Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm

Abstract

Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bézier curve-based model for path planning. The control points of the Bézier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bézier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.

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References

  1. 1.

    Elhoseny, M., Tharwat, A., Farouk, A., Hassanien, A.E.: K-coverage model based on genetic algorithm to extend wsn lifetime. IEEE sensors letters 1(4), 1–4 (2017)

    Article  Google Scholar 

  2. 2.

    Li, R., Wu, W., Qiao, H.: The compliance of robotic hands-from functionality to mechanism. Assem. Autom. 35(3), 281–286 (2015)

    Article  Google Scholar 

  3. 3.

    Robinson, D.C., Sanders, D.A., Mazharsolook, E.: Ambient intelligence for optimal manufacturing and energy efficiency. Assem. Autom. 35(3), 234–248 (2015)

    Article  Google Scholar 

  4. 4.

    Manikas, T.W., Ashenayi, K., Wainwright, R.L.: Genetic algorithms for autonomous robot navigation. IEEE Instrum. Meas. Mag. 10(6), 26–31 (2007)

    Article  Google Scholar 

  5. 5.

    Metawa, N., Hassan, M.K., Elhoseny, M.: Genetic algorithm based model for optimizing bank lending decisions. Expert Syst. Appl. 80, 75–82 (2017)

    Article  Google Scholar 

  6. 6.

    Elhoseny, M., Shehab, A., Yuan, X.: Optimizing robot path in dynamic environments using genetic algorithm and bezier curve. J. Intell. Fuzzy Syst. 33(4), 2305–2316 (2017)

    MATH  Article  Google Scholar 

  7. 7.

    Tharwat, A.: Linear vs. quadratic discriminant analysis classifier: a tutorial. Int. J. Appl. Pattern Recognit. 3(2), 145–180 (2016)

    Article  Google Scholar 

  8. 8.

    Elhoseny, M., Tharwat, A., Hassanien, A.E.: Bezier curve based path planning in a dynamic field using modified genetic algorithm. J. Comput. Sci. (2017). https://doi.org/10.1016/j.jocs.2017.08.004

  9. 9.

    Roberge, V., Tarbouchi, M., Labonté, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time uav path planning. IEEE Trans. Ind. Inform. 9(1), 132–141 (2013)

    Article  Google Scholar 

  10. 10.

    Contreras-Cruz, M.A., Ayala-Ramirez, V., Hernandez-Belmonte, U.H.: Mobile robot path planning using artificial bee colony and evolutionary programming. Appl. Soft Comput. 30, 319–328 (2015)

    Article  Google Scholar 

  11. 11.

    Das, P., Behera, H., Panigrahi, B.: A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol. Comput. 28, 14–28 (2016)

    Article  Google Scholar 

  12. 12.

    Gálvez, A., Iglesias, A., Cabellos, L.: Tabu search-based method for Bézier curve parameterization. Int. J. Softw. Eng. Appl. 7, 283–296 (2013)

    Google Scholar 

  13. 13.

    Li, B., Liu, L., Zhang, Q., Lv, D., Zhang, Y., Zhang, J., Shi, X.: Path planning based on firefly algorithm and Bezier curve. In: IEEE International Conference on Information and Automation (ICIA), IEEE, pp. 630–633 (2014)

  14. 14.

    Arana-Daniel, N., Gallegos, A.A., López-Franco, C., Alanis, A.Y.: Smooth global and local path planning for mobile robot using particle swarm optimization, radial basis functions, splines and Bezier curves. In: IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 175–182 (2014)

  15. 15.

    Ziolkowski, M., Gratkowski, S.: Genetic algorithm coupled with Bézier curves applied to the magnetic field on a solenoid axis synthesis. Arch. Electr. Eng. 65(2), 361–370 (2016)

    Article  Google Scholar 

  16. 16.

    Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning. Springer, New York, pp. 760–766 (2010)

  17. 17.

    Maitra, M., Chatterjee, A.: A hybrid cooperative-comprehensive learning based pso algorithm for image segmentation using multilevel thresholding. Expert Syst. Appl. 34(2), 1341–1350 (2008)

    Article  Google Scholar 

  18. 18.

    Ibrahim, A., Tharwat, A., Gaber, T., Hassanien, A.E.: Optimized superpixel and adaboost classifier for human thermal face recognition. Signal Image Video Process. (2017). https://doi.org/10.1007/s11760-017-1212-6

  19. 19.

    Tharwat, A., Hassanien, A.E., Elnaghi, B.E.: A ba-based algorithm for parameter optimization of support vector machine. Pattern Recogn. Lett. 93, 13–22 (2017)

    Article  Google Scholar 

  20. 20.

    Tharwat, A., Gaber, T., Ibrahim, A., Hassanien, A.E.: Linear discriminant analysis: a detailed tutorial. AI Commun. 30(2), 169–190 (2017)

    MathSciNet  Article  Google Scholar 

  21. 21.

    Subasi, A.: Classification of emg signals using pso optimized svm for diagnosis of neuromuscular disorders. Comput. Biol. Med. 43(5), 576–586 (2013)

    Article  Google Scholar 

  22. 22.

    Van der Merwe, D., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In: The 2003 Congress on Evolutionary Computation, CEC’03, vol. 1., IEEE, pp. 215–220 (2003)

  23. 23.

    Tharwat, A.: Principal component analysis-a tutorial. Int. J. Appl. Pattern Recogn. 3(3), 197–240 (2016)

    Article  Google Scholar 

  24. 24.

    Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation, CEC2004, vol. 2, IEEE, pp. 1980–1987 (2004)

  25. 25.

    Miyatake, M., Veerachary, M., Toriumi, F., Fujii, N., Ko, H.: Maximum power point tracking of multiple photovoltaic arrays: a pso approach. IEEE Trans. Aerosp. Electron. Syst. 47(1), 367–380 (2011)

    Article  Google Scholar 

  26. 26.

    Molazei, S., Ghazizadeh-Ahsaee, M.: Mopso algorithm for distributed generator allocation. In: Fourth International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), IEEE, pp. 1340–1345 (2013)

  27. 27.

    Gandomi, A.H., Yang, X.S.: Chaotic bat algorithm. J. Comput. Sci. 5(2), 224–232 (2014)

    MathSciNet  Article  Google Scholar 

  28. 28.

    Wang, G.G., Guo, L., Gandomi, A.H., Hao, G.S., Wang, H.: Chaotic krill herd algorithm. Inf. Sci. 274, 17–34 (2014)

    MathSciNet  Article  Google Scholar 

  29. 29.

    Gharooni-fard, G., Moein-darbari, F., Deldari, H., Morvaridi, A.: Scheduling of scientific workflows using a chaos-genetic algorithm. Proc. Comput. Sci. 1(1), 1445–1454 (2010)

    Article  Google Scholar 

  30. 30.

    Talatahari, S., Azar, B.F., Sheikholeslami, R., Gandomi, A.: Imperialist competitive algorithm combined with chaos for global optimization. Commun. Nonlinear Sci. Numer. Simul. 17(3), 1312–1319 (2012)

    MathSciNet  MATH  Article  Google Scholar 

  31. 31.

    Gandomi, A., Yang, X.S., Talatahari, S., Alavi, A.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)

    MathSciNet  MATH  Article  Google Scholar 

  32. 32.

    Ma, Y., Zamirian, M., Yang, Y., Xu, Y., Zhang, J.: Path planning for mobile objects in four-dimension based on particle swarm optimization method with penalty function. In: Mathematical Problems in Engineering (2013)

  33. 33.

    Liang, J., Song, H., Qu, B., Liu, Z.: Comparison of three different curves used in path planning problems based on particle swarm optimizer. In: Mathematical Problems in Engineering (2014)

  34. 34.

    Sahingoz, O.K.: Generation of Bezier curve-based flyable trajectories for multi-uav systems with parallel genetic algorithm. J. Intell. Robotic Syst. 74(1–2), 499–511 (2014)

    Article  Google Scholar 

  35. 35.

    Gardner, B., Selig, M.: Airfoil design using a genetic algorithm and an inverse method. In: 41st Aerospace Sciences Meeting and Exhibit, pp. 1–12 (2003)

  36. 36.

    Jolly, K., Kumar, R.S., Vijayakumar, R.: A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. Robot. Auton. Syst. 57(1), 23–33 (2009)

    Article  Google Scholar 

  37. 37.

    Giannakoglou, K.: A design method for turbine-blades using genetic algorithms on parallel computers. Comput. Fluid Dyn. 98(1), 1–2 (1998)

    Google Scholar 

  38. 38.

    Chen, L., Wang, S., Hu, H., McDonald-Maier, K.: Bézier curve based trajectory planning for an intelligent wheelchair to pass a doorway. In: International Conference on Control (CONTROL), IEEE, pp. 339–344 (2012)

  39. 39.

    Choi, J.w., Curry, R., Elkaim, G.: Path planning based on Bézier curve for autonomous ground vehicles. In: Advances in Electrical and Electronics Engineering-IAENG Special Edition of the World Congress on Engineering and Computer Science, (WCECS’08), IEEE, pp. 158–166 (2008)

  40. 40.

    Wagner, R., Birbach, O., Frese, U.: Rapid development of manifold-based graph optimization systems for multi-sensor calibration and slam. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 3305–3312 (2011)

  41. 41.

    Heppner, F., Grenander, U.: A stochastic nonlinear model for coordinated bird flocks. Ubiquity Chaos 99, 233–238 (1990)

    Google Scholar 

  42. 42.

    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. ACM Siggraph Comput. Graph. 21(4), 25–34 (1987)

    Article  Google Scholar 

  43. 43.

    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, vol. 1., New York, pp. 39–43 (1995)

  44. 44.

    Yang, X.S.: Nature-Inspired Optimization Algorithms, 1st edn. Elsevier, Amsterdam (2014)

    Google Scholar 

  45. 45.

    Ren, B., Zhong, W.: Multi-objective optimization using chaos based pso. Inf. Technol. J. 10(10), 1908–1916 (2011)

    Article  Google Scholar 

  46. 46.

    Vohra, R., Patel, B.: An efficient chaos-based optimization algorithm approach for cryptography. Commun. Netw. Secur. 1(4), 75–79 (2012)

    Google Scholar 

  47. 47.

    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  48. 48.

    Masehian, E., Sedighizadeh, D.: A multi-objective pso-based algorithm for robot path planning. In: Proceedings of IEEE International Conference on Industrial Technology (ICIT), IEEE, pp. 465–470 (2010)

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Correspondence to Mohamed Elhoseny.

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Tharwat, A., Elhoseny, M., Hassanien, A.E. et al. Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm. Cluster Comput 22, 4745–4766 (2019). https://doi.org/10.1007/s10586-018-2360-3

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Keywords

  • Particle Swarm Optimization (PSO)
  • Bézier curve
  • Path planning
  • Chaos