Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 71 | Automatic Puzzle Solver |
Alex Kim Conor Devlin Eric Chen |
Angquan Yu | design_document2.pdf final_paper1.pdf other1.zip photo1.jpeg photo2.jpeg presentation1.pptx proposal2.pdf |
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| # Automatic Puzzle Solver for Accessibility and User Convenience Team Members: - Eric Chen (egchen2) - Alex Kim (alexk4) - Conor Devlin (conorbd2) # Problem Jigsaw puzzles remain a popular pastime, offering enjoyment and cognitive benefits. However, manual assembly can be challenging for individuals with motor skill limitations, visual impairments, or limited attention spans. Existing automated solutions are often expensive, complex, or limited in puzzle sizes and complexities. This project addresses the need for an accessible and user-friendly automatic jigsaw puzzle solver. Our solution aims to empower individuals of all abilities to enjoy the benefits of puzzle solving while reducing frustration and increasing user satisfaction. # Solution This project will deliver an accessible and user-friendly solution to enhance the puzzle-solving experience for individuals of all abilities. We propose an innovative Automatic Jigsaw Puzzle Solver equipped with a precision-controlled robotic arm and computer vision system. # Solution Components ## 3D Movement System Function: Precisely position the robotic arm above puzzle pieces. Components: - Stepper motors (e.g., Nema 17 series) with high torque and speed for accurate movement. - Belt/pulley system or leadscrew system for linear motion on X and Y axes. - End-stop switches for precise positioning. ## Rotation System Function: Rotate puzzle pieces for proper orientation before pickup. Components: - Servo motor (e.g., MG996) with sufficient torque for desired rotation angle. - Gears/belt system for rotating a platform holding the puzzle piece. - Limit switch for accurate positioning at specific angles. ## Piece Picking System Function: Securely lift and place puzzle pieces without damage. Components: - Vacuum suction cup(s) with size and material suitable for puzzle pieces (e.g., foam or silicone). - Venturi vacuum generator with sufficient flow rate and pressure for suction. - Compressed air supply with regulator for controlling suction strength. ## Computer Vision System Function: Identify and locate puzzle pieces within the complete image. Components: - Camera sensor (e.g., ArduCam OV5642 or Olimex OV7670) with high resolution and auto-focus capability. - Microcontroller (e.g., Raspberry Pi Zero W, Raspberry Pi 3, STMicroelectronics STM32F103C8T6) for initial image processing and communication. - Processing Unit (e.g., dedicated AI accelerator or cloud-based processing) for intensive image analysis (optional). ## Control Software Function: Orchestrate the entire system, interpret vision data, and control robotic movements. Environment: Open-source libraries like OpenCV for image processing and Python for overall control. Modularity: Designed for easy maintenance and future improvements. # Criterion For Success - Camera Accuracy: 95% of puzzle pieces correctly identified and oriented within the complete image. - Arm Performance: 90% success rate in accurately picking and placing puzzle pieces. - Puzzle Completion Time: Solve a 100-piece puzzle of moderate complexity within 60 minutes. |
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