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Bennulf, M., Svensson, B. & Danielsson, F. (2018). Verification and deployment of automatically generated robot programs used in prefabrication of house walls. Paper presented at Conference of 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 ; Conference Date: 16 May 2018 Through 18 May 2018. Procedia CIRP, 72, 272-276
Open this publication in new window or tab >>Verification and deployment of automatically generated robot programs used in prefabrication of house walls
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 272-276Article in journal (Refereed) Published
Abstract [en]

This paper presents a method for automating the generation, verification and deployment of robot programs used in prefabrication of walls for family houses. The making of robot programs is today performed manually by experts, i.e. implying high costs. This is a huge disadvantage since each wall can be unique. The work demonstrates, with implementation and testing, a method to automate the generation of robot programs for fabrication of walls made of wood. This includes the task of generating collision free paths, automatic verification of path performance and deploying to a real industrial robot with minimal human interaction. © 2018 The Authors. Published by Elsevier B.V.

Keywords
Manufacture, Deployment; Flexible manufacturing; MoveIt; Path generation; RobotStudio, Human robot interaction
National Category
Robotics
Research subject
ENGINEERING, Computer engineering; Production Technology
Identifiers
urn:nbn:se:hv:diva-13049 (URN)10.1016/j.procir.2018.03.025 (DOI)2-s2.0-85049562354 (Scopus ID)
Conference
Conference of 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 ; Conference Date: 16 May 2018 Through 18 May 2018
Available from: 2018-10-26 Created: 2018-10-26 Last updated: 2019-05-24Bibliographically approved
Glorieux, E., Svensson, B., Danielsson, F. & Lennartson, B. (2017). Constructive cooperative coevolution for large-scale global optimisation. Journal of Heuristics, 23(6), 449-469
Open this publication in new window or tab >>Constructive cooperative coevolution for large-scale global optimisation
2017 (English)In: Journal of Heuristics, ISSN 1381-1231, E-ISSN 1572-9397, Vol. 23, no 6, p. 449-469Article in journal (Refereed) Published
Abstract [en]

This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework.

Keywords
Evolutionary optimisation, Cooperative coevolution, Algorithm design and analysis, Large-scale optimisation
National Category
Robotics
Research subject
Production Technology
Identifiers
urn:nbn:se:hv:diva-11264 (URN)10.1007/s10732-017-9351-z (DOI)000414074300002 ()2-s2.0-85024487069 (Scopus ID)
Funder
Region Västra Götaland, PROSAM 612-0974-14
Available from: 2017-08-02 Created: 2017-08-02 Last updated: 2019-05-23Bibliographically approved
Glorieux, E., Svensson, B., Danielsson, F. & Lennartson, B. (2017). Multi-objective constructive cooperative coevolutionary optimization of robotic press-line tending. Engineering optimization (Print), 49(10), 1685-1703
Open this publication in new window or tab >>Multi-objective constructive cooperative coevolutionary optimization of robotic press-line tending
2017 (English)In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 49, no 10, p. 1685-1703Article in journal (Refereed) Published
Abstract [en]

This article investigates multi-objective optimization of the robot trajectories and position-based operation-coordination of complex multi-robot systems, such as press lines, to improve the production rate and obtaining smooth motions to avoid excessive wear of the robots’ components. Different functions for handling the multiple objectives are evaluated on realworld press lines, including both scalarizing single-objective functions and Pareto-based multi-objective functions. Additionally, the Multi-Objective Constructive Cooperative Coevolutionary (moC3) algorithm is proposed, for Pareto-based optimization, which uses a novel constructive initialization of the subpopulations in a co-adaptive fashion. It was found that Paretobased optimization performs better than the scalarizing single-objective functions. Furthermore, moC3 gives substantially better results compared to manual online tuning, as currently used in the industry. Optimizing robot trajectories and operation-coordination of complex multi-robot systems using the proposed method with moC3 significantly improves productivity and reduces maintenance. This article hereby addresses the lack of systematic methods for effectively improving the productivity of press lines.

Keywords
Multi-objective optimization, coevolutionary optimization, press tending, multi-robot coordination
National Category
Production Engineering, Human Work Science and Ergonomics Robotics
Research subject
ENGINEERING, Manufacturing and materials engineering; Production Technology
Identifiers
urn:nbn:se:hv:diva-10341 (URN)10.1080/0305215X.2016.1264220 (DOI)000408952800003 ()2-s2.0-85006124128 (Scopus ID)
Note

Kolla upp ScopusID

Available from: 2016-12-19 Created: 2016-12-19 Last updated: 2019-05-23Bibliographically approved
Glorieux, E., Svensson, B., Parthasarathy, P. & Danielsson, F. (2016). An energy model for press line tending robots. In: José Evora-Gomez & José Juan Hernandez-Cabrera (Ed.), ESM'2016, the 2016 European simulation and Modelling Conference: Modelling and Simulation '2016. Paper presented at 30th European Simulation and Modelling Conference - ESM'2016, October 26-28, 2016, Las Palmas, Gran Canaria, Spain (pp. 377-383). Eurosis
Open this publication in new window or tab >>An energy model for press line tending robots
2016 (English)In: ESM'2016, the 2016 European simulation and Modelling Conference: Modelling and Simulation '2016 / [ed] José Evora-Gomez & José Juan Hernandez-Cabrera, Eurosis , 2016, p. 377-383Conference paper, Published paper (Refereed)
Abstract [en]

Today most industries aim at reducing energy consumption to become sustainable and environment-friendly. The automotive industry, with mass production and large volumes, is one such example. With many robots working round the clock, there is great potential to save energy. In this climate there is a need for robot simulation models that can be used for motion and task execution optimisation and which are aimed lowering energy consumption. This paper presents an energy consumption model for 2D-belt robots for press line tending in the automotive sector. The energy model is generic for 2D-belt robots and is entirely based on component specifications (e.g., dimensions, masses, inertia). An implementation and validation against a real 2D-belt tending robot used in the automotive industry is performed and presented. The purpose and usefulness of the energy model is also demonstrated by two application cases; the investigation of potential energy reductions achieved by reducing the weight of gripper tools, and by using mechanical brakes when the robot is idle.

Place, publisher, year, edition, pages
Eurosis, 2016
Keywords
Industrial robots, energy model, energy consumption, energy minimisation
National Category
Robotics
Research subject
Production Technology; ENGINEERING, Manufacturing and materials engineering
Identifiers
urn:nbn:se:hv:diva-10133 (URN)2-s2.0-85016052034 (Scopus ID)9789077381953 (ISBN)
Conference
30th European Simulation and Modelling Conference - ESM'2016, October 26-28, 2016, Las Palmas, Gran Canaria, Spain
Note

This work was performed at University West’s Production Technology West research centre and supported in part by Västra Götalandsregionen under the grant PROSAM+ RUN 612-0208-16.

Available from: 2016-11-10 Created: 2016-11-10 Last updated: 2019-02-07Bibliographically approved
Glorieux, E., Parthasarathy, P., Svensson, B. & Danielsson, F. (2016). Energy Consumption Model for 2D-Belt Robots. In: 7th Swedish Production Symposium Conference proceedings: . Paper presented at 7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016 (pp. 1-7). Lund: SPS16
Open this publication in new window or tab >>Energy Consumption Model for 2D-Belt Robots
2016 (English)In: 7th Swedish Production Symposium Conference proceedings, Lund: SPS16 , 2016, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

Production that incorporates robotics consumes energy and the trend today is to reduce consumed energy not only to lower the cost but also to be a more energy efficient entity. Energy models can be used to predict the energy consumed by robot(s) for optimising the input parameters which determine robot motion and task execution. This paper presents an energy model to predict the energy consumption of 2D-belt robots used for press line tending. Based on the components’ specifications and the trajectory, an estimation of the energy consumption is computed. The capabilities of the proposed energy model to predict the energy consumption during the planning-phase (i.e. before installation), avoiding the need for physical experiments, are demonstrated. This includes predicting potential energy reductions achieved by reducing the weight of the gripper tools. Additionally, it is also shown how to investigate the energy saving achieved by using mechanical brakes when the robot is idle. This effectively illustrates the purpose and usefulness of the proposed energy model.

Place, publisher, year, edition, pages
Lund: SPS16, 2016
Keywords
Industrial robots, energy model, energy consumption, energy minimisation
National Category
Robotics
Research subject
Production Technology
Identifiers
urn:nbn:se:hv:diva-10131 (URN)
Conference
7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016
Available from: 2016-11-10 Created: 2016-11-10 Last updated: 2018-08-12Bibliographically approved
Glorieux, E., Svensson, B., Danielsson, F. & Lennartson, B. (2016). Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation. In: Computational Intelligence, 2015 IEEE Symposium Series on: . Paper presented at 2015 IEEE Symposium on Computational Intelligence SSCI 8-10 December 2015 Cape Town, South Africa (pp. 1703-1710). IEEE, Article ID 7376815.
Open this publication in new window or tab >>Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation
2016 (English)In: Computational Intelligence, 2015 IEEE Symposium Series on, IEEE, 2016, p. 1703-1710, article id 7376815Conference paper, Published paper (Refereed)
Abstract [en]

The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution (C3DE) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE), which removes several limitations with the previous version. A novel element of C3iDE is the advanced initialisation of the subpopulations. C3iDE initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of C3iDE on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with C3iDE is also investigated in this paper.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Benchmark testing Collaboration Complexity theory, Evolutionary computation, Optimization Partitioning, algorithms
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production Technology
Identifiers
urn:nbn:se:hv:diva-8900 (URN)10.1109/SSCI.2015.239 (DOI)2-s2.0-84964940225 (Scopus ID)978-1-4799-7560-0 (ISBN)
Conference
2015 IEEE Symposium on Computational Intelligence SSCI 8-10 December 2015 Cape Town, South Africa
Available from: 2016-01-18 Created: 2016-01-18 Last updated: 2019-03-13Bibliographically approved
Danielsson, F., Svensson, B. & Reddy, D. (2015). A genetic algorithm with shuffle for job shop scheduling problems. In: Marwan Al-Akaidi & Aladdin Ayesh (Ed.), Modelling and simulation 2015: The European simulation and modelling conference 2015, ESM 2015, October 26-28 Leicester, United Kingdom. Paper presented at The 29th annual European simulation and modelling conference 2015, ESM 2015, October 26-28 Leicester, United Kingdom (pp. 363-367). Ostend: ESM
Open this publication in new window or tab >>A genetic algorithm with shuffle for job shop scheduling problems
2015 (English)In: Modelling and simulation 2015: The European simulation and modelling conference 2015, ESM 2015, October 26-28 Leicester, United Kingdom / [ed] Marwan Al-Akaidi & Aladdin Ayesh, Ostend: ESM , 2015, p. 363-367Conference paper, Published paper (Refereed)
Abstract [en]

Job shop scheduling problems are computationally complex combinatorial optimization problems. Genetic algorithms have been used in various forms and in combination with other algorithms to solve job shop scheduling problems. A partially flexible job shop with precedence constraints increases this complex behaviour. There are two main parts to optimizing ajob shop, the routing and the scheduling. The objective here is to get consistent optimal makespan using a genetic algorithm. This paper firstly, presents a simulation approach for the considered partially flexible job shop scheduling problem. Which take into account the precedence constraints and reduce situations of deadlock. To solve the partially flexible job shop scheduling problem a genetic algorithm was used and improved. It utilise a genetic crossovers for routing and a new random shuffle feature is introduced for the scheduling. The computational results have shown that the algorithm performs well in terms of finding a consistent optimal schedule for the given problem

Place, publisher, year, edition, pages
Ostend: ESM, 2015
Keywords
Simulation based optimisation, genetic algorithm, job shop scheduling, random shuffle
National Category
Robotics
Research subject
ENGINEERING, Manufacturing and materials engineering; Production Technology
Identifiers
urn:nbn:se:hv:diva-8622 (URN)2-s2.0-84963615118 (Scopus ID)978-90-77381-90-8 (ISBN)
Conference
The 29th annual European simulation and modelling conference 2015, ESM 2015, October 26-28 Leicester, United Kingdom
Available from: 2015-11-06 Created: 2015-11-06 Last updated: 2018-08-12Bibliographically approved
Glorieux, E., Danielsson, F., Svensson, B. & Lennartson, B. (2015). Constructive cooperative coevolutionary optimisation for interacting production stations. The International Journal of Advanced Manufacturing Technology, 78(1-4), 673-688
Open this publication in new window or tab >>Constructive cooperative coevolutionary optimisation for interacting production stations
2015 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 78, no 1-4, p. 673-688Article in journal (Refereed) Published
Abstract [en]

Optimisation of the control function for multiple automated interacting production stations is a complex problem, even for skilled and experienced operators or process planners. When using mathematical optimisation techniques, it often becomes necessary to use simulation models to represent the problem because of the high complexity (i.e. simulation-based optimisation). Standard optimisation techniques are likely to either exceed the practical time frame or under-perform compared to the manual tuning by the operators or process planners. This paper presents the Constructive cooperative coevolutionary (C3) algorithm, which objective is to enable effective simulation-based optimisation for the control of automated interacting production stations within a practical time frame. C3 is inspired by an existing cooperative coevolutionary algorithm. Thereby, it embeds an algorithm that optimises subproblems separately. C3 also incorporates a novel constructive heuristic to find good initial solutions and thereby expedite the optimisation. In this work, two industrial optimisation problems, involving interaction production stations, with different sizes are used to evaluate C3. The results illustrate that with C3, it is possible to optimise these problems within a practical time frame and obtain a better solution compared to manual tuning.

Keywords
Manufacturing automation, metaheuristic optimisation algorithm, optimised production technology, interacting production stations, sheet metal press line
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
ENGINEERING, Manufacturing and materials engineering
Identifiers
urn:nbn:se:hv:diva-7586 (URN)10.1007/s00170-015-7012-7 (DOI)000359835000055 ()2-s2.0-84939260196 (Scopus ID)
Note

Published online 2 April 2015

Available from: 2015-05-07 Created: 2015-05-07 Last updated: 2019-05-14Bibliographically approved
Svensson, B. & Danielsson, F. (2015). P-SOP -€“ A multi-agent based control approach for flexible and robust manufacturing. Robotics and Computer-Integrated Manufacturing, 36, 109-118, Article ID 1301.
Open this publication in new window or tab >>P-SOP -€“ A multi-agent based control approach for flexible and robust manufacturing
2015 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 36, p. 109-118, article id 1301Article in journal (Refereed) Published
Abstract [en]

In a truly flexible manufacturing system the description of the control strategy must be updated every day. Hence, a new way to handle changes in the environment down to control system deployment and production is required. This paper presents a novel approach, based on P-SOP, to handle multi-agent based control and verification. The P-SOP approach addresses flexibility, robustness and deployment in the best possible manner with the least waste of time and effort. P-SOP includes a description language where the control strategy based on actual circumstances easily can be defined. Based on the description multi-agents, to control the manufacturing, are automatically generated. An industrial advantage is that the multi-agent generator creates IEC 61131-3 PLC code that can be executed on standard PLC’s. This feature eliminates the need for experts in PLC programming and reduce deployment time to become more efficient. Hence, this flexibility enables small series down to one off production in a competitive way. With multi-agent control it is also possible to handle rebalancing due to market changes, scheduling of available humans, introduction of new part types, and rerouting due to a machine break down or planned service. The generated agents are not optimised for a final solution with specific timings. All decisions are made on-line and the generated solution adapts to the circumstances that arise. With the P-SOP multi-agents it is easy to manually remove or introduce parts to the manufacturing cell without disturbing the system, e.g. for manually random inspections, removal of parts due to restart. The formulated description language and the multi-agent generator has been successfully tested and evaluated in an industrial environment.

Keywords
Multi-agent, Flexible manufacturing, IEC 61131-3, Code generation, PLC
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Research subject
ENGINEERING, Manufacturing and materials engineering
Identifiers
urn:nbn:se:hv:diva-7665 (URN)10.1016/j.rcim.2014.12.005 (DOI)000358464700013 ()2-s2.0-84936890418 (Scopus ID)
Note

Available online 7 February 2015

Available from: 2015-06-03 Created: 2015-06-02 Last updated: 2018-01-11Bibliographically approved
Glorieux, E., Svensson, B., Danielsson, F. & Lennartson, B. (2015). Simulation-based Time and Jerk Optimisation for Robotic Press Tending. In: Modellling and Simulation: The European simulation and modelling conference 2015, ESM 2015. Paper presented at The 29th annual European simulation and modelling conference 2015, Leicester, United Kingdom, October 26-28, 2015 (pp. 377-384). Ostende: ESM
Open this publication in new window or tab >>Simulation-based Time and Jerk Optimisation for Robotic Press Tending
2015 (English)In: Modellling and Simulation: The European simulation and modelling conference 2015, ESM 2015, Ostende: ESM , 2015, p. 377-384Conference paper, Published paper (Refereed)
Abstract [en]

Increased production rate and robot motion smoothness in a sheet metal press line are essential. Smooth robot motions avoid unplanned production interruptions and excessive wear of the robots. Reaching high production rate and smooth motions requires tuning of the tending press robot control to minimise the cycle time and jerk. Doing this for a press line with multiple robots is a complex large-scale problem. To model such problems for the optimisation process, computer simulations become almost essential. This work presents simulation-based optimisation of the time and jerk of robotic press tending operations and investigates the importance of including the robot motion’s smoothness. An optimiser works in concert with a simulation model of a sheet metal press line and its parametrised control system. The effect of including jerk minimisation in the objective function is tested on a real-world problem concerning a sheetmetal press line. The results illustrate the importance of including jerk-minimisation as an objective in the optimisation.Furthermore, the performance of this approach is compared with manual tuning by experienced operators. The results show that the proposed simulation-based optimisation approach outperforms manual tuning.

Place, publisher, year, edition, pages
Ostende: ESM, 2015
Keywords
Production, Optimization, Manufacturing, Automatic control, Industrial control
National Category
Robotics
Research subject
Production Technology
Identifiers
urn:nbn:se:hv:diva-8624 (URN)2-s2.0-84963512911 (Scopus ID)978-90-77381-90-8 (ISBN)
Conference
The 29th annual European simulation and modelling conference 2015, Leicester, United Kingdom, October 26-28, 2015
Available from: 2015-11-06 Created: 2015-11-06 Last updated: 2019-03-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6604-6904

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