A team of researchers working at Johannes Kepler University has developed an autonomous drone using optical sectioning algorithm technology to improve search-and-rescue efforts.

Johannes Kepler University, Linz, Austria.

In their paper published in the journal Science Robotics, the group describes their drone modifications. Andreas Birk with Jacobs University Bremen has published a Focus piece in the same journal issue outlining the work by the team in Austria.

The design, presented in the June 23 issue of Science Robotics, integrates thermal imaging, machine learning, and a relatively new optical algorithm to help the drone see lost persons through the trees.

“Our main motivation is to provide a potentially life-saving technology which is more flexible, faster, and possibly more efficient than manned helicopters,” said corresponding author Oliver Bimber, professor of computer graphics at Johannes Kepler University Linz in Austria.

“Drones can also fly in weather conditions where helicopters can’t, and AI-based person classification together with the AOS [algorithm] is potentially much more reliable than an observing pilot.”

Finding people lost (or hiding) in the forest is difficult because of the tree cover. People in planes and helicopters have difficulty seeing through the canopy to the ground below, where people might be walking or even laying down. The same problem exists for thermal applications—heat sensors cannot pick up readings adequately through the canopy.

Efforts have been made to add drones to search-and-rescue operations, but they suffer from the same problems because they are remotely controlled by pilots using them to search the ground below. In this new effort, the researchers have added new technology that both helps to see through the tree canopy and to highlight people that might be under it.

The new technology is based on what the researchers describe as an airborne optical sectioning algorithm—it uses the power of a computer to defocus occluding objects such as the tops of trees. The second part of the new device uses thermal imaging to highlight the heat emitted from a warm body. A machine-learning application then determines if the heat signals are those of humans, animals or other sources.

The new hardware was then affixed to a standard autonomous drone. The computer in the drone uses both locational positioning to determine where to search and cues from the AOS and thermal sensors. If a possible match is made, the drone automatically moves closer to a target to get a better look.

In 17 field experiments conducted over conifer, broadleaf, and mixed forests that vary in light levels, temperature, and seasonality, the AOS-equipped drone found 38 out of 42 hidden persons in free flying conditions. For experiments with predefined flight paths, the drone demonstrated an average success rate of 86%.

Robots offer many advantages over humans in search and rescue. They can operate from a safe distance, cover a wide range, and are packed with sensors to detect signs of life and recognize threats more accurately. They are also faster and more cost-effective.

By processing all of its data on board, the drone could operate effectively even in areas without stable network coverage.

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