Skip to main content
Springer Nature Link
Log in

Poses Optimisation Methodology for High Redundancy Robotic Systems

  • Conference paper
  • First Online:
ROBOT 2017: Third Iberian Robotics Conference (ROBOT 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 694))

Included in the following conference series:

  • 2670 Accesses

Abstract

The need for efficient automation methods has prompted the fast development in the field of Robotics. However, most robotic solutions found in industrial environments lack in both flexibility and adaptability to be applied to any generic task. A particular problem arises when robots are integrated in work cells with extra degrees of freedom, such as external axis or positioners. The specification/design of high redundancy systems, including robot selection, tool and fixture design, is a multi-variable problem with strong influence in the final performance of the work cell. This work builds on top of optimisation techniques to deal with the optimal poses reachability for high redundancy robotic systems. In this paper, it will be proposed a poses optimisation approach to be applicable within high redundancy robotic systems. The proposed methodology was validated by using real environment existent infrastructures, namely, the national CoopWeld project.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alatartsev, S., Stellmacher, S., Ortmeier, F.: Robotic task sequencing problem: a survey. J. Intell. Robot. Syst. 80(2), 279–298 (2015)

    Article  Google Scholar 

  2. Andrisano, A.O., Leali, F., Pellicciari, M., Pini, F., Vergnano, A., Pini, F.: Integrated design of robotic workcells for high quality machining (2011)

    Google Scholar 

  3. Bennewitz, M., Burgard, W., Thrun, S.: Optimizing schedules for prioritized path planning of multi-robot systems, vol. 1, pp. 271–276 (2001)

    Google Scholar 

  4. Cheng, F.S.: Methodology for developing robotic workcell simulation models, vol. 2, pp. 1265–1271 (2000)

    Google Scholar 

  5. Chu, B., Jung, K., Chu, Y., Hong, D., Lim, M.T., Park, S., Lee, Y., Lee, S.U., Min Chul, K., Kang Ho, K.: Robotic automation system for steel beam assembly in building construction, pp. 38–43 (2009)

    Google Scholar 

  6. Deb, K.: Introduction to genetic algorithms. Sadhana - Acad. Proc. Eng. Sci. 24(4–5), 293–315 (1999)

    MathSciNet  MATH  Google Scholar 

  7. Fathi, M., Álvarez, M., Rodríguez, V.: A new heuristic-based bi-objective simulated annealing method for U-shaped assembly line balancing. Eur. J. Ind. Eng. 10(2), 145–169 (2016)

    Article  Google Scholar 

  8. Geller, E., Matthews, C.: Impact of robotic operative efficiency on profitability. Am. J. Obstet. Gynecol. 209(1), 20e1–20e5 (2013)

    Article  Google Scholar 

  9. Graetz, G., Michaels, G.: Robots at Work (2015)

    Google Scholar 

  10. Gueta, L., Chiba, R., Arai, T., Ueyama, T., Ota, J.: Compact design of work cell with robot arm and positioning table under a task completion time constraint, pp. 807–813 (2009)

    Google Scholar 

  11. Hauer, S., M.V.H.C.S.K.: Design and simulation of modular robot work cells, pp. 1801–1802 (2009)

    Google Scholar 

  12. Kamezaki, M., Hashimoto, S., Iwata, H., Sugano, S.: Development of a dual robotic arm system to evaluate intelligent system for advanced construction machinery, pp. 1299–1304 (2010)

    Google Scholar 

  13. Kamoun, H., Hall, N., Sriskandarajah, C.: Scheduling in robotic cells: heuristics and cell design. Oper. Res. 47(6), 821–835 (1999)

    Article  MATH  Google Scholar 

  14. Kirkpatrick, S., Gelatt Jr., C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  15. Mansouri, S.: A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines. Eur. J. Oper. Res. 167(3), 696–716 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  16. Pellegrinelli, S., Pedrocchi, N., Tosatti, L.M., Fischer, A., Tolio, T.: Multi-robot spot-welding cells for car-body assembly: design and motion planning. Robot. Comput.-Integr. Manufact. 44, 97–116 (2017)

    Article  Google Scholar 

  17. Yang, J., Yang, J.: Intelligence optimization algorithms: a survey 3(4), 144–152 (2011)

    Google Scholar 

Download references

Acknowledgments

A special word to SARKKIS robotics and INESC-TEC (in particular the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project «POCI-01-0145-FEDER-006961») for their commitment in research and development of revolutionary state-of-the-art algorithms and for their contribution regarding software tools and engineering hours availability.

Author information

Authors and Affiliations

  1. FEUP - Faculty of Engineering of University of Porto, Porto, Portugal

    Pedro Tavares, Pedro Costa, Germano Veiga & António Paulo Moreira

  2. INESC TEC - INESC Technology and Science formerly INESC Porto, Porto, Portugal

    Pedro Costa, Germano Veiga & António Paulo Moreira

  3. SARKKIS Robotics, Porto, Portugal

    Pedro Tavares

Authors
  1. Pedro Tavares
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Germano Veiga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. António Paulo Moreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro Tavares .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spain

    Anibal Ollero

  2. Institut de Robòtica I Informàtica Industrial (CSIC-UPC), Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Sanfeliu

  3. Departamento de Informática e Ingeniería de Sistemas, Escuela de Ingeniería y Arquitectura, Instituto de Investigación en Ingeniería de Aragón, Zaragoza, Spain

    Luis Montano

  4. Institute of Electronics and Telematics Engineering of Aveiro (IEETA), Universidade de Aveiro, Aveiro, Portugal

    Nuno Lau

  5. IDMEC, Instituto Superior Técnico de Lisboa, Universidade de Lisboa, Lisbon, Portugal

    Carlos Cardeira

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tavares, P., Costa, P., Veiga, G., Moreira, A.P. (2018). Poses Optimisation Methodology for High Redundancy Robotic Systems. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70836-2_55

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70835-5

  • Online ISBN: 978-3-319-70836-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics