Geometrical block assembly path planning using Faster RCNN and Q-learning
- Geometrical block assembly path planning using Faster RCNN and Q-learning
- YOHG HO JU
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- Recently, assembling robots have been used, not only in general processing, but also
in a eld where new assembling methods and procedures for various objects are
applied, and the assembling process is frequently changed. Accordingly, situations
have occurred in which it is dicult for non-experts to respond promptly to the
situation. The reason for this is that, if the robot does the work, it is necessary
to change its procedure according to the robot assembly process plan, such as
Robot Grasping Planning, Robot Motion Planning, and Robot Assembly Planning.
However, such robot control algorithms are not easy to change quickly, even for
robot experts, and it is even more dicult for non-experts who are not accustomed
to this. A simpler, countermeasure is needed to apply in the eld. As a method to
solve this problem, this paper provides an implementation of an assembly process
that can minimize human intervention by using an articial intelligence algorithm.
The simulator for verication of the results of this paper is divided into two
stages based on Gazebo, which is one of the 3D simulates of the Robot Operating
System (ROS). Block assembly simulation is also implemented. First, as the center
of the articial intelligence algorithm, Faster R-CNN is implemented as a step of
recognizing the block and grasping the gripping position of the block to be assembled.
The second step focuses on the articial intelligence algorithm of Reinforcement
Learning, which is modeled by trajectory generation based on two points of pose
and trajectory, generated after gripping the block and moving to the position to
assemble the block position.
This paper shows that it is possible to assemble by articial intelligence algorithms
other than Robot Grasping Planning, Robot Motion Planning, and Robot Assembly
Planning, which was used in assembly processes and showed the possibility of block
assembly through assembly sequence and methods. This paper also proposes that
non-experts can quickly respond to articial intelligence algorithms and geometric
trajectories at the site where the assembly process is frequently changed.
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- GRADUATE SCHOOL[S](대학원) > INTERDISCIPLINARY ENGINEERING SYSTEMS(융합시스템학과) > Theses (Master)
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