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Visual Servoing Platform
version 3.3.0
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57 #include <visp3/core/vpConfig.h>
58 #include <visp3/core/vpDebug.h>
59 #if (defined(VISP_HAVE_AFMA4) && defined(VISP_HAVE_DC1394))
61 #include <visp3/core/vpDisplay.h>
62 #include <visp3/core/vpImage.h>
63 #include <visp3/gui/vpDisplayGTK.h>
64 #include <visp3/gui/vpDisplayOpenCV.h>
65 #include <visp3/gui/vpDisplayX.h>
66 #include <visp3/sensor/vp1394TwoGrabber.h>
68 #include <visp3/blob/vpDot2.h>
69 #include <visp3/core/vpException.h>
70 #include <visp3/core/vpHomogeneousMatrix.h>
71 #include <visp3/core/vpIoTools.h>
72 #include <visp3/core/vpLinearKalmanFilterInstantiation.h>
73 #include <visp3/core/vpMath.h>
74 #include <visp3/core/vpPoint.h>
75 #include <visp3/io/vpParseArgv.h>
76 #include <visp3/robot/vpRobotAfma4.h>
77 #include <visp3/visual_features/vpFeatureBuilder.h>
78 #include <visp3/visual_features/vpFeaturePoint.h>
79 #include <visp3/vs/vpAdaptiveGain.h>
80 #include <visp3/vs/vpServo.h>
81 #include <visp3/vs/vpServoDisplay.h>
84 #define GETOPTARGS "hK:l:"
86 typedef enum { K_NONE, K_VELOCITY, K_ACCELERATION } KalmanType;
97 void usage(
const char *name,
const char *badparam, KalmanType &kalman)
100 Tests a control law with the following characteristics:\n\
101 - eye-in-hand control\n\
102 - camera velocity are computed\n\
103 - servo on 1 points.\n\
104 - Kalman filtering\n\
107 %s [-K <0|1|2|3>] [-h]\n", name);
112 Set the constant gain. By default adaptive gain. \n\
118 2: acceleration model\n\
121 Print the help.\n", (
int)kalman);
124 fprintf(stderr,
"ERROR: \n");
125 fprintf(stderr,
"\nBad parameter [%s]\n", badparam);
143 bool getOptions(
int argc,
const char **argv, KalmanType &kalman,
bool &doAdaptativeGain,
152 kalman = (KalmanType)atoi(optarg);
155 doAdaptativeGain =
false;
159 usage(argv[0], NULL, kalman);
164 usage(argv[0], optarg, kalman);
170 if ((c == 1) || (c == -1)) {
172 usage(argv[0], NULL, kalman);
173 std::cerr <<
"ERROR: " << std::endl;
174 std::cerr <<
" Bad argument " << optarg << std::endl << std::endl;
181 int main(
int argc,
const char **argv)
184 KalmanType opt_kalman = K_NONE;
186 bool doAdaptativeGain =
true;
188 int opt_cam_frequency = 60;
191 if (getOptions(argc, argv, opt_kalman, doAdaptativeGain, lambda) ==
false) {
201 std::string username;
206 std::string logdirname;
207 logdirname =
"/tmp/" + username;
215 std::cerr << std::endl <<
"ERROR:" << std::endl;
216 std::cerr <<
" Cannot create " << logdirname << std::endl;
220 std::string logfilename;
221 logfilename = logdirname +
"/log.dat";
224 std::ofstream flog(logfilename.c_str());
231 switch (opt_cam_frequency) {
244 for (
int i = 0; i < 10; i++)
248 vpDisplayX display(I, 100, 100,
"Current image");
249 #elif defined(VISP_HAVE_OPENCV)
251 #elif defined(VISP_HAVE_GTK)
258 std::cout << std::endl;
259 std::cout <<
"-------------------------------------------------------" << std::endl;
260 std::cout <<
"Test program for target motion compensation using a Kalman "
263 std::cout <<
"Eye-in-hand task control, velocity computed in the camera frame" << std::endl;
264 std::cout <<
"Task : servo a point \n" << std::endl;
267 switch (opt_kalman) {
269 std::cout <<
"Servo with no target motion compensation (see -K option)\n";
272 std::cout <<
"Servo with target motion compensation using a Kalman filter\n"
273 <<
"with constant velocity modelization (see -K option)\n";
276 std::cout <<
"Servo with target motion compensation using a Kalman filter\n"
277 <<
"with constant acceleration modelization (see -K option)\n";
280 std::cout <<
"-------------------------------------------------------" << std::endl;
281 std::cout << std::endl;
285 std::cout <<
"Click on the dot..." << std::endl;
316 std::cout << std::endl;
333 unsigned int nsignal = 2;
337 unsigned int state_size = 0;
339 switch (opt_kalman) {
344 sigma_state.
resize(state_size * nsignal);
345 sigma_state = 0.00001;
346 sigma_measure = 0.05;
348 kalman.
initFilter(nsignal, sigma_state, sigma_measure, rho, dummy);
352 case K_ACCELERATION: {
356 sigma_state.
resize(state_size * nsignal);
357 sigma_state = 0.00001;
358 sigma_measure = 0.05;
359 double dt = 1. / opt_cam_frequency;
360 kalman.
initFilter(nsignal, sigma_state, sigma_measure, rho, dt);
388 std::cout <<
"\nHit CTRL-C to stop the loop...\n" << std::flush;
392 double Tv = (double)(t_0 - t_1) / 1000.0;
441 dedt_mes = (err_0 - err_1) / (Tv_1)-task.
J1 * vm_0;
452 switch (opt_kalman) {
459 for (
unsigned int i = 0; i < nsignal; i++) {
460 dedt_filt[i] = kalman.
Xest[i * state_size];
467 v2 = -J1p * dedt_filt;
490 flog << v[0] <<
" " << v[1] <<
" " << v[2] <<
" " << v[3] <<
" " << v[4] <<
" " << v[5] <<
" ";
495 flog << task.
error[0] <<
" " << task.
error[1] <<
" ";
501 flog << dedt_mes[0] <<
" " << dedt_mes[1] <<
" ";
504 flog << dedt_filt[0] <<
" " << dedt_filt[1] <<
" ";
524 std::cout <<
"Catch a ViSP exception: " << e << std::endl;
532 std::cout <<
"You do not have an afma4 robot connected to your computer..." << std::endl;
@ STATE_VELOCITY_CONTROL
Initialize the velocity controller.
void initFromConstant(double c)
Use the X11 console to display images on unix-like OS. Thus to enable this class X11 should be instal...
vpImagePoint getCog() const
@ stateConstVelWithColoredNoise_MeasureVel
void buildFrom(double x, double y, double Z)
Generic class defining intrinsic camera parameters.
vpMatrix J1
Task Jacobian .
vpMatrix getTaskJacobianPseudoInverse() const
@ vpVIDEO_MODE_640x480_MONO8
This tracker is meant to track a blob (connex pixels with same gray level) on a vpImage.
static void create(vpFeaturePoint &s, const vpCameraParameters &cam, const vpDot &d)
unsigned int getHeight() const
Implementation of column vector and the associated operations.
The vpDisplayOpenCV allows to display image using the OpenCV library. Thus to enable this class OpenC...
Implementation of a matrix and operations on matrices.
void setServo(const vpServoType &servo_type)
VISP_EXPORT double measureTimeMs()
unsigned int getWidth() const
virtual vpRobotStateType setRobotState(const vpRobot::vpRobotStateType newState)
static bool parse(int *argcPtr, const char **argv, vpArgvInfo *argTable, int flags)
void initStandard(double gain_at_zero, double gain_at_infinity, double slope_at_zero)
static const vpColor green
void print(const vpServo::vpServoPrintType display_level=ALL, std::ostream &os=std::cout)
static void display(const vpImage< unsigned char > &I)
The vpDisplayGTK allows to display image using the GTK 3rd party library. Thus to enable this class G...
Adaptive gain computation.
void initFilter(unsigned int nsignal, vpColVector &sigma_state, vpColVector &sigma_measure, double rho, double dt)
void initTracking(const vpImage< unsigned char > &I, unsigned int size=0)
@ stateConstAccWithColoredNoise_MeasureVel
Class for firewire ieee1394 video devices using libdc1394-2.x api.
Class that defines a 2D point in an image. This class is useful for image processing and stores only ...
void addFeature(vpBasicFeature &s, vpBasicFeature &s_star, unsigned int select=vpBasicFeature::FEATURE_ALL)
void setStateModel(vpStateModel model)
void resize(unsigned int i, bool flagNullify=true)
Control of Irisa's cylindrical robot named Afma4.
Class that defines a 2D point visual feature which is composed by two parameters that are the cartes...
static const vpColor blue
vpColVector computeControlLaw()
static void flush(const vpImage< unsigned char > &I)
static void displayCross(const vpImage< unsigned char > &I, const vpImagePoint &ip, unsigned int size, const vpColor &color, unsigned int thickness=1)
void track(const vpImage< unsigned char > &I, bool canMakeTheWindowGrow=true)
This class provides an implementation of some specific linear Kalman filters.
void filter(vpColVector &z)
void setVelocity(const vpRobot::vpControlFrameType frame, const vpColVector &vel)
static void display(const vpServo &s, const vpCameraParameters &cam, const vpImage< unsigned char > &I, vpColor currentColor=vpColor::green, vpColor desiredColor=vpColor::red, unsigned int thickness=1)
error that can be emited by ViSP classes.
void setGraphics(bool activate)
unsigned int getStateSize()