Artificial Intelligence Robotic Racing series, The Drone Racing League Inc., New York, USA

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Organizer - The Drone Racing League Inc.

Contributor - Lockheed Martin

 

[Administrator]

Lockheed Martin AlphaPilot AI Drone Innovation Challenge

Sep 5, 2018

Lockheed Martin and Drone Racing League announced an innovation competition, challenging teams to develop artificial intelligence (AI) technology that will enable an autonomous drone to race a pilot-operated drone – and win. Participating teams will compete in a series of challenges for their share of over $2 million in prizes.

 

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Introducing RacerAI

Oct 6, 2019

The DRL RacerAI is the first autonomous drone designed to defeat the best human FPV drone pilot in the world. Drone Racing fans, technologists and AI/ML enthusiasts can watch the historic DRL Racer AI race head-to-head through unique tracks in DRL’s new Artificial Intelligence Robotic Racing (AIRR) circuit.

 

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Article "Meet the robot racing drone that could beat human pilots by 2023"
Teams start competing Tuesday in the Drone Racing League's new battle of autonomous drones. Here's an exclusive first look at the AI-powered flying machine.

by Stephen Shankland
October 8, 2019

 

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AlphaPilot: Autonomous Drone Racing (RSS 2020)

May 27, 2020

We present a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the 2019 AlphaPilot Challenge. Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. The global map and drift-compensated state estimate allow the drone to navigate through the race course even when the gates are not immediately visible and further enable to plan a near time-optimal path through the race course in real time based on approximate drone dynamics. The proposed system has been demonstrated to successfully guide the drone through tight race courses reaching speeds up to 8m/s and ranked second at the 2019 AlphaPilot Challenge.

Reference:
P. Foehn, D. Brescianini, E. Kaufmann, T. Cieslewski, M. Gehrig, M. Muglikar, D. Scaramuzza,
"AlphaPilot: Autonomous Drone Racing",
Robotics: Science and Systems (RSS), 2020
PDF: rpg.ifi.uzh.ch/docs/RSS20_Foehn.pdf

For more info about our research page on :
1. Vision-based quadrotor flight: rpg.ifi.uzh.ch/research_mav.html
2. Drone Racing: rpg.ifi.uzh.ch/research_drone_racing.html
3. Aggressive flight: rpg.ifi.uzh.ch/aggressive_flight.html


Affiliations:
All the authors are with the Dept. of Informatics, University of Zurich, and Dept. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland rpg.ifi.uzh.ch

 

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[ICRA21 Autonomous Racing] - Davide Scaramuzza (U of Zurich): Autonomous Drone Racing

Jun 10, 2021

Davide Scaramuzza is a Professor of Robotics and Perception at both departments of Informatics (University of Zurich) and Neuroinformatics (joint between the University of Zurich and ETH Zurich), where he directs the Robotics and Perception Group. His research lies at the intersection of robotics, computer vision, and machine learning, using standard cameras and event cameras, and aims to enable autonomous, agile, navigation of micro drones in search-and-rescue applications. In 2018, his team won the IROS 2018 Autonomous Drone Race and in 2019 it ranked second in the AlphaPilot Drone Racing world championship.

From the ICRA 2021 Full-Day workshop on Opportunities and Challenges with Autonomous Racing.
linklab-uva.github.io/icra-autonomous-racing

Workshop Chairs:
Madhur Behl (University of Virginia)
Johannes Betz (University of Pennsylvania)
Rahul Mangharm (University of Pennsylvania)
Venkat Krovi (Clemson University)