Preprints of the 2013 IFAC Conference on Manufacturing Modelling, Management, and Control, Saint Petersburg State University and Saint Petersburg National Research University of Information Technologies, Mechanics, and Optics, Saint Petersburg, Russia, June 19-21, 2013
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Modelling and Scheduling Autonomous Mobile Robot for a Real-World Industrial Application Quang-Vinh Dang*. Izabela Nielsen* Simon Bøgh*. Grzegorz Bocewicz.** *Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg East, Denmark (e-mail:
[email protected],
[email protected],
[email protected]) **Department of Computer Science and Management, Koszalin University of Technology, Poland (e-mail:
[email protected]) Abstract: The paper deals with a real-world implementation of an autonomous industrial mobile robot performing an industrial application at a pump manufacturing factory. In the implementation, the multicriteria optimization problem of scheduling tasks of a mobile robot is taken into account. The paper proposes an approach composing RI D PRELOH URERW V\VWHP GHVLJQ ³/LWWOH +HOSHU´ DQ DSSURSULDWH DQG comprehensive industrial application (multiple-part feeding tasks), an implementation concept for industrial environments (the bartender concept), and a real-time heuristics integrated into Mission Planning and Control software to schedule the mobile robot in the industrial application. Results from the real-world implementation VKRZ WKDW ³/LWWOH +HOSHU´ LV FDSDEOH RI VXFFHVVIXOO\ VHUYLQJ IRXU SDUW IHHGHUV in three production cells within a given planning horizon using the best schedule generated from the realtime heuristics. The results also demonstrated that the proposed real-time heuristics is capable of finding the best schedule in online production mode. Keywords: Robotics, Modelling, Scheduling, Heuristics, Pumps.
1. INTRODUCTION The shift in paradigm from mass production to customized production and the resumption of production in wageLQWHQVLYH FRXQWULHV KDYH FUHDWHG QHHGV IRU ÀH[LELOLW\ transformability and cost-HI¿FLHQF\ HVSHFLDOO\ LQ WKH ¿HOG RI automation and robotics (Jovan et al., 2003). Robots are widely used in industry to perform 4D tasks; dumb, dangerous, dull, and/or dirty. Therefore, industrial robotics forms an essential part of the manufacturing backbone, but WKH URERWV RI WRGD\ DUH UDWKHU LQÀH[LEOH DV WKH\ DUH RIWHQ dedicated and in ¿[HG positions (High Level Group, 2006). To improve this, mobile robots with manipulation arms, which have the capability of moving around within the manufacturing environment and are not fixed to one physical location, could be employed. Moreover, it is possible for mobile robots to adapt to changing environments and perform a variety of tasks. Therefore, mobile robot technology holds great potential in the manufacturing industries (EUROP, 2009). Despite considerable attention within the manufacturing domain, real-world implementations of mobile robots have been limited (Stopp et al., 2003; Katz et al., 2006; Datta et al., 2008; Hentout et al., 2010) although the needs for transIRUPDEOH DQG ÀH[LEOH DXWRPDWLRQ are present (Hvilshøj and Bøgh, 2011). In addition, the problem of planning and scheduling the tasks of mobile robots in the real-world industrial applications has received little attention in the literature (Maimon et al., 2000; Hurink and Knust, 2002;
Zacharia and Aspragathos, 2005). Although, this problem could be modelled in some respects comparable to the Travelling Salesman Problems (TSP) which belongs to the class of NP-hard combinatorial optimization problems, the surveyed approaches are not well suited and cannot be directly used to solve this problem. Therefore, new initiatives are required to realize industrial acceptance and maturation of the autonomous industrial mobile robots. In general, the mobile robot technology finds most suitable applications within the logistic area (transportation and part feeding). The logistics tasks are either very suited for mobile robots (easily manageable work pieces) or out of scope (unmanageable work pieces, e.g. due to large size and/or weight). In contrast, pre-assembly, inspection and process execution tasks are generally not suited for the current state of mobile robots. Often, these tasks are tailored for human operators and not suited for automation, because they require dexterous manipulation skills and/or experience. Other application categories look promising, e.g. machine tending and cleaning but they are either few in numbers or low in suitability score. In addition, some application categories are generally out of scope, e.g. maintenance, repair and overhaul, as these tasks require handling of large parts (e.g. injection moulds) or task execution in inaccessible areas (e.g. the backside of manufacturing equipment). In summary, mobile robot technology, DW LWV FXUUHQW VWDJH ¿QGV its most suitable applications within the logistic area, moves towards assistive tasks, and in the future more service and other nonproduction-related tasks.
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Preprints of the 2013 IFAC Conference on Manufacturing Modelling, Management, and Control, Saint Petersburg State University and Saint Petersburg National Research University of Information Technologies, Mechanics, and Optics, Saint Petersburg, Russia, June 19-21, 2013
Overall, the necessary mobile robot technologies (hardware and software) exist at a mature level. One significant reason that no autonomous mobile robots have yet been implemented in industrial environments is that research in the right applications have not been carried out. In this paper, the mobile robot technology is transferred from laboratory experiments to a real-world environment and application by proposing an approach consisting of: x a mobile robot system design (Little Helper), x an appropriate and comprehensive industrial application (multiple-part feeding), x an implementation concept for industrial environments (the bartender concept), and x a real-time heuristics to schedule the mobile robot in the industrial application.
7KH ³/LWWOH +HOSHU´ V\VWHP GHVLJQ shown in Fig. 2 relies on standardized, industrially accepted and commercial-off-theshelf (COTS) hardware components and software packages, ZKLFK RIIHU VLJQL¿FDQW VDYLQJV LQ SURFXUHPHQW GHYHORSPHQW DQG PDLQWHQDQFH ,Q DGGLWLRQ ³/LWWOH +HOSHU´ GHSHQGV RQ mass customization principles, in terms of modularization, product families and product platforms. The architecture consists of different modules (basic building blocks) such as mobile platform, vision, robot manipulator, and tooling with pre-defined interfaces and skills/actions. In this way, it is possible to configure the mobile robot for versatile industrial applications and/or environments. Path planning Obstacle avoidance ...
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The paper is organized as follows. Section 2 presents a methodology supporting the mobile robot technology within real-world manufacturing environments. Section 3 describes the necessary preparation and customization steps for the multiple-part feeding implementation at a company which produces pumps. Section 4 presents results from the conducted implementation in term of system performances. Finally, the paper concludes and discusses options for future work within the field of autonomous industrial mobile robots in Section 5.
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2.1 Little Helper TKH DXWRQRPRXV LQGXVWULDO PRELOH URERW ³/LWWOH +HOSHU´ was developed by Hvilshøj and Bøgh (2011). The overall vision is shown in Fig. 1, which depicts a typical work schedule. ³/LWWOH +HOSHU´ FDQ EH XVHG IRU YDULRXV PDQXIDFWXULQJ WDVNV e.g. carrying tasks, preparatory and/or post-processing tasks, and even pre-assembly, quality inspection, and machine tending. Furthermore, the mobile robot is able to work fully automatic in a full shift of a working day ,Q WKLV ZD\ ³/LWWOH +HOSHU´ EHFRPHV D ÀH[LEOH DXWRPDWLRQ WHFKQRORJ\ ZKLFK contributes to the realization of transformable production systems.
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06.00 ± 06.30: feed all workstations with parts 06.30 ± 09.30: pre-assembly at workstation 3 09.30 ± 10.00: feed workstation 1 with needed parts 10.00 ± 10.30: feed workstation 2 with needed parts 10.30 ± 13.30: pre-assembly at workstation 3 13.30 ± 14.00: feed workstation 1 with needed parts 14.00 ± 14.30: feed workstation 2 with needed parts 14.30 ± 17.30: pre-assembly at workstation 3 18.00 ± 19.00: transport products to warehouse A 19.00 ± 20.00: transport products to warehouse B
Fig. 1. A typical work day of a mobile robot.
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Fig. 2. A modular autonomous industrial mobile robot system (the first ³/LWWOH +HOSHU´ SURWRW\SH . 2.2 Multiple-part feeding Currently, the most suitable process for the mobile robot is multiple-part feeding which is the task of loading several parts or components at a time into feeders (e.g. step feeders) and/or machines (e.g. turning-milling machines). Typical manufacturing facilities consist of several automatic production lines, where the feeding of parts or components is manually performed. Multiple-part feeding is a non-value adding manufacturing task and it is quite often disruptive (inbetween and periodic) for the factory workers. Therefore, automation of multiple-part feeding holds great industrial potential. Utilizing autonomous mobile robots will make multiple-part feeding tasks more flexible and complete. 2.3 The bartender concept Multiple-part feeding task is to some extend similar to material handling: pickup 7 transportation 7 empty at feeder 7 transportation 7 place. At the same time they differ in other aspects, e.g. the parts to be handled, the feeders to be serviced, and the workstations to be localized. To address this, the bartender concept is proposed for solving multiple-part feeding tasks by mobile robots (Fig. 3).
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