Kewb

This is a Rubik's cube solver that uses Kociemba's two-phase algorithm and can also be used as a library for manipulating the 3x3 Rubik's cube. However, please note that this is still a work in progress and the implementation is not yet efficient. The solver does not currently use symmetric reductions, pre-moves, or multi-threaded search.

Usage

CLI

By default, there is no timeout, which means the solver will return the first solution it finds. However, by adding a timeout, the solver will continue searching until the timeout has elapsed and return the shortest solution found or nothing. Specifying a lower search depth can result in better solution quality (around 21 to 23 moves), but it can also make the search slower if the depth is less than 20 moves. Nevertheless, it has been proven that all cases can be solved in 20 moves or fewer.

kewb help
kewb solve --scramble "R U R' U'" --max 22 --timeout 1 --details
kewb solve -s "R U R' U'" -m 22 -t 1 -d
# default values: max = 23, timeout = none, details = false
kewb scramble
kewb scramble -n 5
# default values: number = 1
kewb solve --facelet DRBLUURLDRBLRRBFLFFUBFFDRUDURRBDFBBULDUDLUDLBUFFDBFLRL

Library

use std::io;
use kewb::{
    solve, Solver, State, FaceCube,
    fs::read_table,
    utils::scramble_from_string,
};

fn main() -> Result<(), io::Error> {
    let scramble = scramble_from_string("R U R' U'").unwrap();
    let state = State::from(&scramble);
    let solution = solve(state, 23, None).unwrap();

    println!("{}", solution);

    let faces = "DRBLUURLDRBLRRBFLFFUBFFDRUDURRBDFBBULDUDLUDLBUFFDBFLRL";
    let face_cube = FaceCube::try_from(faces).unwrap();
    let state = State::try_from(&face_cube).unwrap();
    let (move_table, pruning_table) = read_table()?;
    let mut solver = Solver::new(&move_table, &pruning_table, 23, None);
    let solution = solver.solve(state).unwrap();

    println!("{}", solution);

    Ok(())
}

Build

NB: You must have the rust toolchain installed

Clone the repository and run:

cargo build --release # build in target/build/
# or
cargo install --path . # install to ~/.cargo/bin/

Testing

You can run the tests by running:

cargo test

Todo

• Add Documentation
• More CLI features
• Algorithm optimization

References

• Two phase algorithm overview: http://kociemba.org/cube.htm

• Two phase algorithm implementation in python: https://qiita.com/7y2n/items/55abb991a45ade2afa28