mpc_python_learn/notebooks/2.2-MPC-v2-car-reference-fr...

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.integrate import odeint\n",
"from scipy.interpolate import interp1d\n",
"import cvxpy as cp\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.style.use(\"ggplot\")\n",
"\n",
"import time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## V2 Use Dynamics w.r.t Robot Frame\n",
"\n",
"explanation here...\n",
"\n",
"benefits:\n",
"* slightly faster mpc convergence time -> more variables are 0, this helps the computation?\n",
"* no issues when vehicle heading ~PI in world"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"Control problem statement.\n",
"\"\"\"\n",
"\n",
"N = 4 # number of state variables\n",
"M = 2 # number of control variables\n",
"T = 20 # Prediction Horizon\n",
"DT = 0.2 # discretization step\n",
"\n",
"\n",
"def get_linear_model(x_bar, u_bar):\n",
" \"\"\"\n",
" Computes the LTI approximated state space model x' = Ax + Bu + C\n",
" \"\"\"\n",
"\n",
" L = 0.3 # vehicle wheelbase\n",
"\n",
" x = x_bar[0]\n",
" y = x_bar[1]\n",
" v = x_bar[2]\n",
" theta = x_bar[3]\n",
"\n",
" a = u_bar[0]\n",
" delta = u_bar[1]\n",
"\n",
" A = np.zeros((N, N))\n",
" A[0, 2] = np.cos(theta)\n",
" A[0, 3] = -v * np.sin(theta)\n",
" A[1, 2] = np.sin(theta)\n",
" A[1, 3] = v * np.cos(theta)\n",
" A[3, 2] = v * np.tan(delta) / L\n",
" A_lin = np.eye(N) + DT * A\n",
"\n",
" B = np.zeros((N, M))\n",
" B[2, 0] = 1\n",
" B[3, 1] = v / (L * np.cos(delta) ** 2)\n",
" B_lin = DT * B\n",
"\n",
" f_xu = np.array(\n",
" [v * np.cos(theta), v * np.sin(theta), a, v * np.tan(delta) / L]\n",
" ).reshape(N, 1)\n",
" C_lin = DT * (\n",
" f_xu - np.dot(A, x_bar.reshape(N, 1)) - np.dot(B, u_bar.reshape(M, 1))\n",
" )\n",
"\n",
" return np.round(A_lin, 4), np.round(B_lin, 4), np.round(C_lin, 4)\n",
"\n",
"\n",
"\"\"\"\n",
"the ODE is used to update the simulation given the mpc results\n",
"I use this insted of using the LTI twice\n",
"\"\"\"\n",
"\n",
"\n",
"def kinematics_model(x, t, u):\n",
" \"\"\"\n",
" Returns the set of ODE of the vehicle model.\n",
" \"\"\"\n",
"\n",
" L = 0.3 # vehicle wheelbase\n",
" dxdt = x[2] * np.cos(x[3])\n",
" dydt = x[2] * np.sin(x[3])\n",
" dvdt = u[0]\n",
" dthetadt = x[2] * np.tan(u[1]) / L\n",
"\n",
" dqdt = [dxdt, dydt, dvdt, dthetadt]\n",
"\n",
" return dqdt\n",
"\n",
"\n",
"def predict(x0, u):\n",
" \"\"\" \"\"\"\n",
"\n",
" x_ = np.zeros((N, T + 1))\n",
"\n",
" x_[:, 0] = x0\n",
"\n",
" # solve ODE\n",
" for t in range(1, T + 1):\n",
"\n",
" tspan = [0, DT]\n",
" x_next = odeint(kinematics_model, x0, tspan, args=(u[:, t - 1],))\n",
"\n",
" x0 = x_next[1]\n",
" x_[:, t] = x_next[1]\n",
"\n",
" return x_\n",
"\n",
"\n",
"\"\"\"\n",
"MODIFIED TO INCLUDE FRAME TRANSFORMATION\n",
"\"\"\"\n",
"\n",
"\n",
"def compute_path_from_wp(start_xp, start_yp, step=0.1):\n",
" \"\"\"\n",
" Computes a reference path given a set of waypoints\n",
" \"\"\"\n",
"\n",
" final_xp = []\n",
" final_yp = []\n",
" delta = step # [m]\n",
"\n",
" for idx in range(len(start_xp) - 1):\n",
" section_len = np.sum(\n",
" np.sqrt(\n",
" np.power(np.diff(start_xp[idx : idx + 2]), 2)\n",
" + np.power(np.diff(start_yp[idx : idx + 2]), 2)\n",
" )\n",
" )\n",
"\n",
" interp_range = np.linspace(0, 1, np.floor(section_len / delta).astype(int))\n",
"\n",
" fx = interp1d(np.linspace(0, 1, 2), start_xp[idx : idx + 2], kind=1)\n",
" fy = interp1d(np.linspace(0, 1, 2), start_yp[idx : idx + 2], kind=1)\n",
"\n",
" # watch out to duplicate points!\n",
" final_xp = np.append(final_xp, fx(interp_range)[1:])\n",
" final_yp = np.append(final_yp, fy(interp_range)[1:])\n",
"\n",
" dx = np.append(0, np.diff(final_xp))\n",
" dy = np.append(0, np.diff(final_yp))\n",
" theta = np.arctan2(dy, dx)\n",
"\n",
" return np.vstack((final_xp, final_yp, theta))\n",
"\n",
"\n",
"def get_nn_idx(state, path):\n",
" \"\"\"\n",
" Computes the index of the waypoint closest to vehicle\n",
" \"\"\"\n",
"\n",
" dx = state[0] - path[0, :]\n",
" dy = state[1] - path[1, :]\n",
" dist = np.hypot(dx, dy)\n",
" nn_idx = np.argmin(dist)\n",
"\n",
" try:\n",
" v = [\n",
" path[0, nn_idx + 1] - path[0, nn_idx],\n",
" path[1, nn_idx + 1] - path[1, nn_idx],\n",
" ]\n",
" v /= np.linalg.norm(v)\n",
"\n",
" d = [path[0, nn_idx] - state[0], path[1, nn_idx] - state[1]]\n",
"\n",
" if np.dot(d, v) > 0:\n",
" target_idx = nn_idx\n",
" else:\n",
" target_idx = nn_idx + 1\n",
"\n",
" except IndexError as e:\n",
" target_idx = nn_idx\n",
"\n",
" return target_idx\n",
"\n",
"\n",
"def normalize_angle(angle):\n",
" \"\"\"\n",
" Normalize an angle to [-pi, pi]\n",
" \"\"\"\n",
" while angle > np.pi:\n",
" angle -= 2.0 * np.pi\n",
"\n",
" while angle < -np.pi:\n",
" angle += 2.0 * np.pi\n",
"\n",
" return angle\n",
"\n",
"\n",
"def get_ref_trajectory(state, path, target_v):\n",
" \"\"\"\n",
" modified reference in robot frame\n",
" \"\"\"\n",
" xref = np.zeros((N, T + 1))\n",
" dref = np.zeros((1, T + 1))\n",
"\n",
" # sp = np.ones((1,T +1))*target_v #speed profile\n",
"\n",
" ncourse = path.shape[1]\n",
"\n",
" ind = get_nn_idx(state, path)\n",
" dx = path[0, ind] - state[0]\n",
" dy = path[1, ind] - state[1]\n",
"\n",
" xref[0, 0] = dx * np.cos(-state[3]) - dy * np.sin(-state[3]) # X\n",
" xref[1, 0] = dy * np.cos(-state[3]) + dx * np.sin(-state[3]) # Y\n",
" xref[2, 0] = target_v # V\n",
" xref[3, 0] = normalize_angle(path[2, ind] - state[3]) # Theta\n",
" dref[0, 0] = 0.0 # steer operational point should be 0\n",
"\n",
" dl = 0.05 # Waypoints spacing [m]\n",
" travel = 0.0\n",
"\n",
" for i in range(T + 1):\n",
" travel += abs(target_v) * DT # current V or target V?\n",
" dind = int(round(travel / dl))\n",
"\n",
" if (ind + dind) < ncourse:\n",
" dx = path[0, ind + dind] - state[0]\n",
" dy = path[1, ind + dind] - state[1]\n",
"\n",
" xref[0, i] = dx * np.cos(-state[3]) - dy * np.sin(-state[3])\n",
" xref[1, i] = dy * np.cos(-state[3]) + dx * np.sin(-state[3])\n",
" xref[2, i] = target_v # sp[ind + dind]\n",
" xref[3, i] = normalize_angle(path[2, ind + dind] - state[3])\n",
" dref[0, i] = 0.0\n",
" else:\n",
" dx = path[0, ncourse - 1] - state[0]\n",
" dy = path[1, ncourse - 1] - state[1]\n",
"\n",
" xref[0, i] = dx * np.cos(-state[3]) - dy * np.sin(-state[3])\n",
" xref[1, i] = dy * np.cos(-state[3]) + dx * np.sin(-state[3])\n",
" xref[2, i] = 0.0 # stop? #sp[ncourse - 1]\n",
" xref[3, i] = normalize_angle(path[2, ncourse - 1] - state[3])\n",
" dref[0, i] = 0.0\n",
"\n",
" return xref, dref"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CVXPY Optimization Time: Avrg: 0.1655s Max: 0.1952s Min: 0.1495s\n"
]
}
],
"source": [
"track = compute_path_from_wp(\n",
" [0, 3, 4, 6, 10, 12, 14, 6, 1, 0], [0, 0, 2, 4, 3, 3, -2, -6, -2, -2], 0.05\n",
")\n",
"\n",
"# track = compute_path_from_wp([0,10,10,0],\n",
"# [0,0,1,1],0.05)\n",
"\n",
"sim_duration = 200 # time steps\n",
"opt_time = []\n",
"\n",
"x_sim = np.zeros((N, sim_duration))\n",
"u_sim = np.zeros((M, sim_duration - 1))\n",
"\n",
"MAX_SPEED = 1.5 # m/s\n",
"MAX_ACC = 1.0 # m/ss\n",
"MAX_D_ACC = 1.0 # m/sss\n",
"MAX_STEER = np.radians(30) # rad\n",
"MAX_D_STEER = np.radians(30) # rad/s\n",
"\n",
"REF_VEL = 1.0 # m/s\n",
"\n",
"# Starting Condition\n",
"x0 = np.zeros(N)\n",
"x0[0] = 0 # x\n",
"x0[1] = -0.25 # y\n",
"x0[2] = 0.0 # v\n",
"x0[3] = np.radians(-0) # yaw\n",
"\n",
"# starting guess\n",
"u_bar = np.zeros((M, T))\n",
"u_bar[0, :] = MAX_ACC / 2 # a\n",
"u_bar[1, :] = 0.0 # delta\n",
"\n",
"for sim_time in range(sim_duration - 1):\n",
"\n",
" iter_start = time.time()\n",
"\n",
" # dynamics starting state w.r.t. robot are always null except vel\n",
" x_bar = np.zeros((N, T + 1))\n",
" x_bar[2, 0] = x_sim[2, sim_time]\n",
"\n",
" # prediction for linearization of costrains\n",
" for t in range(1, T + 1):\n",
" xt = x_bar[:, t - 1].reshape(N, 1)\n",
" ut = u_bar[:, t - 1].reshape(M, 1)\n",
" A, B, C = get_linear_model(xt, ut)\n",
" xt_plus_one = np.squeeze(np.dot(A, xt) + np.dot(B, ut) + C)\n",
" x_bar[:, t] = xt_plus_one\n",
"\n",
" # CVXPY Linear MPC problem statement\n",
" x = cp.Variable((N, T + 1))\n",
" u = cp.Variable((M, T))\n",
" cost = 0\n",
" constr = []\n",
"\n",
" # Cost Matrices\n",
" Q = np.diag([20, 20, 10, 20]) # state error cost\n",
" Qf = np.diag([30, 30, 30, 30]) # state final error cost\n",
" R = np.diag([10, 10]) # input cost\n",
" R_ = np.diag([10, 10]) # input rate of change cost\n",
"\n",
" # Get Reference_traj\n",
" # dont use x0 in this case\n",
" x_ref, d_ref = get_ref_trajectory(x_sim[:, sim_time], track, REF_VEL)\n",
"\n",
" # Prediction Horizon\n",
" for t in range(T):\n",
"\n",
" # Tracking Error\n",
" cost += cp.quad_form(x[:, t] - x_ref[:, t], Q)\n",
"\n",
" # Actuation effort\n",
" cost += cp.quad_form(u[:, t], R)\n",
"\n",
" # Actuation rate of change\n",
" if t < (T - 1):\n",
" cost += cp.quad_form(u[:, t + 1] - u[:, t], R_)\n",
" constr += [\n",
" cp.abs(u[0, t + 1] - u[0, t]) / DT <= MAX_D_ACC\n",
" ] # max acc rate of change\n",
" constr += [\n",
" cp.abs(u[1, t + 1] - u[1, t]) / DT <= MAX_D_STEER\n",
" ] # max steer rate of change\n",
"\n",
" # Kinrmatics Constrains (Linearized model)\n",
" A, B, C = get_linear_model(x_bar[:, t], u_bar[:, t])\n",
" constr += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t] + C.flatten()]\n",
"\n",
" # Final Point tracking\n",
" cost += cp.quad_form(x[:, T] - x_ref[:, T], Qf)\n",
"\n",
" # sums problem objectives and concatenates constraints.\n",
" constr += [x[:, 0] == x_bar[:, 0]] # starting condition\n",
" constr += [x[2, :] <= MAX_SPEED] # max speed\n",
" constr += [x[2, :] >= 0.0] # min_speed (not really needed)\n",
" constr += [cp.abs(u[0, :]) <= MAX_ACC] # max acc\n",
" constr += [cp.abs(u[1, :]) <= MAX_STEER] # max steer\n",
"\n",
" # Solve\n",
" prob = cp.Problem(cp.Minimize(cost), constr)\n",
" solution = prob.solve(solver=cp.OSQP, verbose=False)\n",
"\n",
" # retrieved optimized U and assign to u_bar to linearize in next step\n",
" u_bar = np.vstack(\n",
" (np.array(u.value[0, :]).flatten(), (np.array(u.value[1, :]).flatten()))\n",
" )\n",
"\n",
" u_sim[:, sim_time] = u_bar[:, 0]\n",
"\n",
" # Measure elpased time to get results from cvxpy\n",
" opt_time.append(time.time() - iter_start)\n",
"\n",
" # move simulation to t+1\n",
" tspan = [0, DT]\n",
" x_sim[:, sim_time + 1] = odeint(\n",
" kinematics_model, x_sim[:, sim_time], tspan, args=(u_bar[:, 0],)\n",
" )[1]\n",
"\n",
"print(\n",
" \"CVXPY Optimization Time: Avrg: {:.4f}s Max: {:.4f}s Min: {:.4f}s\".format(\n",
" np.mean(opt_time), np.max(opt_time), np.min(opt_time)\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1080x720 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot trajectory\n",
"grid = plt.GridSpec(4, 5)\n",
"\n",
"plt.figure(figsize=(15, 10))\n",
"\n",
"plt.subplot(grid[0:4, 0:4])\n",
"plt.plot(track[0, :], track[1, :], \"b+\")\n",
"plt.plot(x_sim[0, :], x_sim[1, :])\n",
"plt.axis(\"equal\")\n",
"plt.ylabel(\"y\")\n",
"plt.xlabel(\"x\")\n",
"\n",
"plt.subplot(grid[0, 4])\n",
"plt.plot(u_sim[0, :])\n",
"plt.ylabel(\"a(t) [m/ss]\")\n",
"\n",
"plt.subplot(grid[1, 4])\n",
"plt.plot(x_sim[2, :])\n",
"plt.ylabel(\"v(t) [m/s]\")\n",
"\n",
"plt.subplot(grid[2, 4])\n",
"plt.plot(np.degrees(u_sim[1, :]))\n",
"plt.ylabel(\"delta(t) [rad]\")\n",
"\n",
"plt.subplot(grid[3, 4])\n",
"plt.plot(x_sim[3, :])\n",
"plt.ylabel(\"theta(t) [rad]\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:.conda-jupyter] *",
"language": "python",
"name": "conda-env-.conda-jupyter-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}