notebook/Rover2DOdom.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9795d87-1a39-4ace-9c34-89029f9ed5da",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from cyecca import lie\n",
    "import casadi as ca\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pathlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94608fc-b59d-45c8-9205-335d4a6e113c",
   "metadata": {
    "tags": []
   },
   "source": [
    "$\\dot{x} = u \\cos(\\theta)$\n",
    "\n",
    "$\\dot{y} = u \\sin(\\theta)$\n",
    "\n",
    "$\\dot{\\theta} = \\dfrac{u}{l} \\tan{\\delta}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "69251611-e912-4ed9-9492-29b1db9dfac9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# define symbols\n",
    "x = ca.SX.sym(\"x\")  # x position in world frame of rear axle (north)\n",
    "y = ca.SX.sym(\"y\")  # y position in world frame of rear axle (east)\n",
    "theta = ca.SX.sym(\"theta\")  # angular heading in world frame (rotation about down)\n",
    "u = ca.SX.sym(\"u\")  # forward velocity, along body x\n",
    "delta = ca.SX.sym(\"delta\")  # steering angle, positive rotation about body down\n",
    "l = ca.SX.sym(\"l\")  # length between rear wheel and front wheels\n",
    "dt = ca.SX.sym(\"dt\")  # time stemp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7139804c-e00c-4baa-bf6f-5e79a2a3f0d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SX(@1=cos(theta), @2=sin(theta), \n",
       "[[@1, (-@2), x], \n",
       " [@2, @1, y], \n",
       " [00, 00, 1]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = lie.SE2\n",
    "X = G.elem(ca.vertcat(x, y, theta))\n",
    "X.to_Matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94a13b65-74e6-4e97-9e0a-97965c3658d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'casadi.casadi.SX'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SX(@1=((u/l)*tan(delta)), \n",
       "[[00, (-@1), u], \n",
       " [@1, 00, 0], \n",
       " [00, 00, 00]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "omega = u / l * ca.tan(delta)\n",
    "v = G.algebra.elem(ca.vertcat(u, 0, omega))\n",
    "v.to_Matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b09a06d7-506c-4482-ac0b-c39a55c0566f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SE2LieGroup: SX(@1=0, [@1, @1, @1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.algebra.elem(ca.vertcat(0, 0, 0)).exp(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "280b0de8-c744-4a03-abf3-539ca388bb55",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SE2LieGroup: SX(@1=((u/l)*tan(delta)), @2=1e-07, @3=(fabs(@1)<@2), @4=1, @5=(fabs(@1)<@2), [(((@3?((@4+(-0.166667*sq(@1)))+(0.00833333*sq(sq(@1)))):0)+((!@3)?(sin(@1)/@1):0))*u), (((@5?(((0.5*@1)+(-0.0416667*(@1*sq(@1))))+(0.00138889*(@1*sq(sq(@1))))):0)+((!@5)?((@4-cos(@1))/@1):0))*u), @1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.exp(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb6f8e91-b89b-4ac7-9830-15867a25dd0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SE2LieGroup: SX(@1=cos(theta), @2=((u/l)*tan(delta)), @3=1e-07, @4=(fabs(@2)<@3), @5=1, @6=(((@4?((@5+(-0.166667*sq(@2)))+(0.00833333*sq(sq(@2)))):0)+((!@4)?(sin(@2)/@2):0))*u), @7=sin(theta), @8=(fabs(@2)<@3), @9=(((@8?(((0.5*@2)+(-0.0416667*(@2*sq(@2))))+(0.00138889*(@2*sq(sq(@2))))):0)+((!@8)?((@5-cos(@2))/@2):0))*u), [(((@1*@6)-(@7*@9))+x), (((@7*@6)+(@1*@9))+y), (theta+@2)])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X + v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad7785d6-7a4d-4f06-bdcf-e36e55204443",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X1 = X + v\n",
    "f_predict = ca.Function(\n",
    "    \"predict\", [X.param, delta, u, l], [X1.param], [\"x0\", \"delta\", \"u\", \"l\"], [\"x1\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d8d0e23-7116-4bd6-ad4a-5b0400d0a69c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def simulate():\n",
    "    x = np.array([0, 0, 0])\n",
    "    l = 0.225\n",
    "    dt = 0.01\n",
    "    tf = 10\n",
    "    data = {\n",
    "        \"t\": [],\n",
    "        \"x\": [],\n",
    "        \"u\": [],\n",
    "        \"delta\": [],\n",
    "    }\n",
    "    for t in np.arange(0, tf, dt):\n",
    "        data[\"t\"].append(np.array(t))\n",
    "        data[\"x\"].append(x)\n",
    "        u = np.sin(2 * np.pi * 2 * 0.4 * t) * dt\n",
    "        delta = np.cos(2 * np.pi * 0.1 * t)\n",
    "        x = np.array(f_predict(x, delta, u, l)).reshape(-1)\n",
    "\n",
    "    for k in data.keys():\n",
    "        data[k] = np.array(data[k])\n",
    "\n",
    "    return data\n",
    "\n",
    "\n",
    "data = simulate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b29759e-28d2-4740-8580-9e0d71f0bc27",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb3242fa740>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"x\"][:, 0], data[\"x\"][:, 1], \"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5faaae9e-d1ff-447c-a345-072378035fbb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "eqs = {\"rover2d\": {\"predict\": f_predict}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5cffcc51-e998-4cd8-9545-bef29cba3012",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "\n",
    "def generate_code(eqs: dict, dest_dir: str, **kwargs):\n",
    "    dest_dir = Path(dest_dir)\n",
    "    p = {\n",
    "        \"verbose\": True,\n",
    "        \"mex\": False,\n",
    "        \"cpp\": False,\n",
    "        \"main\": False,\n",
    "        \"with_header\": True,\n",
    "        \"with_mem\": False,\n",
    "        \"with_export\": False,\n",
    "        \"with_import\": False,\n",
    "        \"include_math\": True,\n",
    "        \"avoid_stack\": True,\n",
    "    }\n",
    "    for k, v in kwargs.items():\n",
    "        assert k in p.keys()\n",
    "        p[k] = v\n",
    "\n",
    "    for name, eqs in eqs.items():\n",
    "        filename = \"{:s}.c\".format(name)\n",
    "        gen = ca.CodeGenerator(filename, p)\n",
    "        for f_name in eqs:\n",
    "            gen.add(eqs[f_name])\n",
    "\n",
    "        dest_dir = pathlib.Path(dest_dir)\n",
    "        dest_dir.mkdir(exist_ok=True)\n",
    "\n",
    "        gen.generate(str(dest_dir) + pathlib.os.sep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "09c53189-674c-4cd2-9a52-4598c3c122fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "generate_code(eqs, \"gen\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}