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Abstract and Figures

Automatic welding of tubular TKY joints is an important and challenging task for the marine and offshore industry. In this paper, a framework for tubular joint detection and motion planning is proposed. The pose of the real tubular joint is detected using RGB-D sensors, which is used to obtain a real-to-virtual mapping for positioning the workpiece in a virtual environment. For motion planning, a Bi-directional Transition based Rapidly exploring Random Tree (BiTRRT) algorithm is used to generate trajectories for reaching the desired goals. The complete framework is verified with experiments, and the results show that the robot welding torch is able to transit without collision to desired goals which are close to the tubular joint.
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AbstractAutomatic welding of tubular TKY joints is an
important and challenging task for the marine and offshore
industry. In this paper, a framework for tubular joint detection
and motion planning is proposed. The pose of the real tubular
joint is detected using RGB-D sensors, which is used to obtain a
real-to-virtual mapping for positioning the workpiece in a virtual
environment. For motion planning, a Bi-directional Transition-
based Rapidly exploring Random Tree (BiTRRT) algorithm is
used to generate trajectories for reaching the desired goals. The
complete framework is verified with experiments, and the results
show that the robot welding torch is able to transit without
collision to desired goals which are close to the tubular joint.
I. INTRODUCTION
Deployment of intelligent robots for industrial automation is
a growing trend, particularly for the marine and offshore
industry. Due to the availability of robotic manipulators with
specialized sensors, simple welding tasks can be performed
with high accuracy and repeatability. However, welding of
tubular TKY joints is still carried out manually. This is due to
the complex geometry which requires multi-pass welding. An
example of a tubular joint is shown in Fig. 1. Conventionally,
the welding joint is divided into several sections. The welding
section is switched after every pass in order to avoid joint
deformation. As such, it is necessary to sense the environment
for motion planning.
The major aspects of welding automation include object
detection/seam finding and motion planning. Seam finding
refers to adjusting the welding torch of the manipulator to the
correct pose in relation to the welding groove. Optical sensor
based smart systems for seam finding are offered by an
increasing number of companies. Examples include Servo
Robot Inc (Robo-Find, Power-Trac, i-CUBE) and Meta Vision
Systems (Laser Pilot). For these systems, seam finding is
carried out using highly accurate laser sensors and CCD
cameras. There are also tactile solutions which make use of the
filler material or the gas nozzle as a mechanical sensor [1].
However, tactile solutions can only be used to determine the
pose of simple welding geometries. In addition, most of these
solutions are designed for close range sensing (within a few
centimeters).
For motion planning, the common industrial solutions [2, 3]
are online and walk-through programming. Using online
programming, the waypoints of the trajectory are recorded as
the user guides the robot using a teaching pendant. On the
other hand, Kinetiq teaching [5] is an example of walk-though
Syeda Mariam Ahmed1 and Chee Meng Chew1 are with the Department of
Mechanical Engineering, National University of Singapore (email:
mpesyed@nus.edu.sg ).
Yan Zhi Tan2 and Chee Khiang Pang2 are with the Department of Electrical
Engineering, National University of Singapore.
Gim Hee Lee1,2 is with the Department of Mechanical Engineering and the
Department of Electrical Engineering, National University of Singapore.
Fig 1. Robotic manipulator and tubular joint in V-REP simulation
programming, where the robot welding torch is guided by
hand to the desired location. This method is effective for
simple joints which require single pass welding and do not
involve complicated, large welding seams [4].
An alternative approach will be to develop an offline
simulation environment which is a scaled replica of the real
robot environment [6, 7]. Examples of commercial softwares
which allow development of such workcells include
RobotStudio and Robotmaster. In these softwares, the robot
kinematic model is imported from a library along with 3D
CAD models of the workpieces. One of the significant
research efforts to implement offline programming was by Pan
et al., where an Automated OffLine Programming (AOLP)
software was developed for welding path planning using high
degree of freedom robots [8]. In AOLP, the trajectories for
transitions between welding tasks are computed using
Probabilistic RoadMap (PRM).
The Rapidly-exploring Random Trees (RRT) algorithm [9]
is also commonly used for automatic trajectory generation in
high-order workspace for robotic manipulators. The initial
point is regarded as the root of a tree, and the workspace is
explored using random sampling. Feasible waypoints are
added as a node of the tree which expands towards the desired
goal. The RRT connect planner is one of the early adaptations,
where bi-directional trees expand alternatively from initial and
goal nodes [10]. In order to generate a cost-optimal trajectory,
Object Detection and Motion Planning for Automated Welding of
Tubular Joints
Syeda Mariam Ahmed1, Yan Zhi Tan2, Gim Hee Lee1,2, Chee Meng Chew1, and Chee Khiang Pang2
the RRT* [11] algorithm is proposed. Other variations include
the anytime RRT [12] and lazy RRT [13] algorithms. The
latest extensions include informed-RRT* [14] and wrapping-
based informed RRT* [15], where the sampling space is
limited to a hyperellipsoid.
In this paper, a framework is proposed for tubular welding
joint detection and motion planning. The proposed framework
uses open source libraries without the need for licensed
software. Besides, the simulation environment is easily
updated based on real world information by an object
detection component of the framework. In addition, a
comparative study of motion planners for transitioning
between welding tasks is carried out.
II. FRAMEWORK FOR OBJECT DETECTION AND MOTION
PLANNING
Welding automation for tubular joints can be divided into
two phases. For the first phase, object detection and motion
planning for transitions between welding tasks is carried out.
Welding trajectory planning and laser-based seam tracking is
the main objective of the second phase. In this paper, the focus
is on the implementation of the first phase. The proposed
framework is shown in Fig. 2, which consists of two main
components:
1) Object detection: Perception of the real workshop
environment is carried out using two RGB-D sensors as
shown in Fig. 2. A virtual environment is created using V-
REP [16] by importing CAD models of the robot, tubular
joints and a workbench. This environment can also serve
as a user interface, such as allowing the user to change the
pose of the welding torch. The pointclouds from the
sensors are used to perform a real-to-virtual world
mapping. This mapping determines the misalignment in
pose between the real and virtual workpieces, and can be
used to update the simulation for all subsequent motion
planning tasks.
2) Motion planning: Bi-directional Transition-based Rapidly
exploring Random Tree (BiTRRT) algorithm [17] is used
for generating the trajectories for transitions between the
welding segments. Optimality for the generated trajectories
is based on minimizing the Integral of the Cost (IC) along a
path. The planned trajectories are visualized in the virtual
environment before uploading to the robot controller.
In the following subsections, a more detailed description of the
proposed framework will be provided.
A. Object Detection
Perception of environment is an essential prerequisite for
welding automation. In this section, the RGB-D setup,
pointcloud generation from CAD models, preprocessing of
pointclouds, and registration for determining real-to-virtual
mapping are described with reference to Fig. 2.
The two RGB-D sensors are mounted on tripod stands and
placed perpendicularly to each other at known distances from
the workbench as shown in Fig. 2. The sensor positions are
fixed and the environment can be reconstructed using simple
transformations. Additional RGB-D sensor can always be
deployed in order to obtain a complete pointcloud of the
workpiece with all key features included.
Fig 2. Overall framework comprising of object detection and motion
planning for automating the welding of a tubular joint.
A point cloud from the V-REP environment is generated
using CAD models. A CAD model can be used to generate a
STereoLithography (STL) file which can subsequently be
converted to a Polygon File Format (PLY). The PLY model is
primarily a sparse pointcloud comprising of only the vertices
of the CAD model. A denser pointcloud is generated by
raytracing using the Point Cloud Library (PCL) [18]. In PCL,
raytracing is implemented by creating a spherical truncated
icosahedron around the PLY model. A virtual camera pointed
towards the origin is placed at each vertex of the icosahedron.
Depth information of the model is used to simulate the input of
a depth sensor, and multiple snapshots from each pose are
combined to create the dense pointcloud.
In order to perform real-to-virtual world mapping,
registration between the pointclouds is required. As the focus
is on the tubular joint, segmentation is carried out prior to
registration, and the workbench in the real and virtual worlds
are pre-aligned. The alignment is carried out by assigning a
work frame to the edge of the workbench. The real position of
this work frame is recorded by jogging the robot to the physical
edge and reading the coordinates from the teaching pendant.
This position is used to update the workbench placement in V-
REP. After segmentation and workbench alignment, noise
filtering using the Difference of Normal (DoN) operator [19] is
applied to the pointcloud obtained from the RGB-D sensors in
order to improve registration accuracy. The DoN operator can
be defined as
2121
21
21
ˆ,,,
2),(
ˆ
),(
ˆ
),,( rrrr
rpnrpn
rrp
n
(1)
Environment
Perception using
RGB-D Sensors
CAD Models
+
Object Detection
OMPL Path Planner
in V-REP Simulation
Robotic Manipulator
Filtering
Registration
Pointcloud
Information
Update Pose of
Tubular Joint
Implement
Trajectory
Motion Planning
Fig 3. Pointclouds from real and virtual environment before (top) and after (bottom) ICP registration
where
),(
ˆ1
rpn
and
),(
ˆ2
rpn
represents the estimated surface
normals at a point p considering support radii r1 and r2,
respectively. The result of applying the DoN operator to the
pointcloud is a vector map of
n
ˆ
, where
]1,0[
ˆ n
. The
vector map represents the difference in surface normal
considering a varying support radius, and noise can be filtered
out by specifying a threshold for
n
ˆ
. Registration is
performed using the point-to-point Iterative Closest Point
(ICP) algorithm in PCL. ICP employs brute force approach
which consists of two main steps to align two point clouds.
First, the nearest neighbor in the target point cloud is
determined for every point from the source pointcloud. Next,
the optimal transformation which minimizes the sum of
squared distances between the corresponding points is
determined. The nearest neighbor search in PCL is
implemented using Fast Library for Approximate Nearest
Neighbors (FLANN) [23]. FLANN consists of a collection of
algorithms optimized for fast nearest neighbor search in large
datasets. From the ICP algorithm, a transformation matrix is
obtained which is used to update the pose of the tubular joint
in the virtual environment.
B. Motion Planner for Transition between Welding Tasks
Motion planning is carried out after the pose of the tubular
joint is updated. The welding task for the tubular joint is
divided into several sections, and the welding sections are
switched after every pass in order to avoid joint deformation.
In this paper, the objective of motion planning is to ensure a
safe transition for the robotic manipulator between these
sections. As such, drastic configuration changes should be
avoided in order to prevent damage to the equipment. In
addition, another motivation for automated offline motion
planning is to avoid singularities.
The V-REP environment is recently integrated with the
Open Motion Planning Library (OMPL) [20] through APIs.
As such, the motion planning task can be easily defined for the
planner. A kinematic model for the robotic manipulator is set
up in the environment, and the user is required to define the
elements of the kinematic chain. The V-REP environment
provides the flexibility to choose between the pseudo-inverse
and the Damped Least Squares (DLS) method for inverse
kinematics computation. With the virtual environment and the
robot kinematic model, various path planners can be analyzed
in order to determine the most appropriate planner for the task.
For our welding tasks, the BiTRRT [17] algorithm is used
to generate the transition trajectories. The BiTRRT algorithm
is a combination of RRT and the stochastic-optimization
method which is used to compute the global minima in
complex spaces. The expansion of BiTRRT is similar to the
RRT connect planner [10]. At each iteration, the new node is
subjected to a transition test based on the Metropolis criterion.
Using the Metropolis criterion [22], the cost of the new
configuration is compared with the cost of its parent
configuration. If the criterion is satisfied by the new node, the
node is connected to the nearest neighbor in the other tree. The
planned trajectories are visualized in the virtual environment
and uploaded to the robot controller.
III. EXPERIMENTS AND RESULTS
In this section, the proposed framework is implemented on a
6 degree-of-freedom robotic manipulator from ABB in our
workshop. The workpiece considered is shown in Fig. 1,
which is a typical tubular joint. Besides verifying the object
detection component by aligning the real and virtual tubular
joints, the robustness of the ICP algorithm is explored by
considering additional position and orientation offsets. For
optimal motion planning, cost functions for measuring the
divergence of the robot pose from the goal throughout the
trajectory are introduced.
RGB-D
CAD
Model
Disparity
x
y
z
y
x
z
Fig 4. ICP convergence score considering various position (top) and
orientation (bottom) offsets between the real and virtual environments.
A. Tubular Joint Alignment Using Registration
The pointclouds of the real environment is obtained using two
RGB-D sensors as shown in Fig. 2. In order to prevent
interference between the RGB-D sensors, recording is carried
out using one sensor at a time. The pointcloud is segmented
using the thresholding technique in Cartesian space in order to
retain only the tubular joint. For a change in workpiece position
within the setup, an identical threshold can be used for
segmentation as the sensors remain at fixed positions. The DoN
operator for noise filtering is implemented using r1 = 5, r2 = 50
with the threshold for
n
ˆ
set to 0.1. The threshold value is
chosen such that features of the TKY joint are preserved. The
segmented and filtered pointcloud of the tubular joint is as
shown in blue in Fig. 3.
Registration is carried out using ICP from PCL, and the
results after ten iterations are shown in Fig .3. The pointclouds
before and after registration are shown in the top and bottom
rows of Fig. 3, respectively. The green pointclouds in Fig. 3
represent the pointclouds from the CAD model. The
transformation matrix for real-to-virtual mapping is mainly a
translation of 200 mm in the x-direction, and the virtual
environment is updated accordingly.
From Fig. 3, disparity between the pointclouds from the real
and virtual environment can be observed. The green
pointclouds from the CAD model do not have fixtures as
compared to the pointclouds of the actual tubular joint. The
fixtures are not included in the CAD model for ICP
registration as these fixtures are not precisely positioned for
every joint. Besides, the position of the vertical pipe may vary
within an allowable range.
A series of simulations were also conducted to evaluate the
robustness of the ICP algorithm in the presence of such
disparity. The position offsets considered are up to 600 mm in
each of the x, y and z directions. Similarly, the orientation
offsets are up to 18° about the z-axis. The algorithm converges
to a score which is slightly higher than the minimum ICP
convergence score as shown in Fig. 4. This may be due to the
points belonging to the fixtures which are not aligned with any
points from CAD model. The results show that the ICP
convergence score increases significantly only when an offset
of 600 mm in the x and z-direction or a rotation of 18° about
the z-axis is given. This range is higher than the working range
of commercial optical and tactile sensors-based systems for
seam-finding. Besides, such level of misalignment in the z-
axis is unlikely as the workbenches are pre-aligned.
B. Cost Analysis of Bidirectional Transition-Based RRT
Collision-free and cost-optimal paths are required for
transitioning between the welding tasks. The IC criterion is
used to assess the quality of the trajectories generated by the
motion planner. A discrete approximation of the integral cost
cp for a path 𝜋 can be defined as
)),(()( 1kc
n
L
cn
k
p
(2)
where L represents the length of the path, and n denotes the
number of subdivisions along the path. Besides, c in (2)
represents a continuous differentiable cost function that can be
replaced with either cpos and corient which are defined as
,
arg ettkpos XXc
(3)
 
.)(,)(min argarg ettkettkorient qqqqc
(4)
In (3) and (4), Xk, qk, Xtarget, and qtarget represent the current
position, current orientation, goal position, and goal
orientation vectors considering the tip of the welding torch as
the tool center position, respectively. The Euler angle
representation is used for the orientation vectors. Using the IC
criterion, the divergence of the path from the goal is evaluated
at every intermediate node as compared to the maximal or
average cost criteria.
Paths for reaching two goal poses are generated by the
BiTRRT algorithm over fifteen trials, and the IC values
computed using the two cost functions cpos and corient are shown
in Fig. 5. The IC values considering cpos are observed to be low
for multiple trials, in particular for the trajectory to reach Goal
1 as shown in Fig. 6. The variation in IC values is larger for
corient due to differences in the avoidance of the fixture by the
robotic manipulator. However, the optimal paths generated by
the planner are consistent as indicated by the IC values which
remain within a close range for both trajectories.
Fig 5. Evaluation of cp for BiTRRT planner using cpos and corient functions.
Fig 6. Simulation verification of proposed framework. (top) Trajectory generated to reach Goal 1. (bottom) Trajectory to transit from Goal 1 to Goal 2.
Trajectories generated by the BiTRRT algorithm for the two
goal poses and implementation results on the robotic
manipulator are shown in Fig. 6. The first trajectory from the
robot home position to Goal 1 is shown in the top row, where
Goal 1 represents the approach point for the first welding task.
The intermediate trajectory to transit from Goal 1 to Goal 2 for
the next welding task is shown in the bottom row of Fig. 6.
Similar results are obtained when the trajectories are
implemented on the real welding robot. Various motion
planners will be compared in the next section based on plots
of cost functions cpos and corient in order to verify the
effectiveness of the BiTRRT algorithm.
C. Comparative Study of Motion Planners
Optimal motion planning for a robotic manipulator can be
described by the divergence of the intermediate nodes within
the trajectory. Besides, the generated path should avoid drastic
configuration changes in order to prevent damage to the
welding and monitoring equipment as mentioned in Section II-
B. A comparative analysis of planners available in OMPL is
performed to analyze the cost functions cpos and corient as defined
in (3) and (4). Each planner is evaluated considering the two
goals specified in Section III-B, and the following algorithms
are compared:
a) Bidirectional Transition Based RRT (BiTRRT),
b) Probabilistic Roadmap Star (PRMStar),
c) Lower-Bound Tree RRT (LBT-RRT) [21],
d) RRT Connect, and
e) RRT Star.
The plots of cpos and corient over the time taken to reach Goal 1
are shown in Figs. 7 (a) and (c). The plots of cpos and corient for
Goal 2 are similarly shown in Figs. 7(b) and (d). The area
under the curves is largest for Goal 1 using the LBT-RRT
algorithm. For Goal 2, LBT-RRT and PRMStar results in the
largest area for corient and cpos, respectively. The area is larger
due to the divergence of the intermediate states from the goal,
which results in a longer trajectory for the robotic manipulator.
On the other hand, the BiTRRT algorithm is able to generate
a path with a minimum area under the curve. As the rest of the
algorithms have comparable performance, the BiTRRT is
chosen due to the consistency observed in IC values for paths
generated over multiple iterations as shown in Fig. 5.
IV. CONCLUSION AND FUTURE WORK
A framework for tubular TKY joint detection and motion
planning is proposed in this paper. The proposed framework
involves environment perception to align workpieces in the
virtual world based on real world positioning, which is a
common problem for deploying intelligent robotic
manipulators in the industry. This issue is addressed by
capturing a pointcloud of the real world using RGB-D sensors
and generating a similar pointcloud from CAD models. A real-
to-virtual world mapping is obtained through a series of pre-
processing steps followed by registration. Subsequently, a
BiTRRT motion planner is used to plan trajectories for
transitions between the welding tasks. Comparative evaluation
of different motion planners supported by the OMPL shows
that the BiTRRT algorithm can consistently generate low cost
paths. Besides, the implemented trajectories for a tubular joint
are shown.
Future works include considering the RGB-D sensor to be
mounted on the robotic manipulator. This will improve the
scalability of the proposed framework and facilitate
deployment in industries.
ACKNOWLEDGMENTS
The authors thank the National Research Foundation, Keppel
Corporation and National University of Singapore for
supporting this research that is carried out in the Keppel-NUS
Corporate Laboratory. The conclusions put forward reflect the
views of the authors alone, and not necessarily those of the
institutions within the Corporate Laboratory.
Goal 1
Goal 2
Fig 7. Comparison of motion planners using plots of cpos and corient functions. (a) cpos values for Goal 1. (b) cpos values for Goal 2. (c) corient values for Goal 1.
(d) corient values for Goal
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