SegbotDemo-Alonzo Experimentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "middle-acoustic",
   "metadata": {},
   "source": [
    "# Differential drive robot (i.e., \"segbot\") in artificial gravity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "working-minority",
   "metadata": {},
   "source": [
    "Import modules and configure the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dimensional-contamination",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This module is part of the python standard library\n",
    "import time\n",
    "\n",
    "# These modules are part of other existing libraries\n",
    "import sympy as sym\n",
    "import numpy as np\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "import asyncio\n",
    "\n",
    "# This is my own script (it is an interface to the pybullet simulator)\n",
    "import ae353_segbot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b723c8",
   "metadata": {},
   "source": [
    "Create an instance of the `Simulator` class, which is an interface to the [pybullet](http://pybullet.org) simulation engine.\n",
    "\n",
    "There are several optional arguments:\n",
    "\n",
    "* `display` (`True` or `False`) is whether or not to show the simulation window;\n",
    "* `bumpy` (`True` or `False`) is whether or not the station is \"bumpy\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "359ab3ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can open the visualizer by visiting the following URL:\n",
      "http://127.0.0.1:7000/static/\n"
     ]
    }
   ],
   "source": [
    "simulator = ae353_segbot.Simulator(\n",
    "    display=True,\n",
    "    bumpy=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc2c9d86",
   "metadata": {},
   "source": [
    "Create a slider to allow steering the robot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd0421db",
   "metadata": {},
   "outputs": [],
   "source": [
    "slider = widgets.FloatSlider(\n",
    "    value=0.,\n",
    "    min=-3.,\n",
    "    max=3.,\n",
    "    step=0.01,\n",
    "    description='Centerline:',\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.2f',\n",
    "    layout=widgets.Layout(width='75%', border='solid 2px', margin='10px')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a0f224",
   "metadata": {},
   "source": [
    "Choose a camera view from the following options:\n",
    "* `camera_sideview()`\n",
    "* `camera_backview()`\n",
    "* `camera_wideview()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96875836",
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator.camera_backview()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75738c69",
   "metadata": {},
   "source": [
    "Define a controller that maps sensor measurements to actuator commands. By default, it chooses to apply zero torque to both the right and left wheels. Modify the `run` function to make the controller behave differently. This function will be called 100 times per second (i.e., at 100 Hz) by the simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "34d837e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dimensions of chassis\n",
    "dx = 0.4\n",
    "dy = 0.6\n",
    "dz = 0.8\n",
    "\n",
    "# Distance between axle and COM of chassis\n",
    "h = 0.3\n",
    "\n",
    "# Half-distance between wheels\n",
    "a = 0.7 / 2\n",
    "\n",
    "# Mass of chassis\n",
    "mb = 12.\n",
    "\n",
    "# MOI of chassis\n",
    "Jbx = (mb / 12) * (dy**2 + dz**2)\n",
    "Jby = (mb / 12) * (dx**2 + dz**2)\n",
    "Jbz = (mb / 12) * (dx**2 + dy**2)\n",
    "\n",
    "# Radius of each wheel\n",
    "r = 0.325\n",
    "\n",
    "# Width of each wheel\n",
    "hw = 0.075\n",
    "\n",
    "# Mass of each wheel\n",
    "mw = 1.2\n",
    "\n",
    "# MOI of each wheel\n",
    "Jw = (mw / 2) * r**2\n",
    "Jwt = (mw / 12) * (3 * r**2 + hw**2)\n",
    "\n",
    "# Total mass\n",
    "m = mb + 2 * mw\n",
    "\n",
    "# Total MOI\n",
    "Jx = Jbx + 2 * Jwt\n",
    "Jy = Jby\n",
    "Jz = Jbz + 2 * Jwt\n",
    "\n",
    "# Station parameters\n",
    "station_velocity = -0.5 # <-- FIXME (change the velocity to change gravity)\n",
    "station_radius = 20.    # <-- radius in meters of inside surface of station\n",
    "\n",
    "# Acceleration of artifical gravity\n",
    "g = station_velocity**2 * station_radius\n",
    "\n",
    "# Dimensions\n",
    "h = sym.nsimplify(h)\n",
    "a = sym.nsimplify(a)\n",
    "r = sym.nsimplify(r)\n",
    "\n",
    "# Masses\n",
    "mb = sym.nsimplify(mb)\n",
    "mw = sym.nsimplify(mw)\n",
    "m = sym.nsimplify(m)\n",
    "\n",
    "# MOIs\n",
    "Jx = sym.nsimplify(Jx)\n",
    "Jy = sym.nsimplify(Jy)\n",
    "Jz = sym.nsimplify(Jz)\n",
    "Jw = sym.nsimplify(Jw)\n",
    "Jwt = sym.nsimplify(Jwt)\n",
    "\n",
    "# Gravity\n",
    "g = sym.nsimplify(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f2f9a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "(e_l,\n",
    " e_h,\n",
    " v,\n",
    " w,\n",
    " theta,\n",
    " thetadot,\n",
    " tau_R,\n",
    " tau_L) = sym.symbols('e_l, e_h, v, w, theta, thetadot, tau_R, tau_L', real=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c7e75677",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = sym.Matrix([[m + 2 * Jw / r**2, 0, mb * h * sym.cos(theta)],\n",
    "                [0, (Jx + mb * h**2) * sym.sin(theta)**2 + Jz * sym.cos(theta)**2 + (2 * Jw * a**2 / r**2) + 2 * mw * a**2, 0],\n",
    "                [mb * h * sym.cos(theta), 0, Jy * mb * h**2]])\n",
    "N = sym.Matrix([[mb * h * (w**2 + thetadot**2) * sym.sin(theta)],\n",
    "                [-2 * (Jx - Jz + m * h**2) * sym.cos(theta) * sym.sin(theta) * w * thetadot - mb * h * sym.sin(theta) * v * w],\n",
    "                [(Jx - Jz + mb * h**2) * sym.cos(theta) * sym.sin(theta) * w**2 + mb * g * h * sym.sin(theta)]])\n",
    "R = sym.Matrix([[1 / r, 1 / r],\n",
    "                [-a / r, a / r],\n",
    "                [-1, -1]])\n",
    "f = sym.simplify(M.inv() * (N + R * sym.Matrix([[tau_L], [tau_R]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e71647be",
   "metadata": {},
   "outputs": [],
   "source": [
    "omega_p = sym.symbols('omega_p')\n",
    "f = f.subs(thetadot, omega_p)\n",
    "equations_M = sym.Matrix([[v * sym.sin(e_h)],\n",
    "                [w],\n",
    "                [omega_p],\n",
    "                [f]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ec54cea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "we, omega_pe, e_he, tau_Re, tau_Le, theta_e, ve = 0., 0., 0., 0., 0., 0., 2.5\n",
    "\n",
    "\n",
    "x = sym.Matrix([e_l, e_h, v, w, theta, omega_p])\n",
    "u = sym.Matrix([tau_R, tau_L])\n",
    "\n",
    "dyn = equations_M.jacobian(x)\n",
    "controls = equations_M.jacobian(u)\n",
    "\n",
    "a = sym.lambdify((e_h, e_l, v, theta, w, tau_R, tau_L, omega_p), dyn)\n",
    "A = np.array(a(e_he, 0, ve, theta_e, we, tau_Re, tau_Le, omega_pe))\n",
    "\n",
    "b = sym.lambdify((e_h, e_l, v, theta, w, tau_R, tau_L, omega_p), controls)\n",
    "B = np.array(b(e_he, 0, ve, theta_e, we, tau_Re, tau_Le, omega_pe))\n",
    "\n",
    "W = np.block([B, A@B, A@A@B, A@A@A@B, A@A@A@A@B, A@A@A@A@A@B])\n",
    "print(np.linalg.matrix_rank(W))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "cc8b4139",
   "metadata": {},
   "outputs": [],
   "source": [
    "poles = [-2., -2.15, -2.3, -2.45, -2.6, -2.75]\n",
    "K_list = signal.place_poles(A, B, poles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "43a8d467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.75, -2.6 , -2.45, -2.3 , -2.15, -2.  ])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K_list.requested_poles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5758c798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.75      , -2.59999999, -2.45      , -2.3       , -2.14999994,\n",
       "       -2.        ])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K_list.computed_poles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "211fa708",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = K_list.gain_matrix\n",
    "v_ini = 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e8164ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Controller:\n",
    "    def __init__(self):\n",
    "        self.lateral_error = 0.\n",
    "        self.heading_error = 0.\n",
    "        self.forward_speed = v_ini\n",
    "        self.turning_rate = 0.\n",
    "        self.pitch_angle = 0.\n",
    "        self.pitch_rate = 0.\n",
    "    \n",
    "    def reset(self):\n",
    "        self.lateral_error = 0.\n",
    "        self.heading_error = 0.\n",
    "        self.forward_speed = v_ini\n",
    "        self.turning_rate = 0.\n",
    "        self.pitch_angle = 0.\n",
    "        self.pitch_rate = 0.\n",
    "    \n",
    "    def run(\n",
    "            self,\n",
    "            t,\n",
    "            lateral_error,\n",
    "            heading_error,\n",
    "            forward_speed,\n",
    "            turning_rate,\n",
    "            pitch_angle,\n",
    "            pitch_rate,\n",
    "        ):\n",
    "        \n",
    "        # FIXME: replace the following lines\n",
    "        x = np.array([[lateral_error - self.lateral_error], \n",
    "                      [heading_error - self.heading_error], \n",
    "                      [forward_speed - self.forward_speed],\n",
    "                      [turning_rate - self.turning_rate],\n",
    "                      [pitch_angle - self.pitch_angle],\n",
    "                      [pitch_rate - self.pitch_rate]])\n",
    "        \n",
    "        u = -K@x\n",
    "        right_wheel_torque = u[0,0]\n",
    "        left_wheel_torque = u[1,0]\n",
    "        \n",
    "        return right_wheel_torque, left_wheel_torque"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc096551",
   "metadata": {},
   "source": [
    "Create an instance of the controller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "93300860",
   "metadata": {},
   "outputs": [],
   "source": [
    "controller = Controller()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf56b66",
   "metadata": {},
   "source": [
    "Reset the simulator with given initial conditions. You may also change the angular velocity of the station if you like, but - beware! - doing so will change the amount of artificial gravity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "cf8a455d",
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator.reset(\n",
    "    initial_speed=v_ini,\n",
    "    initial_lateral_error=0.,\n",
    "    initial_heading_error=0.,\n",
    "    initial_pitch=0.,\n",
    "    station_velocity=-0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74613036",
   "metadata": {},
   "source": [
    "Reset the controller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "9a0fa5ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "controller.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f724eeda",
   "metadata": {},
   "source": [
    "Reset the slider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "19b47e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "slider.value = 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c326b31f",
   "metadata": {},
   "source": [
    "Run the simulator with steering. Note that this function runs **asynchronously** to allow user input. You **must** wait until the simulator finishes running before doing anything else in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1b037ae2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acebbb4d92c046a58f65d1f3e0238e1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=0.0, description='Centerline:', layout=Layout(border_bottom='solid 2px', border_left='solid …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated 1001 time steps in 18.4177 seconds (54.3498 time steps per second)\n"
     ]
    }
   ],
   "source": [
    "# Show the slider, to allow steering the robot\n",
    "display(slider)\n",
    "\n",
    "# Run the simulation asynchronously, to allow using the slider\n",
    "obj = asyncio.ensure_future(\n",
    "    simulator.run(\n",
    "        controller,           # <-- required (an instance of your Controller class)\n",
    "        max_time=10.0,         # <-- optional (how long you want to run the simulation in seconds)\n",
    "        data_filename=None,   # <-- optional (save data to this file, e.g., 'my_data.json')\n",
    "        video_filename=None,  # <-- optional (save video to this file, e.g., 'my_video.mov')\n",
    "        slider=slider,        # <-- optional (allow steering)\n",
    "        print_debug=True,     # <-- optional (say when the simulator has finished running)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a688b353",
   "metadata": {},
   "source": [
    "Get results of running the simulator with steering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "de960140",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You cannot get results until after the simulator is done running.\n"
     ]
    }
   ],
   "source": [
    "if obj.done():\n",
    "    data = obj.result()\n",
    "else:\n",
    "    print('You cannot get results until after the simulator is done running.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99afc2e9",
   "metadata": {},
   "source": [
    "Run the simulator with no steering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2828993d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = await simulator.run(\n",
    "    controller,           # <-- required (an instance of your Controller class)\n",
    "    max_time=10.0,         # <-- optional (how long you want to run the simulation in seconds)\n",
    "    data_filename=None,   # <-- optional (save data to this file, e.g., 'my_data.json')\n",
    "    video_filename=None,  # <-- optional (save video to this file, e.g., 'my_video.mov')\n",
    "    slider=None,          # <-- optional (allow no steering)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601a7926",
   "metadata": {},
   "source": [
    "Get, show, and save a snapshot of the simulation after the last time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0cff3241",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Get snapshot as height x width x 4 numpy array of RGBA values\n",
    "# rgba = simulator.snapshot()\n",
    "\n",
    "# # Display snapshot\n",
    "# plt.figure(figsize=(8, 8))\n",
    "# plt.imshow(rgba)\n",
    "\n",
    "# # Save snapshot\n",
    "# plt.imsave('my_snapshot.png', rgba)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c95e55",
   "metadata": {},
   "source": [
    "Plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0ff24bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with three subplots, all of which share the same x-axis\n",
    "fig, (ax_pos, ax_vel, ax_pitch, ax_torque) = plt.subplots(4, 1, figsize=(8, 8), sharex=True)\n",
    "\n",
    "# Plot lateral and heading error\n",
    "ax_pos.plot(\n",
    "    data['t'], data['lateral_error'],\n",
    "    label='lateral_error (m)', linewidth=4,\n",
    ")\n",
    "ax_pos.plot(\n",
    "    data['t'], data['heading_error'],\n",
    "    '--', label='heading_error (rad)', linewidth=4,\n",
    ")\n",
    "ax_pos.grid()\n",
    "ax_pos.legend(fontsize=16)\n",
    "ax_pos.tick_params(labelsize=14)\n",
    "\n",
    "# Plot forward speed and turning rate\n",
    "ax_vel.plot(\n",
    "    data['t'], data['forward_speed'],\n",
    "    label='forward speed (m/s)', linewidth=4,\n",
    ")\n",
    "ax_vel.plot(\n",
    "    data['t'], data['turning_rate'],\n",
    "    '--', label='turning rate (rad / s)', linewidth=4,\n",
    ")\n",
    "ax_vel.grid()\n",
    "ax_vel.legend(fontsize=16)\n",
    "ax_vel.tick_params(labelsize=14)\n",
    "\n",
    "# Plot pitch angle and pitch rate\n",
    "ax_pitch.plot(\n",
    "    data['t'], data['pitch_angle'],\n",
    "    label='pitch angle (rad)', linewidth=4,\n",
    ")\n",
    "ax_pitch.plot(\n",
    "    data['t'], data['pitch_rate'],\n",
    "    '--', label='pitch rate (rad)', linewidth=4,\n",
    ")\n",
    "ax_pitch.grid()\n",
    "ax_pitch.legend(fontsize=16)\n",
    "ax_pitch.tick_params(labelsize=14)\n",
    "\n",
    "# Plot torques\n",
    "ax_torque.plot(\n",
    "    data['t'], data['right_wheel_torque_command'],\n",
    "    label='right wheel torque command (N-m)', linewidth=4,\n",
    ")\n",
    "ax_torque.plot(\n",
    "    data['t'], data['right_wheel_torque'],\n",
    "    '--', label='right wheel torque (N-m)', linewidth=4,\n",
    ")\n",
    "ax_torque.plot(\n",
    "    data['t'], data['left_wheel_torque_command'],\n",
    "    label='left wheel torque command (N-m)', linewidth=4,\n",
    ")\n",
    "ax_torque.plot(\n",
    "    data['t'], data['left_wheel_torque'],\n",
    "    '--', label='left wheel torque (N-m)', linewidth=4,\n",
    ")\n",
    "ax_torque.plot(\n",
    "    data['t'], -np.ones_like(data['t']) * simulator.tau_max,\n",
    "    ':', linewidth=4, color='C4', zorder=0,\n",
    ")\n",
    "ax_torque.plot(\n",
    "    data['t'], np.ones_like(data['t']) * simulator.tau_max,\n",
    "    ':', linewidth=4, color='C4', zorder=0,\n",
    ")\n",
    "ax_torque.grid()\n",
    "ax_torque.legend(fontsize=16)\n",
    "ax_torque.tick_params(labelsize=14)\n",
    "ax_torque.set_ylim(-1.2 * simulator.tau_max, 1.2 * simulator.tau_max)\n",
    "\n",
    "# Set x-axis properties (only need to do this on the last\n",
    "# subplot since all subplots share the same x-axis)\n",
    "ax_torque.set_xlabel('time (s)', fontsize=20)\n",
    "ax_torque.set_xlim([data['t'][0], data['t'][-1]])\n",
    "\n",
    "# Make the arrangement of subplots look nice\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407559d4",
   "metadata": {},
   "source": [
    "Save the figure (without transparent background)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d3c1b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('my_figure.png', facecolor='white', transparent=False)"
   ]
  }
 ],
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