Automated Ball Striker Joseph Black David Caloccia Gina Rophael Paul Savickas.

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Transcript of Automated Ball Striker Joseph Black David Caloccia Gina Rophael Paul Savickas.

  • Slide 1
  • Automated Ball Striker Joseph Black David Caloccia Gina Rophael Paul Savickas
  • Slide 2
  • Presentation Outline Motivation Objective Specifications Design Approach Results Assessment Conclusion
  • Slide 3
  • Motivation Extension of the Ping Pong Shooter from Team 2 of Spring 2005 Inspiration: to develop training equipment for ping pong players.
  • Slide 4
  • Objectives To strike a vertically launched ping pong ball. Launch a ping pong vertically at various heights Visually locate the launched ping pong ball Process data to determine balls position and velocity Develop trajectory for paddles motion Design a controller to track the desired path
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  • Specifications Step response: Steady State Error0.195% Percent Undershoot 12.58% Rise Time 0.43 s Settling Time 1.43 s Other Specifications: Range of Motion (Pan) -80to +100 Range of Motion (Tilt) 0to +90 Payload 200 grams Noise Tolerance 95% Weight of the System is ~1Kg Commercial Cost $2462.75 Speed is 8rad/sec for pan and for tilt 10rad/sec Images processing rate 30 frames/second Sampling time for the controller 10 micro sec
  • Slide 6
  • Design Approach Modeling Control Design Launcher Vision Physics Model Trajectory Generation Integration
  • Slide 7
  • Modeling General Equation of Motion Disconnect between model and system A simpler model was pursued
  • Slide 8
  • Modeling Simplified Equation of Motion Steady State Friction Identification Transient Response Inertia
  • Slide 9
  • Friction Identification Viscous Friction =.0087 NmS/rad Coulomb Friction =.0141 Nm
  • Slide 10
  • Model Validation Open loop response
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  • Model Validation Closed loop response Differences between model and system The final model was useful in understanding the system, but control design was done on the actual system.
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  • Control Design Control applied using the FPGA PID block in LabVIEW. The PID for set point control with sampling of 100 microseconds, 512 proportional for pan and tilt, 3/128 integration for pan and 1/128 for tilt. Swing controller pan 10 microsecond sampling, proportional 1792, derivative 7/128, derivative control may not have much affect with such a high sampling rate.
  • Slide 13
  • Control Design
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  • Launcher Final Design Ball Support Multiple Balls Performance Launch Height Verticality
  • Slide 16
  • Vision Collect frames at 30 frames per second, threshold the individual images and filter out small objects, to remove dust and the black balls reflecting light back to camera. Check for ball in frame, first time records the position and time of image after 2 nd image of ball subtracts position and time from previous image to determine velocity
  • Slide 17
  • Vision Used LabVIEW Vision Assistant to make calibration information image from a image of a dot matrix in the plane of the launch, with black dots at a known distance from each other fed to LabVIEW.
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  • Physics Model Ball Motion 1D Motion Initial Velocity Position/Time Calculations Air Resistance Time Until Swing
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  • Trajectory Generation The choice of start and end positions Development in Trajectory Ability to choose between trajectories
  • Slide 20
  • Integration Physical Integration Base, Launcher, Webcam, Pan-Tilt Software (LabVIEW) Physics Model Image Processing
  • Slide 21
  • Integration A laptop is used as a host to connect to the web cam and retrieve images of the ball and calculate position, velocity and time to swing information from physics model. Laptop communicates to begin the swing to the CRIO using VI Server, breaking the CRIO out of a set point loop into the swing signal loop. The CRIO was used as a Real Time system to control the FPGA and to send the swing path and set points to FPGA at 1 kHz. The FPGA is used for control of the system and monitoring the position and velocity
  • Slide 22
  • Integration Testing Subsystem Overall System Uncertainty Launcher Physics Model Swing Time Strike Zone
  • Slide 23
  • Results Probability of successful striking Demonstration Video
  • Slide 24
  • Probability of Successful Striking
  • Slide 25
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  • Video Demonstration
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  • Assessment Ball Launching Inconsistent launch Vision/Physics Model Reliable for successful launches Trajectory Generation and Tracking Accurate tracking of trajectory Overall Success
  • Slide 28
  • Conclusion Recommendations for improvement Improvements to launcher would greatly benefit the rest of the system Ability to aim ball toward target zones State-space control
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  • Questions?