We learn from challenges. Marco Belinelli and Comau Racer robot
Published on Oct 27, 2014
Basketball player Marco Belinelli teaches Comau Racer robot how to shoot hoops. An example of successful man-robot cooperation.
Stanford students engineer hoop-shooting robots
Published on Mar 17, 2015
This year's annual battle of the bots featured robots that score points by shooting or dunking as many "basketballs" as possible in under two minutes. Students in "Introduction to Mechatronics" learn about electronics, mechanics, and computer programing.
Racer playing basketball at Automate 2015
Published on Apr 1, 2015
Since it played with Marco Belinelli, our Racer has became a champion at basketball. Here you can see it at our stand at Automate 2015, the largest solutions-based showcase of automation technologies in North America.
RC helicopter BASKETBALL competition - the future of NBA?
Published on Apr 24, 2015
Some said you can't do it, others said it should be easy. Both were wrong! Doing a 1-on-1 basketball battle with RC helicopters turned out to be one of the toughest nuts to crack. Chances are even the big NBA stars like Michael Jordan (best ever?!) or Dirk Nowitzki would burst into tears how hard it is to put a ball through that hoop. Main reason is the missing depth perception of the pilot: From the side of the field you don't see the basket until the very last moment, and whether or not you're flying in the plane of the hoop. On the other hand, when both pilot, heli and basket are in one line, then you don't know how close to the hoop you are.
Sounds not so difficult but as you can see in the video it took something like 80 approaches to finally score. But all the more it was one of the fastest and coolest games we've played so far, with plenty of development and testing that had gone into it. Hope you love it! Leave us a thumbs up please!
Published on Nov 9, 2013
Most industrial robots nowadays still employ
strategies that neglect or minimize the effects of task dynamics.
Some tasks, however, are intrinsically dynamic and can only be
accomplished by considering their dynamic aspects. We address
ball catching as a prominent and widely studied example
for such a task. The paper follows a special approach to
accomplish the task: the nonprehensile catching, which means
catching without a form- or force-closure grasp. Depending on
the tracked ball velocity, two different catching methods are
proposed: First, catching of the ball during the initial contact.
Second, catching the ball after an initial rebounce during the
subsequent contact. For both approaches, the ball trajectory is
predicted with a recursive least squares algorithm. The dynamic
manipulability measure is used for the contact point selection.
Once a permanent contact between ball and end effector is
established, a balancing control based on force/torque feedback
is applied. Both methods are experimentally validated using a
six DoF industrial robot.
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