BEE_Simulator_ArmControl_VREP_trajectories-generator_v1-SQUARE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook connects to V-REP to read the joint angles from Baxter\n",
    "\n",
    "# SQUARE\n",
    "\n",
    "## Baxter arm specifications:\n",
    "http://sdk.rethinkrobotics.com/wiki/Hardware_Specifications#Baxter_Arm_Specifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import vrep\n",
    "import numpy\n",
    "import time\n",
    "import sys\n",
    "import subprocess\n",
    "import save_load_file as slf\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Controls if the results are saved to a file\n",
    "#\n",
    "\n",
    "save2file = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Location of the V-REP simulator main file:\n",
    "vrep_location = \"/Applications/V-REP_PRO_EDU_V3_2_2_Mac/vrep.app/Contents/MacOS/vrep\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load movement_generation_training.py\n",
    "\n",
    "# Generates the movements according to:\n",
    "# Flash, Tamar and Neville Hogan. 1985. The Coordination of Arm Movements: An Experimentally Confirmed \n",
    "# Mathematical Model. The Journal of Neuroscience 5 (7): 1688-1703\n",
    "def movement_generation_training(xstart,xdest,MT,t):\n",
    "    '''\n",
    "    xstart,ystart: initial position of the trajectory\n",
    "    MT: total time spent doing the trajectory\n",
    "    t: current time\n",
    "    \n",
    "    returns a matrix: [[x0,y0],[x1,y1],...]\n",
    "    '''\n",
    "    x_t=xstart+(xstart-xdest)*(15*(t/MT)**4-6*(t/MT)**5-10*(t/MT)**3)\n",
    "    return numpy.array(x_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sim_set = \"square\" # basically is the name of the folder where the data is read/saved\n",
    "base_dir = \"BaxterArm_VREP_simulation_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MT=0.5 # The SQUARE is made of four straight lines (0.5s x 4 = 2s)\n",
    "time_step=2E-3\n",
    "t_mov=numpy.arange(0, MT, time_step) # t starts in 0s and steps time_step(s) until reaches MT(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Square\n",
    "trajectories = [[[-0.5,-0.5],[0.5,-0.5]],\\\n",
    "                [[0.5,-0.5],[0.5,0.5]],\\\n",
    "                [[0.5,0.5],[-0.5,0.5]],\\\n",
    "                [[-0.5,0.5],[-0.5,-0.5]]]\n",
    "\n",
    "orig_trajectories = numpy.array(trajectories)\n",
    "\n",
    "trajectories = numpy.array(trajectories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creates an empty matrix to store the points\n",
    "XY_movement = numpy.zeros((0,2))\n",
    "\n",
    "for tji in range(4):\n",
    "\n",
    "    xstart=trajectories[tji][0][0]\n",
    "    ystart=trajectories[tji][0][1]\n",
    "    xdest=trajectories[tji][1][0]\n",
    "    ydest=trajectories[tji][1][1]\n",
    "\n",
    "    x_movement = movement_generation_training(xstart,xdest,MT,t_mov)\n",
    "    y_movement = movement_generation_training(ystart,ydest,MT,t_mov)\n",
    "    \n",
    "    XY_movement_temp = numpy.zeros((len(t_mov),2))\n",
    "\n",
    "    XY_movement_temp[:,0]=x_movement\n",
    "    XY_movement_temp[:,1]=y_movement\n",
    "\n",
    "    # Concatenates all the lines to make a square\n",
    "    XY_movement=numpy.concatenate((XY_movement,numpy.round(XY_movement_temp/6.0,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAJZCAYAAAAZPBl8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc94150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tji_orig = 0\n",
    "plt.figure(figsize =(10,10))\n",
    "plt.plot(XY_movement[:,0],XY_movement[:,1])\n",
    "plt.plot([XY_movement[0,0]],[XY_movement[0,1]],'go')\n",
    "plt.plot([XY_movement[-1,0]],[XY_movement[-1,1]],'ro')\n",
    "# plt.xlim(-1,1)\n",
    "# plt.ylim(-1,1)\n",
    "plt.title(\"Cartesian Movement\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Saves the original values to a file\n",
    "# \n",
    "\n",
    "if save2file:\n",
    "    slf.save_to_file(XY_movement,base_dir+\"/\"+sim_set+\"/\"+sim_set+\".pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load vrep_training.py\n",
    "\n",
    "import os\n",
    "\n",
    "p = None\n",
    "\n",
    "# To automate the process is necessary to avoid the firewall message by deactivating it!!!!\n",
    "\n",
    "\n",
    "p = subprocess.Popen([vrep_location, '-h', '-s', '-q', \\\n",
    "                      os.getcwd()+'/VREP_scenes/Baxter_IK_felt_pen_pick-and-place_learning_ICONIP2016.ttt'])  \n",
    "\n",
    "time.sleep(1.0)\n",
    "\n",
    "# Object names (used inside the simulation)\n",
    "# They are used to retrieve the object handles\n",
    "s0_name = 'Baxter_leftArm_joint1'\n",
    "s1_name = 'Baxter_leftArm_joint2'\n",
    "e0_name = 'Baxter_leftArm_joint3'\n",
    "e1_name = 'Baxter_leftArm_joint4'\n",
    "w0_name = 'Baxter_leftArm_joint5'\n",
    "w1_name = 'Baxter_leftArm_joint6'\n",
    "w2_name = 'Baxter_leftArm_joint7'\n",
    "XY_pos = 'IK_XY_MASTER' # Controls the cartesian X,Y values (pen)\n",
    "Z_pos = 'IK_Z_MASTER' # Controls the cartesian Z value (pen)\n",
    "\n",
    "# simxPauseCommunication(clientID,1);\n",
    "# simxSetJointPosition(clientID,joint1Handle,joint1Value,simx_opmode_oneshot);\n",
    "# simxSetJointPosition(clientID,joint2Handle,joint2Value,simx_opmode_oneshot);\n",
    "# simxSetJointPosition(clientID,joint3Handle,joint3Value,simx_opmode_oneshot);\n",
    "# simxPauseCommunication(clientID,0);\n",
    "# Above's 3 joints will be received and set on the V-REP side at the same time\n",
    "\n",
    "print 'Program started'\n",
    "vrep.simxFinish(-1) # just in case, close all opened connections\n",
    "\n",
    "# Connects to the simulator\n",
    "clientID=vrep.simxStart('127.0.0.1',19999,True,True,5000,5)\n",
    "\n",
    "if clientID!=-1:\n",
    "    print 'Connected to remote API server'\n",
    "    res,objs=vrep.simxGetObjects(clientID,vrep.sim_handle_all,vrep.simx_opmode_oneshot_wait) # gets ALL object handles\n",
    "    if res==vrep.simx_return_ok:\n",
    "        print 'Number of objects in the scene: ',len(objs)\n",
    "        res0,XY=vrep.simxGetObjectHandle(clientID,XY_pos,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the IK_XY_MASTER\n",
    "        res1,Z=vrep.simxGetObjectHandle(clientID,Z_pos,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the IK_Z_MASTER\n",
    "        \n",
    "        res2,s0=vrep.simxGetObjectHandle(clientID,s0_name,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the s0 joint\n",
    "        res3,s1=vrep.simxGetObjectHandle(clientID,s1_name,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the s1 joint\n",
    "#         resX,e0=vrep.simxGetObjectHandle(clientID,e0_name,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the e0 joint\n",
    "        res4,e1=vrep.simxGetObjectHandle(clientID,e1_name,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the e1 joint\n",
    "#         resX,w0=vrep.simxGetObjectHandle(clientID,w0_name,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the w0 joint\n",
    "        res5,w1=vrep.simxGetObjectHandle(clientID,w1_name,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the w1 joint\n",
    "#         resX,w2=vrep.simxGetObjectHandle(clientID,w2_name,vrep.simx_opmode_oneshot_wait) # gets specifically the handle for the w2 joint\n",
    "\n",
    "        if (res1*res2*res3*res4*res5)==vrep.simx_return_ok:\n",
    "            print \"Ok, I'm in!\"\n",
    "\n",
    "            joint_list = [s0,s1,e1,w1]\n",
    "            joint_positions = []\n",
    "\n",
    "            #\n",
    "            # These are the equivalent to X,Y,Z = 0,0,0 in my system:\n",
    "            #\n",
    "            \n",
    "            # Reads the current XY_Master position [X,Y]\n",
    "            print \"Reads the current XY_Master position [X,Y]\"\n",
    "            res,posXY=vrep.simxGetObjectPosition(clientID,XY,vrep.sim_handle_parent,vrep.simx_opmode_oneshot_wait)\n",
    "            time.sleep(0.5)\n",
    "            \n",
    "            # Reads the current Z_Master position [Z]\n",
    "            print \"Reads the current Z_Master position [Z]\"\n",
    "            res,posZ=vrep.simxGetObjectPosition(clientID,Z,vrep.sim_handle_parent,vrep.simx_opmode_oneshot_wait)            \n",
    "            time.sleep(0.5)\n",
    "            \n",
    "            print \"Initial XY Master Position\", posXY\n",
    "            print \"Initial Z Master Position\", posZ\n",
    "\n",
    "\n",
    "            # Lifts the pen to position it in the trajectory's starting point\n",
    "            # This is acomplished by increasing the Z value (posZ[2])\n",
    "            print \"Lifts the pen to position it in the trajectory's starting point\"\n",
    "            # I need a smooth movements, otherwise the pen touches the table by mistake.\n",
    "            for k in range(2):\n",
    "                res = vrep.simxSetObjectPosition(clientID,Z,vrep.sim_handle_parent,[posZ[0], posZ[1], posZ[2]+0.001*(k+1)],vrep.simx_opmode_oneshot_wait)\n",
    "                time.sleep(0.5)\n",
    "                        \n",
    "            # Moves to the first position\n",
    "            # This movement is relative to the current XY position!!!\n",
    "            print \"Moves to the first position\"\n",
    "            res = vrep.simxSetObjectPosition(clientID,XY,vrep.sim_handle_parent,[posXY[0]+XY_movement[0,0], posXY[1]+XY_movement[0,1], posXY[2]],vrep.simx_opmode_oneshot_wait)\n",
    "            time.sleep(0.5)\n",
    "            \n",
    "            \n",
    "            # Puts down the pen\n",
    "            print \"Puts down the pen\"\n",
    "            # I need smooth movements, otherwise the pen touches the table by mistake.\n",
    "            # Reads the current Z_Master position [Z]\n",
    "            res,posZcurrent=vrep.simxGetObjectPosition(clientID,Z,vrep.sim_handle_parent,vrep.simx_opmode_oneshot_wait)            \n",
    "            time.sleep(0.5)\n",
    "            for k in range(2):\n",
    "                res = vrep.simxSetObjectPosition(clientID,Z,vrep.sim_handle_parent,[posZcurrent[0], posZcurrent[1], posZcurrent[2]-0.001*(k+1)],vrep.simx_opmode_oneshot_wait)\n",
    "                time.sleep(0.5)            \n",
    "            \n",
    "            if res!=0:\n",
    "                vrep.simxFinish(clientID)\n",
    "                print 'Remote API function call returned with error code (start-up): ',res\n",
    "          \n",
    "            # Reads the current XY_Master position [X,Y]\n",
    "            print \"Reads the current XY_Master position [X,Y]\"\n",
    "            res,posXYcurrent=vrep.simxGetObjectPosition(clientID,XY,vrep.sim_handle_parent,vrep.simx_opmode_oneshot_wait)\n",
    "            time.sleep(0.5)\n",
    "            \n",
    "            # Reads the current Z_Master position [Z]\n",
    "            print \"Reads the current Z_Master position [Z]\"\n",
    "            res,posZcurrent=vrep.simxGetObjectPosition(clientID,Z,vrep.sim_handle_parent,vrep.simx_opmode_oneshot_wait)            \n",
    "            time.sleep(0.5)            \n",
    "\n",
    "            print \"XY Master Position:\", posXYcurrent\n",
    "            print \"Z Master Position:\", posZcurrent\n",
    "            \n",
    "            \n",
    "            i = 0\n",
    "            for hi in XY_movement:\n",
    "                # Reads and saves the current joint positions\n",
    "                temp = []\n",
    "                for ji in joint_list:\n",
    "                    res,joint_pos=vrep.simxGetJointPosition(clientID,ji,vrep.simx_opmode_oneshot_wait)\n",
    "                    temp.append(joint_pos)\n",
    "                    time.sleep(0.0025)\n",
    "                joint_positions.append(temp)\n",
    "                \n",
    "                if i==0:\n",
    "                    if save2file:\n",
    "                        numpy.save(base_dir+\"/\"+sim_set+\"/starting_joint_pos.npy\",numpy.array(temp))                \n",
    "\n",
    "                cmd_pos = numpy.array(posXY)+numpy.concatenate([hi,[0]]) # Sums X and Y in the pos and [hi[0],hi[1],0] arrays\n",
    "\n",
    "                i+=1\n",
    "                # Sets the new position\n",
    "                res = vrep.simxSetObjectPosition(clientID,XY,vrep.sim_handle_parent,cmd_pos,vrep.simx_opmode_oneshot_wait)\n",
    "                time.sleep(0.0025) # 0.05 here was generating too many time-out errors!                \n",
    "                \n",
    "                if res!=0:\n",
    "                    vrep.simxFinish(clientID)\n",
    "                    print 'Remote API function call returned with error code (main loop): ',res\n",
    "                    break\n",
    "            \n",
    "\n",
    "            # Lifts the pen to position it in the trajectory's starting point\n",
    "            # I need a smooth movements, otherwise the pen touches the table by mistake.\n",
    "            # Reads the current Z_Master position [Z]\n",
    "            res,posZcurrent=vrep.simxGetObjectPosition(clientID,Z,vrep.sim_handle_parent,vrep.simx_opmode_oneshot_wait)            \n",
    "            time.sleep(0.5)            \n",
    "        \n",
    "            for k in range(2):\n",
    "                res = vrep.simxSetObjectPosition(clientID,Z,vrep.sim_handle_parent,[posZcurrent[0], posZcurrent[1], posZcurrent[2]+0.001*(k+1)],vrep.simx_opmode_oneshot_wait)\n",
    "                time.sleep(0.5)            \n",
    "\n",
    "            \n",
    "            # Moves it back to the first position\n",
    "            res = vrep.simxSetObjectPosition(clientID,XY,vrep.sim_handle_parent,posXY,vrep.simx_opmode_oneshot_wait)\n",
    "            time.sleep(0.5)\n",
    "            \n",
    "            # Puts down the pen\n",
    "            # I need a smooth movements, otherwise the pen touches the table by mistake.\n",
    "            # Reads the current Z_Master position [Z]\n",
    "            res,posZcurrent=vrep.simxGetObjectPosition(clientID,Z,vrep.sim_handle_parent,vrep.simx_opmode_oneshot_wait)            \n",
    "            time.sleep(0.5)\n",
    "            for k in range(2):\n",
    "                res = vrep.simxSetObjectPosition(clientID,Z,vrep.sim_handle_parent,[posZcurrent[0], posZcurrent[1], posZcurrent[2]-0.001*(k+1)],vrep.simx_opmode_oneshot_wait)\n",
    "                time.sleep(0.5)      \n",
    "                \n",
    "            if res!=0:\n",
    "                vrep.simxFinish(clientID)\n",
    "                print 'Remote API function call returned with error code (last position): ',res\n",
    "                \n",
    "        else:\n",
    "            print 'Remote API function call returned with error code (object handles): ',res\n",
    "    else:\n",
    "        print 'Remote API function call returned with error code (first connection): ',res\n",
    "#     returncode=vrep.simxStopSimulation(clientID,vrep.simx_opmode_oneshot_wait)\n",
    "    vrep.simxFinish(clientID)\n",
    "else:\n",
    "    print 'Failed connecting to remote API server'\n",
    "print 'Program ended'\n",
    "\n",
    "if p:\n",
    "    # Terminates the process, in the case the connection above failed.\n",
    "    p.terminate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "joint_positions = numpy.array(joint_positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint_positions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4IAAANwCAYAAAB6U9RzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyTJKBEVERERERLKMEkEREREREZEs87/hQ6yAqN8hTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bce8510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize =(15,15));\n",
    "\n",
    "plt.subplot(7,1,1)\n",
    "plt.plot(XY_movement[:,0],XY_movement[:,1])\n",
    "plt.title(\"X and Y\")\n",
    "\n",
    "plt.subplot(7,1,2)\n",
    "plt.plot(XY_movement[:,0])\n",
    "plt.xlim(0,len(XY_movement))\n",
    "plt.title(\"Only X\")\n",
    "\n",
    "plt.subplot(7,1,3)\n",
    "plt.plot(XY_movement[:,1])\n",
    "plt.xlim(0,len(XY_movement))\n",
    "plt.title(\"Only Y\")\n",
    "\n",
    "plt.subplot(7,1,4)\n",
    "plt.plot(joint_positions[:,0])\n",
    "plt.xlim(0,len(joint_positions))\n",
    "plt.title(\"Joint s0\")\n",
    "\n",
    "plt.subplot(7,1,5)\n",
    "plt.plot(joint_positions[:,1])\n",
    "plt.xlim(0,len(joint_positions))\n",
    "plt.title(\"Joint s1\")\n",
    "\n",
    "plt.subplot(7,1,6)\n",
    "plt.plot(joint_positions[:,2])\n",
    "plt.xlim(0,len(joint_positions))\n",
    "plt.title(\"Joint e1\")\n",
    "\n",
    "plt.subplot(7,1,7)\n",
    "plt.plot(joint_positions[:,3])\n",
    "plt.xlim(0,len(joint_positions))\n",
    "plt.title(\"Joint w1\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Only here the points are saved to a file\n",
    "#\n",
    "\n",
    "if save2file:\n",
    "    numpy.save(base_dir+\"/\"+sim_set+\"/XY_movement_\"+sim_set+\".npy\",joint_positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.11"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}