kinematics/inverse_kinematics_2d_jax.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverse Kinematics (2D)\n",
    "with https://github.com/google/jax\n",
    "\n",
    "*note*\n",
    "* running on GPU with colab: https://colab.research.google.com/drive/1guZnXsFOEVLb7IOXVzUgRc8pbq3Z50Qf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "from matplotlib import pylab as plt\n",
    "from numpy import random, pi\n",
    "from __future__ import division\n",
    "from IPython import display\n",
    "from ipywidgets import interact, fixed\n",
    "\n",
    "import jax.numpy as np\n",
    "from jax import grad, jit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coordinate Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans(x, y, a):\n",
    "    '''create a 2D transformation'''\n",
    "    s = np.sin(a)\n",
    "    c = np.cos(a)\n",
    "    return np.asarray([[c, -s, x],\n",
    "                       [s,  c, y],\n",
    "                       [0,  0, 1]])\n",
    "\n",
    "def from_trans(m):\n",
    "    '''get x, y, theta from transform matrix'''\n",
    "    a = np.arctan2(m[1, 0], m[0, 0])\n",
    "    return np.asarray([m[0, -1], m[1, -1], a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1. -0.  0.]\n",
      " [ 0.  1.  0.]\n",
      " [ 0.  0.  1.]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xu/miniconda3/envs/robocup/lib/python3.8/site-packages/jax/lib/xla_bridge.py:125: UserWarning: No GPU/TPU found, falling back to CPU.\n",
      "  warnings.warn('No GPU/TPU found, falling back to CPU.')\n"
     ]
    }
   ],
   "source": [
    "print(trans(0., 0., 0.))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters of robot arm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [0, 3, 2, 1]\n",
    "#l = [0, 3, 2, 1, 1]\n",
    "#l = [0, 3, 2, 1, 1, 1]\n",
    "#l = [1] * 30\n",
    "N = len(l) - 1  # number of links\n",
    "max_len = sum(l)\n",
    "a = random.random_sample(N)  # angles of joints\n",
    "T0 = trans(0, 0, 0)  # base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forward Kinematics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_kinematics(T0, l, a):\n",
    "    T = [T0]\n",
    "    for i in range(len(a)):\n",
    "        Ti = np.dot(T[-1], trans(l[i], 0, a[i]))\n",
    "        T.append(Ti)\n",
    "    Te = np.dot(T[-1], trans(l[-1], 0, 0))  # end effector\n",
    "    T.append(Te)\n",
    "    return T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_robot_arm(T):\n",
    "    plt.cla()\n",
    "    x = [Ti[0,-1] for Ti in T]\n",
    "    y = [Ti[1,-1] for Ti in T]\n",
    "    plt.plot(x, y, '-or', linewidth=5, markersize=10)\n",
    "    plt.plot(x[-1], y[-1], 'og', linewidth=5, markersize=10)\n",
    "    plt.xlim([-max_len, max_len])\n",
    "    plt.ylim([-max_len, max_len]) \n",
    "    ax = plt.axes()\n",
    "    ax.set_aspect('equal')\n",
    "    t = np.arctan2(T[-1][1, 0], T[-1][0,0])\n",
    "    ax.annotate('[%.2f,%.2f,%.2f]' % (x[-1], y[-1], t), xy=(x[-1], y[-1]), xytext=(x[-1], y[-1] + 0.5))\n",
    "    plt.show\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inverse Kinematics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical Solution: jax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "        return fig.key_event(event, event['data']);\n",
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       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
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       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
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       "\n",
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       "    nav_element.attr('style', 'width: 100%');\n",
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       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "\n",
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       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
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       "\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
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       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-d35263df4e47>:9: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = plt.axes()\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af0fc7587de74d05b1b78f01eb8e6d52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=4.991220474243164, description='x_e', max=6.0, step=0.01), FloatSlider…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def error_func(theta, target):\n",
    "    Ts = forward_kinematics(T0, l, theta)\n",
    "    Te = Ts[-1]\n",
    "    e = target - Te\n",
    "    return np.sum(e * e)\n",
    "\n",
    "theta = random.random(N)\n",
    "def inverse_kinematics(x_e, y_e, theta_e, theta):\n",
    "    target = trans(x_e, y_e, theta_e)\n",
    "    func = lambda t: error_func(t, target)\n",
    "    func_grad = jit(grad(func))\n",
    "    \n",
    "    for i in range(1000):\n",
    "        e = func(theta)\n",
    "        d = func_grad(theta)\n",
    "        theta -= d * 1e-2\n",
    "        if e < 1e-4:\n",
    "            break\n",
    "    \n",
    "    return theta\n",
    "\n",
    "T = forward_kinematics(T0, l, theta)\n",
    "show_robot_arm(T)\n",
    "Te = np.asarray([from_trans(T[-1])])\n",
    "\n",
    "@interact(x_e=(0, max_len, 0.01), y_e=(-max_len, max_len, 0.01), theta_e=(-pi, pi, 0.01), theta=fixed(theta))\n",
    "def set_end_effector(x_e=Te[0,0], y_e=Te[0,1], theta_e=Te[0,2], theta=theta):\n",
    "    theta = inverse_kinematics(x_e, y_e, theta_e, theta)\n",
    "    T = forward_kinematics(T0, l, theta)\n",
    "    show_robot_arm(T)\n",
    "    return theta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:robocup]",
   "language": "python",
   "name": "conda-env-robocup-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": "3.8.3"
  },
  "widgets": {
   "state": {
    "0065ff23834146a99efd42e25d3294dc": {
     "views": []
    },
    "00c59707f24e4d80b90a6ca59c255d84": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "1cfd9a6f210b4054bcb39b0429cc55de": {
     "views": []
    },
    "1d683e58238c426daa981843821bbe42": {
     "views": []
    },
    "1da0339e3c6e4a44884e772f18fa4e2b": {
     "views": []
    },
    "1dee2d42496b4c838041027fdf78954d": {
     "views": []
    },
    "1e92889027a541ddb44f454e7f14a387": {
     "views": []
    },
    "1f76e9cb47ce4bf9b0b3452b6bd854a0": {
     "views": []
    },
    "212aab5aa2bf4660a541d175db422584": {
     "views": []
    },
    "22df1d38fd2d43539cc7d9fccbaf577f": {
     "views": []
    },
    "22f22aafaf554defab70e6fe1bf02f58": {
     "views": []
    },
    "2310bd576c5b4f88a20f09a7d851a497": {
     "views": []
    },
    "236f20f3353a427796cd3e1e2d4475f9": {
     "views": []
    },
    "23cf28a2d9f5434696f27517edb1e019": {
     "views": []
    },
    "23e68019becd47078dbd546b0907a08f": {
     "views": []
    },
    "2494fdbd167046d8a7a0fe196ff53f54": {
     "views": []
    },
    "2600b6c694094bffb6043623d1819ea9": {
     "views": []
    },
    "26406e17a6ff431688684397e3d84ecd": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "26795a5634004f179a4433436959f69c": {
     "views": []
    },
    "28f6bbda5c7d487fa65a3ccfcb890b44": {
     "views": []
    },
    "28fbf1d342404c90b19e51eb06b656a6": {
     "views": []
    },
    "29291248cbaf449dad8c04aefea3c251": {
     "views": []
    },
    "29324c74ce44472d816ddb618e5e7154": {
     "views": []
    },
    "29d7ec8815be42d180e90f0867e537a5": {
     "views": []
    },
    "29ef4faa80594de0ab95b9e1e31a0500": {
     "views": []
    },
    "2aa04ae23f974820977ecc02dc8b6b43": {
     "views": []
    },
    "2ab47ef47437437c8cfaa90fb663193c": {
     "views": []
    },
    "2af796b503d34c988689c3b04df5de24": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 24,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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6lU2tdcmqFBJJUmvdcxULiXelOxpz9/LWJnFkAgCAR1rWkQl4NEcmAACAJmICAABoIiYA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\nAQAANBETAABAEzEBAAA0ERM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           "text/plain": "<matplotlib.figure.Figure at 0x7f7e70534390>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "from numpy import sin, cos, pi, matrix\nfrom math import atan2, acos\n\nT0 = trans(0, 0, 0)\nlv = [0] + range(N, 0, -1)  # length of link, l[0] is ingored\nbf = B\nfor i in range(N):\n    bf = bf.subs(l[i + 1], lv[i + 1])\n\ndef inverse_kinematics(x_e, y_e, theta_e):\n    b = bf.subs(x, x_e).subs(y, y_e).subs(theta, theta_e)\n    b = (b.subs('I', 1).subs('pi', pi).tolist())\n    b = [float(i[0]) for i in b]\n    return b\n    \n@interact(x_e=(0, max_len, 0.1), y_e=(-max_len, max_len, 0.1), theta_e=(-pi, pi, 0.1))\ndef set_end_effector(x_e=5, y_e=0, theta_e=0):\n    b = inverse_kinematics(x_e, y_e, theta_e)\n    T = forward_kinematics(T0, lv, b)\n    show_robot_arm(T)"
       },
       "cell_index": 31,
       "root": true
      }
     ]
    },
    "2b08bea1b4f54c9ba077c00da9f32df4": {
     "views": []
    },
    "2b93cbb0c4224f89849f2dde2fc15157": {
     "views": []
    },
    "2bc083e7865744218921a220b1dcd2bd": {
     "views": []
    },
    "2bd0e43b75c644de982f6ed6526efdd4": {
     "views": []
    },
    "2c34626d64f64cee97c80b20eca8bcca": {
     "views": []
    },
    "2d47a8fbe94e44558b858df765718bf6": {
     "views": []
    },
    "2da22ed89495462aa8367d393c2a7eb3": {
     "views": []
    },
    "2daf53bd4a7446b789c98057a1e8a250": {
     "views": []
    },
    "2dcbbf3b132149afa31062be6a07554c": {
     "views": []
    },
    "2e214dcc0b3c44e9b2328d1951fa682f": {
     "views": []
    },
    "2f158a0aa1a64a06b1e040595d5f3786": {
     "views": []
    },
    "2f2278ef4cfa4853819ae1f6527a3b73": {
     "views": []
    },
    "2f6d2e4e681c4aa69eb193d1231e33eb": {
     "views": []
    },
    "2fbda6a400e84a79aef5b70e619abb96": {
     "views": []
    },
    "3041d69ed88044cea4de4bd0f450c27d": {
     "views": []
    },
    "30d84e2c052640189dbdcf9392defa4c": {
     "views": []
    },
    "31cf7dfafa144ec4a2596eb29add3718": {
     "views": []
    },
    "31e0aedd3ffa43dea7a1c25a5500b106": {
     "views": []
    },
    "32abfd69cd694c50aa2ebee92df33674": {
     "views": []
    },
    "32d299c4eabe4ab0b7f7ce7d320c85bf": {
     "views": []
    },
    "32e5479e6e41479e984f122b75b58d11": {
     "views": []
    },
    "330fab827b204b90ad49370b88945728": {
     "views": []
    },
    "335ed94a336e40a2b69bd63a6f675584": {
     "views": []
    },
    "33602b70c8ec4149afabed51291da3d7": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "33b2e7d8d4c84e768423aa3803e2b9f5": {
     "views": []
    },
    "33d1a4a3e2984b5999adf4a82ed6d243": {
     "views": []
    },
    "34acdada4843453d86dc7812e4271c09": {
     "views": []
    },
    "356a4ddeac674b17a489dc73c02f2f57": {
     "views": []
    },
    "358e60da5d7c45278a2640a67c6af292": {
     "views": []
    },
    "36aa1b263e224022a6b374b09c44b6fb": {
     "views": []
    },
    "36ff2aeae7a14f91a7be43d3c47a0597": {
     "views": []
    },
    "374ce53463064914b8233dfced2d9d52": {
     "views": []
    },
    "37513503a13b45cfbd26e56238d8c949": {
     "views": []
    },
    "386c80b57d6a404992acaf6469506db3": {
     "views": []
    },
    "389ea982a40d46d591bebd0ee559801e": {
     "views": []
    },
    "38b47640815546939304ec2a6d71c3ad": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "38bc0f4ca75245689ea57a229c49472a": {
     "views": []
    },
    "38d0989572be494b80d396336d933313": {
     "views": []
    },
    "39240ffe45674b32aa64d099d6e61bd6": {
     "views": []
    },
    "3a5050c9cd9e457fae55d0a598eb4b4f": {
     "views": []
    },
    "3a80053831f94247947bda7171a375f5": {
     "views": []
    },
    "3b0b75b35ae34a6d9576ab19dea42ccd": {
     "views": []
    },
    "3b667c3b4fc4424d8893d82380ed312a": {
     "views": []
    },
    "3bd271b7ed304418a7d058b88a46817c": {
     "views": []
    },
    "3bfb2f44245f4631af2733821fc24f2d": {
     "views": []
    },
    "3c160aed9d4645a3867ab97e9d4428e8": {
     "views": []
    },
    "3c4064aa9a404ca0802954fe0baf05ad": {
     "views": []
    },
    "3c8d97fabeff4d4882982ae88559ca1d": {
     "views": []
    },
    "3ce5b9ab8fbc4af092b37669aef932a9": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "3cfd0856c6ad4d72957c378772dddceb": {
     "views": []
    },
    "3d5f1dddcb724d6f8776e5beb45145be": {
     "views": []
    },
    "3dc13517176b4d059867e0c5c14b333e": {
     "views": []
    },
    "3e69ea50d1bc4d33a564d6e3dc695803": {
     "views": []
    },
    "3e7ec1eec9bb432ab76638a80530882e": {
     "views": []
    },
    "3ec90899379e473a8a3d9537a9fd3e6a": {
     "views": []
    },
    "3ee2ee71c4df41fe8bcc0e36c035bdc9": {
     "views": []
    },
    "3f1c69d137514a16a98abd88055e502c": {
     "views": []
    },
    "3f419880ead047eda91f3d02a9958b64": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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        "execution_count": 7,
        "metadata": {
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        "outputs": [
         {
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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "5dbe6899de6941e4baa16dc072df40c9": {
     "views": []
    },
    "5e8769162a4140d5bc70b8aec03e7ad8": {
     "views": []
    },
    "5e87aee9f185402f8ff57a465fbe73e7": {
     "views": []
    },
    "5ef7e2e41c0043bc8a55756ce22df3e9": {
     "views": []
    },
    "5fef33cf88954c079bf52d5dbbb0339f": {
     "views": []
    },
    "61ac09cef9924de2a2146dcb2b2e852e": {
     "views": []
    },
    "621c02cb75364b558abc79df43f8b70e": {
     "views": []
    },
    "63511e5c5aa34ec3a58130ae0d8b6bdf": {
     "views": []
    },
    "63775a25bb2b4ee9b8446ceef21818fc": {
     "views": []
    },
    "639c23cf0db44536a5f78136ea5b449e": {
     "views": []
    },
    "63d34734668d4d4e8a9abd079cfffcc9": {
     "views": []
    },
    "647b63c63fed43daa91f41fe25b22482": {
     "views": []
    },
    "64976a06e0c24cbebbe7fe4ebe3ee1cd": {
     "views": []
    },
    "654d4b7dbb4a4a18bcb54cc292d2f4bb": {
     "views": []
    },
    "6628109154014be7a19c42fb56a91ef8": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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    "697a98b6e58844049a81cc4b5da87be0": {
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    "69d28d94f5eb40a3879b21f9507e6121": {
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    "6a212e13893a4ab0a241a89a6007906c": {
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    "6abd33d635584ac3bbe8902f7ddd90d4": {
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    "6b17c7b15ea1410a93477b40977226b5": {
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    "6ca5197312004a9683d386408cc08392": {
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    "6d017c1c4b01499693c498de96d7f97a": {
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    "6d17774c8b64462f93b8462a77e58415": {
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    "6db471bbc3794e53a4fda8828961360a": {
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    "6de8f2d679c1445fbc3ce051fec75a1e": {
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    "6e070ef4748e4e40a987b6aacf122e0c": {
     "views": []
    },
    "6e82cccbb1344bc3b20c3175f52608d0": {
     "views": []
    },
    "6e9a98ffa31b449d9c137b9f1f0359f3": {
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    },
    "6ecb03b3ef024b1085696a8f3b41d3ba": {
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    },
    "6ee8f1f634974b8ba9558e4322b4c6d4": {
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    "6f3a909c6f774a7b8d713440c87b0850": {
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    "7005e042210046bbbcb8774e321ad2de": {
     "views": []
    },
    "70e70828620b42e4a1691ab4a0002e47": {
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    },
    "71356f391594493c9b6618e8439701bc": {
     "views": []
    },
    "7157f92cb89a403785ac94702c38681a": {
     "views": []
    },
    "715a4c01b195434c9c1c082a04e7adde": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "71fe3ab37df047f8af2367d8f9a79994": {
     "views": []
    },
    "72231c63ee8d4c2196df2b889e895eb7": {
     "views": []
    },
    "7224f8df15bf4c81b657da552ca9ed24": {
     "views": []
    },
    "730a7d996a3a4ff18f7d3dd9ec5f5ae8": {
     "views": []
    },
    "73736b6f6f69435d971a25fe39a572df": {
     "views": []
    },
    "73b62c199ef741e6b2e86487476ed81d": {
     "views": []
    },
    "745aea5ce26e4cbd9559786ae4f2c9a5": {
     "views": []
    },
    "74932e9cb8f64a30b15936223eb666e6": {
     "views": []
    },
    "74ec10ad98f44cae8ec7cb23bca2e7e8": {
     "views": []
    },
    "755577b3878e4659a9d79c14d1a8b663": {
     "views": []
    },
    "75a0ef9fd2144683a0c221172e75686c": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O\nS84+O/dcf31etGdPHpvkC0neu3Nn6vTT83+uukoAMYm9e/fmpy68MDd/8IN51k035TF33ZU7HvrQ\nfOiUU3LSc56TC9/0pmP6RfaxrNux2bw/T7/xxvzgnj1JsjzGWJ/32mAKU4fPbyd55xjj3VV1XpL/\nNMb4tv3GCB/gmLZ79+6ce+KJefOXv5ynHGD7NUl+9BGPyGWf/7z44Yjs3bs3rz733LzqIx/JmXff\n/fe2f2LHjlz8nd+Zn73ssmPqBXYH3Y7N/vuznmR5Y5PwoY3JwqeqHpXks0keOca4Z/bYbUnOGWPc\nuGmc8AGOad9/5pn5z9ddd8Do2ecPk1x03HF535OffLSWRUP/46ab8tw77siZ9zHm2iSrxx+fH3vC\nE47Sqkj6HZv990f40NGU7/E5Kclt+6Jn5uYkj09y44G/BeDYsnv37ozrr7/P6EmSpyb56u7d2f2x\nj8U5Hw7H3iS3JPf5wjpJzkpy6e23Z+/tt2fxzyv00O3YbHV/4FjnU90ADsGll16aF21c936/XpTk\n7du7HBq7Msmztjj2WUmu2sa1cG/djs2h7A8cy6Y843NLkhOq6kGbzvo8Phtnff6eXbt2ZefOnUmS\nlZWVrKysTLgUgO1x++c+l6ducexjk1y3nYuhtS8lecwWxz4myV9u41q4t27HZt/+rM7+JMnWfr0D\nx5bJwmeM8cWquibJS5K8q6pekOSWze/v2eyiiy7yHh/gmHP8aaflC1sc+4Ukx2/nYmjtkUnu2OLY\nO5I8ehvXwr11Ozb79ucFSfb9Gno9yc/ObUWwPab+VLcnJnlnkm9MspbkZWOMT+83xocbAMes3bt3\n58WPeER+bQuXu31fVd77tKfl6x7kqmIO3d577slrP/nJXPyVr9zv2Ase9rC89clPzo6qo7Ayuh2b\nA+2PDzego0lvYDrG+EySs6ecE2CRHHfccanTT881W/hUtwd/67fm637v947W0mjmIUlOeu1r84mL\nLz7gxyXvc+2OHTn5/POz481vPnqLe4Drdmy2uj9wrJv0jM+WntAZH+AYt+8+Pj/z5S8f8P0+f5jk\n37uPDxPYd2+VCz7ykZx1gBek1+7Ykf99DN0rppNux2b//XHGh46ED8Bh2L17d15yzjm554//OD+w\nZ08em4339Lx35848+Iwz8u4rrxQ9TGLv3r35qde/Pjd/8IN55o035jF33ZU7HvrQfOiUU3Lyykr+\nw0/+5DHxwrqjbsdm8/58+w035KUbl/QKH9oQPgBHYPfu3Xn729+e22+4Icefempe+cpXCh62xd69\ne3PVVVflS7fdlkeecELOPvvsY+pFdWfdjs3evXtz+eWX53nPe14ifGhE+AAAcC/r6+tZXl5OhA+N\n+KghAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0\nJ3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe\n8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvC\nBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7Qkf\nAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wA\nAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEA\nANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAA\naE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACg\nPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0N0n4VNWP\nVNUfVdV1VfWJqnrxFPMCAABMYcdE83wqydljjDur6sQk11bVVWOMmyaaHwAA4LBNcsZnjHHFGOPO\n2d8/n+T2JCdNMTcAAMCRmvw9PlX1PUm+IcnHp54bAADgcGzpUrequirJafs/nGQkOWuMcets3JOS\n/HySF44x/nbKhQIAAByuLYXPGOPs+xtTVWckeX+Sl44xrr6/8bt27crOnTuTJCsrK1lZWdnKUgAA\n2Aarq6tZXV1NkuzZs2fOq4Hp1RjjyCepOj3J/0ty/hjj8vsZu5RkbW1tLUtLS0f83AAATGt9fT3L\ny8tJsjzGWJ/3emAKU73H5y1JlpK8saquraprqurZE80NAABwRCb5OOsxxnOmmAcAAGA7TP6pbgAA\nAItG+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAA\noD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA\n9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADa\nEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP\n+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3h\nAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQP\nAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4A\nAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAA\nAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAA\ntCd8AACA9oQPAADQnvABAADamzR8qurRVXV7Vf3qlPMCAAAcianP+LwtyQcmnhMAAOCITBY+VfXy\nJDcm+d2p5gQAAJjCJOFTVack+aEkb5hiPgAAgCnt2MqgqroqyWn7P5xkJHlKkp9L8poxxl1VVVuZ\nc9euXdm5c2eSZGVlJSsrK1teNAAA01pdXc3q6mqSZM+ePXNeDUyvxhhHNkHVUpIbktw5e+jhSR6W\n5OoxxrMPMn5tbW0tS0tLR/TcAABMb319PcvLy0myPMZYn/d6YApbOuNzX2Y/DI/a93VV/WCSfznG\n+P4jnRsAAGAK7uMDAAC0N3n4jDHe5WwPAACwSJzxAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA9\n4QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaE\nDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+\nAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQn\nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQ3WfhU1XlV9cmq+qPZ\n/z5+qrkBAACOxCThU1VnJfnvSZ49xnhSku9I8hdTzM3Rtbq6Ou8lcB8cn8Xl2Cwux2axOT7A0TLV\nGZ/XJfnpMcYdSTLG+JsxxlcmmpujyD9Ai83xWVyOzeJybBab4wMcLVOFzxlJTq6q36mqP6yq/1ZV\nNdHcAAAAR2THVgZV1VVJTtv/4SQjyVmzec5M8pzZ39+f5IIkFx9szvX19cNYLtttz549js0Cc3wW\nl2OzuBybxeb4LCbHhI5qjHHkk1R9IMn7xhjvnH39qiRPH2P82wOMfVySzx/xkwIAsN1OHGPcOu9F\nwBS2dMZnC96T5F9U1buSPDgbZ34+epCxX0hyYpI7J3puAACm9/BsvG6DFqY641NJ/leS5yW5OxvR\n89oxxt1HPDkAAMARmiR8AAAAFtlkNzA9HG56utiq6tFVdXtV/eq818Lfqaofmf3MXFdVn6iqF897\nTQ9kVXVaVV1ZVX9aVb9XVafPe01sqKqHVtWvVdWfVNW1VbVaVafOe13cW1W9rKruqarnz3stbKiq\nnVX11qr6zOzfml+Y95pgClO9x+eQbbrp6TPHGHdU1dcn+eq81sMBvS3JB5J847wXwr18KsnZY4w7\nq+rEJNdW1VVjjJvmvbAHqEuSvG2M8e6qOi/Ju5J825zXxN+5ZIxxWZJU1auTvCPJM+e7JPapqpOT\nvCLJ1fNeC/fyxiT3jDGemGz8InTO64FJzPOMj5ueLrCqenmSG5P87rzXwr2NMa4YY9w5+/vnk9ye\n5KT5ruqBqaoeleSpSX4xScYY70tyUlV901wXRpJkjHHXvuiZ+ViSk+e1Hu5t9v7gdyR5TZI9c14O\nM1V1XJKXJ3nDvsfGGH8xvxXBdOYZPm56uqCq6pQkP5RN/9FjMVXV9yT5hiQfn/daHqBOSnLbGOOe\nTY/dnMRlu4vptUl+fd6L4Gtel+SjY4xr570Q7uXUJF9K8oaq+nhVfbiqnjXvRcEUtu1St+246SnT\nuJ9j85QkP5fkNWOMu8To0Xd/Pzv77qdQVU9K8vNJXjjG+Nuju0o4tlTVrmy8oDt/3mshqapvSXJe\nkmfMey38PTuycWb0U2OMH6+qM5NcXlVnjDG+OOe1wRHZtvAZY5x9X9ur6uZs3PR0T5I9szfQPz3C\nZ9vd17GpqqUkT0ryS7PmeXiSh1XV5WOMZx+lJT6g3d/PTpJU1RnZ+GXBS8cYro2fn1uSnFBVD9p0\n1ufx2Tjrw4KoqguTfF+S73ZJ9cJ4RjZeXH929gu245NcWlUnjDEume/SHvBuzsZ7rt+TJGOMT1TV\nTdl4bfCheS4MjtQ8L3V7T5Ln1IYd2Tjzc90c10OSMcb6GONRY4xvGmN8U5ILk3xQ9CyO2aeG/WaS\n88cY/hGao9lvP69J8pIkqaoXJLlljHHjXBfG11TV65K8KMmz9703jvkbY7xtjPG42b81p2Tj/Vfn\ni575G2P8ZZLfTnJu8rXL35+Q5Po5LgsmMc/weW+SW5N8OhsvHG5N8pY5rgeOFW9JspTkjbOP6L2m\nqoTp/Pxwkh+qqj9N8h+TvGzO62Gmqh6X5E1JlpNcMft5cYZ0Mbmp4GK5IMnrq+qTSX41G1F625zX\nBEfMDUwBAID25noDUwAAgKNB+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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        "outputs": [
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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "9a19791b387e4c5a913a92790141f553": {
     "views": []
    },
    "9a3dd9ada08d4312b729bc1891e43f1a": {
     "views": []
    },
    "9a81582415584ce588f6a1c53b927d32": {
     "views": []
    },
    "9ae0d4a38f1e4b498b0bc4801df700ec": {
     "views": []
    },
    "9aef67ddda784eb7b93dc9c617ef03ec": {
     "views": []
    },
    "9b917897eeb24144930072d47c8a7571": {
     "views": []
    },
    "9bd57fc229d24f9aa65f8366028e3007": {
     "views": []
    },
    "9bdd3a0d16de4cd7801d67957b196382": {
     "views": []
    },
    "9c13a849122d47458c4ab77c56edf863": {
     "views": []
    },
    "9c9748ffe74d4a5cbc26aef71c6e4780": {
     "views": []
    },
    "9ce4a1bcfd694843a536393891af83be": {
     "views": []
    },
    "9d7f35226a3a4f6c9ae12362454a9716": {
     "views": []
    },
    "9dba4d8a3c4d420492b5b5ac732b5fa0": {
     "views": []
    },
    "9dd653229bd649df86e7ce3d5688e963": {
     "views": []
    },
    "9dddfa92060a4a4ab09253e94473d45e": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "b0939de0ddd64138a947665fc7d23e20": {
     "views": []
    },
    "b097623d1e7a4a48b11b1f405d98d4e8": {
     "views": []
    },
    "b1318684110d42d79687a6386cbae811": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 25,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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HZBAxO/0aq2plkj9M8u4dLDto+Pqfbg2aGlz17Q1JXjb7PJiqWrrdFdhekOSIJLdW1UGz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\nAACgO0IHAADojtABAAC6I3QAAIDu/P+zHD7CA7x3IAAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6b4d6ad0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "from numpy import asarray\ntheta = asarray([0.1] * N)\nlambda_ = 0.001\ndef inverse_kinematics(x_e, y_e, theta_e, theta):\n    target = matrix([[x_e, y_e, theta_e]])\n    while True:\n        T = forward_kinematics(T0, lv, theta)\n        Te = matrix([from_trans(T[-1])])\n        e = target - Te\n        T = matrix([from_trans(i) for i in T[1:-1]])\n        J = Te - T\n        J = J.T\n        J[-1, :] = 1  # angular velocity\n        JJT = J * J.T\n        d_theta = lambda_ * J.T * JJT.I * e.T\n        theta += asarray(d_theta.T)[0]\n        if  np.linalg.norm(d_theta) < 1e-4:\n            break\n    return theta\n\nT = forward_kinematics(T0, lv, theta)\nTe = matrix([from_trans(T[-1])])\n\n@interact(x_e=(0, max_len, 0.01), y_e=(-max_len, max_len, 0.01), theta_e=(-pi, pi, 0.01), theta=fixed(theta))\ndef set_end_effector(x_e=Te[0,0], y_e=Te[0,1], theta_e=Te[0,2], theta=theta):\n    theta = inverse_kinematics(x_e, y_e, theta_e, theta)\n    T = forward_kinematics(T0, lv, theta)\n    show_robot_arm(T)\n\n# NOTE\n# while numerical inverse kinematics is easy to implemente, two issues have to be keep in mind:\n# * stablility: the correction step (lambda_) has to be small, but it will take longer time to converage\n# * singularity: there are singularity poses (all 0, for example), the correction will be 0, so the algorithm won't work. That's why many robots bends its leg when walking"
       },
       "cell_index": 33,
       "root": true
      }
     ]
    },
    "b249266f72ce41b096bfeafd4789ed36": {
     "views": []
    },
    "b27d4b54be3c499bac6ce346de64e0bb": {
     "views": []
    },
    "b28e241d2ac14db39d89dc3952ff8974": {
     "views": []
    },
    "b402a799328c49e99abd5beb99ad63cb": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "b491570fd738421e971d2f01f6e5e025": {
     "views": []
    },
    "b4cd5a2f926e43be8c8028d2c071e2e5": {
     "views": []
    },
    "b579ef0051d945a9adc2c1c7a0387ab9": {
     "views": []
    },
    "b7203daacce34cedab333faf601f8080": {
     "views": []
    },
    "b74a7887033349f59d4dcce5df469ebe": {
     "views": []
    },
    "b7da1a920b6441d8b303aacc5472a743": {
     "views": []
    },
    "b7ebf419b324467196b93aec2ddfb2e5": {
     "views": []
    },
    "b7f65fb78b1f4963923e4b2b889ae71d": {
     "views": []
    },
    "b80d613ff47c4b5e8a92d15ce973c058": {
     "views": []
    },
    "b80d7b1129344995a0e08645bd02c43c": {
     "views": []
    },
    "b8af5204f7bd46e49556c9b9339a61d1": {
     "views": []
    },
    "b8f0724bbf9c4379af6c6b24598a2e45": {
     "views": []
    },
    "b94bcf1136124faa83d0d49f6ffd01ef": {
     "views": []
    },
    "b94d48ddfc0c4b9bbe799e23d02bdb23": {
     "views": []
    },
    "b9a4a36b6f0541d08765760f39d5979d": {
     "views": []
    },
    "b9b0e906c50b4fe194ba0561860a48d2": {
     "views": []
    },
    "b9b20e2b81ef469fbc2147fe4fd1fbcd": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "b9eee2008f4e4bbdb794dc096ad66eed": {
     "views": []
    },
    "bb317461e530495584bed769e3907371": {
     "views": []
    },
    "bb4935d65aa4419a903c269ee1c245c6": {
     "views": []
    },
    "bb5048ede7214dbc95ddd20b2860d967": {
     "views": []
    },
    "bc375a0bce7f422988cf8184f1b766e4": {
     "views": []
    },
    "bcdf58e3772d46bc85c99b311fca3597": {
     "views": []
    },
    "bd0e575537f84a4dbbe73055036d545f": {
     "views": []
    },
    "bda506ba1987430e8e52e2370d61f1a7": {
     "views": []
    },
    "bde65c86e6d1468fb60ae401c4c8b29a": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "be1acc039ba446ee8faa23f50a109cad": {
     "views": []
    },
    "bf5d09791dc84a05a89a788915401157": {
     "views": []
    },
    "c04926eeeee849949eb61738e5e66675": {
     "views": []
    },
    "c05c848c83c649bca51c6b180a176e4a": {
     "views": []
    },
    "c1778dbcd3454ee190824e9101a0b647": {
     "views": []
    },
    "c17e1418e17445ef8f2a0f8e1dad0c38": {
     "views": []
    },
    "c1eaf2eb5c684f2db956365800471e0d": {
     "views": []
    },
    "c205b755237d444eb87a20babac93ad5": {
     "views": []
    },
    "c229e9376d6a4e1b8243aa035744b4a8": {
     "views": []
    },
    "c296feae9fc241f6b7cc2155c35d2a21": {
     "views": []
    },
    "c298b40a15fc4980a8bec3c3c36e1cdf": {
     "views": []
    },
    "c2a9da2b5f9a441c86c7ca1d98bdfb5c": {
     "views": []
    },
    "c2ba4e3cb0024641939db9301b068ace": {
     "views": []
    },
    "c2bae26c415844b19c575a61f4e98967": {
     "views": []
    },
    "c2fa97d314134fdf8ed6e6db1428cd88": {
     "views": []
    },
    "c3703cc105c14aa29739a9c4aa53923c": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "c39014455211404cae26ef0b4179cc29": {
     "views": []
    },
    "c3ae4ee5304c468f84b15b2f834369c3": {
     "views": []
    },
    "c40830ec69ab4c61882d3bb657705a29": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
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    "da8213350eb44e03930067f7616a30ba": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "dae71c9df74f476d9bdca7dbdc9c4813": {
     "views": []
    },
    "db36bc23e8224a13bd5bd39d59b341ea": {
     "views": []
    },
    "db74c7e7bc0445af81e1a1a8cd4c1b2a": {
     "views": []
    },
    "dd7767d3ee1e4fdf9457356dcb9d8856": {
     "views": []
    },
    "dd9547c6a0de46bfbb8766c95faad93a": {
     "views": []
    },
    "de9d33d2fd504e6591a42183a04245c4": {
     "views": []
    },
    "dea19948b29b4852a2fafd8eb5ed81ba": {
     "views": []
    },
    "df3f2a8c0b8d4450b9c81e9bb6bf5a48": {
     "views": []
    },
    "df69964a0a784d678379ef980433bac2": {
     "views": []
    },
    "dfef404d9e874f5ba4bcb626c407e772": {
     "views": []
    },
    "e07f7328f0cd427d9af41fac575919a2": {
     "views": []
    },
    "e0fbecdda5c24dd0b7ae34203dd4bd08": {
     "views": []
    },
    "e14cff4f97544aa2850e91e886f48705": {
     "views": []
    },
    "e172cd6cdcb1425f9686213f3e73af38": {
     "views": []
    },
    "e21b7ba964e54981ac5505b6a6d8e9d4": {
     "views": []
    },
    "e27ff57b6a15404a9f220a4f234d012f": {
     "views": []
    },
    "e35aa9674b9c4be1a8bf4a6b6c3f61b0": {
     "views": []
    },
    "e399e06d7f1d45e98f6db2a87d658e37": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "ebd1bb1a6e184910a6ea9194bb246247": {
     "views": []
    },
    "ec331fec5dc849a096d6fc4614f5c6a1": {
     "views": []
    },
    "ed2373b2c86e4249963f1f0c1539875b": {
     "views": []
    },
    "ed5515d5ada341d5bddc42285601a968": {
     "views": []
    },
    "ee6badce55564b0bb690ba653a5c311b": {
     "views": []
    },
    "ee6e242f9f5b4d1197ca01c6bd49119c": {
     "views": []
    },
    "eebf627136a94f8db9d06a66bc1f5ff7": {
     "views": []
    },
    "eef0260f78af4a21ad250a3641b7a8d8": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
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