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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| DARPA Grand Challenge | 3/4 | https://en.wikipedia.org/wiki/DARPA_Grand_Challenge | reference | science, encyclopedia | 2026-05-05T12:46:15.106307+00:00 | kb-cron |
The DARPA Subterranean Challenge tasked teams, consisting of university and corporate entities from around the world, to build robotic systems and virtual solutions to autonomously map, navigate, and search subterranean environments. Such areas can be difficult and dangerous for humans, making robotic teams a desirable option for exploration and search and rescue operations. These environments pose significant challenges to robots as well, including a lack of lighting, dripping water, thick smoke, cluttered or irregularly shaped environments and potential loss of GPS capabilities and communications with their handlers. The Challenge was meant to help close gaps in four technical areas: autonomy, perception, networking and mobility. The Challenge started in September 2018 and consisted of a Systems Competition (in which teams compete with physical robots) and a Virtual Competition (in which teams compete in a virtual environment in the ROS Gazebo virtual simulator). The competition was split into three stages (Development Stage, Circuit Stage, and Finals Stage. The SubT Challenge consisted of four events, the Tunnel Circuit (August 2019), which was held at an experimental mine in Pittsburgh, PA; the Urban Circuit (February 2020), which featured an abandoned nuclear power plant in Elma, WA; the Cave Circuit (November 2020), which was held virtual only due to the COVID-19 Pandemic; and the Final Event (September 2021), which featured elements from all three domains (tunnel urban underground, and natural cave networks was held in Louisville, KY. Teams came from 11 countries (Australia, Canada, the Czech Republic, England, Germany, Norway, South Korea, Spain, Sweden, Switzerland, and the United States) and 20 universities. On September 24, 2021, Team CERBERUS won the Final Systems Competition using four ANYmal C legged systems. Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) team came in second to Team CERBERUS, with an equal number of points, but a slightly slower time. Team Dynamo won the Final Virtual Competition. One important strategy was to build a team of robots with diverse capabilities. With a mix of navigational capabilities such as treads, wheels, rotors and legs, robots were able to navigate a variety of spaces. Different types of robots have different capabilities. Walking robots can deal with uneven terrain such as stairs and piles of rubble. Robots with wheels or treads can carry heavier payloads, including large batteries, and operate for a longer time. “Marsupials” can carry other robots, including small flying robots which have short battery lives. Flying robots can be strategically deployed to map large or difficult-to-access spaces. Using diverse detection instruments, such as lights, radar, sonar and thermal imaging, enables a team of robots and their handlers to gather information about air and visibility conditions and respond to a broader range of conditions. Because conditions can interfere with communications between robots and their handlers, the teams that developed robots with some degree of autonomy were most successful at the challenge task of mapping and searching a complex subterranean space. Such robots could explore on their own, and then return to radio contact with each other and their handlers to exchange information about what they had found. Australia’s CSIRO team even designed its robots to make cooperative decisions about what tasks to undertake. For example, a robot that was too large to fit into a corridor could notify other robots that it existed, so that a smaller robot could explore there. A robot exploring an area could also for a communications node to be dropped to expand the contact area. A robot deep in a cavern could relay information back to a robot closer to the surface, which could more quickly walk back to a point where it could report the information to the human operators. This changed the way in which humans worked with the robots: the human operator used the control system to set goals and direct overall strategy, leaving the robots to assess on-the-ground conditions and choose how to get the job done.
== 2018 Launch Challenge ==