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Gustavsson, P. & Syberfeldt, A. (2018). A New Algorithm Using the Non-dominated Tree to improve Non-dominated Sorting. Evolutionary Computation, 26(1), 89-116
Open this publication in new window or tab >>A New Algorithm Using the Non-dominated Tree to improve Non-dominated Sorting
2018 (English)In: Evolutionary Computation, ISSN 1063-6560, E-ISSN 1530-9304, Vol. 26, no 1, p. 89-116Article in journal (Refereed) Published
Abstract [en]

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This paper presents a new, more efficient, algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the paper, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.

Place, publisher, year, edition, pages
MIT Press, 2018
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-13336 (URN)10.1162/EVCO_a_00204 (DOI)000426562300004 ()2-s2.0-85042773821 (Scopus ID)
Available from: 2017-01-25 Created: 2017-01-25 Last updated: 2018-03-20Bibliographically approved
Gustavsson, P., Holm, M., Syberfeldt, A. & Wang, L. (2018). Human-robot collaboration – towards new metrics for selection of communication technologies. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 123-128
Open this publication in new window or tab >>Human-robot collaboration – towards new metrics for selection of communication technologies
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 6p. 123-128Article in journal (Refereed) Published
Abstract [en]

Industrial robot manufacturers have in recent years developed collaborative robots and these gains more and more interest within the manufacturing industry. Collaborative robots ensure that humans and robots can work together without the robot being dangerous for the human. However, collaborative robots themselves are not enough to achieve collaboration between a human and a robot; collaboration is only possible if a proper communication between the human and the robot can be achieved. The aim of this paper is to identify and categorize technologies that can be used to enable such communication between a human and an industrial robot.

Place, publisher, year, edition, pages
Elsevier, 2018. p. 6
Keywords
human-robot collaboration, human-robot interaction
National Category
Robotics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15935 (URN)10.1016/j.procir.2018.03.156 (DOI)2-s2.0-85049566186 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-07-06 Created: 2018-07-06 Last updated: 2018-10-31Bibliographically approved
Syberfeldt, A., Danielsson, O. & Gustavsson, P. (2017). Augmented Reality Smart Glasses in the Smart Factory: Product Evaluation Guidelines and Review of Available Products. IEEE Access, 5, 9118-9130, Article ID 7927376.
Open this publication in new window or tab >>Augmented Reality Smart Glasses in the Smart Factory: Product Evaluation Guidelines and Review of Available Products
2017 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 5, p. 9118-9130, article id 7927376Article in journal (Refereed) Published
Abstract [en]

Augmented reality smart glasses (ARSG) are increasingly popular and have been identified as a vital technology supporting shop-floor operators in the smart factories of the future. By improving our knowledge of how to efficiently evaluate and select ARSG for the shop-floor context, this paper aims to facilitate and accelerate the adoption of ARSG by the manufacturing industry. The market for ARSG has exploded in recent years, and the large variety of products to select from makes it not only difficult but also time consuming to identify the best alternative. To address this problem, this paper presents an efficient step-by-step process for evaluating ARSG, including concrete guidelines as to what parameters to consider and their recommended minimum values. Using the suggested evaluation process, manufacturing companies can quickly make optimal decisions about what products to implement on their shop floors. The paper demonstrates the evaluation process in practice, presenting a comprehensive review of currently available products along with a recommended best buy. The paper also identifies and discusses topics meriting research attention to ensure that ARSG are successfully implemented on the industrial shop floor.

Keywords
Augmented reality smart glasses, smart factory, augmented reality, industrial operator support
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13922 (URN)10.1109/ACCESS.2017.2703952 (DOI)000404270600043 ()2-s2.0-85028751835 (Scopus ID)
Available from: 2017-07-15 Created: 2017-07-15 Last updated: 2019-01-24Bibliographically approved
Gustavsson, P., Syberfeldt, A., Brewster, R. & Wang, L. (2017). Human-Robot Collaboration Demonstrator Combining Speech Recognition and Haptic Control. In: Mitchell M. Tseng, Hung-Yin Tsai, Yue Wang (Ed.), Manufacturing Systems 4.0 - Proceedings of the 50th CIRP Conference on Manufacturing Systems: . Paper presented at The 50th CIRP Conference on Manufacturing Systems, Taichung City, Taiwan on May 3rd – 5th, 2017 (pp. 396-401). , 63
Open this publication in new window or tab >>Human-Robot Collaboration Demonstrator Combining Speech Recognition and Haptic Control
2017 (English)In: Manufacturing Systems 4.0 - Proceedings of the 50th CIRP Conference on Manufacturing Systems / [ed] Mitchell M. Tseng, Hung-Yin Tsai, Yue Wang, 2017, Vol. 63, p. 396-401Conference paper, Published paper (Refereed)
Abstract [en]

In recent years human-robot collaboration has been an important topic in manufacturing industries. By introducing robots into the same working cell as humans, the advantages of both humans and robots can be utilized. A robot can handle heavy lifting, repetitive and high accuracy tasks while a human can handle tasks that require the flexibility of humans. If a worker is to collaborate with a robot it is important to have an intuitive way of communicating with the robot. Currently, the way of interacting with a robot is through a teaching pendant, where the robot is controlled using buttons or a joystick. However, speech and touch are two communication methods natural to humans, where speech recognition and haptic control technologies can be used to interpret these communication methods. These technologies have been heavily researched in several research areas, including human-robot interaction. However, research of combining these two technologies to achieve a more natural communication in industrial human-robot collaboration is limited. A demonstrator has thus been developed which includes both speech recognition and haptic control technologies to control a collaborative robot from Universal Robots. This demonstrator will function as an experimental platform to further research on how the speech recognition and haptic control can be used in human-robot collaboration. The demonstrator has proven that the two technologies can be integrated with a collaborative industrial robot, where the human and the robot collaborate to assemble a simple car model. The demonstrator has been used in public appearances and a pilot study, which have contributed in further improvements of the demonstrator. Further research will focus on making the communication more intuitive for the human and the demonstrator will be used as the platform for continued research.

Series
Procedia CIRP, ISSN 2212-8271 ; 63
Keywords
Human-robot collaboration, Speech recognition, Haptic control
National Category
Robotics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13986 (URN)10.1016/j.procir.2017.03.126 (DOI)000418465500067 ()2-s2.0-85028657899 (Scopus ID)
Conference
The 50th CIRP Conference on Manufacturing Systems, Taichung City, Taiwan on May 3rd – 5th, 2017
Available from: 2017-08-14 Created: 2017-08-14 Last updated: 2019-01-24Bibliographically approved
Gustavsson, P. (2016). Using Speech Recognition, Haptic Control and Augmented Reality to enable Human-Robot Collaboration in Assembly Manufacturing: Research Proposal.
Open this publication in new window or tab >>Using Speech Recognition, Haptic Control and Augmented Reality to enable Human-Robot Collaboration in Assembly Manufacturing: Research Proposal
2016 (English)Report (Other academic)
Abstract [en]

In recent years robots have become more adaptive and aware of the surroundings which enables them for use in human-robot collaboration. By introducing robots into the same working cell as the human, then the two can collaborate by letting the robot deal with heavy lifting, repetitive and high accuracy tasks while the human focuses on tasks that needs the flexibility of the human. Collaborative robots already exists today in the market but the usage of these robots are mainly to work in close proximity.

Usually a teaching pendant is used to program a robot by moving it using a joystick or buttons. Using this teaching pendant for programming is usually quite slow and requires training which means that few can operate it. However, recent research shows that there exist several application using multi-modal communication systems to improve the programming of a robot. This kind of programming will be necessary to collaborate with a robot in the industry since the human in a collaborative task might have to teach the robot how to execute its task.

This project aims to introduce a programming-by-guidance system into assembly manufacturing where the human can assist the robot by teaching the robot how to execute its task. Three technologies will be combined, speech recognition, haptic control, and augmented reality. The hypothesis is that with these three technologies an effective and intuitive programming-by-guidance system can be used within the assembly manufacturing industry. This project have three main motivators: Allowing workers, with no robot programming expertise, to teach the robot how to execute its task in an assembly manufacturing system; Reducing the development time of the robot by introducing advanced programming-by-guidance technology; Showing that augmented reality can add additional information that is useful when programming the robot.

Publisher
p. 10
Keywords
Human-Robot Collaboration, Speech Recognition, Haptic Control, Augmented Reality
National Category
Robotics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12845 (URN)
Note

Research proposal, PhD programme, University of Skövde

Available from: 2016-08-30 Created: 2016-08-30 Last updated: 2018-03-28Bibliographically approved
Syberfeldt, A. & Gustavsson, P. (2015). Robust product sequencing through evolutionary multi-objective optimisation. International Journal of Manufacturing Research, 10(4), 371-383
Open this publication in new window or tab >>Robust product sequencing through evolutionary multi-objective optimisation
2015 (English)In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 10, no 4, p. 371-383Article in journal (Refereed) Published
Abstract [en]

This paper describes a study on efficient optimisation of real-world product sequencing problems with the aim of finding robust solutions. Robust solutions are insensitive to unforeseen disturbances in a manufacturing process, which is a critical characteristic for a successful realisation of optimisation results in manufacturing. In the paper, the traditional method of achieving robust solutions is extended by using standard deviation as an additional optimisation objective. This transforms the original single-objective optimisation problem into a multi-objective problem. Using standard deviation as an additional objective focuses the optimisation on solutions that have both high performance and a high degree of robustness (that is, a low standard deviation). In order to optimise the two objectives simultaneously, a multi-objective evolutionary algorithm based on the Pareto approach is used. The multi-objective method for increased robustness is evaluated using both a benchmark problem and a real-world test case. The real-world test case is from GKN Aerospace in Sweden which manufactures components for aircraft engines and aero-derivative gas turbines. Results from the evaluation show that the method successfully increases the robustness while maintaining high performance of the optimisation.

Place, publisher, year, edition, pages
InderScience Publishers, 2015
Keywords
product sequencing, manufacturing industry, case study, robustness, evolutionary algorithms, multi-objective optimisation, aircraft engine components, aero-derivative gas turbines, standard deviation, GKN Aerospace, Sweden
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12016 (URN)10.1504/IJMR.2015.074823 (DOI)2-s2.0-84959326080 (Scopus ID)
Available from: 2016-03-02 Created: 2016-03-02 Last updated: 2017-12-19Bibliographically approved
Syberfeldt, A., Gustavsson, P., Svantesson, J. & Almgren, T. (2014). A Case Study of Evolutionary Simulation Based Optimization in Aircraft Engine Manufacturing. In: Industrial Simulation Conference, Skövde, June 11-13, 2014: . Paper presented at Industrial Simulation Conference 2014, ISC'2014, June 11-13, 2014, University of Skövde, Skövde, Sweden (pp. 91-96).
Open this publication in new window or tab >>A Case Study of Evolutionary Simulation Based Optimization in Aircraft Engine Manufacturing
2014 (English)In: Industrial Simulation Conference, Skövde, June 11-13, 2014, 2014, p. 91-96Conference paper, Published paper (Refereed)
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-9739 (URN)2-s2.0-84922135698 (Scopus ID)978-90-77381-83-0 (ISBN)
Conference
Industrial Simulation Conference 2014, ISC'2014, June 11-13, 2014, University of Skövde, Skövde, Sweden
Available from: 2014-08-20 Created: 2014-08-20 Last updated: 2018-05-07Bibliographically approved
Syberfeldt, A. & Gustavsson, P. (2014). Increased Robustness of Product Sequencing using Multi-Objective Optimization. In: Proceeding of 47th CIRP Conference on Manufacturing Systems: . Paper presented at 47th CIRP Conference on Manufacturing Systems (pp. 434-439). Elsevier, 17
Open this publication in new window or tab >>Increased Robustness of Product Sequencing using Multi-Objective Optimization
2014 (English)In: Proceeding of 47th CIRP Conference on Manufacturing Systems, Elsevier, 2014, Vol. 17, p. 434-439Conference paper, Published paper (Refereed)
Abstract [en]

Almost all manufacturing processes are subject to uncontrollable variations, caused, for example, by human operators or worn-out machines. When optimizing real-world product sequencing problems, it is of importance to find solutions that are robust, that is, whose performance remains relatively unchanged when exposed to uncertain conditions. In this paper, an extension of the traditional method of handling variations through replications is suggested that aims at finding solutions with an increased degree of robustness. The basic idea is to use standard deviation as an additional optimization objective and transform the single-objective problem into a multi-objective problem. Using standard deviation as an additional objective aims to focus the optimization on solutions that exhibit both high performance and high robustness (that is, having low standard deviation). In order to optimize the two objectives simultaneously, a multi-objective evolutionary algorithm is utilized. The proposed method for improved robustness is evaluated using a real-world test case found at the company GKN Aerospace in Sweden. GKN Aerospace manufactures a variety of different components for aircraft engines and aero derivative gas turbines. The company has recently installed a new workshop, and the focus of the study is on the x-ray stations in this workshop. For performing optimizations the company has created a simulation model that realistically mimics the workshop. As an optimization technique, a multi-objective evolutionary algorithm called NSGA-2 is being used. The algorithm considers the mean value and standard deviation from replications of the stochastic simulation as objectives, optimizing both of them simultaneously. Results from the study show that the optimization is able to successfully find robust solutions using the proposed method. However, the general increase in algorithm performance expected with the proposed method is absent, and possible reasons for this are discussed in the paper.

Place, publisher, year, edition, pages
Elsevier, 2014
Series
Procedia CIRP, ISSN 2212-8271
Keywords
Evolutionary Algorithm, Multi-objective optimization, Product Sequencing, Robustness
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-9737 (URN)10.1016/j.procir.2014.01.141 (DOI)000345458000074 ()2-s2.0-84904510168 (Scopus ID)
Conference
47th CIRP Conference on Manufacturing Systems
Available from: 2014-08-20 Created: 2014-08-20 Last updated: 2018-05-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8874-0676

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