#!/usr/bin/env python3
import os
import time
import numpy as np
from casadi import SX, vertcat, sin, cos
# WARNING: imports outside of constants will not trigger a rebuild
from openpilot.selfdrive.modeld.constants import T_IDXS
if __name__ == '__main__': # generating code
from openpilot.third_party.acados.acados_template import AcadosModel, AcadosOcp, AcadosOcpSolver
else:
from openpilot.selfdrive.controls.lib.lateral_mpc_lib.c_generated_code.acados_ocp_solver_pyx import AcadosOcpSolverCython
LAT_MPC_DIR = os.path.dirname(os.path.abspath(__file__))
EXPORT_DIR = os.path.join(LAT_MPC_DIR, "c_generated_code")
JSON_FILE = os.path.join(LAT_MPC_DIR, "acados_ocp_lat.json")
X_DIM = 4
P_DIM = 2
COST_E_DIM = 3
COST_DIM = COST_E_DIM + 2
SPEED_OFFSET = 10.0
MODEL_NAME = 'lat'
ACADOS_SOLVER_TYPE = 'SQP_RTI'
N = 32
def gen_lat_model():
model = AcadosModel()
model.name = MODEL_NAME
# set up states & controls
x_ego = SX.sym('x_ego')
y_ego = SX.sym('y_ego')
psi_ego = SX.sym('psi_ego')
psi_rate_ego = SX.sym('psi_rate_ego')
model.x = vertcat(x_ego, y_ego, psi_ego, psi_rate_ego)
# parameters
v_ego = SX.sym('v_ego')
rotation_radius = SX.sym('rotation_radius')
model.p = vertcat(v_ego, rotation_radius)
# controls
psi_accel_ego = SX.sym('psi_accel_ego')
model.u = vertcat(psi_accel_ego)
# xdot
x_ego_dot = SX.sym('x_ego_dot')
y_ego_dot = SX.sym('y_ego_dot')
psi_ego_dot = SX.sym('psi_ego_dot')
psi_rate_ego_dot = SX.sym('psi_rate_ego_dot')
model.xdot = vertcat(x_ego_dot, y_ego_dot, psi_ego_dot, psi_rate_ego_dot)
# dynamics model
f_expl = vertcat(v_ego * cos(psi_ego) - rotation_radius * sin(psi_ego) * psi_rate_ego,
v_ego * sin(psi_ego) + rotation_radius * cos(psi_ego) * psi_rate_ego,
psi_rate_ego,
psi_accel_ego)
model.f_impl_expr = model.xdot - f_expl
model.f_expl_expr = f_expl
return model
def gen_lat_ocp():
ocp = AcadosOcp()
ocp.model = gen_lat_model()
Tf = np.array(T_IDXS)[N]
# set dimensions
ocp.dims.N = N
# set cost module
ocp.cost.cost_type = 'NONLINEAR_LS'
ocp.cost.cost_type_e = 'NONLINEAR_LS'
Q = np.diag(np.zeros(COST_E_DIM))
QR = np.diag(np.zeros(COST_DIM))
ocp.cost.W = QR
ocp.cost.W_e = Q
y_ego, psi_ego, psi_rate_ego = ocp.model.x[1], ocp.model.x[2], ocp.model.x[3]
psi_rate_ego_dot = ocp.model.u[0]
v_ego = ocp.model.p[0]
ocp.parameter_values = np.zeros((P_DIM, ))
ocp.cost.yref = np.zeros((COST_DIM, ))
ocp.cost.yref_e = np.zeros((COST_E_DIM, ))
# Add offset to smooth out low speed control
# TODO unclear if this right solution long term
v_ego_offset = v_ego + SPEED_OFFSET
# TODO there are two costs on psi_rate_ego_dot, one
# is correlated to jerk the other to steering wheel movement
# the steering wheel movement cost is added to prevent excessive
# wheel movements
ocp.model.cost_y_expr = vertcat(y_ego,
v_ego_offset * psi_ego,
v_ego_offset * psi_rate_ego,
v_ego_offset * psi_rate_ego_dot,
psi_rate_ego_dot / (v_ego + 0.1))
ocp.model.cost_y_expr_e = vertcat(y_ego,
v_ego_offset * psi_ego,
v_ego_offset * psi_rate_ego)
# set constraints
ocp.constraints.constr_type = 'BGH'
ocp.constraints.idxbx = np.array([2,3])
ocp.constraints.ubx = np.array([np.radians(90), np.radians(50)])
ocp.constraints.lbx = np.array([-np.radians(90), -np.radians(50)])
x0 = np.zeros((X_DIM,))
ocp.constraints.x0 = x0
ocp.solver_options.qp_solver = 'PARTIAL_CONDENSING_HPIPM'
ocp.solver_options.hessian_approx = 'GAUSS_NEWTON'
ocp.solver_options.integrator_type = 'ERK'
ocp.solver_options.nlp_solver_type = ACADOS_SOLVER_TYPE
ocp.solver_options.qp_solver_iter_max = 1
ocp.solver_options.qp_solver_cond_N = 1
# set prediction horizon
ocp.solver_options.tf = Tf
ocp.solver_options.shooting_nodes = np.array(T_IDXS)[:N+1]
ocp.code_export_directory = EXPORT_DIR
return ocp
class LateralMpc():
def __init__(self, x0=None):
if x0 is None:
x0 = np.zeros(X_DIM)
self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N)
self.reset(x0)
def reset(self, x0=None):
if x0 is None:
x0 = np.zeros(X_DIM)
self.x_sol = np.zeros((N+1, X_DIM))
self.u_sol = np.zeros((N, 1))
self.yref = np.zeros((N+1, COST_DIM))
for i in range(N):
self.solver.cost_set(i, "yref", self.yref[i])
self.solver.cost_set(N, "yref", self.yref[N][:COST_E_DIM])
# Somehow needed for stable init
for i in range(N+1):
self.solver.set(i, 'x', np.zeros(X_DIM))
self.solver.set(i, 'p', np.zeros(P_DIM))
self.solver.constraints_set(0, "lbx", x0)
self.solver.constraints_set(0, "ubx", x0)
self.solver.solve()
self.solution_status = 0
self.solve_time = 0.0
self.cost = 0
def set_weights(self, path_weight, heading_weight,
lat_accel_weight, lat_jerk_weight,
steering_rate_weight):
W = np.asfortranarray(np.diag([path_weight, heading_weight,
lat_accel_weight, lat_jerk_weight,
steering_rate_weight]))
for i in range(N):
self.solver.cost_set(i, 'W', W)
self.solver.cost_set(N, 'W', W[:COST_E_DIM,:COST_E_DIM])
def run(self, x0, p, y_pts, heading_pts, yaw_rate_pts):
x0_cp = np.copy(x0)
p_cp = np.copy(p)
self.solver.constraints_set(0, "lbx", x0_cp)
self.solver.constraints_set(0, "ubx", x0_cp)
self.yref[:,0] = y_pts
v_ego = p_cp[0, 0]
# rotation_radius = p_cp[1]
self.yref[:,1] = heading_pts * (v_ego + SPEED_OFFSET)
self.yref[:,2] = yaw_rate_pts * (v_ego + SPEED_OFFSET)
for i in range(N):
self.solver.cost_set(i, "yref", self.yref[i])
self.solver.set(i, "p", p_cp[i])
self.solver.set(N, "p", p_cp[N])
self.solver.cost_set(N, "yref", self.yref[N][:COST_E_DIM])
t = time.monotonic()
self.solution_status = self.solver.solve()
self.solve_time = time.monotonic() - t
for i in range(N+1):
self.x_sol[i] = self.solver.get(i, 'x')
for i in range(N):
self.u_sol[i] = self.solver.get(i, 'u')
self.cost = self.solver.get_cost()
if __name__ == "__main__":
ocp = gen_lat_ocp()
AcadosOcpSolver.generate(ocp, json_file=JSON_FILE)
# AcadosOcpSolver.build(ocp.code_export_directory, with_cython=True)