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Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field

Abstract

In this paper, we introduce the concept of Parallel Evolutionary Artificial Potential Field (PEAPF) as a new method for path planning in mobile robot navigation. The main contribution of this proposal is that it makes possible controllability in complex real-world sceneries with dynamic obstacles if a reachable configuration set exists. The PEAPF outperforms the Evolutionary Artificial Potential Field (EAPF) proposal, which can also obtain optimal solutions but its processing times might be prohibitive in complex real-world situations. Contrary to the original Artificial Potential Field (APF) method, which cannot guarantee controllability in dynamic environments, this innovative proposal integrates the original APF, evolutionary computation and parallel computation for taking advantages of novel processors architectures, to obtain a flexible path planning navigation method that takes all the advantages of using the APF and the EAPF, strongly reducing their disadvantages. We show comparative experiments of the PEAPF against the APF and the EAPF original methods. The results demonstrate that this proposal overcomes both methods of implementation; making the PEAPF suitable to be used in real-time applications.

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References

  1. Aghababa, M.P.: 3d path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles. Elsevier - Appl Ocean Res. 38(0), 48–62 (2012)

    Article  Google Scholar 

  2. Berger, J., Jabeur, K., Boukhtouta, A., Guitouni, A., Ghanmi, A.: A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment. IEEE - Evolutionary Computation, pp. 1–8 (2010)

  3. Bhattacharyya, A., Singla, E., Dasgputa, B.: Robot path planning using silhouette method. In: 13th National Conference on Mechanisms and Machines. (NaCoMM07), Bangalore, India (2007)

    Google Scholar 

  4. Botzheim, J., Toda, Y., Kubota, N.: Path planning for mobile robots by bacterial memetic algorithm. In: IEEE - Robotic Intelligence in Informationally Structured Space, pp. 107–112 (2011)

  5. Chen, F., Di, P., Huang, J., Sasaki, H., Fukuda, T.: Evolutionary artificial potential field method based manipulator path planning for safe robotic assembly. In: IEEE, pp. 92–97 (2009)

  6. Dozier, G., Homaifar, A., Bryson, S., Moore, L.: Artificial potential field based robot navigation, dynamic constrained optimization, and simple genetic hill-climbing. In: IEEE, pp. 189–194 (1998)

  7. Durr, P., Mattiussi, C., Soltoggio, A., Floreano, D.: Evolvability of neuromodulated learning for robots. In: ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS ’08, pp. 41–46 (2008)

  8. Eppstein, D.: Geometry in action: Voronoi diagrams. http://www.ics.uci.edu/%7Eeppstein/gina/voronoi.html

  9. Garcia, E., Jimenez, M.A., Gonzalez, P., Armada, M.: The evolution of robotic research. In: IEEE - Robotics and Automation Magazine, pp. 90–103 (2007)

  10. Ge, S.S., Cui, Y.J.: New potential functions for mobile robot path planning. In: IEEE - Transactions on Robotics and Automation, pp. 615–620 (2000)

  11. Ge, S.S., Cui, Y.J.: Dynamic motion planning for mobile robots using potential field method. Auton. Robot. 13, 207–222 (2002)

    MATH  Article  Google Scholar 

  12. Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous uav guidance. Springer - Journal of Intelligent & Robotic Systems, pp. 65 – 100 (2010)

  13. He, L.G., Gao, W.H., Nan, L.Y.: A route planning method based on improved artificial potential field algorithm. In: IEEE, pp. 550–554 (2011)

  14. Hermes, H.: Large and small time local controllability. In: Proceedings of the 33rd IEEE Conference on Decision and Control, vol. 2, pp. 1280–1281. doi:10.1109/CDC.1994.411147 (1994)

  15. Hocaoglu, C., Sanderson, A.: Planning multiple paths with evolutionary speciation. In: IEEE - Evolutionary Computation, pp. 169–191 (2001)

  16. Jin, Y., Meng, Y.: Special issue on evolutionary and development robotics. In: IEEE - Computational Intelligence Magazine (2010)

  17. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: The International Journal of Robotic Research, pp. 90–98 (1986)

  18. Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: IEEE - Conference on Robotics and Automation, pp. 1398–1404 (1991)

  19. Krogh, B.: A generalized potential field approach to obstacle avoidance control. In: ASME Conference of Robotic Research: The Next Five Years and Beyond (1984)

  20. LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)

    MATH  Book  Google Scholar 

  21. Lee, L.F.: Decentralized motion planning within an artificial potential framework (apf) for cooperative payload transport by multi-robot collectives. Master’s thesis, USA. State University of New York, New York (2004)

    Google Scholar 

  22. Li, S., Ding, M., Cai, C., Jiang, L.: Efficient path planning method based on genetic algorithm combining path network. In: IEEE - Genetic and Evolutionary Computing, pp. 194–197 (2010)

  23. Li, W., Huang, Y.: A distributed parallel genetic algorithm oriented adaptive migration strategy. In: Natural Computation ICNC, Eighth International Conference, pp. 592–595 (2012)

  24. Masehian, E., Sedighizaddeh, D.: Classic and heuristic approaches in robot motion planning - a chronological review. In: World Academy of Science, Engineering and Technology, Germany, vol. 23, pp. 101–106 (2007)

  25. Masoud, A.A.: Solving the narrow corridor problem in potential field-guided autonomous robots. In: IEEE - International Conference on Robotics and Automation, pp. 2920–2925 (2005)

  26. Mekki, H., Chtourou, M.: Variable structure neural networks for online identification of continuous-time dynamical systems using evolutionary artificial potential fields. In: IEEE - International Multi-Conference on Systems, Signals and Devices, pp. 1–6 (2012)

  27. Meuth, R., Saad, E., Wunsch, D., Vian, J.: Memetic mission management. In: IEEE - Computational Intelligence, pp. 32–40 (2010)

  28. Montiel, O., Sepulveda, R., Castillo, O., Melin, P.: Ant colony test center for planning autonomous mobile robot navigation. Comput. Appl. Eng. Educ. 21(2), 214–229 (2013). doi:10.1002/cae.20463

    Article  Google Scholar 

  29. Ozalp, N., Sahingoz, O.: Optimal uav path planning in a 3d threat environment by using parallel evolutionary algorithms. In: 2012 ICUAS International Conference on IEEE - Unmanned Aircraft Systems, pp. 308–317 (2013)

  30. Pacheco, P.: An Introduction to Parallel Programming. Elsevier, Morgan (2013)

    Google Scholar 

  31. Paterega, I.: Artificial evolution mechanisms in robot navigation. In: IEEE - International Conference The Experience of Designing and Application of CAD Systems in Microelectronics, pp. 281–286 (2011)

  32. Qixing, C., Yanwen, H., Jingliang, Z.: An evolutionary artificial potential field algorithm for dynamic path planning of mobile robot. In: IEEE/RSJ - International Conference on Intelligent Robots and Systems, pp. 3331–3336 (2006)

  33. Rakshit, P., Konar, A., Bhowmik, P., Goswami, I., Das, S., Jain, L., Nagar, A.: Realization of an adaptive memetic algorithm using differential evolution and q-learning: A case study in multirobot path planning Systems. In: IEEE -Man, and Cybernetics: Systems, pp. 1–18 (2012)

  34. Siegwart, R., Nourbakhsh, I.R., Scaramuzza, D.: Introduction to Autonomous Mobile Robot. Second Edition. The MIT Press, England (2011)

    Google Scholar 

  35. Vadakkepat, P., Lee, T.H., Xin, L.: Application of evolutionary artificialc potential field in robot soccer system. In: IEEE, pp. 2781–2785 (2001)

  36. Vadakkepat, P., Tan, K.C., Wang, M.L.: Evolutionary artificial potential fields and their application in real time robot path planning. In: IEEE - Congress on Evolutionary Computation, pp. 256–263 (2000)

  37. Volpe, R., Khosla, P.: Manipulator control with superquadric artificial potential functions: Theory and experiments. IEEE Trans. Syst. Man Cybern. 20(6) (1990)

  38. Wang, Y., Mulvaney, D., Sillitoe, I., Swere, E.: Robot navigation by waypoints. Springer - Journal of Intelligent & Robotic Systems, pp. 175–207 (2008)

  39. Weijun, S., Rui, M., Chongchong, Y.: A study on soccer robot path planning with fuzzy artificial potential field. In: IEEE - International Conference on Computing, Control and Industrial Engineering, pp. 386–390 (2010)

  40. Zhang, B., Chen, W., Fei, M.: An optimized method for path planning based on artificial potential field. In: IEEE - Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (2006)

  41. Zhang, K., Collins, E.G., Barbu, A.: An efficient stochastic clustering auction for heterogeneous robotic collaborative teams. J. Intell. Amp. Robot. Syst. 72(3-4), 541–558 (2013). doi:10.1007/s10846-012-9800-8

    Article  Google Scholar 

  42. Zhang, K., Collins, E.G., Shi, D.: Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction. ACM Trans. Auton. Adapt. Syst. 7(2), 21:1–21:22 (2012). doi:10.1145/2240166.2240171

    Article  Google Scholar 

  43. Zhang, L.F., Zhou, C.X.: Self organized parallel genetic algorithm to automatically realize diversified convergence. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–9 (2012)

  44. Zhang, Q., Chen, D., Chen, T.: An obstacle avoidance method of soccer robot based on evolutionary artificial potential field. Elsevier - International Conference on Future Energy, Environment and Materials, pp. 1792–1798 (2012)

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Montiel, O., Sepúlveda, R. & Orozco-Rosas, U. Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field. J Intell Robot Syst 79, 237–257 (2015). https://doi.org/10.1007/s10846-014-0124-8

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  • DOI: https://doi.org/10.1007/s10846-014-0124-8

Keywords

  • Mobile robot navigation
  • Path planning
  • Artificial potential field
  • APF
  • PEAPF
  • Parallel evolutionary computation