Abstract

Kinematic redundancy of manipulators is a well-understood topic, and various methods were developed for the redundancy resolution in order to solve the inverse kinematics problem, at least for serial manipulators. An important question, with high practical relevance, is whether the inverse kinematics solution is cyclic, i.e., whether the redundancy solution leads to a closed path in joint space as a solution of a closed path in task space. This paper investigates the cyclicity property of two widely used redundancy resolution methods, namely the projected gradient method (PGM) and the augmented Jacobian method (AJM), by means of examples. Both methods determine solutions that minimize an objective function, and from an application point of view, the sensitivity of the methods on the initial configuration is crucial. Numerical results are reported for redundant serial robotic arms and for redundant parallel kinematic manipulators. While the AJM is known to be cyclic, it turns out that also the PGM exhibits cyclicity. However, only the PGM converges to the local optimum of the objective function when starting from an initial configuration of the cyclic trajectory.

Highlights

  • Redundant serial kinematic manipulators (SKM) are gaining importance for dedicated solution in flexible automation due their advantageous properties

  • This paper investigates the cyclicity property of two widely used redundancy resolution methods, namely the projected gradient method (PGM) and the augmented Jacobian method (AJM), by means of examples

  • The augmented Jacobian method was shown to lead to cyclic trajectories while the projected gradient method for the inverse kinematics was only used to maximize an objective function

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Summary

Introduction

Redundant serial kinematic manipulators (SKM) are gaining importance for dedicated solution in flexible automation due their advantageous properties. The complexity is due to the non-uniqueness of the solution to the geometric inverse kinematics problem (IKP), which consists of finding a vector q ∈ Vn of generalized coordinates (joint coordinates in SKM) for a given vector p ∈ Rm of end-effector (EE) coordinates satisfying the forward kinematics relation p = f (q). Null space methods: A particular solution qof (2) is determined by adding a vector in the null-space of the Jacobian. The latter is usually the gradient of a scalar objective function that is to be maximized (or minimized)

Task augmentation methods
Numerical Simulations
Serial 4R
Serial 6R
Conclusions
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