virtual jspace::Status update(jspace::Model const & model)
{
//////////////////////////////////////////////////
// Update the state of our task. Again, this is not critical
// here, but important later when we want to integrate several
// operational space tasks into a hierarchy.
actual_ = model.getState().position_;
//////////////////////////////////////////////////
// Compute PD control torques and store them in command_ for
// later retrieval. If enabled, add the estimated effect of
// gravity in order to make the robot behave as if was
// weightless.
command_ = kp_ * (goal_ - actual_) - kd_ * model.getState().velocity_;
if (enable_gravity_compensation_) {
jspace::Vector gg;
if ( ! model.getGravity(gg)) {
return jspace::Status(false, "failed to retrieve gravity torque");
}
command_ += gg;
}
jspace::Status ok;
return ok;
}
// used as fall-back controller
static void StepFloat(jspace::Model const & model, jspace::Vector & tau)
{
// this simplistic float mode can only handle a signle scalar kd
// value...
if (gain_kd.size() != 1) {
tau = jspace::Vector::Zero(7);
endme(269);
return;
}
jspace::Vector gravity_torque;
model.getGravity(gravity_torque);
jspace::Matrix massInertia;
model.getMassInertia(massInertia);
tau = gravity_torque - gain_kd[0] * massInertia * model.getState().velocity_;
}
static void StepTaskPosture(jspace::Model const & model, jspace::Vector & tau)
{
static taoDNode * right_hand(0);
if ( ! right_hand) {
right_hand = model.getNodeByName("right-hand");
if ( ! right_hand) {
tau = jspace::Vector::Zero(7);
endme(378);
return;
}
}
//////////////////////////////////////////////////
// task
jspace::Transform eepos;
model.computeGlobalFrame(right_hand, 0.0, -0.15, 0.0, eepos);
jspace::Matrix Jfull;
model.computeJacobian(right_hand,
eepos.translation()[0],
eepos.translation()[1],
eepos.translation()[2],
Jfull);
jspace::Matrix Jx(Jfull.block(0, 0, 3, 7));
jspace::Matrix invA;
model.getInverseMassInertia(invA);
jspace::Matrix invLambda_t(Jx * invA * Jx.transpose());
Eigen::SVD<jspace::Matrix> svdLambda_t(invLambda_t);
svdLambda_t.sort();
int const nrows_t(svdLambda_t.singularValues().rows());
jspace::Matrix Sinv_t;
Sinv_t = jspace::Matrix::Zero(nrows_t, nrows_t);
for (int ii(0); ii < nrows_t; ++ii) {
if (svdLambda_t.singularValues().coeff(ii) > 1e-3) {
Sinv_t.coeffRef(ii, ii) = 1.0 / svdLambda_t.singularValues().coeff(ii);
}
}
jspace::Matrix Lambda_t(svdLambda_t.matrixU() * Sinv_t * svdLambda_t.matrixU().transpose());
dbg_invLambda_t = invLambda_t;
dbg_Lambda_t = Lambda_t;
static jspace::Vector eegoal0;
if (0 == eegoal0.size()) {
if (goal.rows() < 3) {
tau = jspace::Vector::Zero(7);
endme(256);
return;
}
eegoal0 = goal.block(0, 0, 3, 1);
}
struct timeval now;
gettimeofday(&now, 0);
double const alpha((1.0 * now.tv_sec + 1.0e-6 * now.tv_usec) * M_PI / 2.0);
jspace::Vector eegoal(eegoal0);
eegoal.coeffRef(1) += 0.2 * cos(alpha);
eegoal.coeffRef(2) += 0.2 * sin(alpha);
jspace::Vector poserror(eepos.translation() - eegoal);
jspace::Vector g;
model.getGravity(g);
static jspace::Vector eegain_kp, eegain_kd;
if (0 == eegain_kp.size()) {
if (3 <= gain_kp.size()) {
eegain_kp = gain_kp.block(0, 0, 3, 1);
}
else {
eegain_kp = gain_kp[0] * jspace::Vector::Ones(3);
}
if (3 <= gain_kd.size()) {
eegain_kd = gain_kd.block(0, 0, 3, 1);
}
else {
eegain_kd = gain_kd[0] * jspace::Vector::Ones(3);
}
}
jspace::Vector
tau_task(Jx.transpose() * (-Lambda_t)
* ( eegain_kp.cwise() * poserror
+ eegain_kd.cwise() * Jx * model.getState().velocity_));
//////////////////////////////////////////////////
// posture
jspace::Matrix Jbar(invA * Jx.transpose() * Lambda_t);
jspace::Matrix nullspace(jspace::Matrix::Identity(7, 7) - Jbar * Jx);
static jspace::Vector goalposture;
if (7 != goalposture.size()) {
if (goal.size() >= 10) {
goalposture = goal.block(3, 0, 7, 1);
goalposture *= M_PI / 180;
}
else {
goalposture = jspace::Vector::Zero(7);
goalposture[1] = 30 * M_PI / 180; // shoulder "sideways" 30 degrees
goalposture[3] = 60 * M_PI / 180; // ellbow at 60 degrees
}
}
jspace::Matrix invLambda_p(nullspace * invA);
Eigen::SVD<jspace::Matrix> svdLambda_p(invLambda_p);
svdLambda_p.sort();
int const nrows_p(svdLambda_p.singularValues().rows());
jspace::Matrix Sinv_p;
Sinv_p = jspace::Matrix::Zero(nrows_p, nrows_p);
for (int ii(0); ii < nrows_p; ++ii) {
if (svdLambda_p.singularValues().coeff(ii) > 1e-3) {
Sinv_p.coeffRef(ii, ii) = 1.0 / svdLambda_p.singularValues().coeff(ii);
}
}
jspace::Matrix Lambda_p(svdLambda_p.matrixU() * Sinv_p * svdLambda_p.matrixU().transpose());
dbg_invLambda_p = invLambda_p;
dbg_Lambda_p = Lambda_p;
static jspace::Vector posturegain_kp, posturegain_kd;
if (0 == posturegain_kp.size()) {
if (10 <= gain_kp.size()) {
posturegain_kp = gain_kp.block(3, 0, 7, 1);
}
else {
if (1 == gain_kp.size()) {
posturegain_kp = gain_kp[0] * jspace::Vector::Ones(7);
}
else {
posturegain_kp = gain_kp[1] * jspace::Vector::Ones(7);
}
}
if (10 <= gain_kd.size()) {
posturegain_kd = gain_kd.block(3, 0, 7, 1);
}
else {
if (1 == gain_kd.size()) {
posturegain_kd = gain_kd[0] * jspace::Vector::Ones(7);
}
else {
posturegain_kd = gain_kd[1] * jspace::Vector::Ones(7);
}
}
}
jspace::Vector tau_posture(nullspace.transpose() * (-Lambda_p)
* (posturegain_kp.cwise() * (model.getState().position_ - goalposture)
+ posturegain_kd.cwise() * model.getState().velocity_));
//////////////////////////////////////////////////
// sum it up...
tau = tau_task + tau_posture + g;
}