A Reconfigurable Parallel Robot for On-Structure Machining of Large Structures

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper presents a novel walking hybrid-kinematics robot that can be reconfigured to have three, five, and six degrees of freedom (DOFs) for adsorption machining of large structures. A symmetric 3PRPR or 3PRRR parallel mechanism with three translational (3T) DOFs is used to perform three-axis machining tasks. Three attachment pads connected to passive spherical joints are used to attach the robot to the surface of a large structure. Two or three rotational degrees of freedom can be added to the robot to adapt to a large structure’s irregular surface geometry and perform five- or six-axis machining tasks. This is achieved through modular reassembly or joint locking that reconfigures the robot from a three-DOF robot to a five- or six-DOF robot. A serial module providing two rotational DOFs can be added to the 3T parallel mechanism to provide five DOFs. A parallel module, namely 3SPR or 3SU mechanism, can be added to the 3T parallel mechanism to provide six DOFs. The mobility, pose kinematics, differential kinematics, singularities, and workspace of the 3SPR and 3SU parallel mechanisms alone and combined with the 3T mechanism are discussed in this paper. It is shown that the singularities of the mechanism can be easily avoided by making the moving platform of the 3SPR or 3SU mechanism smaller than the base, limiting the range of some joints, and having an appropriate length of the links. Furthermore, a method to optimize the workspace of the mechanism was also discussed.

Original languageBritish English
Article number110
JournalRobotics
Volume11
Issue number5
DOIs
StatePublished - Oct 2022

Keywords

  • hybrid robot
  • machining of large structures
  • on-structure machining
  • walking robot

Fingerprint

Dive into the research topics of 'A Reconfigurable Parallel Robot for On-Structure Machining of Large Structures'. Together they form a unique fingerprint.

Cite this