YOPO/flightlib/configs/traj_opt.yaml

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# Maximum Speed: applicable when test speed not much higher than training
vel_max: 6.0 # the given weights perform smoothly at speeds between 0 - 6 m/s
# segment_time = 2 * radio / vel_max
# IMPORTANT TRAINING PARAM: weight of penalties (6m/s)
ws: 0.00004 # smoothness cost (reduce ws when increase vel_max in training)
wc: 0.001 # collision cost
wg: 0.0002 # goal cost
wl: 0.00 # trajectory length cost
#ws: 0.00004
#wc: 0.001
#wl: 0.02
#wg: 0.0001
# trajectory and primitive parameters
horizon_num: 5 # grids num in horizon
vertical_num: 3 # grids num in vertical
horizon_camera_fov: 90.0 # horizon camera fov
vertical_camera_fov: 60.0 # vertical camera fov
horizon_anchor_fov: 30 # horizon fov of each gird
vertical_anchor_fov: 30 # vertical fov of each grid
goal_length: 10 # used for standardization of goal penalties (should >= 2 * radio_range)
radio_range: 4.0 # planning horizon: 2 * radio_range
vel_fov: 90.0 # not use currently
radio_num: 1 # 1 just ok (deprecated)
vel_num: 1 # 1 just ok (deprecated)
vel_prefile: 0.0 # 0 just ok (deprecated)
# For data efficiency, we randomly sample multiple vel and acc for each depth image with the following the distribution
# values at normalized speed (actual speed can be denormalized by multiplying v_multiple)
# 单位数据倍数: v_multiple = 0.5 * v_max = radio / time
# v数据的均值 v_mean = v_multiple * v_mean_unit
# v数据的方差 v_var = v_multiple^2 * v_var_unit
# a数据的均值 v_mean = v_multiple^2 * a_mean_unit
# a数据的方差 v_var = v_multiple^4 * a_var_unit
vx_mean_unit: 1.5 # vel_x: skewed distribution
vy_mean_unit: 0.0
vz_mean_unit: 0.0
vx_var_unit: 0.15
vy_var_unit: 0.45
vz_var_unit: 0.1
ax_mean_unit: 0.0
ay_mean_unit: 0.0
az_mean_unit: 0.0
ax_var_unit: 0.0278
ay_var_unit: 0.05
az_var_unit: 0.0278
# collision penalties
alpha: 10.0
d0: 1.2
r: 0.6
# vel penalties (deprecated)
alphav: 2.0
v0: 3.5
rv: 1.5
# acc penalties (deprecated)
alphaa: 2.0
a0: 3.5
ra: 1.5
# vel and acc weight (deprecated)
wv: 0.0
wa: 0.0