sample_planning 0.0.4

Sample Based Planner
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[package]
name = "sample_planning"
version = "0.0.4"
authors = ["Yuan Liu <yuan6liu@gmail.com>"]
edition = "2018"
description = "Sample Based Planner"
repository = "https://github.com/clearlycloudy/sample_planning/"
keywords = [ "planning", "sample", "rrt", "sst" ]
license = "MIT"
documentation = "https://docs.rs/crate/sample_planning/"
readme = "README.md"

[dependencies]
mazth = "0.5.0"
zpatial = "0.3.3"
chrono = "0.4"
log = "0.4.6"
rand = "0.6.5"
pretty_env_logger = "0.3"
kiss3d = "0.20.1"
nalgebra = "0.18.0"
ncollide3d = "0.19.2"
clap = "2.32"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
rayon = "1.0.3"
# crossbeam = "0.7"


[features]
motion_primitives = []
runge_kutta = [] #defaults to Euler stepping for propagation
disable_pruning = []
mo_prim_debug = []
mo_prim_thresh_low = []
mo_prim_thresh_high = []
nn_sample_log = [] #use proportional to log(# nodes) for nearest neighbour query, defaults to sqrt(# nodes)
nn_naive = [] #use linear nearest neighbour query
disable_witness_disturbance = [] #default is active for witness discovery rate of < 10% of recent iterations
state_propagate_sample = [] #frontier node selection, 10 samples, 50%
batch_propagate_sample = [] #control propagation selection, 10 samples, 50%, applicable if nn_naive is NOT enabled
path_optimize = [] #use importance sampling for optimization
gen_obs_3d = [] #use for random box obstacle generation
airplane = [] # temporary workaround; use this for Dubins airplane model

[[bin]]
name = "planner"
path = "src/main.rs"

[[bin]]
name = "gen_obs"
path = "gen_obs/main.rs"

[[bin]]
name = "map2poly"
path = "map2poly/main.rs"