Adaptive solution of the inverse kinematic task by fixed point transformation

In a wide class of robots of open kinematic chain the inverse kinematic task cannot be solved by the use of closed-form analytical formulae. On this reason the traditional approaches apply differential approximation in which the Jacobian of the — normally redundant — robot arm is "inverted" by the use of some "generalized inverse". These pseudo-inverses behave well whenever the robot arm is far from a singular configuration, however, in the singularities and nearby the singular configurations they suffer from a singular or ill-conditioned pseudoinverse. For tackling the problem of singularities normally complementary "tricks" have to be used that so "deform" the original problem that the deformed version leads to the inversion of a well-conditioned matrix. Though the so obtained solution does not exactly solve the original problem, it is accepted as practical "substitute" of the not existing solution in the singularities, and an acceptable approximation of the exact solution outside the singular points. Recently, in [1], an alternative, quasi-differential approach was suggested that was absolutely free of any matrix inversion. It was shown that it converged to one of the — normally ambiguous — exact solutions at the nonsingular configurations, and showed stable convergence in the singular points when a "substitute" of the not existing solution was created. This convenient convergence was guaranteed by the use of the "exact Jacobian" of the robot arm. The interesting question, i.e. what happens if only an "approximate Jacobian" is available, and the motion of the robot arm is precisely measurable with respect to a Cartesian "workshop"-based system of reference, was left open. Now it is shown that the convergence properties of the method can be improved by the application of simple rotational matrices, and on this basis the iterative application of an "Adaptive Inverse Kinematics" becomes possible. This approach has the specialty that no complete information it needs on the Jacobian at a given point. It is content with the observable system behavior only along the realized motion, so it seems to be easily implementable. Its operation is demonstrated for an irregularly extended 6 Degree-of-Freedom (DoF) PUMA-type robot arm, that has 8 rotary axles.

[1]  Francis J. Doyle,et al.  Survey on iterative learning control, repetitive control, and run-to-run control , 2009 .

[2]  S. Chiaverini,et al.  Achieving user-defined accuracy with damped least-squares inverse kinematics , 1991, Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments.

[3]  Radu-Emil Precup,et al.  Constrained Data-Driven Model-Free ILC- based Reference Input Tuning Algorithm , 2015 .

[4]  József K. Tar,et al.  Cost Function-Free Optimization in Inverse Kinematics of Open Kinematic Chains , 2015, RAAD.

[5]  J. Gram Ueber die Entwickelung reeller Functionen in Reihen mittelst der Methode der kleinsten Quadrate. , 1883 .

[6]  Andrej Gams,et al.  Adaptation of Motor Primitives to the Environment Through Learning and Statistical Generalization , 2015, RAAD.

[7]  Suguru Arimoto,et al.  Bettering operation of Robots by learning , 1984, J. Field Robotics.

[8]  Gene H. Golub,et al.  Calculating the singular values and pseudo-inverse of a matrix , 2007, Milestones in Matrix Computation.

[9]  Harvey Lipkin,et al.  A new method of robotic rate control near singularities , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[10]  C. Kelley Solving Nonlinear Equations with Newton's Method , 1987 .

[11]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[12]  Svante Gunnarsson,et al.  On the design of ILC algorithms using optimization , 2001, Autom..

[13]  József K. Tar,et al.  Matrix inversion-free quasi-differential approach in solving the inverse kinematic task , 2016, 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI).

[14]  David H. Owens,et al.  Iterative learning control - An optimization paradigm , 2015, Annu. Rev. Control..

[15]  Harvey Lipkin,et al.  Complex Robotic Inverse Kinematic Solutions , 1993 .

[16]  Vincenzo Piuri,et al.  Adaptive control of underactuated mechanical systems using improved "Sigmoid Generated Fixed Point Transformation" and scheduling strategy , 2016, 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[17]  B. AfeArd CALCULATING THE SINGULAR VALUES AND PSEUDOINVERSE OF A MATRIX , 2022 .

[18]  E. Schmidt Zur Theorie der linearen und nichtlinearen Integralgleichungen , 1907 .