An effective global path planning algorithm with teaching-learning-based optimization

Hazrati Nejad, Emad ; Yigit-Sert, Sevgi ; Emrah Amrahov, Şahin

Kybernetika, Tome 60 (2024) no. 3, pp. 293-316

Abstract (VO)

Due to the widespread use of mobile robots in various applications, the path planning problem has emerged as one of the important research topics. Path planning is defined as finding the shortest path starting from the initial point to the destination in such a way as to get rid of the obstacles it encounters. In this study, we propose a path planning algorithm based on a teaching-learning-based optimization (TLBO) algorithm with Bezier curves in a static environment with obstacles. The proposed algorithm changes the initially randomly selected control points step by step to obtain shorter Bezier curves that do not hit obstacles. We also improve the genetic algorithm-based path planning algorithm. Experimental results show that they provide better paths than other existing algorithms.

Détail

DOI : 10.14736/kyb-2024-3-0293

Classification : 68T40, 68W25, 78M50
Keywords: path planning; mobile robot; teaching-learning based optimization; Bezier curve

Bibliographie

[1] Alguliyev, R. M., Aliguliyev, R. M., Alakbarov, R. G.: Constrained K-means algorithm for resource allocation in mobile cloudlets. Kybernetika 59 (2023), 1, 88-109. | DOI

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

[3] Ang, K. M., El-kenawy, E. S. M., Abdelhamid, A. A., Ibrahim, A., Alharbi, A. H., Khafaga, D. S., Tiang, S. S., Lim, W. H.: Optimal design of convolutional neural network architectures using teaching-learning-based optimization for image classification. Symmetry 14 (2022), 2323. | DOI

[4] Ansari, A. Q., Katiyar, I.: Comparison and analysis of obstacle avoiding path planning of mobile robot by using ant colony optimization and teaching learning based optimization techniques. In: Proc. First International Conference on Information and Communication Technology for Intelligent Systems, Volume 2. Smart Innovation, Systems and Technologies, 2016, pp. 563-574.

[5] Aouf, A., Boussaid, L., Sakly, A.: TLBO-based adaptive neurofuzzy controller for mobile robot navigation in a strange environment. Comput. Intell. Neurosci. 4 (2018). | DOI

[6] Ar, Y.: An initialization method for the latent vectors in probabilistic matrix factorization for sparse datasets. Evolution. Intell. 13 (2020), 2, 269-281. | DOI

[7] Ar, Y., Amrahov, S. Emrah, Gasilov, N., Yigit-Sert, S.: A new curve fitting based rating prediction algorithm for recommender systems. Kybernetika 58 (2022), 3, 440-455. | DOI

[8] Bezier, P.: Style, mathematics and NC. Computer-aided Design 22 (1990), 9, 524-526. | DOI | MR

[9] Bodhale, D., Afzulpurkar, N., Thanh, N. T.: Path planning for a mobile robot in a dynamic environment. In: IEEE International Conference on Robotics and Biomimetics, Thailand 2009, pp. 2115-2120.

[10] Bouchekara, H., Abido, M., Boucherma, M.: Optimal power flow using teaching-learning-based optimization technique. Electric Power Systems Research 114 (2014), 49-59. | DOI

[11] Chaari, I., Koubaa, A., Bennaceur, H., Trigui, S., Al-Shalfan, K.: A hybrid ACO-GA algorithm for robot path planning. In: IEEE Congress on Evolutionary Computation, Brisbane 2012, pp. 1-8.

[12] Chia, S. H., Su, K. L., Guo, J. H., Chung, C. Y.: Ant colony system based mobile robot path planning. In: IEEE International Conference on Genetic and Evolutionary Computing, China 2010, pp. 210-213.

[13] Dai, Y., Yu, J., Zhang, C., Zhan, B., Zheng, X.: A novel whale optimization algorithm of path planning strategy for mobile robots. Appl. Intell. 53 (2023), 10843-10857. | DOI

[14] Duraklı, Z., Nabiyev, V.: A new approach based on bezier curves to solve path planning problems for mobile robots. J. Comput. Sci. 58 (2022), 101542. | DOI

[15] Elhoseny, M., Tharwat, A., Hassanien, A. E.: Bezier curve based path planning in a dynamic field using modified genetic algorithm. J. Comput. Sci. 25 (2018), 339-358. | DOI

[16] Feng, S., Zhang, S., Xu, M., Deng, G.: Parallel navigation for 3-D autonomous vehicles. Kybernetika 59 (2023), 4, 592-611. | DOI | MR

[17] Gasilov, N., Dogan, M., Arici, V.: Two-stage shortest path algorithm for solving optimal obstacle avoidance problem. IETE J. Res. 57 (2011), 3, 278-285. | DOI

[18] Guevara, B. C.: An Overview of the Class of Rapidly-Exploring Random Trees. M.Sc. Thesis, Utrecht University 2018.

[19] Güzel, M. S., Kara, M., Beyazkilic, M. S.: An adaptive framework for mobile robot navigation. Adaptive Behavior 25 (2017), 1, 30-39. | DOI

[20] Hossain, M. A., Ferdous, I.: Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique. In: International Conference on Electrical Information and Communication Technology (EICT), Bangladesh 2014, pp. 1-6. | DOI

[21] Holland, J. H.: Adaptation in natural and artificial systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, 1992. | MR

[22] Ismail, A. T., Sheta, A., Al-Weshah, A.: A mobile robot path planning using genetic algorithm in static environment. J. Computer Sci. 4 (2008), 4, 341-344. | DOI

[23] Kroll, A., Soldan, S.: Survey results on status, needs and perspectives for using mobile service robots in industrial applications. In: 11th International Conference on Control Automation Robotics and Vision, Singapore 2010, pp. 621-626. | DOI

[24] Kumar, A., Ahmad, G., Shahid, M.: Portfolio selection strategy: A teaching-learning-based optimization (TLBO) approach. In: Proc. International Joint Conference on Advances in Computational Intelligence, Singapore 2023, pp. 553-564. | DOI

[25] Li, C., Huang, X., Ding, J., Song, K., Lu, S.: Global path planning based on a bidirectional alternating search A* algorithm for mobile robots. Comput. Industr. Engrg. 168 (2022), 108123. | DOI

[26] Li, J., Chen, Y., Zhao, X., Huang, J.: An improved DQN path planning algorithm. J. Supercomput. 78 (2022) 616-639. | DOI

[27] Li, X., Zhao, G., Li, B.: Generating optimal path by level set approach for a mobile robot moving in static/dynamic environments. Appl. Math. Modell. 85 (2020), 210-230. | DOI | MR

[28] Li, Y., Huang, Z., Xie, Y.: Path planning of mobile robot based on improved genetic algorithm. In: 3rd International Conference on Electron Device and Mechanical Engineering (ICEDME), China 2020, pp. 691-695. | MR

[29] Li, Y., Wei, W., Gao, Y., Wang, D., Fan, Z.: PQ-RRT*: An improved path planning algorithm for mobile robots. Expert Systems Appl.152 (2020), 113425. | DOI

[30] Liu, J., Yang, J., Liu, H., Tian, X., Gao, M.: An improved ant colony algorithm for robot path planning. Soft Comput. 21 (2017), 5829-5839. | DOI

[31] Liu, J., Wei, X., Huang, H.: An improved grey wolf optimization algorithm and its application in path planning. IEEE Access 9 (2021), 121944-121956. | DOI

[32] Low, E. S., Ong, P., Low, C. Y., Omar, R.: Modified q-learning with distance metric and virtual target on path planning of mobile robot. Expert Systems Appl. 199 (2022), 117191. | DOI

[33] Luan, P. G., Thinh, N. T.: Hybrid genetic algorithm based smooth global-path planning for a mobile robot. Mechanics Based Design Structures Machines 51 (2023), 1758-1774. | DOI

[34] Luo, S., Zhang, M., Zhuang, Y., Ma, C., Li, Q.: A survey of path planning of industrial robots based on rapidly exploring random trees. Frontiers Neurorobotics 17 (2023). | DOI

[35] Lyu, D., Chen, Z., Cai, Z., Piao, S.: Robot path planning by leveraging the graph-encoded floyd algorithm. Future Generation Computer Systems 122 (2021), 204-208. | DOI

[36] Ma, J., Liu, Y., Zang, S., Wang, L.: Robot path planning based on genetic algorithm fused with continuous Bezier optimization. Comput. Intell. Neurosci. (2020). | DOI

[37] Maoudj, A., Hentout, A.: Optimal path planning approach based on q-learning algorithm for mobile robots. Appl. Soft Comput. 97 (2020), 106796. | DOI

[38] Miao, C., Chen, G., Yan, C., Wu, Y.: Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Comput. Industr. Engrg. 156 (2021), 107230. | DOI

[39] Mirjalili, S.: Genetic Algorithm, Evolutionary Algorithms and Neural Networks. Springer Cham 2019, 43-55.

[40] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., al., et: Human-level control through deep reinforcement learning. Nature 518 (2015), 529-533. | DOI

[41] Naji, H. F., Kullu, P., Amrahov, S. Emrah: An augmented reality-based system with sound effects for teaching english in primary school. Educat. Inform. Technolog. (2023), 1-13. | DOI

[42] Kartli, N., Bostanci, E., Guzel, M. S.: A new algorithm for the initial feasible solutions of fixed charge transportation problem. In: 7th International Conference on Computer Science and Engineering (UBMK), IEEE, 2022, pp. 82-85. | DOI | MR

[43] Kartli, N., Bostanci, E., Guzel, M. S.: A new algorithm for optimal solution of fixed charge transportation problem. Kybernetika 59 (2023), 1, 45-63. | DOI | MR

[44] Rajinikanth, V., Satapathy, S. C., Fernandes, S. L., Nachiappan, S.: Entropy based segmentation of tumor from brain mr images - a study with teaching learning based optimization. Pattern Recognit. Lett. 94 (2017), 87-95. | DOI

[45] Rao, R. V., Savsani, V. J., Vakharia, D.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided Design 43 (2011), 303-315. | DOI | MR

[46] Sabiha, A. D., Kamel, M. A., Said, E., Hussein, W. H.: Path planning algorithm based on teaching-learning-based-optimization for an autonomous vehicle. Communications 24 (2022), C33-C42. | DOI

[47] Sert, S. Y., Ar, Y., Bostanci, G. E.: Evolutionary approaches for weight optimization in collaborative filtering-based recommender systems. Turkish J. Electr. Engrg. Computer Sci. 27 (2019), 3, 2121-2136. | DOI

[48] Shin, D. H., Ollero, A.: Mobile robot path planning for fine-grained and smooth path spcification. J. Robotic Syst. 12 (1995), 7, 491-503. | DOI

[49] Tang, H., Fang, B., Liu, R., Li, Y., Guo, S.: A hybrid teaching and learning-based optimization algorithm for distributed sand casting job-shop scheduling problem. Appl. Soft Comput. 120 (2022), 108694. | DOI

[50] Tu, H., Deng, Y., Li, Q., Song, M., Zheng, X.: Improved RRT global path planning algorithm based on bridge test. Robotics Autonomous Systems 171 (2024), 104570. | DOI

[51] Wang, J., Chi, W., Li, C., Wang, C., Meng, M. Q. H.: Neural RRT*: Learning-based optimal path planning. IEEE Trans. Automat. Sci. Engrg. 17 (2020), 1748-1758. | DOI

[52] Wang, W., Li, J., Bai, Z., Wei, Z., Peng, J.: Towards optimization of path planning: An RRT*-ACO algorithm. IEEE Access (2024). | DOI

[53] Wu, Z., Fu, W., Xue, R., Wang, W.: A novel global path planning method for mobile robots based on teaching-learning-based optimization. Information 7 (2016), 39. | DOI

[54] Xu, L., Cao, M., Song, B.: A new approach to smooth path planning of mobile robot based on quartic bezier transition curve and improved pso algorithm. Neurocomputing 473 (2022), 98-106. | DOI

[55] Yildirim, H. B., Kullu, K., Amrahov, S. Emrah: A graph model and a three-stage algorithm to aid the physically disabled with navigation. Universal Access Inform. Soc. (2023), 1-11. | DOI

[56] Yuan, X., Yuan, X., Wang, X.: Path planning for mobile robot based on improved bat algorithm. Sensors 21 (2021), 4389. | DOI

[57] Zhang, L., Min, H., Wei, H., Huang, H.: Global path planning for mobile robot based on A* algorithm and genetic algorithm. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), China 2012, pp. 1795-1799.

[58] Zhang, T. W., Xu, G. H., Zhan, X. S., Han, T.: A new hybrid algorithm for path planning of mobile robot. J. Supercomput. 78 (2022), 4158-4181. | DOI

[59] Zhang, Y., Jin, Z., Chen, Y.: Hybrid teaching-learning-based optimization and neural network algorithm for engineering design optimization problems. Knowledge-Based Systems 187 (2020), 104836. | DOI

[60] Zhang, Z., He, R., Yang, K.: A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm. Adv. Manufactur. 10 (2022), 114-130. | DOI