Robot Learning for Object Handling in an Unstructured Environment
Date
2020-12
Authors
Cao, Cheng
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Abstract
In the oil exploration industry, perforated pipe assembly can be a prolonged
process in the manufacturing environment. A pipe keyway must be aligned to
successfully assemble the perforated outer and inner pipes. However, the current
method uses vision devices to rotate a pipe multiple times, eventually rotating to
the angle that meets the requirement, which is time-consuming, leading to a lack of
productivity. Therefore, the purpose of conducting this research is to establish an
automatic rotating angle correction method such that the keyway can be aligned by
only rotating a pipe once.
The system executes a series of processes: recognizing a pipe, picking it up,
detecting the keyway, and rotating it to the desired orientation using only a single
rotation. A General Regression Neural Network (GRNN) model predicts the actual
robot rotation angle needed for correct orientation. The robot will rotate the pipe
using the predicted rotation angle. After rotation, the deviation from the desired
keyway angle must be less than the given threshold.
This research is of importance in the application of machine vision (MV) in
industrial production. In this thesis research, the pipe keyway alignment problem is
addressed using a 2D machine vision method as well as the GRNN algorithm. The
proposed method is tested using a pipe handling process. A steel pipe with a keyway
is to be placed in a random orientation. As the keyway must be aligned in the
following manufacturing process, a robot is used to rotate the pipe to the correct
orientation by applying the machine learning algorithm. The experiment was set up and used to test the proposed machine learning method. Also, for easier automatic
picking up the pipe, we implemented a 3D machine vision recognition procedure.
Compared with the current method, the proposed method allows the robot to only
needs to rotate a pipe once to align the keyway. Hence the proposed method can
greatly increase the manufacturing efficiency and reduce manufacturing cost.
The thesis introduces the experimental system, explains the theories and the
methodologies, describes the procedure of the experiment, and arrives at a result.
The system uses an industrial robot ABB IRB 4400; Cognex DS1300 3D
Displacement Sensor; Cognex In-sight 7000 2D Smart Camera, and a computer with
the GRNN algorithm.
Description
Keywords
robotics, machine vision, keyway alignment, GRNN
Citation
Cao, C. (2020). Robot learning for object handling in an unstructured environment (Unpublished thesis). Texas State University, San Marcos, Texas.