Abstract
Improving a robot’s posture enables it to perform with greater accuracy and repeatability. A stiffer posture also protects the robot from unnecessary vibrations and deflections that may be induced by an applied load. This paper presents a method for choosing high stiffness robot postures. The method is demonstrated on a six-degree-of-freedom Fanuc M10-iA serial manipulator. The posture identification and stiffness modelling were achieved by a reliable and cost-effective alternative to deflection measurement using IEPE accelerometers.
Highlights
Serial kinematic machines (SKMs) date back to the beginning of the industrial revolution, when the primary objective was to replicate the human arm by pushing and pulling objects [1]
Serial robots are used to perform light machining tasks that would otherwise be performed by computer numerically controlled (CNC) machines
A study of serial robotic stiffness began in the early 1980s by Salisbury [3], who developed a method of actively controlling the apparent stiffness of serial manipulators
Summary
Serial kinematic machines (SKMs) date back to the beginning of the industrial revolution, when the primary objective was to replicate the human arm by pushing and pulling objects [1]. Serial robots are used to perform light machining tasks that would otherwise be performed by computer numerically controlled (CNC) machines. These are attractive to manufacturers owing to their extended workspace, reachability, and flexibility [2]. The lower stiffness of serial robots limits their ability to perform machining or precision placement tasks on high loads. ‘Stiffness’ is defined as the ability of a manipulator to sustain loads without excessive changes to its geometry [4, 5], and research into robotic stiffness remains of interest to manufacturers and academics. The adoption of serial robots for high-precision manufacturing tasks is limited in the manufacturing sectors because of insufficient control techniques to improve robot posture and stiffness. Researchers who have focused on this problem include Guo, Dong and Ke [6], Sellami and Respall [7], and Cvitanic, Nguyen and Melkote [8]
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