CubeBot: FPGA-Based Rubik’s Cube Solver
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Date
2026
Authors
Momen Anani
Mohammad Hamdan
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Abstract
Abstract
This project presents the design and implementation of an automated Rubik’s Cube solving
robot using a heterogeneous embedded system architecture that combines FPGA hardware
acceleration, ARM processor coordination, and ESP32-based motor control. Unlike traditional
microcontroller-only or PC-based approaches, the system strategically distributes tasks across
specialized computing platforms to achieve deterministic real-time performance, modular de-
sign, and reliable operation.
The system architecture integrates three cooperating units: a DE1-SoC FPGA fabric im-
plementing hardware-accelerated color extraction and VGA display, an ARM Cortex-A9 Hard
Processor System (HPS) managing high-level coordination and solution computation, and
an ESP32 module handling motor control and wireless dashboard connectivity. The FPGA
processes cube face images with deterministic 14.8ms timing using threshold-based color clas-
sification, while the HPS executes the Kociemba two-phase solving algorithm and validates
cube state consistency. The ESP32 coordinates stepper and servo motors to physically ma-
nipulate the cube with sensor-based alignment feedback.
Communication between subsystems uses a custom UART packet protocol with state
machine-based error recovery, achieving 100% reliability across all testing. The system provides
dual monitoring interfaces through FPGA-based VGA hardware display and ESP32-hosted
wireless web dashboard, enabling comprehensive system visibility and user control.
Experimental results demonstrate 98.7% color detection accuracy, 93.3% solve success
rate, and mean solve time of 46.1 seconds. The modular architecture achieved efficient FPGA
resource utilization (27% ALMs, 2% block memory, 20% DSP blocks) while maintaining
flexibility for future enhancements. Testing across 30 complete solve cycles validated the ef-
fectiveness of the heterogeneous design approach for robotics applications requiring integrated
perception, computation, and actuation.
This work demonstrates how hardware-software co-design principles can address the limita-
tions of monolithic embedded systems, providing a practical architecture for FPGA-accelerated
robotics that balances real-time performance with implementation simplicity and debugging
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