CAD-based off-line robot programming
ABSTRACT Traditional industrial robot programming, using the robot teach pendant, is a tedious and time-consuming task that requires technical expertise. Hence, new and more intuitive ways for people to interact with robots are required to make robot programming easier. The goal is to develop methodologies that help users to program a robot in an intuitive way, with a high-level of abstraction from the robot language. In this paper we present a CAD-based system to program a robot from a 3D CAD environment, allowing users with basic CAD skills to generate robot programs off-line, without stop robot production. This system works as a human-robot interface (HRI) where, through a relatively low cost and commercially available CAD package, the user is able to generate robot programs. The methods used to extract information from the CAD and techniques to treat/convert it into robot commands are presented. The effectiveness of the proposed method is proved through various experiments. The results showed that the system is easy to use and within minutes an untrained user can set up the system and generate a robot program for a specific task. Finally, the time spent in the robot programming task is compared with the time taken to perform the same task but using the robot teach pendant as interface.
- SourceAvailable from: Pedro Neto
Conference Paper: Fuzzy-PI Force Control for Industrial Robotics.[Show abstract] [Hide abstract]
ABSTRACT: Increasingly, robot programs are generated off-line, for example, through a virtual model of a robotic cell. However, when the virtual model does not reproduce exactly the real scenario or the calibration process is not performed correctly it is difficult to generate reliable robot programs. In order to circumvent this problem, it was introduced sensory feedback (force and torque sensing) in a robotic framework. By controlling the robot end-effector pose and specifying its relationship to the interaction/contact forces, robot programmers can ensure that the robot maneuvers correctly, damping possible impacts and also increasing the tolerance to positioning errors from the off-line programming process. In this paper Fuzzy-PI and PI reasoning was proposed as a force control technique. The effectiveness of the proposed approach was evaluated in a serie of 20 experiments that demonstrated that Fuzzy-PI controllers are more suitable to deal with this type of situations.Trends in Intelligent Robotics - 13th FIRA Robot World Congress, FIRA 2010, Bangalore, India, September 15-17, 2010. Proceedings; 01/2010
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ABSTRACT: In this paper, an adaptive and low-cost robotic coating platform for small production series is presented. This new platform presents a flexible architecture that enables fast/automatic system adaptive behaviour without human intervention. The concept is based on contactless technology, using artificial vision and laser scanning to identify and characterize different workpieces travelling on a conveyor. Using laser triangulation, the workpieces are virtually reconstructed through a simplified cloud of three-dimensional (3D) points. From those reconstructed models, several algorithms are implemented to extract information about workpieces profile (pattern recognition), size, boundary and pose. Such information is then used to on-line adjust the “base” robot programmes. These robot programmes are off-line generated from a 3D computer-aided design model of each different workpiece profile. Finally, the robotic manipulator executes the coating process after its “base” programmes have been adjusted. This is a low-cost and fully autonomous system that allows adapting the robot’s behaviour to different manufacturing situations. It means that the robot is ready to work over any piece at any time, and thus, small production series can be reduced to as much as a one-object series. No skilled workers and large setup times are needed to operate it. Experimental results showed that this solution proved to be efficient and can be applied not only for spray coating purposes but also for many other industrial processes (automatic manipulation, pick-and-place, inspection, etc.).International Journal of Advanced Manufacturing Technology 09/2013; · 1.78 Impact Factor
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ABSTRACT: In this manuscript, CAM system for an articulated-type industrial robot RV1A is described from the view point of robotic servo controller. It is defined here that CAM system includes an important function which allows an industrial robot to move along cutter location data (CL data) consisting of position and orientation components. Another important point is that the proposed CAM system has a high applicability to other industrial robots whose servo systems are technically opened to end-user engineers. The CAM system works as a flexible interface without any robot language between CAD/CAM and industrial robots. At the present stage, unfortunately, the relationship between CAD/CAM and industrial robots are not deep compared to NC machine tools widely spread in manufacturing industries. CAM system for NC machine tools is already established for the standardization due to the NC data, however, CAM system for industrial robots has not been sufficiently considered for the standardization yet. An on-line teaching with a teaching pendant is generally required in almost all cases to obtain teaching data according to each industrial robot. Off-line teaching systems by using a CAD/CAM are also proposed, however, there exist too many types of robot languages to standardize them like NC data. Here, in order to raise the relationship between a CAD/CAM and an industrial robot, a simple CAM system without using any robot languages is proposed. The basic design of the CAM system and an experimental result are shown.01/2012;
978-1-4244-1676-9/08 /$25.00 ©2008 IEEE RAM 2008
CAD-Based Off-Line Robot Programming
Pedro Neto, J. Norberto Pires
Department of Mechanical Engineering (CEMUC)
University of Coimbra
A. Paulo Moreira
Institute for Systems and Computer Engineering of Porto
University of Porto
Abstract—Traditional industrial robot programming, using
the robot teach pendant, is a tedious and time-consuming task
that requires technical expertise. Hence, new and more intuitive
ways for people to interact with robots are required to make
robot programming easier. The goal is to develop methodologies
that help users to program a robot in an intuitive way, with a
high-level of abstraction from the robot language. In this paper
we present a CAD-based system to program a robot from a 3D
CAD model, allowing users with basic CAD skills to generate
robot programs off-line, without stop robot production. This
system works as a real human-robot interface (HRI) where,
through the CAD, the user operates in the real robot. The
methods used to extract information from the CAD (position and
orientation of rigid bodies in space) and techniques to
treat/convert it into robot commands are presented in detail. The
effectiveness of the proposed method is proved through various
experiments. The results showed that the system is easy to use
and within minutes an untrained user can set up the system and
generate a robot program for a specific task. Finally, the time
spent in the robot programming task (using this system) is
compared with the time taken to perform the same task but using
the robot teach pendant as interface.
Keywords—human-robot interaction, intuitive programming,
high-level programming, CAD, industrial robotics
Traditional manufacturing systems (often based on fixed
automation) are being replaced by flexible and adjustable
manufacturing systems. Due to its flexibility, programmability
and efficiency, industrial robots are seen as a key element of
modern flexible manufacturing systems. Nevertheless, there are
still some problems that hinder the utilization of robots in
industry, especially in the small and medium-sized enterprises
(SMEs) . Programming an industrial robot by the typical
teaching method, through the use of the robot teach pendant, is
still a tedious and time-consuming task that requires some
technical expertise. In fact, manual teach methods are often
time consuming and imprecise. Nonetheless, the biggest
problem that SMEs are facing is the lack of skilled workers,
especially experts in robot programming and at the same time
in specific manufacturing processes, such as welding and
painting. Therefore, new and more intuitive ways for people to
interact with robots are required to make robot programming
easier. The goal is to develop methodologies that help users to
program a robot with a high-level of abstraction from the robot
specific language. Another important factor is the ability to
program a robot off-line, without stop robot production. Many
different solutions have been proposed in literature to create
intuitive HRIs; through the development and implementation of
user-friendly software interfaces dedicated to a specific
industrial process ; using sensors attached to the human
body to capture arm movements and thus teach the robot by
performing gestures ; using vision-based interfaces ; and
speech . Since over the past few years, computer-aided
design (CAD) packages are becoming more powerful and
accessible, CAD-based solutions related to the HRI problem
have been common (see section II). Notwithstanding the above,
due to the specific characteristics of an industrial environment
it remains difficult to apply such systems in industry (many
systems have not yet reached industrial usage). Thereby, the
teach pendant continues to be the common robot input device
that gives access to all functionalities provided by the robot
(jogging the manipulator, producing and editing programs,
etc.). In the last few years, the robot manufacturers have made
great efforts to make user-friendly
implementing ergonomic design concepts, more intuitive user
interfaces, color touch screens with graphical interfaces, a 3D
joystick, a 6D mouse and developing a wireless teach pendant.
Nevertheless, it is still difficult for an untrained worker to
operate with a robot teach pendant. The teach pendants are not
intuitive to use and require a lot of user experience, besides
being big and heavy .
In this paper is presented a CAD-based system to program a
robot from a 3D CAD model, allowing users with basic CAD
skills to generate robot programs off-line. In addition, the 3D
CAD package (Autodesk Inventor) that interfaces with the user
is a well known CAD package, widespread in the market at a
relative low-cost. This system works as a real HRI where,
through the CAD, the user operates in the real robot. The
methods used to extract information from the CAD (position
and orientation of rigid bodies in space) and techniques to
treat/convert it into robot commands will be presented in detail.
Several experiments were conducted to evaluate the system
performance. The results showed that the proposed system is
easy to use and within minutes an untrained user (without
programming skills) can set up the system and generate a robot
program for a specific task. The time spent in the robot
programming task (using the system here proposed) is
compared with the time taken to perform the same task but
using the robot teach pendant as interface. Experiments were
performed with a six-axis industrial robot in laboratory
environment, giving to the reader a good insight into the
problem. Finally, results are discussed and some considerations
about future work directions are made.
In recent years, CAD technology has become economically
attractive and easy to work with so that today millions of SMEs
worldwide are using it to design and model their products. The
2D and 3D CAD models provide an exceptional way to model
objects with great precision. Already in the 80’s, CAD was
seen as a technology that could help in the development of
robotics . Since then, a variety of research has been
conducted in the field of CAD-based robot planning and
programming, so that today’s users can generate robot
programs from CAD models. CAD-based robot programming
has been used to deal with many different processes, such as
welding, machining, painting, material handling, etc.
Many CAD-based systems have been proposed to assist
people in the robot programming process. A series of studies
have been conducted using CAD as an interface between robots
and humans, for example, extracting motion information from a
CAD data exchange format (DXF) file and converting it into
robot commands . In this system, the user only needs to
define the welding path and the approach/escape paths in the
drawing. Another study presents a method to generate three-
dimensional robot working paths for a robotic adhesive spray
system for shoe outsoles and uppers . A robotic sanding
platform where the robot paths are generated by CAD/CAM
software and a force control system is used to maintain a
constant contact force between the tool and the workpiece is
presented in . Reference  presents a model based robot
programming concept for applications where metal profiles are
processed by robots and only a 2D geometrical representation
of the workpiece is available. An example of a novel process
that benefits from the robots and CAD versatility is the so-
called incremental forming process of metal sheets. Without
using any costly form, metal sheets are clamped in a rigid
frame and the robot produces a given 3D contour by guiding a
tool equipped with a high-frequency oscillating stamp over the
metal surface. The robot’s trajectories are calculated from the
CAD model on the basis of specific material models. Prototype
panels or customized car panels can be economically produced
using this method . Reference  presents a robot path
generator for the polishing process, where the cutter location
data is generated from the postprocessor of a CAD system. As
we have seen above, a variety of research has been done in the
area of CAD-based robot planning and programming.
However, none of the studies so far deals with a “global”
solution for this problem. Even though an abundance of
approaches has been presented, a cost-effective standard
solution has not been established yet.
In this paper, the information extracted from CAD models
will be used to generate robot programs. Through the CAD
interface, any user with basic CAD skills will be able to define
the robot working paths and organise them in the desired
sequence (definition and
positions/orientations, reference frames, and trajectories). After
completing the design, a program converts it into robot
programs (off-line robot programming) (Fig. 1). The generated
parameterisation of robot
programs can be immediately tested for detailed tuning and a
set of tools is then available to speed up corrections, if
necessary. Depending on the complexity of the robotic cell, this
process can be completed in just a few minutes, representing a
huge reduction in programming and set up time.
Figure 1. Off-line robot programming concept. Working in an office
environment, the user can generate robot programs without interrupt
IV. PROPOSED APPROACH
There is a lack of natural interfaces between humans and
robots, something that allows us to show to the robot what it
should do, and with a high-level of abstraction from the robot
language. As we know that one of the major challenges facing
HRI involve the teaching of robots by operators, how we can
interface/interact with robots in an intuitive way is the question.
The HRI system should be intuitive, low-cost, with short
learning curve, and should also allow users to program a robot
in a short time.
Once CAD technology is widespread throughout the
industry, we are proposing a CAD-based system to program an
industrial robot, allowing users with basic CAD skills to
generate robot programs off-line. The CAD package, Autodesk
Inventor, will make the interface between the user and the
robot. The information needed to program the robot will be
extracted from the CAD models through an application
programming interface (API) provided by Autodesk.
A. Application Programming Interface (API)
The Autodesk Inventor API exposes the Inventor’s
functionalities in an object-oriented manner, allowing
developers to interact with Autodesk Inventor using current
programming languages (Visual Basic, Visual C#, Visual
C++), for example, access to CAD data and create CAD
models. There are different ways to access the API (Fig. 2) and
it is important to choose the appropriate way to access it. In our
system, a standalone application was used to extract
information from the CAD and the Apprentice Server used to
display the CAD models.
Figure 2. Different ways of accessing Autodesk Inventor’s API. The white
boxes represent components provided by the API (Autodesk Inventor and
Apprentice Server) and the gray boxes represent programs written by
The process begins with the extraction of data from the
CAD, but first, it is necessary to specify what kind of data will
be extracted and how. Robot programming is essentially based
on the definition of robot paths, or rather, in the definition of a
sequence of tag points that the robot passes through. As we are
working with industrial robots, the tag points will define the
robot end-effector pose, so that the definition of these points
should include not only position but also orientation in space.
The API allows us to extract the transformation matrix of each
part represented in a CAD assembly model. The
transformation matrix of each part contains the rotation matrix
and the position of the origin of that part, both in relation to
the origin of the CAD assembly model. In addition, the API
also gives us information about the position of the Autodesk
Inventor WorkPoints, which are points that can be inserted in
the CAD drawing at any location. Finally, the extracted
information is converted into robot code.
C. Position of Objects in Space
In our proposed approach the tag points are defined by
placing a WorkPoint in the tool model, where the tool is
attached to the robot wrist (Fig. 3).
Figure 3. A simplified model of the robot tool (at left) and the real tool (at
right). The tool can be modeled in a simplified manner but respecting its real
dimensions, or rather, the important dimensions for the robotic process.
As previously mentioned, the transformation matrix of each
part is defined in relation to the origin of the CAD assembly
model, however, the WorkPoints are defined in relation to the
origin of each tool model. Therefore, considering our goal, we
need to define the WorkPoint coordinates in relation to the
origin of the CAD assembly model.
Considering two Cartesian coordinate systems, the first
representing the transformation matrix of each tool model in
relation to the origin of the assembly model (system A), and the
second representing the WorkPoint coordinates in relation to
the origin of the tool model (system B). Making an analogy
with Figure 4, where P is the WorkPoint, P
WorkPoint position in relation to the origin of the tool model,
is the positional vector from the origin of the system A
to the origin of the system B, and P
of point P relative to the origin of the system A.
is the positional vector
Figure 4. Reference systems A and B.
The aim is to obtain the vector P
, which describes the orientation of the system B
relative to the system A, we can write that:
. Considering the rotation
Where, the transformation matrix T
known (provided by the API).
and the vector P
D. Orientation of Objects in Space
The transformation matrix of each tool in the CAD
assembly model will be used to define the robot end-effector
orientation in the form of quaternions (4), (5), (6), (7), where
q is the real quaternion, and
q are the imaginary
quaternions. Since all the components
in t of the rotation matrix
are provided by the API, the quaternions can be calculated.
If the real quaternion
calculate the warning signs of the imaginary quaternions, given
by (8), (9) and (10).
q is nonzero, it becomes necessary to
E. Program Generation
The demand for intuitive ways to program machines has led
to the emergence of techniques to generate code, such as the
automatic generation of CNC code from CAD/CAM software.
So why does not generate robot programs from CAD
drawings? Using information from the CAD models, our
system is able to generate control sequences for the robot
program. In the construction of an algorithm to generate code,
the keyword is “generalise” and never “particularise”, in other
words, the algorithm must be prepared to cover a wide range of
variations in the process.
Several code generation techniques have been developed,
but these tend to have drawbacks such as their suitability of the
plans they produce for a particular application or how well they
are able to generalise the problems. However, for a particular
application with a limited and well known number of process
variations, this kind of systems tend to present good
performance. For this particular approach, the code generation
process is divided into two distinct phases: first, definition and
parameterization of robot positions/orientations, reference
frames, tools, etc.; second, construction of the body of the
program containing predominantly
EXPERIMENTS AND RESULTS
The robotic system presented in this paper was designed to
perform the task of object handling, or more specifically, the
handling of knives in a robotic deburring cell. Briefly the
system works as follows.
a) Create CAD model: The user should create a CAD
assembly model representing the real robotic cell. The CAD
model of a specific robotic cell only needs to be built once, so
that usually the user only needs to make changes in design,
according to the current work plan.
b) Place the robot tool models in the target positions.
c) Define robot parameters: Robot speed, robot tools,
reference frames, etc.
d) Generate the robot program.
e) The generated program can be tested and adjusted if
A. System Architecture
The experimental cell is composed of an industrial robot
(IRB 2400 equipped with the S4C controller, ABB), and a
computer running the CAD package Autodesk Inventor and the
software application that manages the cell (Fig. 5). The
application receives data from the CAD, interprets the received
data and generates robot programs. This application interface
provides a set of capabilities to interact with the robot, allowing
command the robot, upload and download programs to/from
the robot, etc., using for this purpose the PcRob, an ActiveX
created in our laboratory to control and manage the robot
remotely, via Ethernet (Fig. 6).
Figure 5. Software application interface.
Figure 6. Communications and system architecture.
B. Practical Implementation
The CAD assembly model of the robotic cell (Fig. 7) does
not need to accurately represent the real cell in all its aspects,
on the contrary, it can be a simplified model. For example, the
dimensions of the feed table, the relative position of objects
(robot tools, feed table and deburring machine) and the robot
tool length should represent the real scenario, however, the
objects appearance need not be exactly equal to the real objects.
When building the CAD model, it is important that the user
keep in mind that the model will be used to generate a robot
program. An important issue is that one must number the tool
models according to the work flow because the algorithm to
generate code is prepared to acquire data from the first
numbered tool to the last one. Another important issue is
related to the process of calibration, specifically the placement
of the reference frames. In this experiment we are using only
one robot reference frame, and so a vertex of the feed table was
selected to be the origin of the reference frame. This way, all
positions from CAD are related to this reference frame.
After specifying some robot parameters such as robot
speed, approach distances (the robot needs to reduce speed
before placing the workpiece in the target location), etc., the
system acquires data from CAD and convert it into a robot
program. Finally, the generated robot program was tested. It
was generated a robot program to perform the task of object
handling, or more specifically, the handling of unfinished
knives in a robotic deburring cell. The robot starts by picking it
up (from the feed table), moves it to a pose near to the
deburring machine and finally puts it in contact with the
deburring machine (Fig. 8). After this phase the process is
controlled by a force control system . In general, the
generated program works very well. Results will be presented
and discussed in the next section.
Figure 7. CAD assembly model of the robotic deburring cell. A robot
program will be generated from this model.
Figure 8. Robot running the generated program.
VI. RESULTS, DISCUSSION AND FUTURE WORK
The experiments showed that the generated program works
very well, without producing positional errors. The error that
may exist comes from inaccuracies in the calibration process
and from situations where the CAD model does not reproduce
properly the real scenario. This task must be done carefully to
avoid causing errors.
It is important to quantify the time spent in the process.
Thus, the experiment above was conducted with two
participants (P1 and P2), both with basic skills in CAD and 3D
modelling, but had never worked with a robot before. After a
brief explanation on how the system works (10 minutes) the
participants began the test. This explanation was focused
essentially on how the application interface works and the
procedures to be followed at the time when the CAD model is
constructed. The process was divided in five different tasks and
the time spent in each one was reported in Table I. The
construction of the CAD model consumes a great deal of time,
on average about 64% of total time (robot and deburring
machine models are provided by manufacturers), and if it was
considered that the acquisition of dimensions is part of the
construction of the CAD model, then this task would consume
over 85% of total time. Nevertheless, after the cell model is
built, it can be quickly reconfigured to generate code for a
different task in few minutes. Both participants generate the
robot program and put it running on the robot controller with
success, spending on average 33 minutes.
We thought it was interesting to compare the time taken to
program the robot using this CAD-based system and using the
robot teach pendant. The same participants (P1 and P2) were
invited to conduct the experiment. In the first experiment
(using the CAD interface) we spent 10 minutes explaining how
the system works, but for the current experiment (using the
teach pendant) we quickly concluded that this was not enough
time, so we spent 2 hours talking about general robotics (a
quick introduction) and 1 hour talking about robot
programming using the teach pendant. This explanation
focused mainly on practical aspects. Participant P1 spent about
58 minutes to complete the task (most of the time consulting
the robot manual) while the participant P2 gave up. It can be
concluded then that the first experiment took less time,
however, we believe that with practice this 58 minutes may be
reduced. The point to emphasize is that the time spent teaching
participants was much lower for the first experiment (10
minutes) than for the second (3 hours), showing the short
learning curve of the CAD-based system. Finally, when
questioned, participants indicated that the first experiment
(using CAD) is much more intuitive and therefore easy to use.
In future, the calibration process should be simplified and
less susceptible to errors, since this is the most delicate/difficult
task facing this system.
TIME SPENT BY PARTICIPANTS IN EACH TASK
Time Spent (minutes)
Acquisition of real cell dimensions
CAD model construction 20 22
Definition of robot frame(s) 3 3
Robot and process parameterisation 1 1
Code generation 1 1
Total 32 34
A new CAD-based system dedicated to off-line robot
programming was developed. This system works as a real HRI,
allowing users with basic CAD skills to generate robot
programs off-line, in an intuitive way. A common 3D CAD
package (Autodesk Inventor) was selected to make the
interface between user and robot. The effectiveness of the
proposed system was proved through the experiments. These
experiments were conducted with two participants that had
never worked with a robot before. Experiments showed that the
CAD-based system is intuitive and has a short learning curve,
allowing non-experts in robot programming to generate robot
programs in just few minutes. In future, the way the system is
calibrated should be simplified.
This work was supported in part by the Portuguese
Foundation for Science and Technology (FCT), grant no.
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