kinematics/inverse_kinematics_2d_optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverse Kinematics (2D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "from matplotlib import pylab as plt\n",
    "from numpy import sin, cos, pi, matrix, random, linalg, asarray\n",
    "from __future__ import division\n",
    "from math import atan2\n",
    "from IPython import display\n",
    "from ipywidgets import interact, fixed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coordinate Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans(x, y, a):\n",
    "    '''create a 2D transformation'''\n",
    "    s = sin(a)\n",
    "    c = cos(a)\n",
    "    return matrix([[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",
    "    return [m[0, -1], m[1, -1], atan2(m[1, 0], m[0, 0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1., -0.,  0.],\n",
       "        [ 0.,  1.,  0.],\n",
       "        [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans(0, 0, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters of robot arm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_kinematics(T0, l, a):\n",
    "    T = [T0]\n",
    "    for i in range(len(a)):\n",
    "        Ti = T[-1] * trans(l[i], 0, a[i])\n",
    "        T.append(Ti)\n",
    "    Te = T[-1] * trans(l[-1], 0, 0)  # end effector\n",
    "    T.append(Te)\n",
    "    return T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 = atan2(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: optimzation approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
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       "        }\n",
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
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       "\n",
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       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
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       "\n",
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       "\n",
       "\n",
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       "    var format_dropdown = fig.format_dropdown;\n",
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       "    fig.ondownload(fig, format);\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
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       "        fig._resize_canvas(size[0], size[1]);\n",
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       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
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       "    y1 = Math.floor(y1) + 0.5;\n",
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       "    var min_y = Math.min(y0, y1);\n",
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       "        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",
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       "    var cursor = msg['cursor'];\n",
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       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
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       "        break;\n",
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       "        break;\n",
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       "    }\n",
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       "\n",
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       "\n",
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       "    // Request the server to send over a new figure.\n",
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       "}\n",
       "\n",
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       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
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       "    // Called whenever the canvas gets updated.\n",
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       "}\n",
       "\n",
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       "    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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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-6ffa2c90f55e>: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": "83700afed8834926bfb232548fb9ff7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=1.079695330867911, description='x_e', max=30.0, step=0.01), FloatSlide…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import fmin\n",
    "\n",
    "def error_func(theta, target):\n",
    "    Ts = forward_kinematics(T0, l, theta)\n",
    "    Te = matrix([from_trans(Ts[-1])]).T\n",
    "    e = target - Te\n",
    "    return linalg.norm(e)\n",
    "\n",
    "theta = random.random(N)\n",
    "def inverse_kinematics(x_e, y_e, theta_e, theta):\n",
    "    target = matrix([[x_e, y_e, theta_e]]).T\n",
    "    func = lambda t: error_func(t, target)\n",
    "    return fmin(func, theta)\n",
    "\n",
    "T = forward_kinematics(T0, l, theta)\n",
    "show_robot_arm(T)\n",
    "Te = 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))\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": []
  }
 ],
 "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\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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    "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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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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        "outputs": [
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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
      }
     ]
    },
    "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\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
      }
     ]
    },
    "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\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
      }
     ]
    },
    "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\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
      }
     ]
    },
    "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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