Living in a constant world of evolution and change, the need for automation and simplicity in our lives is growing fast. Whether it’s seen in self-driving cars, smart home accessories, or even within the AI compatibility within your smartphone, automation is revolutionizing the technological industry.

                However recently, universities like the Massachusetts Institute of Technology (MIT) and the California Institute of Technology (Caltech) are finding ways to introduce newly found innovations into the world of automation, specifically with aerial vehicles. As seen frequently within the aerospace engineering industry, these universities are finding cost-efficient and safe ways to create navigation systems for aerial vehicles, including drones, planes, and cars.

                One of the largest examples of this has taken place at MIT, by Professor Richard Cockburn Maclaurin of Aeronautics and Astronautics. Along with a team of researchers from the Aerospace Controls Laboratory at MIT, Professor Maclaurin has been able to develop a trajectory planning system that would allow armies and fleets of operating drones to fly in the same airspace, without dangerous collisions. Like mentioned before, this innovation greatly influences the progress of aerial vehicles, especially when considering new traffic and safety regulations for air taxis. Professor Maclaurin and MIT’s work has allowed for new real-world implications and allowing for cost-efficient testing for air taxis, drones, and other aerial vehicles.

                Here’s how it works: the autonomous systems computer a path-planning trajectory and share that information with the rest of the machines utilizing a wireless connection. Now, the UAV’s have a specific trajectory and launch point to finish at, along with a path that strays away from the other UAV’s. However, the biggest issue with the array of trajectory and communication is the eminent delay, which would not allow certain UAV’s to receive the transmission. To solve this, the MIT team worked to create a “perception aware” function, which would allow the systems to yield onboard sensors, meaning the UAV’s can alter their trajectory based on readings from sensors.

                As for the next steps, the team is working on expanding the algorithms and test spaces, to eventually simulate a real-world aerial vehicle example. During this process, engineers will have to build around challenges like the identification of environment of which the vehicle is in, or overcoming the delays of communication between sensors. However, the boundaries are only expanding, as the future of autonomous luxury vehicles and drones are only coming closer.  

Jake Takiguchi

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