Getting Started

Installation

To install CRCBS.jl, start up Julia and type the following code-snipped into the REPL.

julia> ] # enter package mode by typing "]"

(@v1.4) pkg> add https://github.com/kylejbrown17/CRCBS.jl.git

Example

To construct and solve a predefined multi agent path finding (MAPF) problem:

julia> using CRCBS
julia> solver = CBSSolver() # initialize a solverCBSSolver{AStar{Float64}, Float64}
  low_level_planner: AStar{Float64}
  logger: SolverLogger{Float64}
julia> prob = init_mapf_1() # initialize the problemMAPF{CRCBS.CBSEnv.LowLevelEnv{FullCostModel{typeof(sum), Float64, TravelTime}, PerfectHeuristic{CRCBS.var"#129#130"{Dict{Int64, Vector{Int64}}}}, GridFactoryEnvironment{MetaGraphs.MetaGraph{Int64, Float64}}, DiscreteConstraintTable}, CRCBS.CBSEnv.State, CRCBS.CBSEnv.State}(CRCBS.CBSEnv.LowLevelEnv{FullCostModel{typeof(sum), Float64, TravelTime}, PerfectHeuristic{CRCBS.var"#129#130"{Dict{Int64, Vector{Int64}}}}, GridFactoryEnvironment{MetaGraphs.MetaGraph{Int64, Float64}}, DiscreteConstraintTable}
  graph: GridFactoryEnvironment{MetaGraphs.MetaGraph{Int64, Float64}}
  goal: CRCBS.CBSEnv.State
  agent_idx: Int64 -1
  constraints: DiscreteConstraintTable
  cost_model: FullCostModel{typeof(sum), Float64, TravelTime}
  heuristic: PerfectHeuristic{CRCBS.var"#129#130"{Dict{Int64, Vector{Int64}}}}
, CRCBS.CBSEnv.State[CRCBS.CBSEnv.State(1, 0), CRCBS.CBSEnv.State(4, 0)], CRCBS.CBSEnv.State[CRCBS.CBSEnv.State(13, 0), CRCBS.CBSEnv.State(16, 0)])
julia> solution, cost = solve!(solver,prob) # solve it(LowLevelSolution:
   T:  0   1   2   3
   1: [1   5   9   13  ]
   2: [4   8   12  16  ]
, 6.0)
julia> optimal_status(solver) # check if the problem was solved optimallytrue

If you want to build your own MAPF problem from scratch:

# copied from CRCBS/scripts/mapf_demo.jl
using CRCBS

## set up the environment
vtx_grid = initialize_dense_vtx_grid(4,4) # 4 x 4 grid world
#  1   2   3   4
#  5   6   7   8
#  9  10  11  12
# 13  14  15  16
env = construct_factory_env_from_vtx_grid(vtx_grid)

## Define the initial conditions of the robots
starts = [1,4]
goals = [13,16]

cost_model = FullCostModel(maximum,TravelTime())

prob = init_mapf_problem(env,starts,goals,cost_model)

## define solver
solver = CBSSolver() # PIBTPlanner()

# solve the problem
solution, cost = solve!(solver,prob)
# check if the problem was solved to optimality
@show feasible_status(solver)
@show optimal_status(solver)