1-s2.0-S0736584513000665-main

A STEP-compliant Industrial Robot Data Model for robot off-lineprogramming systems

Wenlei Xiao a,n, Ji Huan b, Shuxiang Dong a

a College of Engineering, 100871 Peking University, Beijing, Chinab School of Mechanical Engineering and Automation, 100191 Beihang University, Beijing, China

a r t i c l e i n f o

Article history:Received 19 April 2013Received in revised form7 September 2013Accepted 12 September 2013Available online 17 October 2013

Keywords:STEPData modelingRobot off-line programming systemSTEP-NC

a b s t r a c t

Recently, various robot off-line programming systems have promoted their own robot data models,resulting in a plethora of robot representation methods and unchangeable data les among CAx androbot off-line programming systems. The current paper represents a STEP-compliant Industrial RobotData Model (IRDM) for data exchange between CAx systems and robot off-line programming systems.Using this novel representation method, most resources involved in a robot manufacturing system canbe represented. The geometric and mathematic aspects of industrial robots have been dened in IRDM,so that the robot off-line programming system could have abundant information to represent robotskinematic and dynamic behaviors. In order to validate the proposed models and approaches, a prototyperobot off-line programming system with 3D virtual environment is presented. The functionalitiesof IRDM not only have signicant meaning for providing a unied data platform for robot simulationsystems, but also have the potential capability to represent robot language using the object-orientedconcept.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Modern industries have been more and more heavily depen-dent on industrial robots [1]. In the early applications of robots(such as material transfer, precision assembly, welding and paint-ing), the path planning tasks are usually very simple. Hence, theonline teach in and playback mode can handle with most robotprogramming tasks. However, the increasing demands of 3Dpath planning in traditional and new emerging applications likemachining have gradually revealed the necessity of robot off-lineprogramming systems [2]. Therefore, many academic and com-mercial activities have been conducted into this eld in the latestseveral years, and many software solutions have been released,such as V-Rep, Robotmaster and Robotworks. Besides, even sometraditional machine tool related manufacturing software devel-opers, such as Delcam, VeriCUT and ST-Machine, have also incor-porated their own off-line robot programming capabilities [3].

For the robot off-line programming systems, the representationof robot data model is vital for exchanging data between differentCAx and robot off-line programming systems. The data model of arobot usually consists of geometric aspect and mathematic (kine-matic and dynamic) aspect, whereas most CAD systems can onlyprovide geometric information. There are numerous geometric

resource models reported in literatures together with a range ofcommercial CAD systems for representing geometric models. Butthere is so far even no standardized information model availablefor exchanging data of both aspects for a robot. Subsequently, mostrobot off-line programming developers have to promote theirown kinematic models individually in their systems. Until recently,almost all the robot models of current software systems areprivate and unchangeable, which results in a plethora of robotrepresentation methods and contemporary non-interoperablerobot off-line programming systems.

In recent years, the data model of manufacturing resources hasbeen included in the STEP standard (ISO 10303). STEP-NC, asan extension of STEP in the CNC eld, is being developed as thedata model for a new breed of CNC machine tools [4], and hassuccessfully veried the functionality and competence of the STEPmodel to describe the machining process data and machine tooldata [5,6]. Therefore, the STEP standard is regarded as the mostsuitable format to describe the robot data model. In recentyears, many researches have been undertaken on modeling themanufacturing resources or CNC resources regarding to the STEPstandard [79], since the representation of CNC resources has beenconsidered vital for making efcient and economic manufacturingdecisions [10,11]. For example, the modeling phase (data collectionand model design) was about 60% of the total time spent ona simulation project [12]. In this regard, machine tools havebeen modeled to form a contribution to management of infor-mation and knowledge in manufacturing [13]. Industrial robot is

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Robotics and Computer-Integrated Manufacturing

0736-5845/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.rcim.2013.09.007

n Corresponding author. Tel.: 86 62750071.E-mail addresses: [email protected], [email protected] (W. Xiao).

Robotics and Computer-Integrated Manufacturing 30 (2014) 114123

considered as one of the elements of the CNC manufacturingsystem, thus is also conducted in the modeling work. However,most mathematic data models of previous researches are notable to exactly meet the demand of robot off-line programmingsystems, except for a few exploratory researches [14].

This paper proposes a generic information model for represent-ing the data mode of an industrial robot, namely Industrial RobotData Model (IRDM). In order to make it STEP-compliant, IRDM isaccordingly dened in an EXPRESS le (as requested in the STEPstandard ISO 10303-11) and expressed as EXPRESS-G diagrams.In the data models of IRDM, the data contents for a robot arecategorized into product models, process models, resource modelsand mathematic models, and depicted in Section 2. The concept ofmodel categorization is rstly proposed by Vichare et al. [15],whereas IRDM reveals that the mathematic model can also becovered and can be together with other models integrated into aneutral data le. Based on this concept, the related models in arobot off-line programming system are proposed in Sections 3,4 and 5, which include robot resource data, kinematic and dynamicdata and manufacturing process data, respectively. In Section 6,a prototype system STEP-VM is developed to validate the feasi-bility of integrating IRDM into the robot off-line programmingprocess. The paper concludes and discusses that IRDM not only isbenecial to the robot off-line programming system but also haspotential meaning to dene the future robot language in robotcontrollers.

2. The industrial robot data model (IRDM)

In this paper, the proposed IRDM provides representations ofavailable industrial robots for robot off-line programming systems.In an industrial robot off-line programming system, a 3D simula-tion interface is a necessity, since many complex conditionshave to be inspected, such as collision, joint over limit, workspacedetection, path review, etc. [16] In order to realize above 3Ddetections, the simulation system must have a CAD core, and theIRDM should provide normal exchangeable geometric denitionsof a robot. In the STEP standard, there have two sub-parts (calledApplication Protocol in the STEP standard) providing the geo-metric denitions, AP203 and AP214. Nevertheless, due to theabsence of robot's resource and mathematic models (generallyinclude kinematic and dynamic models), the current STEP modelswith only geometric denitions cannot completely describe allthe properties of an industrial robot. Thus, the proposed IRDM

provides robot denitions, so that it can represent the remainingmanufacturing resources such as kinematic congurations of robots,end effectors, auxiliary devices, and kinematic and dynamic nota-tions. In addition, using the object-oriented concept of STEP-NC,the conventional robot manufacturing tasks are redened asprojects, work plans and working steps. The various types of modelsare categorized into product models, process models, resourcemodels and mathematic models, as illustrated in Fig. 1.

In order to save the amount of modeling work, the IRDMintegrates entities from several previous established STEP sche-mata, including ISO 10303-105, Unied Manufacturing ResourceModel (UMRM, proposed by Vichare et al.), and ISO 14649 (Fig. 2).

Firstly, the development of a human-comprehensive and mathe-matically complete kinematic model needs a lot of knowledge andwork. Fortunately, such kinematic model is provided by the ISO10303-105 standard (usually known as IR 105, where IR standsfor Integrated Resource). IR 105 species an information modelfor the kinematic aspects of a mechanical product as required forthe communication between CAD systems and kinematic analysissystems, and among dissimilar kinematic analysis systems [17].It describes a variety of entities such as kinematic joints, kinematiclinks and kinematic pairs, so that the kinematic structure of amechanism can be determined. The models dened in IR 105support tree kinematic structures and close loop kinematic struc-tures, so it is compatible with most current industrial robots.However, IR 105 does not provide enough robotics denitions, e.g.the denition of the DH notation (DenavitHartenberg notation)[18], which is the most essential data for describing the kinematicsof a robot.

Secondly, the process modeling work is introduced from ISO14649, since the machining task has been successfully describedusing STEP-NC in the last decade. ISO 14649 is a newmodel of datatransfer between CAD/CAM systems and CNC machines, whichreplaces ISO 6983 (G and M code). It remedies the shortcomings ofISO 6983 by specifying machining processes rather than machinetool motion, using the object-oriented concept [6]. The object-oriented concept that can be extended to other manufacturingtasks for an industrial robot is dened as a multipurpose manip-ulator [19]. Therefore, the corresponding workingstep and tech-nology entities have to be redened for different operations andtechnologies.

Thirdly, a robot manufacturing system generally consists of variousmanufacturing resources such as robots, end-effectors, xtures andother auxiliary devices. The representation of manufacturing resourcesis essential for creating an integrated, exchangeable and interactive

Workpiece(Product model)

Robot(Resource model)

Spindle(Resource model)

Toolpath(Process model)

A robot off-line programming example: machining an impeller

Coordinate frames and D-H parameters(Mathematic model)

Product model- Workpiece- Manufacturing feature- Planar face- Surface

...Process model

- Toolpath- Workingstep- Operation- Technology...

Resource model- Machine tool- Robot- End-effector- Auxiliary device...

Mathematic model- Coordinate frame- Kinematic notation- Dynamic notation- Algorithm

Fig. 1. Model categorizations of the IRDM and a instance of robot off-line programming.

W. Xiao et al. / Robotics and Computer-Integrated Manufacturing 30 (2014) 114123 115

environment in the robot off-line programming system. In UMRMproposed by Vichare et al., various manufacturing elements of CNCmachining systems have been dened to represent those manufactur-ing resources. These denitions can be also referenced for representingthe robot manufacturing elements. However, the industrial robot isonly considered as an automated material handling system, which isfar from meeting the requirement of representing the robot manu-facturing system. Thus, the authors in this paper supplement theresource model with extensions that are not covered in the scopeof UMRM.

3. Representation of resource models

This section is concerned with the representation of industrialrobots and provides indispensable information during the robotsimulating and post-processing process.

3.1. Resource abstraction layers

In order to provide manufacturing resource information to adiversity of users, it is important to be able to represent the factoryfrom different viewpoints [11]. Therefore, the resource abstractionlayers have to be established. According to the usability in themanufacturing organization, the manufacturing resource informa-tion is usually abstracted as station, cell, shop and factory [20].Vichare et al. has dened the entity logical_manufacturing_unit torepresent these abstraction layers [15], as shown in Fig. 2(1). In

this paper, the authors introduce the same approach in IRDM torepresent the abstraction layers of the robot systems. As shown inFig. 3, IRDM's abstract entities industrial_robot and device_ele-ment are the abstract supertype representations of the resourcemodels in a robot manufacturing system. Combining themtogether with the resource abstraction layers of UMRM, themanufacturing resource information related in a robot manufac-turing system can be effectively organized.

As described in Fig. 3, all individual robots inherit the attributesof their parent entity industrial_robot, and industrial_robot isdened as a child of mechanical_resource. Thus, all the industrialrobots can be contained by any logical_manufacturing_unit, namelya factory, a shop, a cell or a work_station. The entity device_elementrefers to various end-effectors and auxiliary devices that can beattached to an industrial robot. Those resources include all mannerof end effectors, auxiliary actuators and auxiliary sensors, while theinner links between industrial_robot and device_element will begiven in the following sections.

3.2. Industrial robot

IRDM use the entity industrial_robot to describe an abstractedrepresentation of diverse industrial robots. According to thekinematic congurations, those diverse industrial robots discussedin this paper are classied as the serial robot, the parallel robotand the hybrid robot. Fig. 4 shows some examples of those threekinematic congurations. The schematically illustrated struct-ures are also provided for each conguration, where an arrow

Fig. 2. EXPRESS-G representation of primary entities referenced from UMRM, STEP-NC and IR105.

W. Xiao et al. / Robotics and Computer-Integrated Manufacturing 30 (2014) 114123116

represents a kinematic joint and a circle represents a kinematiclink. If an arrow is attach with a spot, it stands for an active joint,otherwise a passive joint. An active joint means a kinematic jointdriven by a servo axis. By connecting kinematic links and jointstogether, it forms several kinematic chains. The serial robot isdened as a manipulator with only one main serial chain mechan-isms consisting of all active joints, while the parallel robot isdened as a robot that uses several serial chains to support asingle platform, each of which may have both active and passivejoints. The hybrid robot is a combination of the both.

In general, the entity industrial_robot has some common prop-erties, as shown in Fig. 5. The kinematic aspects of an industrialrobot are presented using the attribute its_mechanism, which is atype of mechanism described in ISO 10303-105 [17] (IR105 forshort). According to the denition of IR105, a mechanism pos-sesses a kinematic_structure, which contains a set of kinematicjoints representing the topological relationships among a group ofkinematic links. Therein, each joint connect two adjacent links.The attribute its_controller refers to a robot controller that con-trols the robot. In practice, a robot will need several auxiliarydevices to make its functionalities complete. An auxiliary deviceis described in the entity device_element, and the attribute

its_devices contains all the auxiliary devices that are attached toa robot. its_degree_of_freedom gives the degree of freedom.Normally, it can be determined by the kinematic structure of themechanism, thus is a derived attribute. The coordinate framesare also vital attributes of a robot. Usually, when a robot task isimplemented, its base frame, tool frame and workpiece frame have

A serial robot

A hybrid robot

A parallel robot

Kinematic link

Kinematic joint (active)

Kinematic joint (passive)

Fig. 4. Three main kinematic congurations of industrial robots.

Fig. 5. EXPRESS-G representation of industrial_robot.

Fig. 3. Abstract supertype representation of IRDM.


to be previously set up. Those frames are all given as spatial_posi-tion dened in Section 4. The geometric aspect of the robot is holdthrough the attribute geometry_representation, so that the entityindustrial_robot can represent both geometric and mathematicaspects of a robot.

3.3. Device element

The entity device_element is a abstracted representation of allperipheral resources used by a robot. In IRDM, we classify thoseresources as end_effector, auxiliary_actuator and auxiliary_actua-tor (Fig. 6). As they share a same abstracted entity device_element,they can be hold in the attribute its_devices of industrial_robot asa data set (Fig. 5). The device_element has a mechanism attribute,since in some cases the device may possess some kinematic joints,e.g. an external axis. Likewise, the attribute geometry_representa-tion is included for representing the geometric aspect of a device.

An end effector is a device which a robot uses as its tool tofulll a task. It is further divided into concrete types in terms oftheir application purposes, such as laser, welding, machining, etc.In a certain end effector, the related technology and controlinterface can be assigned, so that the end effector reveals distin-guish functions. For an end effector, the tool center point (TCP) is avital property, and some end effectors may have more than oneTCP. Therefore, the entity end_effector has an attribute its_tool_-center_points dened as a set of spatial_position to hold the TCPsused by an end effector.

An auxiliary actuator is a device with active actuation func-tionalities, e.g. auxiliary machinery (auxiliary axes), wire feedingmachine, power source, air pump, vacuum cleaner, automaticend effector exchanger, etc. According to this assumption, theabstracted entity auxiliary_actuator has a control_interface attri-bute to receive the control signals for actuation.

An auxiliary sensor is a device with the capabilities to measuredifferent physical quantities, such as distance, temperature, force,torque, etc. In some cases, the perception of ambient environmentis essential for sensor-based motion control and instantaneousreactions to unforeseen events. In order to transmit the signal tothe control system, an attribute signal_interface is assigned to theentity auxiliary_sensor.

3.4. Robot controller

The entity robot_controller provides the logical control unit foran industrial robot. In the real world, a robot controller solves theforward and inverse kinematics and dynamics problem for a robot,and provides other PLC control functionalities. Correspondingly, IRDMdenes the entity robot_controller to play the homologous role in thevirtual environment of a robot off-line programming system. Fig. 7depicts the EXPRESS-G representation of robot_controller.

4. Representation of mathematic models

Kinematics and dynamics are vitally important characteristicsof an industrial robot. The motion of a robot is basically deter-mined by the kinematic and dynamic parameters. However, veryfew resource modeling researches have included the considerationof kinematic and dynamic parameters. In order to provide robotoff-line programming systems those key parameters and make itpossible to simulate the robot motion using a universal robotdata model, the entities of kinematic and dynamic parameters aredened in IRDM and presented in Fig. 8. Since the conventionalrobotics studies have all considered the kinematic link as thecarrier of all the data and functionality required to characterize itskinematics and dynamics, the abstracted entity robotic_parameterhas an attribute ref_link referring to the kinematic link. Theattributes its_kinematic_parameter and its_dynamic_parameterare used to identify its kinematic and dynamic parameters.

4.1. Forward kinematics and kinematic notations

The forward kinematic model denes the relation:

0Tn Mq 1where 0Tn is the homogeneous transform representing the posi-tion and orientation of the manipulator tool (frame n) in the baseframe 0.

STRING

STRING

Fig. 6. EXPRESS-G representation of device_element.

robot_controller

Fig. 7. EXPRESS-G representation of robot_controller.

robotic_parameter

Fig. 8. EXPRESS-G representation of robotic_parameter.


su-parameters dh-parameters

kinematic_notation

mdh-parameters

axis2_placement_3d

placement direction

cartesion_point

plueker_line direction

plueker_point

plueker_plane

length_measurescrew_representation

representation_item

homogeneous matrix

REALplane_angle_measurelength_measure

Fig. 9. EXPRESS-G representation of kinematic_notation.

Cartesian Cylindrical

Jointed SCARA Spherical

Delta Stewart Hexa

Tripod Tricept

SIER:ecruoSSIER:ecruoS

Source: KUKA Source: etaminU:ecruoSAKUK

giewhcsnuarBUT,FWI:ecruoSOILA:ecruoSBBA:ecruoS

LStpecirtMKP:ecruoSOILA:ecruoS

Seria

l rob

ots

Para

llel

robo

tsH

ybrid

robo

ts

Fig. 10. Kinematic types dened in IRDM.


In order to solve the forward kinematics problem of a robot, itskinematic notations should be given to establish the coordinateframes for a robot. Currently, the DH notation is most commonlyin use. It usually has two versions, the standard notation (intro-duced by Denavit and Hartenberg in 1955 [18]) and the modiednotation (introduced by Craig in 1989 [21]). Since no denitions ofDH notations have been dened in ISO 10303-105, we proposethe entities dh-parameter and mdh-parameter in IRDM to supportthe standard and modied DH notations respectively. However,only the denitions of DH parameters are absolutely not enough.

Although the DH parameters have been widely used todescribe serial kinematics of robots during the past decades, theyare not well accepted by the parallel and hybrid robots. In parallelmanipulators, spherical and cardan joints are frequently used.Describing these joint types by DH parameters leads to a non-unique representation [22]. Furthermore, the DH representationsometimes fails for calibration as the parameters are discontin-uous with respect to perturbations of nearly parallel joint axes[23]. Therefore, many extended and modied notations have beenproposed to establish the robot kinematics for different purpose.Among them, the SU notation [24] developed by Sheth andUicker in 1971 is the most generalized model for all others. Hence,IRDM supports the SU notation as a unied notation for all serial,parallel and hybrid kinematic structures.

Moreover, the screw theory is usually used for parallel robots todescribe their robotic problems and solutions. In the screw theory,the Plker coordinates are the fundamental notations of a spatialcoordinate [25]. In this regard, IRDM includes the denition ofPlker coordinates.

The above mentioned kinematic notations are all dened intraditional robotics studies. To a normal user outside the specialty,those denitions are somewhat too unintelligible. In fact, there existsome other much more comprehensive methods describing thespatial placement. For example, homogeneous matrix and axis2_placement_3d are common in a CAD system to describe the relativeposition and orientation between two coordinate systems.

In summary, the kinematic notations supported in IRDM include:dh-parameter, mdh-parameter, su-parameter, screw_representa-tion, homogeneous_matrix and axis2_ placement_3d (as shown inFig. 9), where su-parameter and axis2_placement_3d are referencedfrom ISO 10303-105. The selection type kinematic_notation is anabstraction of all notations.

4.2. Inverse kinematics and kinematic types

The inverse kinematic model is dened by

qM10Tn 2In general, this equation allows multiple solutions and can besolved either analytically or numerically. Normally, the iterativenumerical solutions lead to a large number of calculations, thoughit has greater generality [2]. In view of this, many robots have beendesigned to be analytically solvable so that numerical iterationscan be avoided. For example, the most commonly used jointedrobot has its last three axes intersect at a same point. Unlike thegenerality of the numerical solution, different kinematic types mayhave different analytical equations. IRDM denes various robottypes for the robot off-line programming systems to determinewhich kinematics solution to be used.

Currently, ve serial kinematic types are common, namely theCartesian, Cylindrical, Spherical, Jointed and SCARA robots. Paralleland hybrid robots have not so widely in industrial use as serialrobots, whereas they have also some application cases. The parallelkinematic types include the Delta, Stewart and Hexa robots, andthe hybrid kinematic types include Tripod and Tricept robots. Fig. 10illustrates various kinematic types supported in IRDM.

4.3. Dynamics

The dynamic model of a robot is given by

Dq qCq; qGq 3where q is the generalized joint coordinates, is the generalizedforces, Dq is the mass matrix, Cq; q is the Centrifugal andCoriolis forces and Gq is the gravity forces [26].

In order to solve the forward and inverse dynamics problem,the entity dynamic_notation in IRDM consists of following criticalparameters: the mass, the center of mass (denoted by a vector [cx,cy, cz]) and the inertia tensor (denoted by a 33 tensor matrix), asshown in Fig. 11. In addition, the parameters of the motors thatdrive the robot should also be given, which consist of motor rotorinertia, motor gear ratio, motor viscous friction coefcient andmotor Coulomb friction coefcient. Hence, the dynamic model ofthe motor is also dened in IRDM, and presented in Fig. 11.

4.4. Spatial position

The spatial position given for a robot to achieve consists oftranslation and rotation components. The translation is usuallygiven as a translational vector [x, y, z], while the rotation hasdifferent representations. Since no standard has been implemen-ted to industrial robot languages, different robot vendors usetheir own spatial position denitions in the post-processing robotlanguages. For example, ABB uses the quaternion to dene anorientation, while KUKA uses the rotation angles about X, Y and Zaxes. IRDM provides various spatial orientation manners, as shownin Fig. 12.

REAL

REAL

REAL

REAL

Fig. 11. EXPRESS-G representation of dynamic models.

Fig. 12. EXPRESS-G representation of spatial_position.


5. Representation of product and process models

The object-oriented concept of STEP-NC has started a new erafor CNC machining. However, other manufacturing technologieslike welding, laser and painting remain rarely affected by thisadvanced concept. Since these tasks are most common for anindustrial robot, IRDM attempts to cover these robot tasks usingthe STEP framework.

Similar with ISO 14649, the product and process data models ofIRDM share the same top-level entities provided in ISO 14649-10.It uses the entity project to indicate the workplan to be executedupon interpretation of this model, and it may also provide theworkpiece(s) upon which actions are to be performed [6]. Indivi-dual technology oriented schemata have been dened for differentrobot tasks. In this paper, the welding process is exemplied asa case study of representing a robot manufacturing task using theobject-oriented concept, as shown in Fig. 13. Other task models aresimilar, whereas due to the limit of space, they are not described inthis paper.

6. Implementation prototype

In order to validate the IRDM model, a prototype robot off-lineprogramming system STEP-VM has been developed, which canrecently partially support the novel STEP model. As shown inFig. 14, a machining robot is simulated to perform a typical STEP-NC project (the rst example provided in ISO 14649-11). In this

virtual environment, the major components of IRDM are included.The simulation project includes not only the traditional STEP-NCtasks, but also the manufacturing resources involved in the manu-facturing process using a robot. The nal target of this softwaresystem is to use the STEP models to represent the abundantinformation related to a robot manufacturing task, which includesthe geometric and mathematic aspects of a robot, end effectorsinstalled on the robot, auxiliary devices surrounding the robot,manufacturing tasks and its related workpieces, toolpaths, proces-sing strategies and technologies. With those data, many complexand generalized functionalities can be integrated into the STEP-compliant robot off-line programming system, such as kinematicssolution, path optimization, collision detection, postprocessing,etc.

7. Discussion and conclusion

The STEP standard is used to represent the data modelsof industrial robot systems. The authors aim to found a unieddata model to exchange information between different CAx androbot off-line programming systems. Those information contentis categorized into product model, process model, resource modeland mathematic model. In IRDM, the industrial robot resourcesand the kinematic and dynamic aspects of a robot have beensuccessfully represented using the STEP EXPRESS schema. In addi-tion, the object-oriented concept in STEP-NC is extended to othermanufacturing tasks besides machining. The new data models

Fig. 13. EXPRESS-G representation of product and process models for the welding task.


were effectively implemented in an independently developedprototype of robot off-line programming system, and its feasibilitywas successfully veried.

The authors indicate that the unied models under the STEPstandard will not only benet the robot simulation systems, butalso for the improvement of traditional industrial robot control-lers. In the developing progress of STEP-CNC, the STEP-NC tech-nology is rstly implemented in a virtual environment (like thefunction of ST-Machine). Once the corresponding CAD/CAM andCNC technologies are mature, the STEP-CNC controller willdenitely emerge. For reference, Siemens has demonstrated therst prototype of an industrial model of a STEP compliant CNCcontroller in 2000 for milling their own vendor proprietaryarchitecture [27], and in 2009 a hybrid STEP-NC controller madeby Fanuc was tested for a gantry router [28]. The robot controllerwill go through similar periods as the CNC controller. Recently, theKUKA.CNC controller has already supported G-code input [29],which is a great progress of the robot language. It clears the

prevailing misconception, which has existed in the industrial roboteld for a long time, that the G-code denition is not suitable fora robot. Although there are still many customized extensions inthe KUKA.CNC G-code, supporting G-code (ISO 6983) in a robotcontroller reveals the possibility of using a uniform standard todescribe a robot motion. Since that, we can postulate that if therobot controller can parse the STEP-NC code like a STEP-CNCcontroller, it will then understand what it is doing on a task leveland generate the robot paths online.

References

[1] International Federation of Robotics. www.ifr.org; February 2012.[2] Xiao W, Strauss H, Loohss T, Hoffmeister HW, Hesselbach J. Closed-form

inverse kinematics of 6r milling robot with singularity avoidance. ProductionEngineering 2011;5:10310.

[3] Chen Y, Dong F. Robot machining: recent development and future researchissues. The International Journal of Advanced Manufacturing Technology2012:19.

Fig. 14. A robot performing a STEP-NC task (the rst example of ISO 14649-11).


[4] Xu XW, Newman ST. Making CNC machine tools more open, interoperable andintelligenta review of the technologies. Computers in Industry 2006;57(2):14152.

[5] ISO 14649-1. Industrial automation systems and integration physical devicecontrol data model for computerized numerical controllers part 1:overview and fundamental principles. International Organization for Standar-dization; 2003.

[6] ISO 14649-10. Industrial automation systems and integration physical devicecontrol data model for computerized numerical controllers part 10:general process data. International Organization for Standardization; 2004.

[7] Hedlind M, Lundgren M, Archenti A, Kjellberg T, Nicolescu CM. Manufacturingresource modelling for model driven operation planning. In: Process machineinteractions (PMI), Vancouver, Canada, June 1011; 2010.

[8] Yang W, Xu X. Modelling machine tool data in support of STEP-NC basedmanufacturing. International Journal of Computer Integrated Manufacturing2008;21(7):74563.

[9] Nassehi Aydin, Vichare Parag. A step-nc compliant methodology for modellingmanufacturing resources. In: Xu Xun, Nee Andrew YC, editors. Advanceddesign and manufacturing based on STEP, Springer series in advanced manu-facturing. London: Springer; 2009. p. 26181.

[10] ISO Tc184/Sc4. Machine tool data model for general manufacturing processes:new work item proposal. Step Tools, Inc, Funchal, Portugal; 2007.

[11] Vichare P, Nassehi A, Kumar S, Newman ST. A unied manufacturing resourcemodel for representing CNC machining systems. Robotics and Computer-Integrated Manufacturing 2009;25(6):9991007.

[12] Jones A, Umeda S. Simulation in Japan: state-of-the-art update. TechnicalReport NISTIR 6040; 1997.

[13] Kjellberg T, von Euler-Chelpin A, Hedlind M, Lundgren M, Sivard G, Chen D.The machine tool model: a core part of the digital factory. CIRP Annals Manufacturing Technology 2009;58(1):4258.

[14] Li Y, Hedlind M, Kjellberg T. Kinematic error modeling based on STEP AP242.In: 1st International Conference on Virtual Machining Process Technology,Canada; 2012.

[15] Vichare P. A novel methodology for modelling CNC machining systemresources. PhD thesis, University of Bath, 9; 2009.

[16] Xiao W, Huan J. Redundancy and optimization of a 6r robot for ve-axismilling applications: singularity, joint limits and collision. Production Engi-neering 2012;6(3):28796.

[17] ISO 10303-105. Industrial automation systems and integration product datarepresentation and exchange part 105: integrated application resource:kinematics. International Organization for Standardization; 1996.

[18] Denavit J, Hartenberg RS. A kinematic notation for lower pair mechanismsbased on matrices. Journal of Applied Mechanics 1955;77(6):21521.

[19] ISO 8373. Manipulating industrial robots C vocabulary. International Organi-zation for Standardization; 1994.

[20] Molina A, Bell R. A manufacturing model representation of a exiblemanufacturing facility. Proceedings of the Institution of Mechanical Engineers,Part B: Journal of Engineering Manufacture 1999;213(3):22546.

[21] Craig JJ. Introduction to robotics: mechanics and control. 2nd ed. Addison-Wesley Publishing Company; 1989.

[22] Thomas U, Maciuszek I, Wahl FM. A unied notation for serial, parallel, andhybrid kinematic structures. In: IEEE international conference on robotics andautomation, 2002. Proceedings, ICRA '02, vol. 3, 2002. p. 286873.

[23] Newman WS, Birkhimer CE, Horning RJ, Wilkey AT. Calibration of a motomanp8 robot based on laser tracking. In: ICRA'00; 2000. p. 3597602.

[24] Sheth PN, Uicker JJ. A generalized symbolic notation for mechanisms. Journalof Engineering for Industry 1971;93(70):10212.

[25] Huang Z, Zhao WS, Zhao TS. Advanced spatial mechanism. 2nd ed. HigherEducation Press; 2008.

[26] Harib K, Srinivasan K. Kinematic and dynamic analysis of stewart platform-based machine tool structures. Robotica 2003;21(9):54154.

[27] Rosso Jr Roberto SU, Allen RD, Newman Stephen T. Future issues for CAD/CAMand intelligent CNC manufacture. In: The 19th international manufacturingconference; 2002.

[28] STEP Tools, Inc. http://www.steptools.com/library/stepnc/2009_renton; 2009.[29] KUKA Robotics Corp. http://www.kukaconnect.com/wp-content/uploads/2012/

07/kuka_cnc.pdf; 07, 2012.


A STEP-compliant Industrial Robot Data Model for robot off-line programming systemsIntroductionThe industrial robot data model (IRDM)Representation of resource modelsResource abstraction layersIndustrial robotDevice elementRobot controller

Representation of mathematic modelsForward kinematics and kinematic notationsInverse kinematics and kinematic typesDynamicsSpatial position

Representation of product and process modelsImplementation prototypeDiscussion and conclusionReferences

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