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.
Similar content being viewed by others
References
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)
Li, R., Wu, W., Qiao, H.: The compliance of robotic hands-from functionality to mechanism. Assem. Autom. 35(3), 281–286 (2015)
Robinson, D.C., Sanders, D.A., Mazharsolook, E.: Ambient intelligence for optimal manufacturing and energy efficiency. Assem. Autom. 35(3), 234–248 (2015)
Manikas, T.W., Ashenayi, K., Wainwright, R.L.: Genetic algorithms for autonomous robot navigation. IEEE Instrum. Meas. Mag. 10(6), 26–31 (2007)
Metawa, N., Hassan, M.K., Elhoseny, M.: Genetic algorithm based model for optimizing bank lending decisions. Expert Syst. Appl. 80, 75–82 (2017)
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)
Tharwat, A.: Linear vs. quadratic discriminant analysis classifier: a tutorial. Int. J. Appl. Pattern Recognit. 3(2), 145–180 (2016)
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
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)
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)
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)
Gálvez, A., Iglesias, A., Cabellos, L.: Tabu search-based method for Bézier curve parameterization. Int. J. Softw. Eng. Appl. 7, 283–296 (2013)
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)
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)
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)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning. Springer, New York, pp. 760–766 (2010)
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)
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
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)
Tharwat, A., Gaber, T., Ibrahim, A., Hassanien, A.E.: Linear discriminant analysis: a detailed tutorial. AI Commun. 30(2), 169–190 (2017)
Subasi, A.: Classification of emg signals using pso optimized svm for diagnosis of neuromuscular disorders. Comput. Biol. Med. 43(5), 576–586 (2013)
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)
Tharwat, A.: Principal component analysis-a tutorial. Int. J. Appl. Pattern Recogn. 3(3), 197–240 (2016)
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)
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)
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)
Gandomi, A.H., Yang, X.S.: Chaotic bat algorithm. J. Comput. Sci. 5(2), 224–232 (2014)
Wang, G.G., Guo, L., Gandomi, A.H., Hao, G.S., Wang, H.: Chaotic krill herd algorithm. Inf. Sci. 274, 17–34 (2014)
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)
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)
Gandomi, A., Yang, X.S., Talatahari, S., Alavi, A.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)
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)
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)
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)
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)
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)
Giannakoglou, K.: A design method for turbine-blades using genetic algorithms on parallel computers. Comput. Fluid Dyn. 98(1), 1–2 (1998)
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)
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)
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)
Heppner, F., Grenander, U.: A stochastic nonlinear model for coordinated bird flocks. Ubiquity Chaos 99, 233–238 (1990)
Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. ACM Siggraph Comput. Graph. 21(4), 25–34 (1987)
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)
Yang, X.S.: Nature-Inspired Optimization Algorithms, 1st edn. Elsevier, Amsterdam (2014)
Ren, B., Zhong, W.: Multi-objective optimization using chaos based pso. Inf. Technol. J. 10(10), 1908–1916 (2011)
Vohra, R., Patel, B.: An efficient chaos-based optimization algorithm approach for cryptography. Commun. Netw. Secur. 1(4), 75–79 (2012)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
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)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
Rights and permissions
About this article
Cite this article
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 (Suppl 2), 4745–4766 (2019). https://doi.org/10.1007/s10586-018-2360-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-2360-3