Perception and Control for Fast and Agile Super-Vehicles

July 12th, Oregon State University, RSS 2020

Abstract

As autonomous aerial vehicles not only become more robust and capable, but also are slowly being adopted in many industrial tasks, a novel branch of autonomy has recently caught the interest of many researchers: autonomous drone racing. Not only does it combine the difficulties in perception, estimation, planning, control, and their intersections, but it also tests their ability to perform under harsh, real-world conditions. Expert human pilots have demonstrated an astonishing level of control, racing remotely controlled drones at their physical limits, and inspiring roboticists to push the algorithmic limits to a human-competitive level. As advances in algorithmic perception and control for fast and agile robotic vehicles materialize, autonomous racing vehicles are quickly approaching the ability to contend against human pilots in head to head races. Most recently, Lockheed Martin, NVIDIA and the Drone Racing League (DRL) successfully organised the first season of the AlphaPilot program (https://www.herox.com/alphapilot) and the AIRR drone racing challenge (https://thedroneracingleague.com/airr/), where multiple teams have successfully deployed and raced their autonomy algorithms against each other. These advances may ultimately lead to autonomous super-vehicles, i.e., next-generation autonomous robots that are capable of achieving super-human maneuvering and racing capabilities. The resulting algorithms may become invaluable components of high-throughput autonomy software, e.g., to maneuver cars out of traffic accidents. However, the development of these super-vehicles brings significant challenges. While perceiving the environment at high speeds with low latency has been investigated throughout the last decade, many open research questions still remain. On the other side, time-optimal planning with well known or learned dynamic and aerodynamic models could give autonomous drones an advantage over human pilots, or let them learn from each other. The purpose of this workshop is to identify gaps in current techniques, and discuss possible solutions to the remaining and newly uncovered research questions.. Is end to end deep learning a viable option to solve these high speed interactions? What can we model, what can we learn, and could we combine these techniques to achieve superhuman capabilities? What are the transfer gaps between simulation, learning and real world systems, and how can we bridge them to achieve truly superior autonomous mobile robots?

AlphaPilotVideo

Call for Papers

We invite 2-page extended abstract submissions for original work in perception and control for high speed navigation and topics of interest to this workshop. Topics of interest to this workshop are (but not limited to):

  1. Drone racing.
  2. High speed localization and mapping.
  3. Perception aware control and planning.
  4. Trajectory optimization for aggressive flight.
  5. Robust control for high speed flight
  6. Accurate simulation of highly agile and fast aerial vehicles.

Authors will have the opportunity to participate in a poster session at the workshop.

** Important dates:

Abstract submission deadline: April 9th 2020
Acceptance Notification: April 16th 2020
Workshop date: July 12th 2020

Please email all submissions to super-vehicles-rss20-submit@mit.edu with ‘RSS20 Super Vehicles’ in the subject line.

Confirmed Speakers

Teodor Tomic
Affiliation: Skydio

Ashish Kapoor
Affiliation: Microsft

Mac Schwagger
Afilliation: Stanford

Marilyn Smith
Affiliation: Georgia Institute of Technology

Guido de Croon
Affiliation: TU Delft

Workshop Organizers

Varun Murali (Contact Person)
Email: mvarun@mit.edu
Affiliation: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology

Phillip Foehn
Email: foehn@ifi.uzh.ch
Affiliation: University of Zurich

Prof. Davide Scaramuzza
Email: sdavide@ifi.uzh.ch
Affiliation: University of Zurich

Prof. Sertac Karaman
Email: sertac@mit.edu
Affiliation:Laboratory for Information and Decision Systems, Massachusetts Institute of Technology