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discussed and some considerations about future work directions are made. II.RELATED WORKIn 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. Already in the 80’s, CAD was seen as a technology that could help in the development of robotics [7]. Since then, a variety of research has been conducted in the field of CAD-based robot planning and programming. Many CAD-based systems have been proposed to assistpeople 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 [8]. 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 [9]. A robotic sanding platform where the robot paths are generated by CAD/CAM software is presented in [10]. Reference [11] 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. The robot’s trajectories are calculated from the CADmodel on the basis of specific material models. Prototype panels or customized car panels can be economically produced using this method [12]. Reference [13] 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. The system presented here allows to extract data not only from a single CAD model but essentially from a CAD environment representing a robotic cell. We can say that in this case, generate a robot program is simple as the manipulation of virtual parts in a 3D CAD environment. igure 1. Off-line robot programming concept. Working in an office environment, the user can generate robot programs without interrupt production. IV.PROPOSED APPROACHThere is a lack of natural interfaces between humans and robots, something that allows us to show to the robot what it should do. 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 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 III.CAD-BASED INTERFACEC++), for example, access to CAD data and create CAD In this paper, the information extracted from CAD models models. There are different ways to access the API (Fig. 2) will be used to generate robot programs. Through the CAD and it is important to choose the appropriate way to access it. In our system, a standalone application was used to extract interface, any user with basic CAD skills will be able to define the robot working paths and organise them in the desired information from the CAD and the Apprentice Server used to sequence (definition and parameterisation of robot display the CAD models. positions/orientations, reference frames, and trajectories). After B.Method completing the design, a program converts it into robot The process begins with the extraction of data from the programs (off-line robot programming) (Fig. 1). The generated programs can be immediately tested for detailed tuning and a CAD, but first, it is necessary to specify what kind of data will set of tools is then available to speed up corrections, if be extracted and how. Robot programming is essentially based on the definition of robot paths, or rather, in the definition of a necessary. Depending on the complexity of the robotic cell, this process can be completed in just a few minutes, representing a sequence of tag points that the robot passes through. As we are working with industrial robots, the tag points will define the huge reduction in programming and robot setup time.robot end-effector pose, so that the definition of these points 2010IEEEConferenceonRobotics,AutomationandMechatronics
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Considering two Cartesian coordinate systems; the first with origin and orientation coincident with the origin and orientation of the CAD assembly model (system A); and the second attached to the origin of a tool model (system B). Since the transformation matrix of each tool model is known we have the orientation and position of system B relative to system A. It is thus possible to make an analogy with Figure 4, where Pis the WorkPoint, BP represents the WorkPoint position in relation to the origin of the tool model, APBorg is the positional vector from the origin of the system A to the origin of the system B, and AP is the positional vector of point P relative to the origin of the system A. 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 developers. should include not only position but also orientation in space. In our system the robot path is defined by a sequence of tool models that represent the end-effector pose in each point of the desired path. 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). The transformation matrix of each part (including tool models) 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 in which they are attached. Therefore, considering our goal (definition of all the tag points in relation to a common reference), we need to define the WorkPoint coordinates in relation to the origin of the CAD assembly model or in relation to a reference frame defined by the user into the drawing. Figure 4. Reference systems A and B. The aim is to obtain the vector AP. Considering the rotation matrix ABR, which describes the orientation of the system B relative to the system A, we can write that: AABAP=ABR⋅P+PBorg (1) BAABP=ABR⋅P+PBorg=BT⋅P (2) ⎡AP⎤⎡ABR=⎢⎥⎢⎣1⎦⎣0APBorg⎤⎡BP⎤AAB⎥⋅⎢⎥⇒P=BT⋅P (3) 1⎦⎣1⎦Awhere the transformation matrix BT and the vector known (provided by the API). BP are 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 is the real quaternion, and qx, qy, qz are the imaginary qw quaternions. Since all the components tin of the rotation matrix are provided by the API, the quaternions can be calculated. 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. qw=1+t11+t22+t332 (4) 518
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qx=according to the current work plan. b)Place the robot tool models in the target positions. 1−t11+t22−t33 (6) qy=c)Define robot parameters: Robot speed, robot tools, 2reference frames, etc.d)Generate the robot program.1−t11−t22+t33 (7) qz=e)The generated program can be tested and adjusted if2necessary.If the real quaternion qw is nonzero, it becomes necessary to A.System Architecture calculate the warning signs of the imaginary quaternions, given The experimental cell is composed of an industrial robot by (8), (9) and (10). (IRB 2400 equipped with the S4C controller, ABB), and a computer running the CAD package Autodesk Inventor and the sign(qx)=sign(qx⋅qw)=sign(t32−t23) (8) software application that manages the cell (Fig. 5). The application receives data from the CAD, interprets the received sign(qy)=sign(qy⋅qw)=sign(t13−t31) (9) 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 sign(qz)=sign(qz⋅qw)=sign(t21−t12) (10) the robot, etc., using for this purpose the PcRob, an ActiveX created in our laboratory to control and manage the robot E.Program Generation remotely (Fig. 6). 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 robot movement instructions (linear, joint, circular or spline robot movement type). The definition of the robot trajectories is an important part of the process, so from (3) we have the trajectory positions AP referring to a specific reference frame and from (4), (5), (6) and (7) we have the quaternions to define the orientation of the robot end-effector. model of a specific robotic cell only needs to be built once, so 1+t11−t22−t33 (5) that usually the user only needs to make changes in design, 2Figure 5. Software application interface. ()V.EXPERIMENTS AND RESULTSThe CAD-based 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 Figure 6. Communications and system architecture. 2010IEEEConferenceonRobotics,AutomationandMechatronics
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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 (transformation matrix and WorkPoints) 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. The orientation for the reference frame is also defined into the CAD environment. 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 can be 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 [14]. In general, the generated program works very well. Results will be presented and discussed in the next section. Figure 8. Robot running the generated program. VI.RESULTS,DISCUSSION AND FUTURE WORKThe 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 is a problem that also affects the off-the-shelf off-line programming software’s. 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 Figure 7. CAD assembly model of the robotic deburring cell. A robot program will be generated from this model. 520
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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. and has a short learning curve, allowing non-experts in robot programming to generate robot programs in just few minutes. This low cost off-line robot programming technology can be an important tool for SMEs who have robots but do not have trained personnel to operate them. ACKNOWLEDGMENT This work was supported in part by the Portuguese Foundation for Science and Technology (FCT), grant no. SFRH/BD/39218/2007. REFERENCES[1]J. N. Pires, K. Nilsson, and H. G. Petersen, “Industrial robotics applications and industry-academia cooperation in Europe,” IEEE Robotics & Automation Magazine, vol. 12, no. 3, pp. 5–6, 2005. [2]J. N. Pires, “Semi-autonomous manufacturing systems: the role of the role human-machine interface software and of the manufacturing tracking software,” Mechatronics: an International Journal, vol. 15, no. 10, pp. 1191–1205, 2005. [3]P. Neto, J. N. Pires, and A. P. Moreira, “Accelerometer-based control of an industrial robotic arm,” in 18th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2009), pp. 1192–1197, Toyama, Japan, 2009. [4]U. Hillenbrand, B. Brunner, C. Borst and G. Hirzinger, “The robutler: a vision-controlled hand-arm system for manipulating botles and glasses,” in Proceedings of the 35th International Symposium on robotics, Paris, France, 2004. [5]S. Yamamoto, J. M. Valin, K. Nakadai, J. Rouat, F. Michaud, T. Ogata, and H. G. Okuno, “Enhanced robot speech recognition based on microphone array source separation and missing feature theory,” in Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), pp. 1477–1482, Barcelona, Spain, 2005. As future work, the way the system is calibrated should be simplified and less susceptible to errors. This will be done [6]B. Hein, M. Hensel, and H. Worn, “Intuitive and model-based on-line programming of industrial robots: a modular on-line programming through a mechanism to on-line adjust the robot pose according environment,” in Proceedings of the 2008 IEEE International to the error reported. Conference on Robotics and Automation (ICRA 2008), pp. 3952–3957, Pasadena, USA, 2008. TABLE I. TIME SPENT BY PARTICIPANTS IN EACH TASK[7]B. Bhanu, “CAD-based robot vision,” IEEE Computer, vol. 20, no. 8, pp. 13–16, 1987. Time Spent (minutes) [8]J. N. Pires, T. Godinho, and P. Ferreira, “CAD interface for automatic P1 P2 robot welding programming,” Industrial Robot: an International Journal, vol. 31, no. 1, pp. 71–76, 2004. Acquisition of real cell dimensions7 7 [9]J. Y. Kim, “CAD-based automated robot programming in adhesive spray CAD model construction 20 22 systems for shoe outsoles and uppers,” Journal of Robotic Systems, vol. 21, pp. 625–634, 2004. Definition of robot frame(s) 3 3 [10]F. Nagata, Y. Kusumoto, Y. Fujimoto, and K. Watanabe, “Robotic sanding system for new designed furniture with free-formed surface,” Robot and process parameterisation 1 1 Robotics and Computer-Integrated Manufacturing, vol. 23, no. 4, pp. 371–379, 2007. Code generation 1 1 [11]T. Pulkkinen, T. Heikkilä, M. Sallinen, S. Kivikunnas, and T. Salmi, Total 32 34 “2D CAD based robot programming for processing metal profiles in short series manufacturing,” in International Conference on Control, Automation and Systems (ICCAS 2008), pp. 156–162, Seoul, Korea, VII.CONCLUSION2008. A new CAD-based system dedicated to off-line robot [12]T. Schaefer, and R. D. Schraft, “Incremental sheet metal forming by programming was developed. This system works as a HRI, industrial robots,” Rapid Prototyping Journal, vol. 11, no. 5, pp. 278–286, 2005. allowing users with basic CAD skills to generate robot [13]L. Feng-yun, and L. Tian-sheng, “Development of a robot system for programs off-line from a CAD environment, and in an intuitive complex surfaces polishing based on CL data,” The International Journal way. A common 3D CAD package (Autodesk Inventor) was of Advanced Manufacturing Technology, vol. 26, pp. 1132–1137, 2005. selected to make the interface between user and robot. The effectiveness of the proposed system was proved through the [14]J. N. Pires, G. Afonso, and N. Estrela, “CAD interface for automatic robot welding programming,” Assembly Automation, vol. 27, no. 2, pp. experiments. These experiments were conducted with two 148–156, 2007. participants that had never worked with a robot before. Experiments showed that the CAD-based system is intuitive 2010IEEEConferenceonRobotics,AutomationandMechatronics
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