Study Explores Using Swarm of Tiny Drones to Transform Search-and-Rescue Operations
Denrie Caila Perez posted on November 08, 2019 |
A joint research team release the results of their project exploring swarm robotics.
Drones explore the environment by flying into different areas. (Image courtesy of TU DELFT/MVLAB.)
Drones explore the environment by flying into different areas. (Image courtesy of TU DELFT/MVLAB.)

Researchers recently released a study detailing the results of their project in the field of swarm robotics. A joint research team worked on the project for four years, exploring the use of tiny, autonomous 33-gram drones. The researchers were inspired by insect swarms. While insects tend to be limited individually, they are able to surpass limitations when operating in a swarm. By applying the same processes to small robots, they become capable of performing tasks typically difficult for large, individual robots.

The aim of the project was to explore the use of swarms of drones in search-and-rescue operations. The main idea is to enable rescue workers to release a swarm of tiny drones for exploring disaster sites, such as buildings on the brink of collapse. The swarm of drones should be able to enter the building, explore it and return to the base station with relevant data. This wallow rescue workers to focus on rescue operations.

To test the technology, the researchers replicated a disaster scenario. The drones were equipped with cameras and deployed in an indoor office environment where they were tasked to locate two dummy victims. A swarm of six drones was able to explore about 80 percent of open rooms within six minutes, which would be impossible if only one drone was available. One drone was able to locate a victim. Due to a hardware failure in the camera, it could not offer images. However, other drones were able to capture images.

The test was able to show the advantages of a swarm compared to having only one drone, but it isn’t without its challenges. According to the team, the most challenging aspect in swarm exploration is making the small robots capable of navigating an unknown environment individually. That is because tiny robots have sensing and computation limitations.

“The biggest challenge in achieving swarm exploration lies at the level of the individual intelligence of the drones,” said Kimberly McGuire, a PhD student who worked on the project. “In the beginning of the project, we focused on achieving basic flight capabilities, such as controlling the velocity and avoiding obstacles. After that, we designed a method for the small drones to detect and avoid each other. We solved this by having each drone carry a wireless communication chip and then making use of the signal strength between these chips. This is like the number of bars shown on your phone that decrease when you move away from your Wi-Fi router in your home. The main advantages of this method are that it does not require extra hardware on the drone and requires very few computations.”

The researchers looked to nature for inspiration. Instead of highly detailed maps, insects rely on retaining landmarks and behaviorally relevant places, such as food sources and their own nest.

“The main idea underlying the new navigation method is to reduce our navigation expectations to the extreme—we only require the robots to be able to navigate back to the base station,” said Guido de Croon, a principal investigator of the project. “The swarm of robots first spreads out into the environment by having each robot follow a different preferred direction. After exploring, the robots return to a wireless beacon located at the base station.”

The team next proposed a bug algorithm as the drones’ primary navigation system.

“Bug algorithms do not make maps of the environment but deal with obstacles on the fly,” McGuire said. “In principle, detailed maps are very convenient because they allow a robot to navigate from any point in the map to any other point along an optimal path. However, the costs of making such a map on tiny robots is prohibitive. The proposed bug algorithm leads to less efficient paths but has the merit that it can even be implemented on tiny robots.”

The joint research team includes researchers from TU Delft, the University of Liverpool and Radboud, and the University of Nijmegen. The project was financed by the Dutch National Science Foundation NWO Natural Artificial Intelligence program.

The complete study was published Oct. 23 and can be found in the Science Robotics journal.

For more robotics new, check out these robots that eat pollution.

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