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159 lines
4.9 KiB
159 lines
4.9 KiB
#!/usr/bin/env python3
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import os
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import numpy as np
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from casadi import SX, vertcat, sin, cos
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from selfdrive.controls.lib.drive_helpers import LAT_MPC_N as N
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from selfdrive.controls.lib.drive_helpers import T_IDXS
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from pyextra.acados_template import AcadosModel, AcadosOcp, AcadosOcpSolver
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LAT_MPC_DIR = os.path.dirname(os.path.abspath(__file__))
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EXPORT_DIR = os.path.join(LAT_MPC_DIR, "c_generated_code")
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JSON_FILE = "acados_ocp_lat.json"
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def gen_lat_model():
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model = AcadosModel()
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model.name = 'lat'
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# set up states & controls
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x_ego = SX.sym('x_ego')
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y_ego = SX.sym('y_ego')
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psi_ego = SX.sym('psi_ego')
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curv_ego = SX.sym('curv_ego')
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v_ego = SX.sym('v_ego')
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rotation_radius = SX.sym('rotation_radius')
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model.x = vertcat(x_ego, y_ego, psi_ego, curv_ego, v_ego, rotation_radius)
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# controls
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curv_rate = SX.sym('curv_rate')
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model.u = vertcat(curv_rate)
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# xdot
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x_ego_dot = SX.sym('x_ego_dot')
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y_ego_dot = SX.sym('y_ego_dot')
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psi_ego_dot = SX.sym('psi_ego_dot')
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curv_ego_dot = SX.sym('curv_ego_dot')
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v_ego_dot = SX.sym('v_ego_dot')
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rotation_radius_dot = SX.sym('rotation_radius_dot')
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model.xdot = vertcat(x_ego_dot, y_ego_dot, psi_ego_dot, curv_ego_dot,
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v_ego_dot, rotation_radius_dot)
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# dynamics model
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f_expl = vertcat(v_ego * cos(psi_ego) - rotation_radius * sin(psi_ego) * (v_ego * curv_ego),
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v_ego * sin(psi_ego) + rotation_radius * cos(psi_ego) * (v_ego * curv_ego),
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v_ego * curv_ego,
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curv_rate,
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0.0,
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0.0)
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model.f_impl_expr = model.xdot - f_expl
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model.f_expl_expr = f_expl
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return model
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def gen_lat_mpc_solver():
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ocp = AcadosOcp()
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ocp.model = gen_lat_model()
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N = 16
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Tf = np.array(T_IDXS)[N]
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# set dimensions
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ocp.dims.N = N
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# set cost module
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ocp.cost.cost_type = 'NONLINEAR_LS'
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ocp.cost.cost_type_e = 'NONLINEAR_LS'
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Q = np.diag([0.0, 0.0])
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QR = np.diag([0.0, 0.0, 0.0])
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ocp.cost.W = QR
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ocp.cost.W_e = Q
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y_ego, psi_ego = ocp.model.x[1], ocp.model.x[2]
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curv_rate = ocp.model.u[0]
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v_ego = ocp.model.x[4]
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ocp.cost.yref = np.zeros((3, ))
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ocp.cost.yref_e = np.zeros((2, ))
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# TODO hacky weights to keep behavior the same
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ocp.model.cost_y_expr = vertcat(y_ego,
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((v_ego +5.0) * psi_ego),
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((v_ego +5.0) * 4 * curv_rate))
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ocp.model.cost_y_expr_e = vertcat(y_ego,
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((v_ego +5.0) * psi_ego))
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# set constraints
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ocp.constraints.constr_type = 'BGH'
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ocp.constraints.idxbx = np.array([2,3])
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ocp.constraints.ubx = np.array([np.radians(90), np.radians(50)])
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ocp.constraints.lbx = np.array([-np.radians(90), -np.radians(50)])
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x0 = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
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ocp.constraints.x0 = x0
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ocp.solver_options.qp_solver = 'PARTIAL_CONDENSING_HPIPM'
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ocp.solver_options.hessian_approx = 'GAUSS_NEWTON'
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ocp.solver_options.integrator_type = 'ERK'
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ocp.solver_options.nlp_solver_type = 'SQP_RTI'
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ocp.solver_options.qp_solver_iter_max = 1
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# set prediction horizon
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ocp.solver_options.tf = Tf
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ocp.solver_options.shooting_nodes = np.array(T_IDXS)[:N+1]
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ocp.code_export_directory = EXPORT_DIR
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return ocp
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class LateralMpc():
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def __init__(self, x0=np.zeros(6)):
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self.solver = AcadosOcpSolver('lat', N, EXPORT_DIR)
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self.reset(x0)
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def reset(self, x0=np.zeros(6)):
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self.x_sol = np.zeros((N+1, 4))
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self.u_sol = np.zeros((N))
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self.yref = np.zeros((N+1, 3))
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self.solver.cost_set_slice(0, N, "yref", self.yref[:N])
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self.solver.cost_set(N, "yref", self.yref[N][:2])
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W = np.eye(3)
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self.Ws = np.tile(W[None], reps=(N,1,1))
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# Somehow needed for stable init
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for i in range(N+1):
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self.solver.set(i, 'x', np.zeros(6))
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self.solver.constraints_set(0, "lbx", x0)
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self.solver.constraints_set(0, "ubx", x0)
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self.solver.solve()
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self.solution_status = 0
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self.cost = 0
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def set_weights(self, path_weight, heading_weight, steer_rate_weight):
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self.Ws[:,0,0] = path_weight
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self.Ws[:,1,1] = heading_weight
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self.Ws[:,2,2] = steer_rate_weight
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self.solver.cost_set_slice(0, N, 'W', self.Ws, api='old')
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#TODO hacky weights to keep behavior the same
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self.solver.cost_set(N, 'W', (3/20.)*self.Ws[0,:2,:2])
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def run(self, x0, v_ego, car_rotation_radius, y_pts, heading_pts):
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x0_cp = np.copy(x0)
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self.solver.constraints_set(0, "lbx", x0_cp)
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self.solver.constraints_set(0, "ubx", x0_cp)
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self.yref[:,0] = y_pts
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self.yref[:,1] = heading_pts*(v_ego+5.0)
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self.solver.cost_set_slice(0, N, "yref", self.yref[:N])
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self.solver.cost_set(N, "yref", self.yref[N][:2])
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self.solution_status = self.solver.solve()
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self.x_sol = self.solver.get_slice(0, N+1, 'x')
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self.u_sol = self.solver.get_slice(0, N, 'u')
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self.cost = self.solver.get_cost()
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if __name__ == "__main__":
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ocp = gen_lat_mpc_solver()
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AcadosOcpSolver.generate(ocp, json_file=JSON_FILE, build=False)
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