Dartboard Watches Your Throw; Catches Perfect Bullseyes

Some people really put a lot of effort into rigging the system. Why spend years practicing a skill and honing your technique to hit a perfect bullseye in darts when you can spend the time building an incredibly complicated auto-bullseye dartboard that’ll do it for you?

In fairness, what [Mark Rober] started three years ago seemed like a pretty simple task. He wanted to build a rig to move the dartboard’s bullseye to meet the predicted impact of any throw. Seems simple, but it turns out to be rather difficult, especially when you choose to roll your own motion capture system.

That system, built around the Nvidia Jetson TX1, never quite gelled, a fact which unfortunately burned through the first two years of the project. [Mark] eventually turned to the not inexpensive Vicon Vantage motion capture system with six IR cameras. A retroreflector on the non-regulation dart is tracked by the system and the resulting XY data is fed into MATLAB to calculate the parabolic path of the dart. An XY-gantry using six steppers quickly shifts the board so the bullseye is in the right place to catch the incoming dart.

It’s a huge amount of work and a lot of money to spend, but the group down at the local bar seemed to enjoy it. We wonder if it can be simplified, though. Perhaps tracking just the thrower’s motions with an IMU-based motion capture system and extrapolating the impact point would work.

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Inexpensive Robot Tracking System is Swarm Ready

RobotWebcam

[Ladvien] has figured an inexpensive way to control a robot from a remote PC with a static webcam. Inspired by swarming robot videos such as those from the UPENN Grasp lab, [Ladvien] wanted to build his own static camera based system. He’s also managed to create one of the more eclectic Instructables we’ve seen. You don’t often find pseudo code for robot suicide mixed in with the project instructions.

Fixed cameras are used in many motion capture systems, such as the Vicon system used by numerous film, game, and animation studios. Vicon and similar systems cost tens of thousands of dollars. This was a bit outside [Ladvien’s] budget. He set about building his own system from scratch. The first step was the hardest – obtaining permission from his wife to screw a webcam into the ceiling. With that problem overcome, [Ladvien] brought openCV and python to bear. He created Overlord, his webcam vision and control system. A vision system with nothing to control would be rather boring, so [Ladvien] created DotMuncher, Overlord’s radio controlled robot slave.

The basic processing system is rather simple. DotMuncher carries a magnetometer on board, which it uses to send heading information to Overlord. Overlord is pre-calibrated with an offset from magnetic north to “video game north” (toward the top of the screen). Overlord then uses openCV’s color detection to find DotMuncher in the current scene.
Overlord finally generates a virtual “Dot” on screen, and directs DotMuncher to drive over to it. When the robot gets to the dot, it is considered munched, and a new dot is generated.

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