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Vehicle Targeting System

Katie Dellaquila Jeremy Nelson Khiem Tong. Vehicle Targeting System. Agenda. Project Overview [KED] Multidisciplinary Aspects [KED ] Motivation (Similar Products) [KED ] System Schematic [JSN ] Components Image Processing [KDT] Mechanical Control [KDT/JSN] Testing [ KED]

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Vehicle Targeting System

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  1. Katie Dellaquila Jeremy Nelson Khiem Tong Vehicle Targeting System

  2. Agenda • Project Overview [KED] • Multidisciplinary Aspects [KED] • Motivation (Similar Products) [KED] • System Schematic [JSN] • Components • Image Processing [KDT] • Mechanical Control [KDT/JSN] • Testing [KED] • Integration [KED] • Project Feasibility and Anticipated Problems [JSN] • Cost [JSN] • Questions

  3. Project Overview • This project will implement a game involving a moving target with a mechanical targeting and shooting system.  • The user playing the game will move the target around while the targeting system attempts to shoot it with foam darts.  • The system will use a web camera placed on a tripod and image processing algorithms to detect, track, and predict the location of the target.

  4. Multidisciplinary Aspects • Mechanical Engineering • Firing Mechanism, Pneumatics • Electrical Engineering • Power considerations, Power MOSFETS • Software Engineering • Software design principles, design patterns, source control.

  5. Motivation (Similar Products) • Modern weapon systems have very complex targeting systems that utilize lasers. • Target tracking for security cameras. • Any application that requires the tracking of a target. Filters and feedback system would change but idea stays the same.

  6. System Schematic

  7. Time-out System • If microcontroller loses contact with laptop after specified time, it will shutdown. • The microcontroller will not move the mechanisms nor fire any darts if it does not receive messages containing these commands from the laptop.

  8. Circuit Diagrams • Power MOSFET or AC Relay for solenoid valve control

  9. Image Processing Frame • Three filters are used to isolate target and get it’s dimensions. • Color filters iterate over array and compare to threshold values • Each new frame captured from the camera generates an event whose listener applies the filters. • Detected object’s coordinates are then used in order to calculate the centroid. Filter HSL Filter Coordinate Translation Grayscale Filter Target Prediction BlobCounter Control signals to μController

  10. Operating Restrictions/Assumptions • Target is a solid, predefined color. • Target is within operating area, defined at calibration. • Lighting conditions are “reasonable”. • No external objects are the same color as the target. • Computation load affects image processing, frame rate. • Disable “RightLight” automatic light adjustment for camera. • Jitter needs to be tested.

  11. GUI Prototype Calibrate functionality allows for adjustments to environmental conditions. Calibrate is done by placing uniquely colored objects in viewing area to form reference points which are stored.

  12. Target Prediction • Simple Technique: Linear Interpolation • The location of the target will be tracked. • This information will be used to determine the next location of the target. • Complicated Technique: Kalman Filter • This technique will probably be more accurate, but it is more costly in terms of performance. • Timing Considerations • The calculations must be done quickly for better accuracy. • Dart flight time is taken into account

  13. Mechanical Firing Mechanism • Based on, with permission, mechanism utilized in the Remote Control Turret by Josh Bookout et al. • Barrel from Nerf gun. • Machine mounting with two holes. • Servo rotates barrel using shaft connected by 2:1 gear ratio for 360˚ rotation. • Barrel is aligned with compressed air tube.

  14. CAD Drawings [Source: Remote Controlled Turret]

  15. Mechanical Specifications • Servo is rated for 2.75 lbs (4.8V) and 0.19 s/60˚ • The firing rate will be limited by a minimum wait time between shots. • Range is limited by pressure which controlled by regulator. • This is not huge concern since range of accurate image processing is only 15-20’.

  16. Testing • Unit tests of individual components. • Mechanical and software tests separately • Mechanical • Empirical tests to produce scatter plot of dart firing locations • Timing for updating servo position, barrel rotation, and travel time. • C# Code components • Image processing (light conditions, camera location, etc.) • Target prediction (accuracy, timing, etc) • Embedded Code components • Serial communication with laptop • Servo control, solenoid valve control

  17. Integration • SVN server setup to help with software integration (and testing). • Iterative integration. • Laptop and microcontroller communication • Microcontroller and mechanical components control • Overall system – accuracy and usability

  18. Feasibility/Projected Problems • Mechanical design components • Borrowed CAD drawings have very precise dimensions, which make machining parts time costly • Lighting conditions • Target prediction/calibration will be challenging.

  19. Cost *Purchased by RIT Department of Computer Engineering ** Received from Mr. Wellin

  20. DEMO • Image processing • GUI • Target Detection

  21. Acknowledgements 1. Remote Controlled Turret, Josh Bookout et al. [Spring 2008]

  22. Questions • Any questions or comments?

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