Onboard sensors and cameras prevent drone collisions

University of the Basque Country (UPV/EHU) researcher Julián Estévez has developed low-cost, autonomous navigation technology to prevent two or more drones whose paths cross in mid-air from colliding with each other. His solution has been published in the journal Aerospace Science and Technology, with the software code available via GitHub.

Most of the drones we are familiar with are manned, even if they are outside the operator’s view. For a drone to be fully autonomous, it has to be able to make flight decisions on its own and without human intervention; in other words, to decide for itself how to avoid collisions, maintain its course in the face of wind gusts, control flight speed, dodge buildings and trees, etc.

“Using simple, low-cost equipment and an algorithm based on artificial vision and colour identification, we have developed a robust piece of technology to satisfactorily prevent collision between drones and which can be easily extrapolated to most commercial and research aerial robots,” Estévez said.

“This work is a small step towards fully autonomous navigation, without any human intervention, so that drones can decide which manoeuvre to perform, which direction to take, thus preventing collisions with each other or with other airborne obstacles. If we assume that, in the future, our airspace will be much more populated by commercial services performed by these drones, our work is a small contribution in this respect.”

Estévez explained that the approach to preventing collisions “does not require drones to exchange information with each other; instead, they rely solely on their onboard sensors and cameras. We get the signal from the camera onboard the drones, and by processing the images, we adjust the reactions of the robots so that they fly smoothly and accurately.”

In experiments, UPV/EHU’s Computational Intelligence Group tried to mimic realistic drone conditions — so scenarios that could occur in a typical urban area under uncontrolled lighting conditions, with drones flying in different directions. Estévez explained, “We equipped each drone with a red card that allows the software algorithm to detect the presence of an approaching drone and measure its proximity.”

Estévez continued, “Each drone is equipped with an onboard camera, the screen of which is divided into two halves (left and right). This camera always seeks out the colour red of the cards mentioned above. Through simple image processing, we can find out what percentage of the camera is occupied by the colour red, and whether most of this red is on the left- or right-hand side of the screen. If most of the red zone is on the left-hand side of the screen, the drone will fly to the right to avoid collision. If the red zone is on the right, it will move to the left.

“When the percentage of the colour red on the screen increases, it means that the drones are approaching each other head-on. So when a threshold is exceeded, the robot knows that it has to perform the avoidance manoeuvre. All this happens autonomously, without the human operator intervening.

“It’s a simple way to prevent collisions and can be performed by low-cost sensors and equipment,” Estévez said. He added that the study successfully validated the use of the technology in commercial drones, in spite of its reduced cost.

Image credit: UPV/EHU.