Abstract

Parallel manipulators, also called parallel kinematics machines (PKM), enable robotic solutions for highly dynamic handling and machining applications. The safe and accurate design and control necessitates high-fidelity dynamics models. Such modeling approaches have already been presented for PKM with simple limbs (i.e. each limb is a serial kinematic chain). A systematic modeling approach for PKM with complex limbs (i.e. limbs that possess kinematic loops) was not yet proposed despite the fact that many successful PKM comprise complex limbs.This paper presents a systematic modular approach to the kinematics and dynamics modeling of PKM with complex limbs that are built as serial arrangement of closed loops. The latter are referred to as hybrid limbs, and can be found in almost all PKM with complex limbs, such as the Delta robot. The proposed method generalizes the formulation for PKM with simple limbs by means of local resolution of loop constraints, which is known as constraint embedding in multibody dynamics. The constituent elements of the method are the kinematic and dynamic equations of motions (EOM), and the inverse kinematics solution of the limbs, i.e. the relation of platform motion and the motion of the limbs. While the approach is conceptually independent of the used kinematics and dynamics formulation, a Lie group formulation is employed for deriving the EOM. The frame invariance of the Lie group formulation is used for devising a modular modeling method where the EOM of a representative limb are used to derived the EOM of the limbs of a particular PKM. The PKM topology is exploited in a parallel computation scheme that shall allow for computationally efficient distributed evaluation of the overall EOM of the PKM. Finally, the method is applied to the IRSBot-2 and a 3RR[2RR]R Delta robot, which is presented in detail.

Highlights

  • Many of the successfully applied parallel kinematics machines (PKM) possess limbs with kinematic loops, which will be referred to as complex limbs

  • This paper presents a systematic modular approach to the kinematics and dynamics modeling of PKM with complex limbs that are built as serial arrangement of closed loops

  • This paper presents a systematic generally applicable modeling approach for rigid body PKM with complex limbs, following the basic concept of the formulation for PKM with simple limbs in [47,49]

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Summary

Introduction

Many of the successfully applied PKM possess limbs with kinematic loops, which will be referred to as complex limbs. Other PKM were proposed whose limbs possess several loops, which are called fundamental cycles (FCs). Almost all such PKM possess complex multi-loop limbs where the loops are arranged in series within the limbs, i.e. the FC share at most one common link. Received 15 July 2021; Received in revised form 25 August 2021; Accepted 3 September 2021.

Müller
Kinematic topology
Forward kinematics of a complex limb
Kinematics of the associated tree-topology system
Cut-joint formulation of loop constraints
Cut-joint constraints
Cut-body formulation of loop constraints
Kinematic loop constraints
Constraint resolution using reciprocal screws
Velocity forward kinematics of limb mechanism
Taskspace velocity
Geometric forward kinematics of limb mechanism
Inverse Kinematics of PKM Mechanism
Velocity and acceleration inverse kinematics of equimobile PKM
Velocity and acceleration inverse kinematics of non-equimobile PKM
Velocity inverse kinematics of general PKM
Geometric inverse kinematics of the limb mechanism
Inverse Kinematics of PKM
Kinematics of a complex limb without platform
EOM of the tree-topology system of a separated limb
EOM of a complex limb
EOM of the platform
Task space formulation of the EOM for PKM with complex limbs
Formulation of the EOM for non-redundant PKM in terms of actuator coordinates
Modular modeling
Applications of the dynamic equations
EOM in terms of taskspace coordinates and velocity
Inverse dynamics formulation in taskspace
Example
Kinematics of representative limb
Dynamic parameters
Implementation and results
Computation Results
10. Conclusion
Methods
Full Text
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