Miscellaneous

Professor Valvano's real-time operating system class (EE345M/EE380L.6) at the University of Texas at Austin has a robot competition. Each robot has two microcontrollers (LM3S8962 and LM3S2110) connected via CAN and runs a RTOS that the students designed built and evaluated.

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The annual Mobot races during CMU Carnival. April 15, 2016.

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Drone Racing is a competitive sport that puts pilots in the driver seat of their custom rigs with a first person view of the action.

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We test Anki Overdrive, Hotwheels AI and Scalextric Arc Air. Each of the sets are put through their paces by checking out:
- The Hook
- Track
- Cars
- Controllers
- Value

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At a recent HackDay event here at Clearpath, a team from the Research division transformed a RC car into a 1/5th scale autonomous driving development platform. They outfitted a Losi RC car with a Velodyne laser scanner, Novatel GPS and Point Grey Flea3 camera, all of which integrated with onboard battery and computer running ROS.

The team took it out to a local go cart race track to test the handling and performance.

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These might not be the robots of the future, but these students are the future of robots.

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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!

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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.

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

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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

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

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(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

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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.

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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

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