Page 2 of 3 FirstFirst 123 LastLast
Results 11 to 20 of 23

Thread: Miscellaneous

  1. #11


    Building the world's largest race drone

    Published on Apr 17, 2017

  2. #12


    Hotel room tiny whoop racing

    Published on May 31, 2017

    Whenever we travel we bring along our Tiny Whoops to do some racing after hours! Anub and RiSCyD battle in Dubai while testing the new TBS LED Micro Racing Gates!

  3. #13


    Drag Racing with DIY drone engineer Zoe Stumbaugh

    Published on Sep 14, 2017

    The ubiquity of drones in the modern world has recently birthed a new sport: drone racing. Motherboard’s Erik Franco went to meet drone racing 'it' girl Zoe Stumbaugh as she prepares for a major race and competes with the fastest model she's ever built.

  4. #14


    These awesome little robocars that drive themselves

    Published on Sep 25, 2017

    Hobbyists build these autonomous cars and race them at an Oakland warehouse.

  5. #15


    World's first Robot Jockey

    Published on Mar 12, 2018

    BetBright has unveiled the World’s first Robot Jockey to celebrate the start of Cheltenham Festival and the BetBright Cup

    · The robot, capable of riding a horse and jumping fences, can also communicate with humans

  6. #16


    Naveen Rao drives AI into the future

    Published on Jun 14, 2018

    Intel’s Naveen Rao talks about driving racecars, growing up in rural Kentucky and opening the throttle on Artificial Intelligence.

  7. #17

  8. #18


    Are we ready for autonomous drone racing? The UZH-FPV drone racing dataset

    Published on May 21, 2019

    (NB. This video is narrated). Despite impressive results in visual-inertial state estimation in recent years, high speed trajectories with six degree of freedom motion remain challenging for existing estimation algorithms. Aggressive trajectories feature large accelerations and rapid rotational motions, and when they pass close to objects in the environment, this induces large apparent motions in the vision sensors, all of which increase the difficulty in estimation. Existing benchmark datasets do not address these types of trajectories, instead focusing on slow speed or constrained trajectories, targeting other tasks such as inspection or driving. We introduce the UZH-FPV Drone Racing dataset, consisting of over 27 sequences, with more than 10 km of flight distance, captured on a first-person-view (FPV) racing quadrotor flown by an expert pilot. The dataset features camera images, inertial measurements, event-camera data, and precise ground truth poses. These sequences are faster and more challenging, in terms of apparent scene motion, than any existing dataset. Our goal is to enable advancement of the state of the art in aggressive motion estimation by providing a dataset that is beyond the capabilities of existing state estimation algorithms.

    Reference:
    J. Delmerico, T. Cieslewski, H. Rebecq, M. Faessler, D. Scaramuzza
    Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset
    IEEE International Conference on Robotics and Automation, 2019

  9. #19


    Drone racing star wars style Pod racing are back!

    Published on Sep 30, 2014

    This is a pre event that happend in france, organized by airgonay, a quadcopter racing fanatic association. Drones are not only for spying or doing the headlines when they fall in the streets.

  10. #20


    Super-human performance in Gran Turismo Sport using deep reinforcement learning

    Aug 19, 2020

    Autonomous car racing raises fundamental robotics challenges such as planning minimum-time trajectories under uncertain dynamics and controlling the car at its friction limits. In this project, we consider the task of autonomous car racing in the top-selling car racing game Gran Turismo Sport. Gran Turismo Sport is known for its detailed physics simulation of various cars and tracks. Our approach makes use of maximum-entropy deep reinforcement learning and a new reward design to train a sensorimotor policy to complete a given race track as fast as possible. We evaluate our approach in three different time trial settings with different cars and tracks. Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers.

    Reference:
    F. Fuchs, Y. Song, E. Kaufmann, D. Scaramuzza, P. Duerr
    Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning
    Arxiv, 2020
    PDF: https://arxiv.org/abs/2008.07971

    More about our research on Deep Learning: http://rpg.ifi.uzh.ch/research_learning.html

Page 2 of 3 FirstFirst 123 LastLast

Социальные закладки

Социальные закладки

Posting Permissions

  • You may not post new threads
  • You may not post replies
  • You may not post attachments
  • You may not edit your posts
  •