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kdl_install/include/kdl/chainiksolvervel_wdls.hpp
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kdl_install/include/kdl/chainiksolvervel_wdls.hpp
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// Copyright (C) 2007 Ruben Smits <ruben dot smits at mech dot kuleuven dot be>
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// Version: 1.0
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// Author: Ruben Smits <ruben dot smits at mech dot kuleuven dot be>
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// Maintainer: Ruben Smits <ruben dot smits at mech dot kuleuven dot be>
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// URL: http://www.orocos.org/kdl
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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#ifndef KDL_CHAIN_IKSOLVERVEL_WDLS_HPP
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#define KDL_CHAIN_IKSOLVERVEL_WDLS_HPP
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#include "chainiksolver.hpp"
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#include "chainjnttojacsolver.hpp"
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#include <Eigen/Core>
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namespace KDL
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{
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/**
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* Implementation of a inverse velocity kinematics algorithm based
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* on the weighted pseudo inverse with damped least-square to calculate the velocity
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* transformation from Cartesian to joint space of a general
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* KDL::Chain. It uses a svd-calculation based on householders
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* rotations.
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*
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* J# = M_q*Vb*pinv_dls(Db)*Ub'*M_x
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*
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* where B = Mx*J*Mq
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*
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* and B = Ub*Db*Vb' is the SVD decomposition of B
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*
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* Mq and Mx represent, respectively, the joint-space and task-space weighting
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* matrices.
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* Please refer to the documentation of setWeightJS(const Eigen::MatrixXd& Mq)
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* and setWeightTS(const Eigen::MatrixXd& Mx) for details on the effects of
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* these matrices.
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*
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* For more details on Weighted Pseudo Inverse, see :
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* 1) [Ben Israel 03] A. Ben Israel & T.N.E. Greville.
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* Generalized Inverses : Theory and Applications,
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* second edition. Springer, 2003. ISBN 0-387-00293-6.
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*
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* 2) [Doty 93] K. L. Doty, C. Melchiorri & C. Boniveto.
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* A theory of generalized inverses applied to Robotics.
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* The International Journal of Robotics Research,
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* vol. 12, no. 1, pages 1-19, february 1993.
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*
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*
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* @ingroup KinematicFamily
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*/
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class ChainIkSolverVel_wdls : public ChainIkSolverVel
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{
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public:
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/// solution converged but (pseudo)inverse is singular
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static const int E_CONVERGE_PINV_SINGULAR = +100;
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/**
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* Constructor of the solver
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*
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* @param chain the chain to calculate the inverse velocity
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* kinematics for
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* @param eps if a singular value is below this value, its
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* inverse is set to zero, default: 0.00001
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* @param maxiter maximum iterations for the svd calculation,
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* default: 150
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*
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*/
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explicit ChainIkSolverVel_wdls(const Chain& chain,double eps=0.00001,int maxiter=150);
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//=ublas::identity_matrix<double>
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~ChainIkSolverVel_wdls();
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/**
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* Find an output joint velocity \a qdot_out, given a starting joint pose
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* \a q_init and a desired cartesian velocity \a v_in
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*
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* @return
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* E_NOERROR=svd solution converged in maxiter
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* E_SVD_FAILED=svd solution failed
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* E_CONVERGE_PINV_SINGULAR=svd solution converged but (pseudo)inverse singular
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*
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* @note if E_CONVERGE_PINV_SINGULAR returned then converged and can
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* continue motion, but have degraded solution
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*
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* @note If E_SVD_FAILED returned, then getSvdResult() returns the error
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* code from the SVD algorithm.
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*/
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virtual int CartToJnt(const JntArray& q_in, const Twist& v_in, JntArray& qdot_out);
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/**
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* not (yet) implemented.
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*
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*/
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virtual int CartToJnt(const JntArray& /*q_init*/, const FrameVel& /*v_in*/, JntArrayVel& /*q_out*/){return -1;};
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/**
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* Set the joint space weighting matrix
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*
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* @param weight_js joint space weighting symmetric matrix,
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* default : identity. M_q : This matrix being used as a
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* weight for the norm of the joint space speed it HAS TO BE
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* symmetric and positive definite. We can actually deal with
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* matrices containing a symmetric and positive definite block
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* and 0s otherwise. Taking a diagonal matrix as an example, a
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* 0 on the diagonal means that the corresponding joints will
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* not contribute to the motion of the system. On the other
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* hand, the bigger the value, the most the corresponding
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* joint will contribute to the overall motion. The obtained
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* solution q_dot will actually minimize the weighted norm
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* sqrt(q_dot'*(M_q^-2)*q_dot). In the special case we deal
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* with, it does not make sense to invert M_q but what is
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* important is the physical meaning of all this : a joint
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* that has a zero weight in M_q will not contribute to the
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* motion of the system and this is equivalent to saying that
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* it gets an infinite weight in the norm computation. For
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* more detailed explanation : vincent.padois@upmc.fr
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*
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* @return success/error code
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*/
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int setWeightJS(const Eigen::MatrixXd& Mq);
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/**
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* Set the task space weighting matrix
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*
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* @param weight_ts task space weighting symmetric matrix,
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* default: identity M_x : This matrix being used as a weight
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* for the norm of the error (in terms of task space speed) it
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* HAS TO BE symmetric and positive definite. We can actually
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* deal with matrices containing a symmetric and positive
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* definite block and 0s otherwise. Taking a diagonal matrix
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* as an example, a 0 on the diagonal means that the
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* corresponding task coordinate will not be taken into
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* account (ie the corresponding error can be really big). If
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* the rank of the jacobian is equal to the number of task
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* space coordinates which do not have a 0 weight in M_x, the
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* weighting will actually not impact the results (ie there is
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* an exact solution to the velocity inverse kinematics
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* problem). In cases without an exact solution, the bigger
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* the value, the most the corresponding task coordinate will
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* be taken into account (ie the more the corresponding error
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* will be reduced). The obtained solution will minimize the
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* weighted norm sqrt(|x_dot-Jq_dot|'*(M_x^2)*|x_dot-Jq_dot|).
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* For more detailed explanation : vincent.padois@upmc.fr
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*
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* @return success/error code
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*/
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int setWeightTS(const Eigen::MatrixXd& Mx);
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/**
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* Set lambda
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*/
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void setLambda(const double lambda);
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/**
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* Set eps
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*/
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void setEps(const double eps_in);
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/**
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* Set maxIter
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*/
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void setMaxIter(const int maxiter_in);
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/**
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* Request the number of singular values of the jacobian that are < eps;
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* if the number of near zero singular values is > jac.col()-jac.row(),
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* then the jacobian pseudoinverse is singular
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*/
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unsigned int getNrZeroSigmas()const {return nrZeroSigmas;};
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/**
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* Request the minimum of the first six singular values
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*/
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double getSigmaMin()const {return sigmaMin;};
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/**
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* Request the six singular values of the Jacobian
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*/
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int getSigma(Eigen::VectorXd& Sout);
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/**
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* Request the value of eps
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*/
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double getEps()const {return eps;};
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/**
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* Request the value of lambda for the minimum
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*/
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double getLambda()const {return lambda;};
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/**
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* Request the scaled value of lambda for the minimum
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* singular value 1-6
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*/
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double getLambdaScaled()const {return lambda_scaled;};
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/**
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* Retrieve the latest return code from the SVD algorithm
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* @return 0 if CartToJnt() not yet called, otherwise latest SVD result code.
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*/
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int getSVDResult()const {return svdResult;};
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/// @copydoc KDL::SolverI::strError()
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virtual const char* strError(const int error) const;
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/// @copydoc KDL::SolverI::updateInternalDataStructures()
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virtual void updateInternalDataStructures();
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private:
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const Chain& chain;
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ChainJntToJacSolver jnt2jac;
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unsigned int nj;
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Jacobian jac;
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Eigen::MatrixXd U;
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Eigen::VectorXd S;
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Eigen::MatrixXd V;
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double eps;
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int maxiter;
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Eigen::VectorXd tmp;
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Eigen::MatrixXd tmp_jac;
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Eigen::MatrixXd tmp_jac_weight1;
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Eigen::MatrixXd tmp_jac_weight2;
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Eigen::MatrixXd tmp_ts;
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Eigen::MatrixXd tmp_js;
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Eigen::MatrixXd weight_ts;
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Eigen::MatrixXd weight_js;
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double lambda;
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double lambda_scaled;
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unsigned int nrZeroSigmas ;
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int svdResult;
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double sigmaMin;
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};
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}
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#endif
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