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Refactor dynamics parameters to use numba typed dict #168

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91 changes: 10 additions & 81 deletions gym/f110_gym/envs/base_classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@
import numpy as np
from numba import njit

from f110_gym.envs.dynamic_models import vehicle_dynamics_st, pid
from f110_gym.envs.dynamic_models import vehicle_dynamics_st, pid, create_numba_params
from f110_gym.envs.laser_models import ScanSimulator2D, check_ttc_jit, ray_cast
from f110_gym.envs.collision_models import get_vertices, collision_multiple

Expand Down Expand Up @@ -94,6 +94,9 @@ def __init__(self, params, seed, is_ego=False, time_step=0.01, num_beams=1080, f
if self.integrator is Integrator.RK4:
warnings.warn(f"Chosen integrator is RK4. This is different from previous versions of the gym.")

# create numba typed dict for dynamics functions
self.numba_params = create_numba_params(params)

# state is [x, y, steer_angle, vel, yaw_angle, yaw_rate, slip_angle]
self.state = np.zeros((7, ))

Expand Down Expand Up @@ -169,6 +172,7 @@ def update_params(self, params):
None
"""
self.params = params
self.numba_params = create_numba_params(params)

def set_map(self, map_path, map_ext):
"""
Expand Down Expand Up @@ -286,88 +290,28 @@ def update_pose(self, raw_steer, vel):
k1 = vehicle_dynamics_st(
self.state,
np.array([sv, accl]),
self.params['mu'],
self.params['C_Sf'],
self.params['C_Sr'],
self.params['lf'],
self.params['lr'],
self.params['h'],
self.params['m'],
self.params['I'],
self.params['s_min'],
self.params['s_max'],
self.params['sv_min'],
self.params['sv_max'],
self.params['v_switch'],
self.params['a_max'],
self.params['v_min'],
self.params['v_max'])
self.numba_params)

k2_state = self.state + self.time_step*(k1/2)

k2 = vehicle_dynamics_st(
k2_state,
np.array([sv, accl]),
self.params['mu'],
self.params['C_Sf'],
self.params['C_Sr'],
self.params['lf'],
self.params['lr'],
self.params['h'],
self.params['m'],
self.params['I'],
self.params['s_min'],
self.params['s_max'],
self.params['sv_min'],
self.params['sv_max'],
self.params['v_switch'],
self.params['a_max'],
self.params['v_min'],
self.params['v_max'])
self.numba_params)

k3_state = self.state + self.time_step*(k2/2)

k3 = vehicle_dynamics_st(
k3_state,
np.array([sv, accl]),
self.params['mu'],
self.params['C_Sf'],
self.params['C_Sr'],
self.params['lf'],
self.params['lr'],
self.params['h'],
self.params['m'],
self.params['I'],
self.params['s_min'],
self.params['s_max'],
self.params['sv_min'],
self.params['sv_max'],
self.params['v_switch'],
self.params['a_max'],
self.params['v_min'],
self.params['v_max'])
self.numba_params)

k4_state = self.state + self.time_step*k3

k4 = vehicle_dynamics_st(
k4_state,
np.array([sv, accl]),
self.params['mu'],
self.params['C_Sf'],
self.params['C_Sr'],
self.params['lf'],
self.params['lr'],
self.params['h'],
self.params['m'],
self.params['I'],
self.params['s_min'],
self.params['s_max'],
self.params['sv_min'],
self.params['sv_max'],
self.params['v_switch'],
self.params['a_max'],
self.params['v_min'],
self.params['v_max'])
self.numba_params)

# dynamics integration
self.state = self.state + self.time_step*(1/6)*(k1 + 2*k2 + 2*k3 + k4)
Expand All @@ -376,22 +320,7 @@ def update_pose(self, raw_steer, vel):
f = vehicle_dynamics_st(
self.state,
np.array([sv, accl]),
self.params['mu'],
self.params['C_Sf'],
self.params['C_Sr'],
self.params['lf'],
self.params['lr'],
self.params['h'],
self.params['m'],
self.params['I'],
self.params['s_min'],
self.params['s_max'],
self.params['sv_min'],
self.params['sv_max'],
self.params['v_switch'],
self.params['a_max'],
self.params['v_min'],
self.params['v_max'])
self.numba_params)
self.state = self.state + self.time_step * f

else:
Expand Down
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