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
# 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.

Electronic Automatic Transmission for Bicycle

Tianqi Liu, Ruijie Qi, Xingkai Zhou

Featured Project

Tianqi Liu(tliu51)

Ruijie Qi(rqi2)

Xingkai Zhou(xzhou40)

Sometimes bikers might not which gear is the optimal one to select. Bicycle changes gears by pulling or releasing a steel cable mechanically. We could potentially automate gear changing by hooking up a servo motor to the gear cable. We could calculate the optimal gear under current condition by using several sensors: two hall effect sensors, one sensing cadence from the paddle and the other one sensing the overall speed from the wheel, we could also use pressure sensors on the paddle to determine how hard the biker is paddling. With these sensors, it would be sufficient enough for use detect different terrains since the biker tend to go slower and pedal slower for uphill or go faster and pedal faster for downhill. With all these information from the sensors, we could definitely find out the optimal gear electronically. We plan to take care of the shifting of rear derailleur, if we have more time we may consider modifying the front as well.

Besides shifting automatically, we plan to add a manual mode to our project as well. With manual mode activated, the rider could override the automatic system and select the gear on its own.

We found out another group did electronic bicycle shifting in Spring 2016, but they didn't have a automatic function and didn't have the sensor set-up like ours. Commercially, both SRAM and SHIMANO have electronic shifting products, but these products integrate the servo motor inside the derailleurs, and they have a price tag over $1000. Only professionals or rich enthusiasts can have a hand on them. As our system could potentially serve as an add-on device to all bicycles with gears, it would be much cheaper.

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