actually fly the planned path. By employing differential flatness, we can quickly check whether a trajectory is feasible,” explains Tal.

This approach allows the creation of complicated flight patterns, including rapidly transitioning between various flight conditions.

Gateway to new possibilities

Professor Sertac Karaman sees these tailsitters as revolutionary. “The autonomy technology we developed suddenly makes them available in many applications, from consumer technology to large-scale industrial inspections,” he says.

MIT's team demonstrated these algorithms' power through rigorous tests, including an 'airshow' where synchronized tailsitters performed loops, sharp turns, and seamless transitions through airborne gates.

According to Tal, such real-time planning wouldn't have been possible without applying differential flatness, an innovation that has expanded the applicability of tailsitters in various domains.

The next phase for the MIT researchers is to extend their algorithms for fully autonomous outdoor flights, accommodating winds and other environmental conditions.

This work represents a step into the future, where tailsitter drones may become the common face of aviation, redefining efficiency, versatility, and excitement in the sky.

The study was published in IEEE Transactions on Robotics

Study abstract:

This article proposes a novel algorithm for aerobatic trajectory generation for a vertical take-off and landing (VTOL) tailsitter flying wing aircraft. The algorithm differs from existing approaches for fixed-wing trajectory generation, as it considers a realistic six-degree-of-freedom (6-DOF) flight dynamics model, including aerodynamic equations. Using a global dynamics model enables the generation of aerobatics trajectories that exploit the entire flight envelope, allowing agile maneuvering through the stall regime, sideways uncoordinated flight, inverted flight, etc. The method uses the differential flatness property of the global tailsitter flying wing dynamics, which is derived in this work. By performing snap minimization in the differentially flat output space, a computationally efficient algorithm, suitable for online motion planning, is obtained. The algorithm is demonstrated in extensive flight experiments encompassing six aerobatic maneuvers, a time-optimal drone racing trajectory, and an airshowlike aerobatic sequence for three tailsitter aircraft.

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