treeiksolvervel_wdls.cpp

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00001 /*
00002  * TreeIkSolverVel_wdls.cpp
00003  *
00004  *  Created on: Nov 28, 2008
00005  *      Author: rubensmits
00006  */
00007 
00008 #include "treeiksolvervel_wdls.hpp"
00009 #include "utilities/svd_eigen_HH.hpp"
00010 
00011 namespace KDL {
00012     using namespace std;
00013     
00014     TreeIkSolverVel_wdls::TreeIkSolverVel_wdls(const Tree& tree_in, const std::vector<std::string>& endpoints) :
00015         tree(tree_in), jnttojacsolver(tree),
00016         J(MatrixXd::Zero(6 * endpoints.size(), tree.getNrOfJoints())),
00017         Wy(MatrixXd::Identity(J.rows(),J.rows())),
00018         Wq(MatrixXd::Identity(J.cols(),J.cols())),
00019         J_Wq(J.rows(),J.cols()),Wy_J_Wq(J.rows(),J.cols()),
00020         U(MatrixXd::Identity(J.rows(),J.cols())),
00021         V(MatrixXd::Identity(J.cols(),J.cols())),
00022         Wy_U(J.rows(),J.rows()),Wq_V(J.cols(),J.cols()),
00023         t(VectorXd::Zero(J.rows())), Wy_t(VectorXd::Zero(J.rows())),
00024         qdot(VectorXd::Zero(J.cols())),
00025         tmp(VectorXd::Zero(J.cols())),S(VectorXd::Zero(J.cols()))
00026     {
00027         
00028         for (size_t i = 0; i < endpoints.size(); ++i) {
00029             jacobians.insert(Jacobians::value_type(endpoints[i], Jacobian(tree.getNrOfJoints())));
00030         }
00031         
00032     }
00033     
00034     TreeIkSolverVel_wdls::~TreeIkSolverVel_wdls() {
00035     }
00036     
00037     void TreeIkSolverVel_wdls::setWeightJS(const MatrixXd& Mq) {
00038         Wq = Mq;
00039     }
00040     
00041     void TreeIkSolverVel_wdls::setWeightTS(const MatrixXd& Mx) {
00042         Wy = Mx;
00043     }
00044     
00045     void TreeIkSolverVel_wdls::setLambda(const double& lambda_in) {
00046         lambda = lambda_in;
00047     }
00048     
00049     double TreeIkSolverVel_wdls::CartToJnt(const JntArray& q_in, const Twists& v_in, JntArray& qdot_out) {
00050         
00051         //First check if we are configured for this Twists:
00052         for (Twists::const_iterator v_it = v_in.begin(); v_it != v_in.end(); ++v_it) {
00053             if (jacobians.find(v_it->first) == jacobians.end())
00054                 return -2;
00055         }
00056         //Check if q_in has the right size
00057         if (q_in.rows() != tree.getNrOfJoints())
00058             return -1;
00059         
00060         //Lets get all the jacobians we need:
00061         unsigned int k = 0;
00062         for (Jacobians::iterator jac_it = jacobians.begin(); jac_it
00063                  != jacobians.end(); ++jac_it) {
00064             int ret = jnttojacsolver.JntToJac(q_in, jac_it->second, jac_it->first);
00065             if (ret < 0)
00066                 return ret;
00067             else {
00068                 //lets put the jacobian in the big matrix and put the twist in the big t:
00069                 J.block(6*k,0, 6,tree.getNrOfJoints()) = jac_it->second.data;
00070                 const Twist& twist=v_in.find(jac_it->first)->second;
00071                 t.segment(6*k,3)   = Eigen::Map<Eigen::Vector3d>(twist.vel.data);
00072                 t.segment(6*k+3,3) = Eigen::Map<Eigen::Vector3d>(twist.rot.data);
00073             }
00074             ++k;
00075         }
00076         
00077         //Lets use the wdls algorithm to find the qdot:
00078         // Create the Weighted jacobian
00079         J_Wq = (J * Wq).lazy();
00080         Wy_J_Wq = (Wy * J_Wq).lazy();
00081         
00082         // Compute the SVD of the weighted jacobian
00083         int ret = svd_eigen_HH(Wy_J_Wq, U, S, V, tmp);
00084         
00085         //Pre-multiply U and V by the task space and joint space weighting matrix respectively
00086         Wy_t = (Wy * t).lazy();
00087         Wq_V = (Wq * V).lazy();
00088         
00089         // tmp = (Si*Wy*U'*y),
00090         for (unsigned int i = 0; i < J.cols(); i++) {
00091             double sum = 0.0;
00092             for (unsigned int j = 0; j < J.rows(); j++) {
00093                 if (i < Wy_t.size())
00094                     sum += U(j, i) * Wy_t(j);
00095                 else
00096                     sum += 0.0;
00097             }
00098             tmp( i) = sum * ((S(i) / (S(i) * S(i) + lambda * lambda)));
00099         }
00100         
00101         // x = Lx^-1*V*tmp + x
00102         qdot_out.data = (Wq_V * tmp).lazy();
00103         
00104         return Wy_t.norm();
00105     }
00106 }

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