RobotAccel_V3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "from sympy import *\n",
    "import scipy\n",
    "from scipy.special import lambertw as W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Per Simulation\n",
    "X = [0.8, 1.2, 3] # Robot Travel Distances\n",
    "\n",
    "s = 5 # Minimum Gear Ratio (5)\n",
    "b = 25 # Maximum Gear Ratio (25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Robot Constants\n",
    "r = 0.051 # Wheel Radius (meters)\n",
    "m = 13 # Robot Mass (Kg)\n",
    "I = 0.0015 # Rotational Inertia of drive system (Kg m^2)\n",
    "E = 1 # Efficiency of wheel (Tank Drive = 1, Mecanum Drive = 0.7)\n",
    "Vo = 12.5 # Battery Voltage (volts)\n",
    "Rb = 0.15 # Battery internal resistance (ohms)\n",
    "\n",
    "# Motor Constants\n",
    "Vm = 12 # Nominal Motor Voltage\n",
    "Ts = 2.1 / 20 # Stall torque (Nm)\n",
    "wo = 5 * 20 # No load speed (Radians / Sec)\n",
    "Io = 0.4 # No load current (Amps)\n",
    "Is = 8.5 # Stall current (Amps)\n",
    "\n",
    "nm = 4 # Number of motors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Motor Constants\n",
    "Km = Ts / Is\n",
    "Ke = Vo / wo\n",
    "Tf = Ts * Io / Is * nm\n",
    "R = Rb + Vm / Is / nm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equation constants\n",
    "c1 = (Vo*Km/R - Tf) / (r**2*m + I*E)\n",
    "c2 = Ke*Km / (2*math.pi*r*R * (r**2 * m + I*E))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timeAtRatio(g, x):\n",
    "    u = float(exp(-g**3*x*c2**2/c1) * g*c2/c1)\n",
    "    time = (W(u) / c2*g**2 + c2*g*x/c1)\n",
    "    return float(time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loopTimeAtRatio(g, targetDist):\n",
    "    x, v, t = 0. , 0., 0.\n",
    "    dt = 0.001\n",
    "    while x < targetDist:\n",
    "        a = g*c1 - g**2*v*c2\n",
    "        v = v + a*dt\n",
    "        x = x + v*dt\n",
    "        t = t + dt\n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "900"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_g = np.arange(1, 10, 0.01)\n",
    "y_t = np.zeros(((10-1)*100))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for j, x in enumerate(X):\n",
    "    \n",
    "    y_t = [loopTimeAtRatio(g, x) for g in x_g]\n",
    "        \n",
    "    ax.plot(x_g, y_t, label = str(X[j]) + \" m, Ratio: \" + str(round(x_g[np.argmin(y_t)], 3)))\n",
    "\n",
    "ax.set_xlabel(\"Gear Reduction\", fontsize=15)\n",
    "ax.set_ylabel(\"Time\", fontsize=18)\n",
    "\n",
    "ax.legend(loc=\"best\")\n",
    "\n",
    "ax.margins(0.1)\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_g = np.arange(1, 10, 0.01)\n",
    "y_t = np.zeros(((10-1)*100))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for j, x in enumerate(X):\n",
    "    for i, g in np.ndenumerate(x_g):\n",
    "        y_t[i] = loopTimeAtRatio(g, x)\n",
    "        \n",
    "    ax.plot(x_g, y_t, label = str(X[j]) + \" m, Ratio: \" + str(round(x_g[np.argmin(y_t)], 3)))\n",
    "\n",
    "ax.set_xlabel(\"Gear Reduction\", fontsize=15)\n",
    "ax.set_ylabel(\"Time\", fontsize=18)\n",
    "\n",
    "ax.legend(loc=\"best\")\n",
    "\n",
    "ax.margins(0.1)\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.230000000000004"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_g[np.argmin(y_t)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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