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Design and simulation of assembly systems with mobile robots

George Michalos, Konstantinos Kaltsoukalas, Panagiotis Aivaliotis, Platon Sipsas,Andreas Sardelis, George Chryssolouris (1)*

Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece

1. Introduction

Robotic equipment has found great application in assemblysystems, mainly in the automotive, electronics, rubber/plastics andmetal/machinery industrial sectors [1]. The intrinsic character-istics of the robots such as high accuracy, speed repeatability,strength and reliability have enabled the production firms to investin large scale installations that can work around the clock withminimal intervention [2].

However, as the manufacturing trends shift towards higherproduct diversification and process commonality, the manufactur-ing systems have presented new requirements in terms ofreconfigurability [3,4]. The operational flexibility of robots tohandle and assemble different parts has been the major driver fortheir adoption. Nevertheless, the resulting system level flexibilityis limited, mainly due to the following two drawbacks. The first oneis that robots require a fixed or well constrained positioning at theshop floor that ensures the adequate supply of consumables (air,current, water etc.) as well as the rigidity in order for high accuracyto be achieved. This does not allow for physical changes in thestructure and form of the assembly system. The second drawbackis the complexity in controls integration and programming of newresources and operations. The existing control architectures makeuse of hierarchical (usually based on Programmable LogicalControllers – PLC) control that requires time and expertise whenit comes to integrating new equipment. Moreover, the lack ofintuitive user interfaces results in large commissioning time evenwhen using Offline Programming techniques. The current require-

By considering an architecture that can overcome thlimitations, this paper proposes an approach for designing

detailing the operation of the assembly system. More specificathe method is aimed at providing feasible reconfigurations ofline along with detailed design of the operations to be carriedby each robotic resource. The reconfiguration is achieved throthe use of mobile robotic manipulators that are capable of dockwithin the assembly stations and undertaking new assembly taThe attempt to commission industrial mobile manipulators

already yielded very promising results. The latest examples invothe introduction of a mobile manipulator for assembly applicati[6], the creation of an autonomous multi-purpose industrial ro[7] as well as the development of a high payload momanipulator for automotive Body in White (BiW) applications

The design activities focus on detailing the assembly operatioterms of motion planning, coordination with control systems etmore detailed description of the processes constituting the destage is presented in Section 2. The method, which is presenteSection 3, employs a semantic representation of resources that althe automated allocation of suitable robots to assembly taskcomplete set of software tools used for each activity is presenteSection 4, while the application of the method on a case stustemming from the automotive industry, is presented in SectioThe last section is dedicated to the drawing of conclusions

providing an outlook towards future research areas.

2. Design of assembly systems

A R T I C L E I N F O

Keywords:

Design method

Assembly

Robot

A B S T R A C T

This paper proposes an approach to the design and operation of assembly systems that include mo

robots. It is based on a semantic model representing the robot/resource skills along with the operat

that a resource has to execute. The method is implemented in a system capable of generating

assignment of operations to the proper robotic resources. Collision free paths can be generated for

assembly system robots with the help of simulation. The method is presented in a case study from

automotive industry.

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Contents lists available at ScienceDirect

CIRP Annals - Manufacturing Technology

journal homepage: http: / /ees.elsevier.com/cirp/default .asp

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ments are towards automating these processes and coming upwith methods that would allow the system’s fast design andeventually reconfiguration with minimal human intervention [5].

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http://dx.doi.org/10.1016/j.cirp.2014.03.102

0007-8506/� 2014 CIRP.

The method attacks the near real time design of the producprocess in order to adapt to both internal and external variatiIn this context, the internal variations refer to changes thatrequired inside the assembly system, but should not under

circumstances, disrupt the production process. The need for

replacement of a malfunctioning robot and the addition of erobots, in a workstation, in order for the process to be sped up,

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G. Michalos et al. / CIRP Annals - Manufacturing Technology 63 (2014) 181–184182

cative examples. External variations refer to the productionem factors that induce requirements for changes within it. The

to re-arrange the resources in order to produce a new modelroduct family are typical examples.he design of assembly system involves the following activities(a) analysis of assembly methods and assembly sequence, (b)ction of the assembly sequence, (c) determination of theuction capacity, (d) tabulation – selection of availableurces and required tasks and (d) detailed workstation andess design. In the approach discussed, activities (a) and (b) areidered being predefined and the method focuses mostly onities (c) and (d). More specifically, the following functionali-are provided by the developed tools:

erivation of assignments between the planned/unplannedasks and the available resources.eneration of assembly layouts that can satisfy each of thesellocations.utomatic detailing of the assembly process by performingollision free motion planning for the mobile platforms and theobotic manipulators.utomatic generation of the robot programmes (including,otion, sensing and communication) to be executed.

mplementing the aforementioned activities requires thattiple design categories such as computer based, representationanalysis [10] be bridged under a common frame. The followingions provide an overview of the proposed activities along withtools and techniques required for their implementation.

ethod description

Semantic representation of resources

he first step of the method involves the assignment of suitables to the robotic resources. The investigation of the process fortifying and evaluating a suitable task for mobile manipulatorse up with a plethora of activities that can be supported by thisnology [11]. In order for the adequate representation of theurces available, their characteristics and their capabilities to be

ed, a data model has been developed in the context of thisk. Within the data model, the complete shop floor can beribed both in terms of physical elements and operations.urces and services are autonomous and perform the decisioning on their own. Following, the implementation of this datael to a knowledge repository, based on the semantic web

technology, was carried out via the OWL 2.0 language and theProtege v4.1 software tool. This technology for implementingontologies has emerged recently for knowledge management ofmanufacturing systems at all levels [12].

Fig. 1, shows the ontology graph where all classes representingthe shop floor elements are depicted. The arrows show therelationships among the elements i.e. the assembly line consists ofstations including docking stations, tool repositories, resources etc.Other information, such as the setup time required for a resource toswitch between operations, the allocation criteria etc. are alsostored into the ontology.

3.2. Assembly configuration and design

The Semantic web technology provides inference and reasoningabilities. In the developed solution, these two features areexploited at the logic level of the autonomous resources whendecision making is required with respect to the reconfigurationactions to be followed. In particular, the ontology repositorymodule includes the ontological representation of the data models,populated with the appropriate shop floor data for each scenario.Further to the online and offline data, the ontology repository isequipped with inference rules, which are used when reasoning isperformed. These rules represent the existing knowledge regard-ing the shop floor manufacturing process and the resources as wellas the production tool compatibility with the production process-es. For example, such a rule can represent the knowledge that forthe spot welding assembly process of a subassembly, the weldinggun’s current should not exceed a certain level. Such informationcan be utilized, at the decision making phase, when the assignmentof the pending manufacturing tasks to a resource is beingperformed [13]. The hierarchical levels of Fig. 2 have been usedto drive the assignment process.

The data model describes the manufacturing tasks with the useof the order, job and task model. In particular, the manufacturing ofa specific product is connected with an order. The latter iscomprises jobs assigned to specific stations and each job iscomposed of tasks, which are assigned to specific resources. Thetasks have precedence constraints revealing the order in whichthey have to be executed. Furthermore, there is a task resourcerelation class that describes which tasks can be performed by theproper manufacturing resource. The tasks are composed ofoperations. Within the data model, an operation describes a

Fig. 2. Hierarchical model for task assignment.

Fig. 1. Manufacturing ontology graph.

hardware or software activity that should be executed by one ofthe resources. For instance, a hardware operation can be themechanical move of a robotic arm and a software operation can bethe calculation of a subassembly position and orientation by avision system.

By using inference rules instead of hard coded rules to representthe existing knowledge, more flexible systems can be achieved.More decision making situations can be modelled and automated,simply by adding new rules to the ontology repository. An alreadyplugged production resource does not require software updates ifnew manufacturing processes are assigned to it. The final step of

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G. Michalos et al. / CIRP Annals - Manufacturing Technology 63 (2014) 181–184 183

the design is the detailing of the assembly process. In this context,open source tools have been used to generate the motion of therobotic units. More specifically, the MoveIt! [14] software packageaccepts the 3D model and the kinematics of the robots as a UnifiedRobot Description Format (URDF) file and can generate collisionfree paths towards a target position. The coordination between theresources, in terms of operations sequence that is traditionallyassigned to a PLC, is also assigned to the ontology service. Eachoperation is registered in the repository together with its pre- andpost-conditions and the ontology service automatically generatesmessages to denote the start and completion of the operations.

3.3. Resource requirements

Fig. 3, presents the structure of a service based architecture thatthe robotic resources will need in order to efficiently exploit theproposed approach. In this figure, Layer 2 contains the tools thatperform the assignment of the resources and the process detailing.Layer 1 involves the services required on the robot and mobile unitcontroller side in order to (a) obtain the data for the current state ofthe resource (position, time to station, alarm states etc.) and (b) tosend the detailed commands for the implementation of the designedreconfiguration. Other resource dedicated services that may be usedfor local control, such as the SLAM (Simultaneous localization andmapping) can be used for the acquisition of the data required for thedecision making. To ensure compatibility with all the involvedresource, the ROS [15] programming framework can be used since itis available for multiple robotic equipment types, including mobileplatforms and industrial manipulators.

4. System implementation

The methods and tools described in Section 3, have beendeveloped and integrated, under a user friendly application thatallows the system’s fast redesigning when needed. The tool hasbeen developed as a web application in the Java programming

language using the Spring framework and it is hosted on an ApaTomcat web server. For the communication with the ontologuses the Jena server module, which is also developed in Java

uses the Jena framework to make SPARQL (SPARQL Protocol

RDF Query Language) queries to the Ontology repository.Fig. 4, illustrates a part of the user interfaces that allows the u

to define a new task and designate suitable resources. The outcois stored directly into the ontology repository and can be udirectly for the task of resource assignments. More specificafunctionalities include: adding resources to workstations, addtools to resources, creating jobs, adding tasks to jobs, modifyinglist of operations of each task, adding suitable resources to taediting tasks’ precedence constraints, executing the schedualgorithm, viewing the assignments generated by the planner

initiating the execution.Furthermore, through this application, the communication w

the robot controller can be achieved in order for the robcoordinates to be automatically retrieved and stored into

ontology. This is important, since it is the main input to the moplanning software (MoveIt!) that is responsible for the moplanning activities.

5. Automotive case study

To examine the applicability of the proposed method, a cstudy stemming from the automotive industry has been selecThis case involves an assembly station with two stationary robthat perform cooperative welding on the floor of a passengvehicle. Mobile robots such as the one depicted in the CAD modeFig. 5 are also present in the shop floor and can be docked tostation. The handling (a) robot is holding the part in mid-air

the welding (b) robot is performing spot welding. The sceninvolves a malfunction of the handling robot, which cancontinue the operation and therefore, needs to be replaced b

Fig. 3. Resource architecture.

Fig. 4. Developed tool. Fig. 5. Path generation for the case study.

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G. Michalos et al. / CIRP Annals - Manufacturing Technology 63 (2014) 181–184184

lar one. At the flange of the handling robot, there is a gripper (c) is equipped with multiple connection points, thus allowingexchange of both the gripper and the part between the twots.pon its breakdown, the handling robot sends a signal to thelogy service that triggers the reconfiguration/design process.tool that was presented in Section 4 is used for generating a

set of assignments for the handling tasks, which aretually allocated to one of the mobile units. The mobile unitarries a robot with a type of a compatible flange as well as anuate payload to carry both the part and the gripper.he first part of the reconfiguration process is completed withommand being sent to the mobile unit to navigate and dock in

station. At the same time, the detailed designing of the mobilet operation is carried out. At first, the current pose (jointes) of the handling robot is retrieved from the ontology. This

s for the identification of the gripper’s exact location in the 3De and also the generation of the collision free paths, since the

functioning robot is now being considered as an additionalacle in the station. Following, and based on the gripper

etry, the location of the gripper’s second connection point isulated. This is the final position of the mobile robot in order to

the gripper. These points are inserted into MoveIt, whichtes the trajectory of the handling robot. The outcome is shownig. 5 and its geometry has been simplified in order for theputational time to be reduced, but without any compromisesg made as regards the derived path’s feasibility.

n order for the operation’s continuation, from the point wherebreakdown occurred to be ensured, the rest of the operationsned to the stationary handling robot are also modified andined in the simulation package for execution feasibility by the

ile robot. This concludes the design process and thelementation of the derived solution can start taking place.use of open controllers for the robots allows the service basedution of tasks by transmitting the motion commands directlye robot’s controller. The use of services also eliminates the

for defining I/O signals and integration with the station PLC.is case, once the mobile robot has docked to the station, itediately starts receiving and executing the generated com-ds that enable the completion of the assigned operations.

onclusions and outlook

his study has presented a method for designing the operationssembly systems that incorporate mobile robotics unitsble of undertaking multiple roles. The method can assignmbly tasks to suitable resources and further detail the processutomatically generating motion plans for the robots. Theware and software architectures that the robotic resources

to comply with in order to exploit this method have also beenented. With this method’s application to an automotive casey, a feasible reconfiguration plan allowing the replacement of atic unit, without any human intervention, has been generated.benefits of adopting such technologies over the traditionalonary robots mainly lay in the shortened reconfiguration timeell as in the reduction of the efforts required for the

missioning of a new robot. The consideration of mobile robotsides the potential for line level changes. In this context,pment mobility is of primary importance for the method.

related to the calibration and accuracy of mobile robots will havebeen resolved with the use of advanced sensors, such methods willallow for the system’s real time reaction without the interventionof humans. To this direction, future research should focus on thestandardization of hardware (electrical and mechanical) andsoftware interfaces for achieving a seamless ‘Plug & Produce’behaviour of the resources. Other preparatory activities such as theconnection of auxiliary devices need to be redesigned in thisdirection since they are important for enabling quick reconfigura-tion when practical applications are being considered. Moreover,the current control architectures on monolithic PLC–control, needto evolve towards a more open, service oriented architecture thatwould permit easier and less complex networking among thedifferent devices. Finally, the method’s expansion to account forprocesses, where humans and robots can cooperate in the sameworkspace, is also a challenge [16]. The hybrid assembly systemscurrently pose as the greatest challenge for the factories of thefuture, whilst methods for designing the operation of such systemsneed to be developed.

Acknowledgements

This work has been funded under the EU FP7 project‘‘Autonomous co-operative machines for highly reconfigurableassembly operations of the future – AUTORECON’’ (www.autor-econ.eu) (Grant no. 285189). The authors would like to thank allproject partners for their valuable feedback on the discussedapproach.

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