If you look into it, the human arsenal will show various different elements, and yet it won’t show you anything more valuable than that desire of ours to improve at a consistent pace. We say this because the stated desire has brought the world some huge milestones, with technology emerging as quite a major member of the group. The reason why we hold technology in such a high regard is, by and large, predicated upon its skill-set, which guided us towards a reality that nobody could have ever imagined otherwise. Nevertheless, upon looking beyond the surface for one hot second, it will become abundantly clear how the whole runner was also very much inspired from the way we applied those skills across a real world environment. The latter component, in fact, did a lot to give the creation a spectrum-wide, and as a result, initiate a full-blown tech revolution. Of course, this revolution eventually went on to scale up the human experience through some outright unique avenues, but even after achieving a feat so notable, technology will somehow continue to bring forth the right goods. The same has turned more and more evident in recent times, and assuming one new discovery ends up with the desired impact, it will only put that trend on a higher pedestal moving forward.

The researching team at Massachusetts Institute of Technology has successfully conceived another major technological breakthrough by developing new algorithms for optimal trajectory planning and control of a tailsitter. First of all, we must acknowledge the fact that a tailsitter is, in simple terms, a fixed-wing aircraft which takes off vertically and then tilts horizontally for forward flight. The stated sort of aircraft is understood to be more efficient than quadcopter drones, boasting the ability to not only fly over a large area like an airplane, but also hover around like a helicopter. Talk about how the algorithms enhance the current tailsitter setup, they deliver the means to seamlessly execute challenging maneuvers like sideways or upside-down flight, and also plan complex trajectories in real-time. As a result, the aircraft provided with the new technology should be able to autonomously perform complex moves in dynamic environments like flying into a collapsed building and avoiding obstacles during on a rapid search for survivors. That being said, tailsitter isn’t exactly a novel concept. Invented by Nikola Tesla, it has been around since 1928, and even the trajectory generation and control algorithms for the same have markedly existed before. However, the problem was that they would mostly focus on calm trajectories and slow transitions, therefore bearing a very limited number of use cases. In contrast, MIT’s latest brainchild is well-equipped to thrive in a relatively hostile environment. Notably enough, to make their algorithms robust, the researchers introduced a global dynamics model, translating to the one that applies to all flight conditions. This included vertical take-off, alongside forward and also sideways flight. Anyway, next up, the team applied a technical property known as differential flatness to ensure that model would perform efficiently.

“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.

Staying on the point of differential flatness, the researching team basically went for it because the move handed them a mathematical function to quickly check whether a particular trajectory is feasible or not. You see, considering  tailsitters are complex systems that carry a tendency to showcase utterly complicated aerial motions, numerous calculations to determine whether a trajectory is feasible almost become unavoidable. So, having differential flatness ensured a must faster alternative.

The team behind these algorithms tested their idea by planning and executing a number of challenging trajectories for tailsitters across MIT’s indoor flight space. In one such test, they showed a tailsitter executing a climbing turn where the aircraft turns to the left and then rapidly accelerates and banks back to the right. Apart from that, they also conducted a tailsitter “airshow” in which three synchronized tailsitters performed loops, sharp turns, and flew impeccably through airborne gates.

“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,” said Sertac Karaman, associate professor of aeronautics and astronautics and director of LIDS. “But, the kind of autonomy technology we developed suddenly makes them available in many applications, from consumer technology to large-scale industrial inspections.”

For the immediate future, the plan is to extend the algorithm to make it compatible for fully autonomous outdoor flight, where winds and other environmental conditions can drastically affect the dynamics of a fixed-wing aircraft.