Visual Servoing Platform  version 3.2.0 under development (2018-08-18)
servoAfma4Point2DCamVelocityKalman.cpp

Example of eye-in-hand control law. We control here a real robot, the Afma4 robot (cylindrical robot, with 4 degrees of freedom). The velocity is computed in the camera frame. The visual feature is the center of gravity of a point.In this example we estimate the velocity of the target in order to reduce the tracking error when the target is moving. The velocity of the target is filtered by a Kalman filter with a constant velocity state model, or a constant acceleration state model.

/****************************************************************************
*
* This file is part of the ViSP software.
* Copyright (C) 2005 - 2017 by Inria. All rights reserved.
*
* This software is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
* See the file LICENSE.txt at the root directory of this source
* distribution for additional information about the GNU GPL.
*
* For using ViSP with software that can not be combined with the GNU
* GPL, please contact Inria about acquiring a ViSP Professional
* Edition License.
*
* See http://visp.inria.fr for more information.
*
* This software was developed at:
* Inria Rennes - Bretagne Atlantique
* Campus Universitaire de Beaulieu
* 35042 Rennes Cedex
* France
*
* If you have questions regarding the use of this file, please contact
* Inria at visp@inria.fr
*
* This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
* WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
*
* Description:
* tests the control law
* eye-in-hand control
* velocity computed in the camera frame
*
* Authors:
* Eric Marchand
* Fabien Spindler
*
*****************************************************************************/
#include <stdlib.h>
#include <visp3/core/vpConfig.h>
#include <visp3/core/vpDebug.h> // Debug trace
#if (defined(VISP_HAVE_AFMA4) && defined(VISP_HAVE_DC1394))
#include <visp3/core/vpDisplay.h>
#include <visp3/core/vpImage.h>
#include <visp3/gui/vpDisplayGTK.h>
#include <visp3/gui/vpDisplayOpenCV.h>
#include <visp3/gui/vpDisplayX.h>
#include <visp3/sensor/vp1394TwoGrabber.h>
#include <visp3/blob/vpDot2.h>
#include <visp3/core/vpException.h>
#include <visp3/core/vpHomogeneousMatrix.h>
#include <visp3/core/vpIoTools.h>
#include <visp3/core/vpLinearKalmanFilterInstantiation.h>
#include <visp3/core/vpMath.h>
#include <visp3/core/vpPoint.h>
#include <visp3/io/vpParseArgv.h>
#include <visp3/robot/vpRobotAfma4.h>
#include <visp3/visual_features/vpFeatureBuilder.h>
#include <visp3/visual_features/vpFeaturePoint.h>
#include <visp3/vs/vpAdaptiveGain.h>
#include <visp3/vs/vpServo.h>
#include <visp3/vs/vpServoDisplay.h>
// List of allowed command line options
#define GETOPTARGS "hK:l:"
typedef enum { K_NONE, K_VELOCITY, K_ACCELERATION } KalmanType;
void usage(const char *name, const char *badparam, KalmanType &kalman)
{
fprintf(stdout, "\n\
Tests a control law with the following characteristics:\n\
- eye-in-hand control\n\
- camera velocity are computed\n\
- servo on 1 points.\n\
- Kalman filtering\n\
\n\
SYNOPSIS\n\
%s [-K <0|1|2|3>] [-h]\n", name);
fprintf(stdout, "\n\
OPTIONS: Default\n\
-l <%%f> \n\
Set the constant gain. By default adaptive gain. \n\
\n\
-K <0|1|2> %d\n\
Kalman filtering:\n\
0: none\n\
1: velocity model\n\
2: acceleration model\n\
\n\
-h\n\
Print the help.\n", (int)kalman);
if (badparam) {
fprintf(stderr, "ERROR: \n");
fprintf(stderr, "\nBad parameter [%s]\n", badparam);
}
}
bool getOptions(int argc, const char **argv, KalmanType &kalman, bool &doAdaptativeGain,
vpAdaptiveGain &lambda) // gain lambda
{
const char *optarg;
int c;
while ((c = vpParseArgv::parse(argc, argv, GETOPTARGS, &optarg)) > 1) {
switch (c) {
case 'K':
kalman = (KalmanType)atoi(optarg);
break;
case 'l':
doAdaptativeGain = false;
lambda.initFromConstant(atof(optarg));
break;
case 'h':
usage(argv[0], NULL, kalman);
return false;
break;
default:
usage(argv[0], optarg, kalman);
return false;
break;
}
}
if ((c == 1) || (c == -1)) {
// standalone param or error
usage(argv[0], NULL, kalman);
std::cerr << "ERROR: " << std::endl;
std::cerr << " Bad argument " << optarg << std::endl << std::endl;
return false;
}
return true;
}
int main(int argc, const char **argv)
{
try {
KalmanType opt_kalman = K_NONE;
vpAdaptiveGain lambda; // Gain de la commande
bool doAdaptativeGain = true; // Compute adaptative gain
lambda.initStandard(4, 0.2, 40);
int opt_cam_frequency = 60; // 60 Hz
// Read the command line options
if (getOptions(argc, argv, opt_kalman, doAdaptativeGain, lambda) == false) {
return (-1);
}
// Log file creation in /tmp/$USERNAME/log.dat
// This file contains by line:
// - the 6 computed cam velocities (m/s, rad/s) to achieve the task
// - the 6 mesured joint velocities (m/s, rad/s)
// - the 6 mesured joint positions (m, rad)
// - the 2 values of s - s*
std::string username;
// Get the user login name
// Create a log filename to save velocities...
std::string logdirname;
logdirname = "/tmp/" + username;
// Test if the output path exist. If no try to create it
if (vpIoTools::checkDirectory(logdirname) == false) {
try {
// Create the dirname
} catch (...) {
std::cerr << std::endl << "ERROR:" << std::endl;
std::cerr << " Cannot create " << logdirname << std::endl;
exit(-1);
}
}
std::string logfilename;
logfilename = logdirname + "/log.dat";
// Open the log file name
std::ofstream flog(logfilename.c_str());
vpServo task;
vp1394TwoGrabber g(false);
switch (opt_cam_frequency) {
case 15:
break;
case 30:
break;
case 60:
break;
}
g.open(I);
for (int i = 0; i < 10; i++) // 10 acquisition to warm up the camera
g.acquire(I);
#ifdef VISP_HAVE_X11
vpDisplayX display(I, 100, 100, "Current image");
#elif defined(VISP_HAVE_OPENCV)
vpDisplayOpenCV display(I, 100, 100, "Current image");
#elif defined(VISP_HAVE_GTK)
vpDisplayGTK display(I, 100, 100, "Current image");
#endif
std::cout << std::endl;
std::cout << "-------------------------------------------------------" << std::endl;
std::cout << "Test program for target motion compensation using a Kalman "
"filter "
<< std::endl;
std::cout << "Eye-in-hand task control, velocity computed in the camera frame" << std::endl;
std::cout << "Task : servo a point \n" << std::endl;
// Kalman filtering
switch (opt_kalman) {
case K_NONE:
std::cout << "Servo with no target motion compensation (see -K option)\n";
break;
case K_VELOCITY:
std::cout << "Servo with target motion compensation using a Kalman filter\n"
<< "with constant velocity modelization (see -K option)\n";
break;
case K_ACCELERATION:
std::cout << "Servo with target motion compensation using a Kalman filter\n"
<< "with constant acceleration modelization (see -K option)\n";
break;
}
std::cout << "-------------------------------------------------------" << std::endl;
std::cout << std::endl;
vpDot2 dot;
std::cout << "Click on the dot..." << std::endl;
dot.setGraphics(true);
dot.initTracking(I);
cog = dot.getCog();
vpRobotAfma4 robot;
double px = 1000;
double py = 1000;
double u0 = I.getWidth() / 2.;
double v0 = I.getHeight() / 2.;
vpCameraParameters cam(px, py, u0, v0);
// Sets the current position of the visual feature
// Sets the desired position of the visual feature
pd.buildFrom(0, 0, 1);
// Define the task
// - we want an eye-in-hand control law
// - robot is controlled in the camera frame
task.setServo(vpServo::EYEINHAND_CAMERA);
// - we want to see a point on a point
std::cout << std::endl;
task.addFeature(p, pd);
// - set the gain
task.setLambda(lambda);
// Display task information
// task.print() ;
//--------------------------------------------------------------------------
//--------------------------------------------------------------------------
// Initialize the kalman filter
unsigned int nsignal = 2; // The two values of dedt
double rho = 0.3;
vpColVector sigma_state;
vpColVector sigma_measure(nsignal);
unsigned int state_size = 0; // Kalman state vector size
switch (opt_kalman) {
case K_VELOCITY: {
// Set the constant velocity state model used for the filtering
state_size = kalman.getStateSize();
sigma_state.resize(state_size * nsignal);
sigma_state = 0.00001; // Same state variance for all signals
sigma_measure = 0.05; // Same measure variance for all the signals
double dummy = 0; // non used parameter dt for the velocity state model
kalman.initFilter(nsignal, sigma_state, sigma_measure, rho, dummy);
break;
}
case K_ACCELERATION: {
// Set the constant acceleration state model used for the filtering
state_size = kalman.getStateSize();
sigma_state.resize(state_size * nsignal);
sigma_state = 0.00001; // Same variance for all the signals
sigma_measure = 0.05; // Same measure variance for all the signals
double dt = 1. / opt_cam_frequency;
kalman.initFilter(nsignal, sigma_state, sigma_measure, rho, dt);
break;
}
default:
break;
}
int iter = 0;
double t_1, Tv_0;
vpColVector vm(6), vm_0(6);
vpColVector v(6), v1(6), v2(6); // robot velocities
// task error
vpColVector err(2), err_0(2), err_1(2);
vpColVector dedt_filt(2), dedt_mes(2);
t_1 = vpTime::measureTimeMs(); // t_1: time at previous iter
Tv_0 = 0;
//
// Warning: In all varaible names,
// _0 means the value for the current iteration (t=0)
// _1 means the value for the previous iteration (t=-1)
// _2 means the value for the previous previous iteration (t=-2)
//
std::cout << "\nHit CTRL-C to stop the loop...\n" << std::flush;
for (;;) {
double t_0 = vpTime::measureTimeMs(); // t_0: current time
// Temps de la boucle d'asservissement
double Tv = (double)(t_0 - t_1) / 1000.0; // temps d'une iteration en s
// !
// std::cout << "time iter : " << Tv << std::endl;
// Update time for next iteration
t_1 = t_0;
// Acquire a new image from the camera
g.acquire(I);
// Display this image
// Achieve the tracking of the dot in the image
dot.track(I);
vpImagePoint cog = dot.getCog();
// Display a green cross at the center of gravity position in the image
// Update the point feature from the dot location
//----------------------------------------------------------------------
//----------------------------------------------------------------------
vm_0 = vm;
// Update current loop time and previous one
double Tv_1 = Tv_0;
Tv_0 = Tv;
// Compute the visual servoing skew vector
v1 = task.computeControlLaw();
err = task.error;
if (iter == 0) {
err_0 = 0;
err_1 = 0;
dedt_mes = 0;
dedt_filt = 0;
} else {
err_1 = err_0;
err_0 = err;
dedt_mes = (err_0 - err_1) / (Tv_1)-task.J1 * vm_0;
}
if (iter <= 1) {
dedt_mes = 0;
}
//----------------------------------------------------------------------
//----------------------- Kalman Filter Equations ----------------------
//----------------------------------------------------------------------
// Kalman filtering
switch (opt_kalman) {
case K_NONE:
dedt_filt = 0;
break;
case K_VELOCITY:
case K_ACCELERATION:
kalman.filter(dedt_mes);
for (unsigned int i = 0; i < nsignal; i++) {
dedt_filt[i] = kalman.Xest[i * state_size];
}
break;
}
vpMatrix J1p = task.getTaskJacobianPseudoInverse();
v2 = -J1p * dedt_filt;
// std::cout << "task J1p: " << J1p.t() << std::endl ;
// std::cout << "dedt_filt: " << dedt_filt.t() << std::endl ;
v = v1 + v2;
// Display the current and desired feature points in the image display
vpServoDisplay::display(task, cam, I);
// std::cout << "v2 : " << v2.t() << std::endl ;
// std::cout << "v1 : " << v1.t() << std::endl ;
// std::cout << "v : " << v.t();
// Apply the camera velocities to the robot
// Save loop time
flog << Tv_0 << " ";
// Save velocities applied to the robot in the log file
// v[0], v[1], v[2] correspond to camera translation velocities in m/s
// v[3], v[4], v[5] correspond to camera rotation velocities in rad/s
flog << v[0] << " " << v[1] << " " << v[2] << " " << v[3] << " " << v[4] << " " << v[5] << " ";
// Save feature error (s-s*) for the feature point. For this feature
// point, we have 2 errors (along x and y axis). This error is
// expressed in meters in the camera frame
flog << task.error[0] << " " << task.error[1] << " ";
// Save feature error (s-s*) in pixels in the image.
flog << cog.get_u() - cam.get_u0() << " " << cog.get_v() - cam.get_v0() << " ";
// Save de/dt
flog << dedt_mes[0] << " " << dedt_mes[1] << " ";
// Save de/dt filtered
flog << dedt_filt[0] << " " << dedt_filt[1] << " ";
flog << std::endl;
// Flush the display
iter++;
}
flog.close(); // Close the log file
// Display task information
task.print();
// Kill the task
task.kill();
return EXIT_SUCCESS;
} catch (const vpException &e) {
std::cout << "Catch a ViSP exception: " << e << std::endl;
return EXIT_FAILURE;
}
}
#else
int main()
{
std::cout << "You do not have an afma4 robot connected to your computer..." << std::endl;
return EXIT_SUCCESS;
}
#endif