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Acceleration of Path Planning Computation Based on Evolutionary Artificial Potential Field for Non-static Environments

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

Abstract

In this work, a mobile robot path-planning algorithm based on the evolutionary artificial potential field (EAPF) for non-static environments is presented. With the aim to accelerate the path planning computation, the EAPF algorithm is implemented employing novel parallel computing architectures. The EAPF algorithm is capable of deriving optimal potential field functions using evolutionary computation to generate accurate and efficient paths to drive a mobile robot from the start point to the goal point without colliding with obstacles in static and non-static environments. The algorithm allows parallel implementation to accelerate the computation to obtain better results in a reasonable runtime. Comparative performance analysis in terms of path length and computation time is provided. The experiments were specifically designed to show the effectiveness and the efficiency of the mobile robot path-planning algorithm based on the EAPF in a sequential implementation on CPU, a parallel implementation on CPU, and a parallel implementation on GPU.

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References

  1. Montiel, O., Sepúlveda, R., Orozco-Rosas, U.: Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. J. Intell. Rob. Syst. 79(2), 237–257 (2015)

    Article  Google Scholar 

  2. Devaurs, D., Siméon, T., Cortés, J.: Optimal path planning in complex cost spaces with sampling-based algorithms. IEEE Trans. Autom. Sci. Eng. 13(2), 415–424 (2016)

    Article  Google Scholar 

  3. Masehian, E., Amin-Naseri, M.R.: Sensor-based robot motion planning—a tabu search approach. IEEE Robot. Autom. Mag. 15(2), 48–57 (2008)

    Article  Google Scholar 

  4. LaValle, S.M.: Planning Algorithms. Cambridge University Press, New York (2006)

    Book  Google Scholar 

  5. Masehian, E., Sedighizadeh, D.: Classic and heuristic approaches in robot motion planning a chronological review. Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng. 1(5), 228–233 (2007)

    Google Scholar 

  6. Park, M.G., Lee, M.C.: A new technique to escape local minimum in artificial potential field based path planning. KSME Int. J. 17(12), 1876–1885 (2003)

    Article  Google Scholar 

  7. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (1985)

    Google Scholar 

  8. Orozco-Rosas, U., Montiel, O., Sepúlveda, R.: Parallel evolutionary artificial potential field for path planning—an implementation on GPU. In Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol. 601, pp. 319–332 (2015)

    Chapter  Google Scholar 

  9. Montiel, O., Sepúlveda, R., Castillo, O., Melin, P.: Ant colony test center for planning autonomous mobile robot navigation. Comput. Appl. Eng. Educ. 21(2), 214–229 (2013)

    Article  Google Scholar 

  10. Zhang, Q., Chen, D., Chen, T.: An obstacle avoidance method of soccer robot based on evolutionary artificial potential field. Energy Procedia 16-C, 1792–1798 (2012)

    Article  Google Scholar 

  11. Vadakkepat, P., Lee, T.H., Xin, L.: Application of evolutionary artificial potential field in robot soccer system. In: Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), Vancouver, BC, Canada (2001)

    Google Scholar 

  12. Vadakkepat, P., Kay, C.T., Wang, M.-L.: Evolutionary artificial potential fields and their application in real time robot path planning. In: Proceedings of the 2000 Congress on Evolutionary Computation (2000)

    Google Scholar 

  13. Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  14. Mitchell, M.: An Introduction to Genetic Algorithms, Cambridge, Massachusetts. Bradford, USA (2001)

    Google Scholar 

  15. Fogel, D.B.: An introduction to evolutionary computation. In: Evolutionary Computation: The Fossil Record, pp. 1–28. Wiley-IEEE Press (1998)

    Google Scholar 

  16. Orozco-Rosas, U., Picos, K., Montiel, O., Sepúlveda, R., Díaz-Ramírez, V.: Obstacle recognition for path planning in autonomus mobile robots. In: Optics and Photonics for Information Processing X (2016)

    Google Scholar 

  17. Tsai, C., Huang, H.C., Chan, C.K.: Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans. Industr. Electron. 58(10), 4813–4821 (2011)

    Article  Google Scholar 

  18. Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE Trans. Neural Netw. 5(1), 3–14 (1994)

    Article  Google Scholar 

  19. Holland, J.H.: Adaptation in natural and artificial systems, 2nd edn. MIT Press. Cambridge (1992). (First edition, Ann Arbor: University of Michigan Press, 1975)

    Google Scholar 

  20. Picos, K., Orozco-Rosas, U., Díaz-Ramírez, V.H., Montiel, O.: Pose estimation in noncontinuous video sequence using evolutionary correlation filtering. Math. Prob. Eng. 1–14 (2018)

    Article  Google Scholar 

  21. Orozco-Rosas, U., Montiel, O, Sepúlveda, R.: High-performance navigation system for mobile robots. In: High Performance Programmig for Soft Computing Application, pp 258–281. CRC Press (2014)

    Google Scholar 

  22. Cheng, J., Grossman, M., McKercher, T.: Professional CUDA C Programming. Wrox-Wiley, Indianapolis, Indiana (2014)

    Google Scholar 

  23. Shin, D.H., Singh, S.: Path Generation for Robot Vehicle Using Composite Clothoid Segments. The Robotic Institute. Carnegie-Mellon University, Pittsburgh, Pennsylvania (1990)

    Book  Google Scholar 

  24. Siegwart, R., Nourbakhsh, I.R., Scaramuzza, D.: Introduction to Autonomous Mobile Robots, 2nd edn. The MIT Press, Cambridge (2011)

    Google Scholar 

  25. Sariff, N., Buniyamin, N.: An overview of autonomous mobile robot path planning algorithms. In: 4th Student Conference on Research and Development SCOReD 2006 (2006)

    Google Scholar 

  26. Orozco-Rosas, U., Montiel, O., Sepúlveda, R.: Mobile robot path planning using membrane evolutionary artificial potential field. Appl. Soft Comput. 77, 236–251 (2019)

    Article  Google Scholar 

  27. Ge, S.S., Cui, Y.J.: New potential functions for mobile robot path planning. IEEE Trans. Robot. Autom. 16(5), 615–620 (2000)

    Article  Google Scholar 

  28. Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: IEEE International Conference on Robotics and Automation (1991)

    Google Scholar 

  29. Aghababa, M.P.: 3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles. Appl. Ocean Res. 38, 48–62 (2012)

    Article  Google Scholar 

  30. Morales, N., Toledo, J., Acosta, L.: Path planning using a multiclass support vector machine. Appl. Soft Comput. 43, 498–509 (2016)

    Article  Google Scholar 

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Acnowledgments

This research was supported in part by the Coordinación Institucional de Investigación of CETYS Universidad, and in part by the Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico).

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Authors and Affiliations

  1. CETYS Universidad, Centro de Innovación y Diseño (CEID), Av. CETYS Universidad No. 4, El Lago, 22210, Tijuana, Baja California, Mexico

    Ulises Orozco-Rosas & Kenia Picos

  2. Instituto Politécnico Nacional, CITEDI-IPN, Av. Instituto Politécnico Nacional No. 1310, Nueva Tijuana, 22435, Tijuana, Baja California, Mexico

    Oscar Montiel

Authors
  1. Ulises Orozco-Rosas
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  2. Kenia Picos
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  3. Oscar Montiel
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Corresponding author

Correspondence to Oscar Montiel .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Orozco-Rosas, U., Picos, K., Montiel, O. (2020). Acceleration of Path Planning Computation Based on Evolutionary Artificial Potential Field for Non-static Environments. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_22

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