kinematics/inverse_kinematics_2d_jacobian.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverse Kinematics (2D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 scipy.linalg import pinv\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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1., -0.,  0.],\n",
       "        [ 0.,  1.,  0.],\n",
       "        [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans(0, 0, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters of robot arm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [0, 3, 2, 1]\n",
    "#l = [0, 3, 2, 1, 1]\n",
    "#l = [0, 3, 2, 1, 1, 1]\n",
    "l = [1] * 30\n",
    "N = len(l) - 1  # number of links\n",
    "max_len = sum(l)\n",
    "a = random.random_sample(N)  # angles of joints\n",
    "T0 = trans(0, 0, 0)  # base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forward Kinematics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_kinematics(T0, l, a):\n",
    "    T = [T0]\n",
    "    for i in range(len(a)):\n",
    "        Ti = 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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_robot_arm(T):\n",
    "    plt.cla()\n",
    "    x = [Ti[0,-1] for Ti in T]\n",
    "    y = [Ti[1,-1] for Ti in T]\n",
    "    plt.plot(x, y, '-or', linewidth=5, markersize=10)\n",
    "    plt.plot(x[-1], y[-1], 'og', linewidth=5, markersize=10)\n",
    "    plt.xlim([-max_len, max_len])\n",
    "    plt.ylim([-max_len, max_len]) \n",
    "    ax = plt.axes()\n",
    "    ax.set_aspect('equal')\n",
    "    t = 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 with Jacobian\n",
    "\n",
    "NOTE: 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_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "        }\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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/site-packages/matplotlib/cbook/deprecation.py:107: 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",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a15a1f248aa4797ae8b5a79ef9d91c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "aW50ZXJhY3RpdmUoY2hpbGRyZW49KEZsb2F0U2xpZGVyKHZhbHVlPTAuMzY4ODA3NzQ1ODg4MTAxOTUsIGRlc2NyaXB0aW9uPXUneF9lJywgbWF4PTMwLjAsIHN0ZXA9MC4wMSksIEZsb2F0U2zigKY=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta = random.random(N)\n",
    "lambda_ = 1\n",
    "max_step = 0.1\n",
    "def inverse_kinematics(x_e, y_e, theta_e, theta):\n",
    "    target = matrix([[x_e, y_e, theta_e]]).T\n",
    "    for i in range(1000):\n",
    "        Ts = forward_kinematics(T0, l, theta)\n",
    "        Te = matrix([from_trans(Ts[-1])]).T\n",
    "        e = target - Te\n",
    "        e[e > max_step] = max_step\n",
    "        e[e < -max_step] = -max_step\n",
    "        T = matrix([from_trans(i) for i in Ts[1:-1]]).T\n",
    "        J = Te - T\n",
    "        dT = Te - T\n",
    "        J[0, :] = -dT[1, :] # x\n",
    "        J[1, :] = dT[0, :] # y\n",
    "        J[-1, :] = 1  # angular\n",
    "        d_theta = lambda_ * pinv(J) * e\n",
    "        theta += asarray(d_theta.T)[0]\n",
    "        if  linalg.norm(d_theta) < 1e-4:\n",
    "            break\n",
    "    return 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  },
  "widgets": {
   "state": {
    "0065ff23834146a99efd42e25d3294dc": {
     "views": []
    },
    "00c59707f24e4d80b90a6ca59c255d84": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "1cfd9a6f210b4054bcb39b0429cc55de": {
     "views": []
    },
    "1d683e58238c426daa981843821bbe42": {
     "views": []
    },
    "1da0339e3c6e4a44884e772f18fa4e2b": {
     "views": []
    },
    "1dee2d42496b4c838041027fdf78954d": {
     "views": []
    },
    "1e92889027a541ddb44f454e7f14a387": {
     "views": []
    },
    "1f76e9cb47ce4bf9b0b3452b6bd854a0": {
     "views": []
    },
    "212aab5aa2bf4660a541d175db422584": {
     "views": []
    },
    "22df1d38fd2d43539cc7d9fccbaf577f": {
     "views": []
    },
    "22f22aafaf554defab70e6fe1bf02f58": {
     "views": []
    },
    "2310bd576c5b4f88a20f09a7d851a497": {
     "views": []
    },
    "236f20f3353a427796cd3e1e2d4475f9": {
     "views": []
    },
    "23cf28a2d9f5434696f27517edb1e019": {
     "views": []
    },
    "23e68019becd47078dbd546b0907a08f": {
     "views": []
    },
    "2494fdbd167046d8a7a0fe196ff53f54": {
     "views": []
    },
    "2600b6c694094bffb6043623d1819ea9": {
     "views": []
    },
    "26406e17a6ff431688684397e3d84ecd": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "26795a5634004f179a4433436959f69c": {
     "views": []
    },
    "28f6bbda5c7d487fa65a3ccfcb890b44": {
     "views": []
    },
    "28fbf1d342404c90b19e51eb06b656a6": {
     "views": []
    },
    "29291248cbaf449dad8c04aefea3c251": {
     "views": []
    },
    "29324c74ce44472d816ddb618e5e7154": {
     "views": []
    },
    "29d7ec8815be42d180e90f0867e537a5": {
     "views": []
    },
    "29ef4faa80594de0ab95b9e1e31a0500": {
     "views": []
    },
    "2aa04ae23f974820977ecc02dc8b6b43": {
     "views": []
    },
    "2ab47ef47437437c8cfaa90fb663193c": {
     "views": []
    },
    "2af796b503d34c988689c3b04df5de24": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 24,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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6lU2tdcmqFBJJUmvdcxULiXelOxpz9/LWJnFkAgCAR1rWkQl4NEcmAACAJmICAABoIiYA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\nAQAANBETAABAEzEBAAA0ERM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           "text/plain": "<matplotlib.figure.Figure at 0x7f7e70534390>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "from numpy import sin, cos, pi, matrix\nfrom math import atan2, acos\n\nT0 = trans(0, 0, 0)\nlv = [0] + range(N, 0, -1)  # length of link, l[0] is ingored\nbf = B\nfor i in range(N):\n    bf = bf.subs(l[i + 1], lv[i + 1])\n\ndef inverse_kinematics(x_e, y_e, theta_e):\n    b = bf.subs(x, x_e).subs(y, y_e).subs(theta, theta_e)\n    b = (b.subs('I', 1).subs('pi', pi).tolist())\n    b = [float(i[0]) for i in b]\n    return b\n    \n@interact(x_e=(0, max_len, 0.1), y_e=(-max_len, max_len, 0.1), theta_e=(-pi, pi, 0.1))\ndef set_end_effector(x_e=5, y_e=0, theta_e=0):\n    b = inverse_kinematics(x_e, y_e, theta_e)\n    T = forward_kinematics(T0, lv, b)\n    show_robot_arm(T)"
       },
       "cell_index": 31,
       "root": true
      }
     ]
    },
    "2b08bea1b4f54c9ba077c00da9f32df4": {
     "views": []
    },
    "2b93cbb0c4224f89849f2dde2fc15157": {
     "views": []
    },
    "2bc083e7865744218921a220b1dcd2bd": {
     "views": []
    },
    "2bd0e43b75c644de982f6ed6526efdd4": {
     "views": []
    },
    "2c34626d64f64cee97c80b20eca8bcca": {
     "views": []
    },
    "2d47a8fbe94e44558b858df765718bf6": {
     "views": []
    },
    "2da22ed89495462aa8367d393c2a7eb3": {
     "views": []
    },
    "2daf53bd4a7446b789c98057a1e8a250": {
     "views": []
    },
    "2dcbbf3b132149afa31062be6a07554c": {
     "views": []
    },
    "2e214dcc0b3c44e9b2328d1951fa682f": {
     "views": []
    },
    "2f158a0aa1a64a06b1e040595d5f3786": {
     "views": []
    },
    "2f2278ef4cfa4853819ae1f6527a3b73": {
     "views": []
    },
    "2f6d2e4e681c4aa69eb193d1231e33eb": {
     "views": []
    },
    "2fbda6a400e84a79aef5b70e619abb96": {
     "views": []
    },
    "3041d69ed88044cea4de4bd0f450c27d": {
     "views": []
    },
    "30d84e2c052640189dbdcf9392defa4c": {
     "views": []
    },
    "31cf7dfafa144ec4a2596eb29add3718": {
     "views": []
    },
    "31e0aedd3ffa43dea7a1c25a5500b106": {
     "views": []
    },
    "32abfd69cd694c50aa2ebee92df33674": {
     "views": []
    },
    "32d299c4eabe4ab0b7f7ce7d320c85bf": {
     "views": []
    },
    "32e5479e6e41479e984f122b75b58d11": {
     "views": []
    },
    "330fab827b204b90ad49370b88945728": {
     "views": []
    },
    "335ed94a336e40a2b69bd63a6f675584": {
     "views": []
    },
    "33602b70c8ec4149afabed51291da3d7": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "33b2e7d8d4c84e768423aa3803e2b9f5": {
     "views": []
    },
    "33d1a4a3e2984b5999adf4a82ed6d243": {
     "views": []
    },
    "34acdada4843453d86dc7812e4271c09": {
     "views": []
    },
    "356a4ddeac674b17a489dc73c02f2f57": {
     "views": []
    },
    "358e60da5d7c45278a2640a67c6af292": {
     "views": []
    },
    "36aa1b263e224022a6b374b09c44b6fb": {
     "views": []
    },
    "36ff2aeae7a14f91a7be43d3c47a0597": {
     "views": []
    },
    "374ce53463064914b8233dfced2d9d52": {
     "views": []
    },
    "37513503a13b45cfbd26e56238d8c949": {
     "views": []
    },
    "386c80b57d6a404992acaf6469506db3": {
     "views": []
    },
    "389ea982a40d46d591bebd0ee559801e": {
     "views": []
    },
    "38b47640815546939304ec2a6d71c3ad": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "38bc0f4ca75245689ea57a229c49472a": {
     "views": []
    },
    "38d0989572be494b80d396336d933313": {
     "views": []
    },
    "39240ffe45674b32aa64d099d6e61bd6": {
     "views": []
    },
    "3a5050c9cd9e457fae55d0a598eb4b4f": {
     "views": []
    },
    "3a80053831f94247947bda7171a375f5": {
     "views": []
    },
    "3b0b75b35ae34a6d9576ab19dea42ccd": {
     "views": []
    },
    "3b667c3b4fc4424d8893d82380ed312a": {
     "views": []
    },
    "3bd271b7ed304418a7d058b88a46817c": {
     "views": []
    },
    "3bfb2f44245f4631af2733821fc24f2d": {
     "views": []
    },
    "3c160aed9d4645a3867ab97e9d4428e8": {
     "views": []
    },
    "3c4064aa9a404ca0802954fe0baf05ad": {
     "views": []
    },
    "3c8d97fabeff4d4882982ae88559ca1d": {
     "views": []
    },
    "3ce5b9ab8fbc4af092b37669aef932a9": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "3cfd0856c6ad4d72957c378772dddceb": {
     "views": []
    },
    "3d5f1dddcb724d6f8776e5beb45145be": {
     "views": []
    },
    "3dc13517176b4d059867e0c5c14b333e": {
     "views": []
    },
    "3e69ea50d1bc4d33a564d6e3dc695803": {
     "views": []
    },
    "3e7ec1eec9bb432ab76638a80530882e": {
     "views": []
    },
    "3ec90899379e473a8a3d9537a9fd3e6a": {
     "views": []
    },
    "3ee2ee71c4df41fe8bcc0e36c035bdc9": {
     "views": []
    },
    "3f1c69d137514a16a98abd88055e502c": {
     "views": []
    },
    "3f419880ead047eda91f3d02a9958b64": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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        "execution_count": 7,
        "metadata": {
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        "outputs": [
         {
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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "5dbe6899de6941e4baa16dc072df40c9": {
     "views": []
    },
    "5e8769162a4140d5bc70b8aec03e7ad8": {
     "views": []
    },
    "5e87aee9f185402f8ff57a465fbe73e7": {
     "views": []
    },
    "5ef7e2e41c0043bc8a55756ce22df3e9": {
     "views": []
    },
    "5fef33cf88954c079bf52d5dbbb0339f": {
     "views": []
    },
    "61ac09cef9924de2a2146dcb2b2e852e": {
     "views": []
    },
    "621c02cb75364b558abc79df43f8b70e": {
     "views": []
    },
    "63511e5c5aa34ec3a58130ae0d8b6bdf": {
     "views": []
    },
    "63775a25bb2b4ee9b8446ceef21818fc": {
     "views": []
    },
    "639c23cf0db44536a5f78136ea5b449e": {
     "views": []
    },
    "63d34734668d4d4e8a9abd079cfffcc9": {
     "views": []
    },
    "647b63c63fed43daa91f41fe25b22482": {
     "views": []
    },
    "64976a06e0c24cbebbe7fe4ebe3ee1cd": {
     "views": []
    },
    "654d4b7dbb4a4a18bcb54cc292d2f4bb": {
     "views": []
    },
    "6628109154014be7a19c42fb56a91ef8": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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    "697a98b6e58844049a81cc4b5da87be0": {
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    "69d28d94f5eb40a3879b21f9507e6121": {
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    "6a212e13893a4ab0a241a89a6007906c": {
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    "6abd33d635584ac3bbe8902f7ddd90d4": {
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    "6b17c7b15ea1410a93477b40977226b5": {
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    "6ca5197312004a9683d386408cc08392": {
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    "6d017c1c4b01499693c498de96d7f97a": {
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    "6d17774c8b64462f93b8462a77e58415": {
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    "6db471bbc3794e53a4fda8828961360a": {
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    "6de8f2d679c1445fbc3ce051fec75a1e": {
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    "6e070ef4748e4e40a987b6aacf122e0c": {
     "views": []
    },
    "6e82cccbb1344bc3b20c3175f52608d0": {
     "views": []
    },
    "6e9a98ffa31b449d9c137b9f1f0359f3": {
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    },
    "6ecb03b3ef024b1085696a8f3b41d3ba": {
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    },
    "6ee8f1f634974b8ba9558e4322b4c6d4": {
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    "6f3a909c6f774a7b8d713440c87b0850": {
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    "7005e042210046bbbcb8774e321ad2de": {
     "views": []
    },
    "70e70828620b42e4a1691ab4a0002e47": {
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    },
    "71356f391594493c9b6618e8439701bc": {
     "views": []
    },
    "7157f92cb89a403785ac94702c38681a": {
     "views": []
    },
    "715a4c01b195434c9c1c082a04e7adde": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "71fe3ab37df047f8af2367d8f9a79994": {
     "views": []
    },
    "72231c63ee8d4c2196df2b889e895eb7": {
     "views": []
    },
    "7224f8df15bf4c81b657da552ca9ed24": {
     "views": []
    },
    "730a7d996a3a4ff18f7d3dd9ec5f5ae8": {
     "views": []
    },
    "73736b6f6f69435d971a25fe39a572df": {
     "views": []
    },
    "73b62c199ef741e6b2e86487476ed81d": {
     "views": []
    },
    "745aea5ce26e4cbd9559786ae4f2c9a5": {
     "views": []
    },
    "74932e9cb8f64a30b15936223eb666e6": {
     "views": []
    },
    "74ec10ad98f44cae8ec7cb23bca2e7e8": {
     "views": []
    },
    "755577b3878e4659a9d79c14d1a8b663": {
     "views": []
    },
    "75a0ef9fd2144683a0c221172e75686c": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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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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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "9a19791b387e4c5a913a92790141f553": {
     "views": []
    },
    "9a3dd9ada08d4312b729bc1891e43f1a": {
     "views": []
    },
    "9a81582415584ce588f6a1c53b927d32": {
     "views": []
    },
    "9ae0d4a38f1e4b498b0bc4801df700ec": {
     "views": []
    },
    "9aef67ddda784eb7b93dc9c617ef03ec": {
     "views": []
    },
    "9b917897eeb24144930072d47c8a7571": {
     "views": []
    },
    "9bd57fc229d24f9aa65f8366028e3007": {
     "views": []
    },
    "9bdd3a0d16de4cd7801d67957b196382": {
     "views": []
    },
    "9c13a849122d47458c4ab77c56edf863": {
     "views": []
    },
    "9c9748ffe74d4a5cbc26aef71c6e4780": {
     "views": []
    },
    "9ce4a1bcfd694843a536393891af83be": {
     "views": []
    },
    "9d7f35226a3a4f6c9ae12362454a9716": {
     "views": []
    },
    "9dba4d8a3c4d420492b5b5ac732b5fa0": {
     "views": []
    },
    "9dd653229bd649df86e7ce3d5688e963": {
     "views": []
    },
    "9dddfa92060a4a4ab09253e94473d45e": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "b0939de0ddd64138a947665fc7d23e20": {
     "views": []
    },
    "b097623d1e7a4a48b11b1f405d98d4e8": {
     "views": []
    },
    "b1318684110d42d79687a6386cbae811": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 25,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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HZBAxO/0aq2plkj9M8u4dLDto+Pqfbg2aGlz17Q1JXjb7PJiqWrrdFdhekOSIJLdW1UGz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\nAACgO0IHAADojtABAAC6I3QAAIDu/P+zHD7CA7x3IAAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6b4d6ad0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "from numpy import asarray\ntheta = asarray([0.1] * N)\nlambda_ = 0.001\ndef inverse_kinematics(x_e, y_e, theta_e, theta):\n    target = matrix([[x_e, y_e, theta_e]])\n    while True:\n        T = forward_kinematics(T0, lv, theta)\n        Te = matrix([from_trans(T[-1])])\n        e = target - Te\n        T = matrix([from_trans(i) for i in T[1:-1]])\n        J = Te - T\n        J = J.T\n        J[-1, :] = 1  # angular velocity\n        JJT = J * J.T\n        d_theta = lambda_ * J.T * JJT.I * e.T\n        theta += asarray(d_theta.T)[0]\n        if  np.linalg.norm(d_theta) < 1e-4:\n            break\n    return theta\n\nT = forward_kinematics(T0, lv, theta)\nTe = matrix([from_trans(T[-1])])\n\n@interact(x_e=(0, max_len, 0.01), y_e=(-max_len, max_len, 0.01), theta_e=(-pi, pi, 0.01), theta=fixed(theta))\ndef set_end_effector(x_e=Te[0,0], y_e=Te[0,1], theta_e=Te[0,2], theta=theta):\n    theta = inverse_kinematics(x_e, y_e, theta_e, theta)\n    T = forward_kinematics(T0, lv, theta)\n    show_robot_arm(T)\n\n# NOTE\n# while numerical inverse kinematics is easy to implemente, two issues have to be keep in mind:\n# * stablility: the correction step (lambda_) has to be small, but it will take longer time to converage\n# * singularity: there are singularity poses (all 0, for example), the correction will be 0, so the algorithm won't work. That's why many robots bends its leg when walking"
       },
       "cell_index": 33,
       "root": true
      }
     ]
    },
    "b249266f72ce41b096bfeafd4789ed36": {
     "views": []
    },
    "b27d4b54be3c499bac6ce346de64e0bb": {
     "views": []
    },
    "b28e241d2ac14db39d89dc3952ff8974": {
     "views": []
    },
    "b402a799328c49e99abd5beb99ad63cb": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "b491570fd738421e971d2f01f6e5e025": {
     "views": []
    },
    "b4cd5a2f926e43be8c8028d2c071e2e5": {
     "views": []
    },
    "b579ef0051d945a9adc2c1c7a0387ab9": {
     "views": []
    },
    "b7203daacce34cedab333faf601f8080": {
     "views": []
    },
    "b74a7887033349f59d4dcce5df469ebe": {
     "views": []
    },
    "b7da1a920b6441d8b303aacc5472a743": {
     "views": []
    },
    "b7ebf419b324467196b93aec2ddfb2e5": {
     "views": []
    },
    "b7f65fb78b1f4963923e4b2b889ae71d": {
     "views": []
    },
    "b80d613ff47c4b5e8a92d15ce973c058": {
     "views": []
    },
    "b80d7b1129344995a0e08645bd02c43c": {
     "views": []
    },
    "b8af5204f7bd46e49556c9b9339a61d1": {
     "views": []
    },
    "b8f0724bbf9c4379af6c6b24598a2e45": {
     "views": []
    },
    "b94bcf1136124faa83d0d49f6ffd01ef": {
     "views": []
    },
    "b94d48ddfc0c4b9bbe799e23d02bdb23": {
     "views": []
    },
    "b9a4a36b6f0541d08765760f39d5979d": {
     "views": []
    },
    "b9b0e906c50b4fe194ba0561860a48d2": {
     "views": []
    },
    "b9b20e2b81ef469fbc2147fe4fd1fbcd": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "b9eee2008f4e4bbdb794dc096ad66eed": {
     "views": []
    },
    "bb317461e530495584bed769e3907371": {
     "views": []
    },
    "bb4935d65aa4419a903c269ee1c245c6": {
     "views": []
    },
    "bb5048ede7214dbc95ddd20b2860d967": {
     "views": []
    },
    "bc375a0bce7f422988cf8184f1b766e4": {
     "views": []
    },
    "bcdf58e3772d46bc85c99b311fca3597": {
     "views": []
    },
    "bd0e575537f84a4dbbe73055036d545f": {
     "views": []
    },
    "bda506ba1987430e8e52e2370d61f1a7": {
     "views": []
    },
    "bde65c86e6d1468fb60ae401c4c8b29a": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "be1acc039ba446ee8faa23f50a109cad": {
     "views": []
    },
    "bf5d09791dc84a05a89a788915401157": {
     "views": []
    },
    "c04926eeeee849949eb61738e5e66675": {
     "views": []
    },
    "c05c848c83c649bca51c6b180a176e4a": {
     "views": []
    },
    "c1778dbcd3454ee190824e9101a0b647": {
     "views": []
    },
    "c17e1418e17445ef8f2a0f8e1dad0c38": {
     "views": []
    },
    "c1eaf2eb5c684f2db956365800471e0d": {
     "views": []
    },
    "c205b755237d444eb87a20babac93ad5": {
     "views": []
    },
    "c229e9376d6a4e1b8243aa035744b4a8": {
     "views": []
    },
    "c296feae9fc241f6b7cc2155c35d2a21": {
     "views": []
    },
    "c298b40a15fc4980a8bec3c3c36e1cdf": {
     "views": []
    },
    "c2a9da2b5f9a441c86c7ca1d98bdfb5c": {
     "views": []
    },
    "c2ba4e3cb0024641939db9301b068ace": {
     "views": []
    },
    "c2bae26c415844b19c575a61f4e98967": {
     "views": []
    },
    "c2fa97d314134fdf8ed6e6db1428cd88": {
     "views": []
    },
    "c3703cc105c14aa29739a9c4aa53923c": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "c39014455211404cae26ef0b4179cc29": {
     "views": []
    },
    "c3ae4ee5304c468f84b15b2f834369c3": {
     "views": []
    },
    "c40830ec69ab4c61882d3bb657705a29": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
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    "da8213350eb44e03930067f7616a30ba": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "dae71c9df74f476d9bdca7dbdc9c4813": {
     "views": []
    },
    "db36bc23e8224a13bd5bd39d59b341ea": {
     "views": []
    },
    "db74c7e7bc0445af81e1a1a8cd4c1b2a": {
     "views": []
    },
    "dd7767d3ee1e4fdf9457356dcb9d8856": {
     "views": []
    },
    "dd9547c6a0de46bfbb8766c95faad93a": {
     "views": []
    },
    "de9d33d2fd504e6591a42183a04245c4": {
     "views": []
    },
    "dea19948b29b4852a2fafd8eb5ed81ba": {
     "views": []
    },
    "df3f2a8c0b8d4450b9c81e9bb6bf5a48": {
     "views": []
    },
    "df69964a0a784d678379ef980433bac2": {
     "views": []
    },
    "dfef404d9e874f5ba4bcb626c407e772": {
     "views": []
    },
    "e07f7328f0cd427d9af41fac575919a2": {
     "views": []
    },
    "e0fbecdda5c24dd0b7ae34203dd4bd08": {
     "views": []
    },
    "e14cff4f97544aa2850e91e886f48705": {
     "views": []
    },
    "e172cd6cdcb1425f9686213f3e73af38": {
     "views": []
    },
    "e21b7ba964e54981ac5505b6a6d8e9d4": {
     "views": []
    },
    "e27ff57b6a15404a9f220a4f234d012f": {
     "views": []
    },
    "e35aa9674b9c4be1a8bf4a6b6c3f61b0": {
     "views": []
    },
    "e399e06d7f1d45e98f6db2a87d658e37": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
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      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
     ]
    },
    "ebd1bb1a6e184910a6ea9194bb246247": {
     "views": []
    },
    "ec331fec5dc849a096d6fc4614f5c6a1": {
     "views": []
    },
    "ed2373b2c86e4249963f1f0c1539875b": {
     "views": []
    },
    "ed5515d5ada341d5bddc42285601a968": {
     "views": []
    },
    "ee6badce55564b0bb690ba653a5c311b": {
     "views": []
    },
    "ee6e242f9f5b4d1197ca01c6bd49119c": {
     "views": []
    },
    "eebf627136a94f8db9d06a66bc1f5ff7": {
     "views": []
    },
    "eef0260f78af4a21ad250a3641b7a8d8": {
     "views": [
      {
       "cell": {
        "cell_type": "code",
        "execution_count": 7,
        "metadata": {
         "collapsed": false,
         "trusted": true
        },
        "outputs": [
         {
          "data": {
           "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
           "text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
          },
          "metadata": {},
          "output_type": "display_data"
         }
        ],
        "source": "for i in range(N):\n    @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n    def set_joint_angle(n, value=0):\n        global a\n        a[n] = value\n        T = forward_kinematics(T0, l, a)\n        show_robot_arm(T)\n"
       },
       "cell_index": 10,
       "root": true
      }
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