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Chess Comes Alive: The Magic of Automation is a documented DIY robotic chessboard, not a commercial game system. A Raspberry Pi manages the chess game and Stockfish engine, while an Infineon XMC4700 microcontroller controls a two-axis gantry, electromagnet, LEDs, and homing sensors. When a move is entered, the gantry positions an electromagnet beneath the board and drags a magnet-equipped piece to its destination.
The result looks like a physical chessboard playing by itself, but the engineering is closer to a coordinated CNC machine than to a conventional smart board. The project is a substantial maker build involving fabrication, embedded control, Linux software, chess rules, calibration, and recovery from mechanical errors.
What this project actually is
The project was published on Hackster.io on July 7, 2025, by the Infineon Team and Maysa Ouaja. It is presented as an MIT-licensed maker project with hardware lists, schematics, custom 3D-printed parts, and software. The published project is best understood as a documented prototype rather than a ready-to-buy appliance. See the original Hackster project for the authors’ files and implementation details.
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- A digital chess game displays a board on a screen.
- An electronic board detects a player’s physical moves.
- A networked board synchronizes games with online services.
- A robotic board physically moves pieces.
This project belongs mainly to the last category. The documented interaction uses terminal input for the player’s move, then physically executes the computer’s response. The available documentation does not establish automatic recognition of arbitrary moves made by a human on the physical board.
Its defining trick is simple to describe: every 3D-printed chess piece contains a small magnet, and an electromagnet mounted beneath the playing surface pulls the selected piece across the board.
The architecture: two computers with different jobs
Player input
↓
Raspberry Pi
├── Python chess state
├── Stockfish engine
├── coordinate calculation
└── I²C commands
↓
XMC4700
├── X/Y stepper motors
├── electromagnet
├── RGB LEDs
└── Hall sensorsThe Raspberry Pi handles the high-level work: maintaining a virtual board, validating moves, invoking Stockfish, calculating square coordinates, and sending commands over I²C.
The XMC4700 handles timing-sensitive hardware control. It drives the two stepper motors, switches the electromagnet, operates the addressable LEDs, reads the Hall-effect sensors, and reports whether the mechanism is still busy.
This division is important. A Linux computer is well suited to Python, chess libraries, and engine processes, but a dedicated microcontroller is better suited to deterministic motor pulses and sensor timing.
How the physical mechanism moves a piece
The board uses an X/Y gantry made from linear rods or axes, timing belts, stepper motors, and a 3D-printed trolley. The electromagnet is attached to the trolley beneath the acrylic playing surface.
- The Raspberry Pi identifies the source and destination squares.
- It converts those squares into X/Y movement values.
- It sends the command to the XMC4700 over I²C.
- The XMC4700 moves the gantry to the source square.
- The electromagnet attracts the magnet inside the piece.
- The gantry drags the piece to its destination.
- The LEDs highlight the relevant squares.
The under-board design avoids a robotic arm above the board. That keeps the playing surface visually clean and allows one actuator to move every piece. The trade-off is that the board, pieces, magnet strength, and mechanical tolerances all have to work together.
A weak magnet, excessive distance between the electromagnet and piece, a rough surface, a heavy piece, poor belt tension, or a neighboring magnetic object can cause a missed move. The pieces are therefore not interchangeable with arbitrary commercial chess pieces; they need compatible embedded magnets, dimensions, weight, and clearance.
Captures are much harder than ordinary moves
A normal move transfers one piece from one square to another. A capture requires the mechanism to clear the destination before moving the attacking piece:
- The gantry moves to the captured piece.
- The captured piece is dragged to a side docking or storage area.
- The gantry returns to the attacking piece’s source square.
- The attacking piece is moved to the destination.
- The virtual board is updated.
The software uses counters and occupancy flags to determine where captured pawns, rooks, knights, bishops, queens, and kings should be parked. This makes captures a significant mechanical and software problem. The side area must have enough room, the software must know which spaces are occupied, and the gantry must travel beyond the normal 8×8 playing area.
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Special moves add further complications. Castling moves two pieces. En passant removes a piece from a square different from the destination. Promotion requires the board to receive or substitute a replacement piece. These cases need explicit mechanical handling rather than assuming that every chess move is a simple source-to-destination transfer.
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The published design specifies an enclosure measuring 600 × 470 × 115 mm, with a 340 × 340 mm chess field and pieces with a stated base diameter of 21 mm. These are values for this build, not universal requirements for robotic chessboards.
Controllers and computing
- Infineon KIT XMC47 RELAX LITE V1 — one
- Infineon KIT XMC1300 IFX9201 stepper shield or evaluation boards — two
- Raspberry Pi 3 Model B — one
Sensors and actuators
- Infineon TLE4964-3M Hall sensor boards — four
- Stepper motors — two
- Grove electromagnet — one
- Bench power supply — one
Lighting and fabrication
- WS2812B addressable RGB LED strip
- Wood panels
- Steel linear axes or rods
- Timing belts
- 3D-printed enclosure, gantry, trolley, joints, labels, chessboard, and pieces
The stated electrical values are 12 V at 0.7 A for the stepper motors and 9 V at 0.5 A for the electromagnet. Do not treat those figures as generic substitution instructions. Motor-driver compatibility, current limits, heat, torque margin, wiring, and power-supply headroom must be checked for the exact parts used.
The source does not provide a complete project cost. The final price depends on whether the builder already owns a Raspberry Pi, printer, tools, bench supply, fasteners, wiring, and fabrication materials, as well as regional availability and shipping.
Homing and calibration with Hall sensors
Stepper motors can move accurately without encoders, but the controller can lose its positional certainty after missed steps, a jam, or a power interruption. This project uses Hall-effect switches at the X and Y limits, with magnets mounted on moving components. When a magnet reaches a sensor, the controller can identify a known edge position and stop the axis.
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- Stop sending movement commands.
- Inspect the gantry for a jam, loose belt, displaced piece, or obstruction.
- Re-home both axes using the Hall sensors.
- Restore the software’s assumed gantry position.
- Check the physical board against the virtual board before resuming.
These sensors primarily establish gantry limits and home positions. They are not equivalent to scanning every chess square. The documentation does not establish independent detection of whether every piece is physically present where the software expects it to be.
LED feedback beneath the board
The project places addressable RGB LEDs beneath an acrylic layer. The published implementation uses a strip containing 390 individually addressable LEDs. Four LEDs are assigned to each of the 64 squares, using 256 LEDs for the square mapping and leaving the remainder for expansion or testing.
The code shown by the project includes:
#include <Adafruit_NeoPixel.h>
#define PIN 7
#define NUMPIXELS 390
Adafruit_NeoPixel pixels(NUMPIXELS, PIN, NEO_GRB + NEO_KHZ800);The mapping uses different green brightness levels to distinguish light and dark squares, while move-related squares can be highlighted. The exact pin, LED count, wiring, library behavior, voltage, and power requirements must be checked against the actual strip and controller. The published values are implementation details, not a universal WS2812B wiring recipe.
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Software flow for a normal move
The project uses Python chess software and Stockfish. A typical turn works approximately like this:
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- Read the player’s move.
- Validate it against the software board.
- Move the gantry to the source square.
- Drag the piece to the destination.
- Update the virtual board.
- Check the game state.
- Send the updated position to Stockfish.
- Receive the computer’s move.
- Execute the computer’s move with the same gantry.
- Update the virtual board again.
The project refers to helper functions including motor_to_src(move), src_to_dest(move), calculate_squares(move), and execute_move(move, turn). A move such as e2e4 contains a source square and destination square, which can be converted into two-dimensional board coordinates.
The project’s example input a2a3 is coordinate-style, also commonly called UCI-style notation. It means move the piece from a2 to a3. That is different from Standard Algebraic Notation, where the same pawn move would normally be written as a3.
This distinction deserves attention before attempting to run the code. The published implementation appears to mix coordinate-style move strings with a call named push_san(). Python chess APIs distinguish SAN and UCI moves, so a builder should inspect the exact downloaded code and installed library API rather than copy this part blindly. The correction may involve using a UCI-compatible push method or converting the move to SAN first, but it should be tested against the project’s actual code and package version.
I²C communication between the Pi and controller
The Raspberry Pi acts as the I²C master and the XMC4700 receives commands. The published Python code includes:
from smbus import SMBus
addr = 0x08
bus = SMBus(1)The Pi sends movement values as bytes and waits while the controller reports that the motors are busy. That busy-state protocol prevents a new movement command from arriving while the previous move is still executing.
Common failure points include:
- I²C not enabled on the Raspberry Pi
- Using the wrong bus number
- An address mismatch between firmware and Python
- No shared ground
- Logic-voltage incompatibility
- Broken wiring or unsuitable pull-ups
- Sending a command before the previous one has finished
- Incorrect handling of negative coordinate deltas or byte ranges
If communication fails, first verify the physical wiring and shared ground, then confirm the bus and address, and finally inspect how signed movement values are encoded. A command protocol needs a defined range and representation; a negative X or Y movement cannot safely be treated as an arbitrary unsigned byte.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Installing the published software
The following is the setup described by the project, not a guarantee that the commands remain unchanged on every Raspberry Pi operating system or in September 2026. Package versions, Python support, compiler behavior, and Stockfish’s repository can change.
python3 -m venv <environment-name>
source <environment-name>/bin/activate
pip install chess
pip install stockfish
pip install smbusThe virtual environment isolates Python dependencies from the rest of the operating system. The stockfish Python package is a wrapper or interface; it is not the chess engine executable itself.
The project’s documented Stockfish build is:
git clone https://github.com/official-stockfish/Stockfish.git
cd Stockfish/src
make -j profile-buildCloning the repository does not by itself produce a runnable engine. The build depends on the operating system, compiler, processor architecture, and current repository state. After building, the program must be configured with the actual path to the compiled Stockfish binary. The official source is available at github.com/official-stockfish/Stockfish.
The documented launch command is:
python wizard_chess.pyThe filename and working directory must match the downloaded project files. Before building, record the Raspberry Pi model, operating system, architecture, Python version, package versions, and any exact installation error. The 2025 commands should be treated as the author’s published setup, not as freshly verified 2026 instructions.
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What can go wrong
The gantry loses position
Stop movement, inspect for mechanical interference, re-home both axes, and reconcile the physical board with the software board. Continuing without restoring both coordinate systems can make every later move incorrect.
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A piece does not move
Check the magnet’s position and polarity, the gap between the electromagnet and piece, acrylic or board thickness, surface friction, piece weight, belt tension, motor current, driver settings, and electromagnet activation time. Also check whether a nearby piece is being pulled unintentionally.
The board and software disagree
A displaced piece, failed capture, dropped piece, or power loss can leave the physical and virtual boards out of sync. Because the available documentation does not establish full-board piece sensing, recovery may require a manual inspection and a software reset or corrected starting position.
Python packages fail
Check whether the package supports the selected Python version and processor architecture. Some packages are pure Python; others require native compilation or system libraries. Avoid assuming that a command from the 2025 project will work unchanged on a current Raspberry Pi OS installation.
What this project does not establish
- It is not a finished commercial chess product or plug-and-play kit.
- It does not establish automatic recognition of arbitrary physical human moves.
- It does not document online-game synchronization, Bluetooth, Wi-Fi pairing, or a mobile app.
- It does not publish verified move time, positioning accuracy, noise, reliability, maximum piece weight, or electromagnet temperature.
- It does not provide a complete current build cost.
- It should not be described as using “AI” merely because it uses Stockfish. Stockfish is a chess engine; the project’s documentation does not make that label necessary.
Likewise, the availability of instructions does not mean the build is easy. Squaring the gantry, aligning rods, tuning belts, matching magnetic force to friction, handling captures, and recovering from faults are the central challenges.
Should you build it?
This project makes sense for a reader who wants to learn how mechanics, embedded electronics, chess software, and computer control fit together. It is particularly suitable for experienced Raspberry Pi makers, robotics hobbyists, students of mechatronics, and chess enthusiasts who value the construction process as much as the finished spectacle.
It is a poor fit for someone who wants to open a box and immediately play, or for anyone expecting a guaranteed commercial alternative to a smart chessboard. A commercial electronic board is more appropriate for convenience and support. A camera-based system avoids magnetizing every piece but introduces computer-vision calibration, lighting, and recognition problems. A simple XY demonstration can show motion with less software and fewer edge cases, but it will not necessarily support complete chess play or captures.
Using a newer Raspberry Pi or different controller may improve availability, but it also changes the project. GPIO behavior, operating-system assumptions, wiring, drivers, and software compatibility all need to be retested. Similarly, a generic Hall sensor, electromagnet, motor, LED strip, or stepper driver is not automatically a drop-in substitute.
Final assessment
The magic of this automated chessboard is not that it contains a mysterious chess-playing mechanism. Its achievement is systems integration: a Python chess state, a Stockfish opponent, I²C messaging, a real-time microcontroller, stepper motors, a magnetized piece set, homing sensors, LEDs, and a recovery strategy all have to agree about the same game.
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That makes Chess Comes Alive: The Magic of Automation an impressive maker project and a useful teaching example. It also makes clear why the build should be approached as a robotics prototype, not as a ready-made game product. The published files provide a starting point, but successful reproduction still depends on careful fabrication, electrical verification, software compatibility checks, calibration, and patience when the physical board disagrees with the virtual one.
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