Port lateral planning to ACADOS (#22080)
* lateral acados * looks good! * add another solve, needed for init somehow * use copy * init correctly * cleanup sconstruct * Update files_common * update cpu usage * reset when invalid * fix cpu usage * cost_set doesnt leak * new ref * non leaky reset Co-authored-by: Adeeb Shihadeh <adeebshihadeh@gmail.com> Co-authored-by: Comma Device <device@comma.ai>pull/22222/head
HaraldSchafer
4 years ago
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GitHub
parent
32db9184d4
commit
7081ab4fb7
11 changed files with 301 additions and 125 deletions
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acados_ocp_lat.json |
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c_generated_code/ |
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Import('env', 'arch') |
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gen = "c_generated_code" |
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casadi_model = [ |
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f'{gen}/lat_model/lat_expl_ode_fun.c', |
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f'{gen}/lat_model/lat_expl_vde_forw.c', |
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] |
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casadi_cost_y = [ |
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f'{gen}/lat_cost/lat_cost_y_fun.c', |
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f'{gen}/lat_cost/lat_cost_y_fun_jac_ut_xt.c', |
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f'{gen}/lat_cost/lat_cost_y_hess.c', |
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] |
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casadi_cost_e = [ |
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f'{gen}/lat_cost/lat_cost_y_e_fun.c', |
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f'{gen}/lat_cost/lat_cost_y_e_fun_jac_ut_xt.c', |
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f'{gen}/lat_cost/lat_cost_y_e_hess.c', |
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] |
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casadi_cost_0 = [ |
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f'{gen}/lat_cost/lat_cost_y_0_fun.c', |
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f'{gen}/lat_cost/lat_cost_y_0_fun_jac_ut_xt.c', |
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f'{gen}/lat_cost/lat_cost_y_0_hess.c', |
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] |
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build_files = [f'{gen}/acados_solver_lat.c'] + casadi_model + casadi_cost_y + casadi_cost_e + casadi_cost_0 |
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# extra generated files used to trigger a rebuild |
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generated_files = [ |
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f'{gen}/Makefile', |
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f'{gen}/main_lat.c', |
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f'{gen}/acados_solver_lat.h', |
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f'{gen}/lat_model/lat_expl_vde_adj.c', |
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f'{gen}/lat_model/lat_model.h', |
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f'{gen}/lat_cost/lat_cost_y_fun.h', |
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f'{gen}/lat_cost/lat_cost_y_e_fun.h', |
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f'{gen}/lat_cost/lat_cost_y_0_fun.h', |
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] + build_files |
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lenv = env.Clone() |
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lenv.Clean(generated_files, Dir(gen)) |
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lenv.Command(generated_files, |
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["lat_mpc.py"], |
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f"cd {Dir('.').abspath} && python lat_mpc.py") |
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lenv["CFLAGS"].append("-DACADOS_WITH_QPOASES") |
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lenv["CXXFLAGS"].append("-DACADOS_WITH_QPOASES") |
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lenv["CCFLAGS"].append("-Wno-unused") |
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lenv["LINKFLAGS"].append("-Wl,--disable-new-dtags") |
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lenv.SharedLibrary(f"{gen}/acados_ocp_solver_lat", |
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build_files, |
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LIBS=['m', 'acados', 'hpipm', 'blasfeo', 'qpOASES_e']) |
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#!/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 = 10 |
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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 = np.column_stack([y_pts, heading_pts*(v_ego+5.0), np.zeros(N+1)]) |
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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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0ada8ee251248f041ed8092abd9b5368f57bbf6e |
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a256ade033538eeead4f34ad7b12be25237ae443 |
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