Science and Technology

How drones can beat rush hour by ‘thinking’ on their landers

K Bharat Kumar | Updated on October 10, 2021

Programing drone trajectories   -  IMAGE COURTESY: IISC, BENGALURU

An IISc team is working on ways to program unmanned aerial vehicles to avoid mid-air traffic jams and, worse, collisions

Before maps software arrived on your phone, sheer experience helped you decide the route you would take to get out of home and back. At specific times of day, a certain junction was best avoided. At other times, you are on velvet as you’d be going against the traffic. Sounds simple enough. But how does a drone — an unmanned aerial vehicle — decide the quickest route for it?

Before we try and answer this question, here are a couple more: Does a drone need to decide which route to take? Yes, it does. Aren’t routes merely straight lines in the air, as the crow flies? Certainly not, as Prof Ashwini Ratnoo’s research shows.

Perception and planning are the two pillars that a drone’s utility depends on. The drone must be able to perceive where it is located, what is around it, and then choose a suitable path towards its destination, says Prof Ratnoo of the Department of Aerospace Engineering, Indian Institute of Science, Bengaluru.

Ratnoo’s lab at the institute works on the planning part of this ‘brain’. For a drone to function autonomously, it must be programed with algorithms that make human-like decisions with precision, says the September edition of the institute’s newsletter Kernel.

“At the outer level, if we have, say, three UAVs needing to service five destinations, then we need to program them to optimise the total time taken,” says the professor.

Beyond optimising, the code needs to take into account the capability of the drone. “Trajectories for the drones need to be decided. A sharp turn may not be possible. Also, we should ensure that the algorithm does not become so computationally intensive as to make the drone inefficient.”

Inefficiency here is not just about the time factor. Ratnoo says, “If the drone is in a confined place, then it can wait till the algorithm computes and tells it what to do. But if it faces a dynamic object — such as another drone approaching it — the algorithm has to help the drone quickly decide what to do.”

Drones can be useful in myriad areas — warfare, agriculture, anti-poaching measures, rescue efforts in disaster-hit unnavigable areas, and delivering emergency healthcare, among others.

Fine-tuning the fliers

What concepts do Ratnoo and his team use to make drones more efficient and safe? “Control systems is a large part of our work. Control theory is being used the world over in the guidance and control of vehicles.” Ratnoo also has a favourite that helps his team keep control of the efficiency aspect — bifurcation theory. “Bifurcation theory helps us use the same control algorithm structure to vary the control design parameters. So, much like flipping only one switch, I get to use the same algorithm to come up with varied trajectories that the drone could use. Moreover, retaining the same feedback structure in the control algorithm presents the best use of the drone’s sensing capabilities.”

That brings us to an even more interesting part of Ratnoo’s work: When multiple drones are in action, what else decides their paths? His team has worked on ‘Drone Skyways’ in collaboration with the Robert Bosch Centre for Cyber Physical Systems (RBCCPS) at the institute. The algorithms help create a corridor, or a virtual road, as the newsletter article puts it, for UAVs to travel safely. The drones fly at an altitude called ‘Class G’, which is closest to the earth’s surface, it explains.

In collaboration with colleague Prof Debasish Ghose, his group has participated in developing ‘CORRIDRONE’, a skyway for the movement of several UAVs that also features geo-fencing — that is, a virtual fence along the corridor. And, these corridors can be dynamic. “Meaning, once a drone passes a location, the geo-fencing around that space disappears and another drone can freely use it.”

“If two drones are in different lanes but likely to cross each other, the algorithm helps decide if one should speed up and clear the junction quickly or wait and let the other drone pass,” Ratnoo says.

Does that mean each drone carries inside itself the equivalent of an airport’s air traffic control system that helps avoid mid-air collisions? Not entirely, says Ratnoo. “Ground control stations are needed when we have multiple drones in a specific area. UAS Traffic Management is an important area of research that is being carried out globally.”

Published on October 10, 2021

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