![]() 117 teams registered for the competition worldwide, with 16 unique entries that have shown up on the qualification leaderboard. In the qualification stage (Oct 15 th to Nov 21 st), teams were asked to submit their entries for a subset or all of the three competition tiers. A set of training binaries with configurable racetracks was made available to the participants initially, for prototyping and verification of algorithms on arbitrary racetracks. The competition is being run in two stages-an initial qualification round and a final round. ![]() Here “drone_2” (pink spline) is the opponent racer going through randomized waypoints in each gate cross section, while “drone_1” (yellow spline) is a representative competitor going through the gate centers. The following animation shows a segment of one of our racing tracks with two drones racing against each other. In each animation, the drone is tracking a minimum jerk trajectory using one of our competition APIs. The animation on the left below shows the ground truth gate poses (Tier 1), while the animation on the right shows the noisy gate poses (Tier 2 and Tier 3). Given the ground truth state estimate for participant drone and noisy estimate for gates, the goal is to race against the opponent racer without colliding with it. Tier 3 – Perception and Planning: This combines Tier 1 and 2.The next gate will not always be in view, but the noisy pose returned by our API will steer the drone roughly in the right direction, after which vision-based control would be necessary. Tier 2 – Perception only: This is a time trial format where the participants are provided with noisy gate poses.The opponent racer follows a minimum jerk trajectory, which goes through randomized waypoints selected in each gate’s cross section. These are accessible via our application-programming interfaces (APIs). Ground truth for gate poses, the opponent drone pose, and the participant drone are provided. The goal is to go through all gates in the minimum possible time, without hitting the opponent drone. Tier 1 – Planning only: The participant’s drone races tête-à-tête with a Microsoft Research opponent racer.The competition focuses on trajectory planning and control, computer vision, and opponent drone avoidance. We are hosting the competition on Microsoft AirSim, our Unreal Engine-based simulator for multirotors. This has given rise to competitions, with the goal of beating human performance in drone racing.Īt the thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), the AirSim research team is working together with Stanford University and University of Zurich to further democratize drone-racing research by hosting a simulation-based competition, Game of Drones. More recently, the unmanned aerial vehicle (UAV) research community has begun to tackle the drone-racing problem. These advances enable increased autonomy and reliability for drones. In parallel, computer vision and machine learning are making rapid progress, along with advances in agile trajectory planning, control, and state estimation for quadcopters. Drone racing has transformed from a niche activity sparked by enthusiastic hobbyists to an internationally televised sport.
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