This paper presents a novel Cable-Driven Parallel Robot dedicated to laser-scanning operations. The proposed device can inspect low-accessibility environments, thanks to a self-deployable end-effector, which can be inserted in a closed container through very small access areas, such as hatches, pipes, etc. The reconfigurable end-effector is suspended and actuated by extendable cables, and is equipped with an optical mirror, which is used to deflect a laser beam produced by a frame-fixed laser distance sensor. Thanks to its large orientation capabilities, the machine can record the position of points belonging to a large portion of the surface to be scanned, primarily by tilting and panning the end-effector. The robot is equipped with a frame-orientation calibration device, which can align the machine frame to earth gravity before operation. The robot capabilities are validated by a prototype, which experimentally reconstruct benchmark surfaces.

A Deployable Cable-Driven Parallel Robot With Large Rotational Capabilities for Laser-Scanning Applications

Ida, Edoardo
;
Carricato, Marco
2020

Abstract

This paper presents a novel Cable-Driven Parallel Robot dedicated to laser-scanning operations. The proposed device can inspect low-accessibility environments, thanks to a self-deployable end-effector, which can be inserted in a closed container through very small access areas, such as hatches, pipes, etc. The reconfigurable end-effector is suspended and actuated by extendable cables, and is equipped with an optical mirror, which is used to deflect a laser beam produced by a frame-fixed laser distance sensor. Thanks to its large orientation capabilities, the machine can record the position of points belonging to a large portion of the surface to be scanned, primarily by tilting and panning the end-effector. The robot is equipped with a frame-orientation calibration device, which can align the machine frame to earth gravity before operation. The robot capabilities are validated by a prototype, which experimentally reconstruct benchmark surfaces.
2020
Ida, Edoardo; Marian, Daniele; Carricato, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/759092
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