We are excited to announce the ICRA2023 Autonomous Drone Racing Competition. The competition will showcase the latest advancements in autonomous drone technology, and we invite you to participate. The task is to develop an autonomous algorithm that can fly a drone through a sequence of gates in minimum time. This is a great opportunity for researchers, engineers, and students to showcase their skills and network with other experts in the field. Register your team now, and let's fly to the future of autonomous drones!

**Detailed description**

The competition will have two challenges: i) state-based drone racing and ii) vision-based drone racing. The state-based challenge aims at benchmarking algorithms for planning and control from the aspect of time-optimal flight and robustness against unknown effects, such as aerodynamics. The vision-based challenge evaluates the entire system, particularly the perception. Building upon the state-based challenge, the vision-based system should be additionally robust against visual distractors, such as illumination and color changes. We will also provide an easy-to-use API to control the drone to lower the entry barrier. We will allow the participants to concentrate exclusively on the racing problem by abstracting away all the complexity of agile drone control. The competition will be hosted on our in-house simulator “Flightmare.” We believe this competition is a great opportunity for researchers, engineers, and students to showcase their skills and developments in the field of autonomous drone technology. It will also be a great platform for participants to network and collaborate with other experts in the field. The registration for the competition is now open, and we encourage you to register your team as soon as possible.

**Dates**

– Preregister now to get all new updates
– Submission deadline: 20th May 2023 (AOE).

For more information about the competition, please visit the competition website. We look forward to welcoming you to the competition and to an exciting future of autonomous drone technology.

Yunlong Song, Leonard Bauersfeld, Angel Romero, Davide Scaramuzza