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