Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 38 | Dual-Arm Robotic System for Cube Rotation |
Keeron Huang Rong Wang Yiming Xu Zhuoyang Shen |
proposal1.pdf |
Meng Zhang | |
| # Dual-Arm Robotic System for Cube Rotation Team members (listed A-Z): - Qixuan Huang - Rong Wang - Yiming Xu - Zhuoyang Shen ## Problem Traditional Rubik’ cube solvers often rely on highly specialized, single-purpose mechanical structures that lack the versatility of human-like manipulation. Conversely, general-purpose bimanual robots struggle with the precision required for cube rotation and the complex coordination needed to prevent jamming. Furthermore, training robust bimanual policies requires massive amounts of data; collecting this in the real world is time-consuming, expensive, and risks damaging expensive hardware. There is a need for a system that leverages advanced simulation data to perform high-precision, robust bimanual manipulation in the physical world. ## Solution Overview We propose an integrated bimanual robotic system that uses RoboTwin 2.0 to bridge the gap between simulation and reality (Sim-to-Real). - Simulation & AI: We will utilize RoboTwin 2.0’s “Strong Domain Randomization” (varying lighting, clutter, and textures) to generate a massive synthetic dataset. This data will be used to train a robust bimanual manipulation policy capable of handling physical uncertainties. - Hardware Implementation: To meet the course's hardware requirements, we will construct a physical dual-arm workstation. The system will feature a custom-designed PCB for power distribution and motor control, ensuring the mechanical arms can execute the trained policy with high torque and precision. - User Interface: The system will adhere to the "One-button start" requirement, where a single physical trigger initiates the vision-scan-solve-rotate sequence autonomously. ## Solution Components - Subsystem I: Mechanical & Actuation (ME Focus) - Bimanual Arm Assembly: Two 3-to-6 DOF robotic arms equipped with specialized grippers. - 3D Printed End-Effectors: Custom-designed high-friction fingertips and cube-stabilizing fixtures to ensure secure grasping during high-speed rotations. - Subsystem II: Electronics & Control (ECE Focus - Core Requirement) - Custom PCB: A dedicated circuit board integrating a voltage regulation module (12V to 5V/3.3V), high-current motor driver ICs (e.g., PCA9685 for PWM expansion), and signal isolation to protect the MCU. - Central MCU: An ESP32 or STM32 microcontroller to handle real-time motor commands and “One-button” logic. - Subsystem III: Vision & Computation - Sensing: A dual-camera or mirror-based vision system for 6-face color recognition. - Edge Computing: A Jetson Nano or PC to run the RoboTwin-trained policy and the Kociemba solving algorithm. ## Criteria of Success - Vision Accuracy: Correct identify the color configuration of all 6 faces of a scrambled cube within 30 seconds under varying ambient light. - Mechanical Stability: The bimanual arms must successfully rotate the cube faces without dropping the cube or causing mechanical jamming in 95% of test trials. - Full Autonomy: Upon pressing the physical start button, the system must autonomously solve the cube from any scrambled state within 3 minutes. - Hardware Integrity: The custom PCB must operate without overheating or voltage drops exceeding 5% during peak motor activity. ## Distribution of Work - Yiming Xu: Develops the simulation environment using RoboTwin 2.0 and is responsible for generating synthetic expert datasets and training the bimanual manipulation policy via domain randomization. - Zhuoyang Shen: Designs the computer vision module for Rubik’s cube state recognition and implements the high-level solving algorithm (e.g., Kociemba) for optimal motion path planning. - Rong Wang: Responsible for the custom PCB design and hardware implementation, including high-current motor drive circuits, power management systems, and low-level MCU firmware for real-time control. - Qixuan Huang: Focuses on the mechanical structure design and fabrication, including 3D-printed specialized bimanual grippers and performing system-wide sim-to-real integration and stability testing. |
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