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Path Planning Optimization Method Based on Genetic Algorithm for Mapping Toxic Environment

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Bioinspired Optimization Methods and Their Applications (BIOMA 2018)

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Abstract

The ionizing radiation is used in the nuclear medicine field during the execution of diagnosis exams. The administration of nuclear radio pharmaceutical components to the patient contaminates the environment. The main contribution of this work is to propose a path planning method for scanning the nuclear contaminated environment with a mobile robot optimizing the traveled distance. The Genetic Algorithm methodology is proposed and compared with other approaches and the final solution is validated in simulated and real environment in order to achieve a closer approximation to reality.

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References

  1. Suetens, P.: Fundamentals of Medical Imaging, 2nd edn. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  2. International Atomic Energy Agency (IAEA). http://nucmedicine.iaea.org/default.asp. Accessed 13 Dec 2017

  3. Tuncer, A., Yildirim, M.: Dynamic path planning of mobile robots with improved genetic algorithm. Comput. Electr. Eng. 38(6), 1564–1572 (2012)

    Article  Google Scholar 

  4. Siegwart, R., Nourbakhsh, I.R.: Introduction to Autonomous Mobile Robots, 1st edn. The MIT Press, Cambridge (2004)

    Google Scholar 

  5. Ma, Y., Zheng, G., Perruquetti, W.: Cooperative path planning for mobile robots based on visibility graph. In: Proceedings of the 32nd Chinese Control Conference, pp. 4915–4920, July 2013

    Google Scholar 

  6. Dong, H., Li, W., Zhu, J., Duan, S.: The path planning for mobile robot based on Voronoi diagram. In: 2010 Third International Conference on Intelligent Networks and Intelligent Systems, pp. 446–449, November 2010

    Google Scholar 

  7. Yang, X., Zeng, Z., Xiao, J., Zheng, Z.: Trajectory planning for RoboCup MSL mobile robots based on Bézier curve and Voronoi diagram. In: 2015 IEEE International Conference on Information and Automation, pp. 2552–2557, August 2015

    Google Scholar 

  8. Kloetzer, M., Mahulea, C., Gonzalez, R.: Optimizing cell decomposition path planning for mobile robots using different metrics. In: 2015 19th International Conference on System Theory, Control and Computing (ICSTCC), pp. 565–570, October 2015

    Google Scholar 

  9. Yu, Z., Yan, J., Zhao, J., Chen, Z.F., Zhu, Y.: Mobile robot path planning based on improved artificial potential field method. Harbin Gongye Daxue Xuebao (J. Harbin Inst. Technol.) 43(1), 50–55 (2011)

    Google Scholar 

  10. Moreira, A.P., Costa, P.J., Costa, P.: Real-time path planning using a modified A* algorithm. In: Proceedings of ROBOTICA 2009-9th Conference on Mobile Robots and Competitions (2009)

    Google Scholar 

  11. Hu, Y., Yang, S.X.: A knowledge based genetic algorithm for path planning of a mobile robot. In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation 2004, ICRA 2004, vol. 5, pp. 4350–4355. IEEE (2004)

    Google Scholar 

  12. Ismail, A., Sheta, A., Al-Weshah, M.: A mobile robot path planning using genetic algorithm in static environment. J. Comput. Sci. 4(4), 341–344 (2008)

    Article  Google Scholar 

  13. Sedighi, K.H., Ashenayi, K., Manikas, T.W., Wainwright, R.L., Tai, H.M.: Autonomous local path planning for a mobile robot using a genetic algorithm. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), vol. 2, pp. 1338–1345, June 2004

    Google Scholar 

  14. Alnasser, S., Bennaceur, H.: An efficient genetic algorithm for the global robot path planning problem. In: 2016 Sixth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), pp. 97–102, July 2016

    Google Scholar 

  15. Miller, C.E., Tucker, A.W., Zemlin, R.A.: Integer programming formulation of traveling salesman problems. J. ACM (JACM) 7(4), 326–329 (1960)

    Article  MathSciNet  Google Scholar 

  16. Piardi, L., Lima, J., Costa, P., Brito, T.: Development of a dynamic path for a toxic substances mapping mobile robot in industry environment. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds.) ROBOT 2017. AISC, vol. 694, pp. 655–667. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70836-2_54

    Chapter  Google Scholar 

  17. Lima, J., Costa, P.: Ultra-wideband time of flight based localization system and odometry fusion for a scanning 3 DoF magnetic field autonomous robot. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds.) ROBOT 2017. AISC, vol. 693, pp. 879–890. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70833-1_71

    Chapter  Google Scholar 

  18. Moharam, R., Morsy, E.: Genetic algorithms to balanced tree structures in graphs. Swarm Evol. Comput. 32, 132–139 (2017)

    Article  Google Scholar 

  19. Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer

    Google Scholar 

  20. Costa, P., Gonçalves, J., Lima, J., Malheiros, P.: Simtwo realistic simulator: a tool for the development and validation of robot software. Theory Appl. Math. Comput. Sci. 1(1), 17 (2011)

    Google Scholar 

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Acknowledgment

This work is financed by Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” financed by the North Portugal Regional Operational behaviour (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the FCT Fundaç\(\tilde{\text {a}}\)o para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects POCI-01-0145-FEDER-006961, POCI-01-0145-FEDER-007043 and UID/CEC/00319/2013.

Author information

Authors and Affiliations

  1. Federal University of Technology - Paraná, Toledo, Brazil

    Luis Piardi

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    Luis Piardi, José Lima & Ana I. Pereira

  3. Faculty of Engineering of University of Porto, Porto, Portugal

    Paulo Costa

  4. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima & Ana I. Pereira

  5. Centre for Robotics in Industry and Intelligent Systems, INESC-TEC, Porto, Portugal

    José Lima & Paulo Costa

  6. Algoritmi R&D Centre, University of Minho, Braga, Portugal

    Ana I. Pereira

Authors
  1. Luis Piardi
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  2. José Lima
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  3. Ana I. Pereira
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  4. Paulo Costa
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Corresponding author

Correspondence to Luis Piardi .

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Editors and Affiliations

  1. Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia

    Peter Korošec

  2. University of Lille, Lille, France

    Nouredine Melab

  3. University of Lille, Lille, France

    El-Ghazali Talbi

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Piardi, L., Lima, J., Pereira, A.I., Costa, P. (2018). Path Planning Optimization Method Based on Genetic Algorithm for Mapping Toxic Environment. In: Korošec, P., Melab, N., Talbi, EG. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2018. Lecture Notes in Computer Science(), vol 10835. Springer, Cham. https://doi.org/10.1007/978-3-319-91641-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-91641-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91640-8

  • Online ISBN: 978-3-319-91641-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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