آمار

تعداد نشریات13
تعداد شماره‌ها238
تعداد مقالات2,418
تعداد مشاهده مقاله3,962,363
تعداد دریافت فایل اصل مقاله2,874,084
Mahdipour, B., Zahiri, S. H., Behravan, I.. (1403). An Intelligent Two and Three Dimensional Path Planning, Based on a Metaheuristic Method. فناوری آموزش, 13(1), 93-116. doi: 10.22061/jecei.2024.10941.751
B. Mahdipour; S. H. Zahiri; I. Behravan. "An Intelligent Two and Three Dimensional Path Planning, Based on a Metaheuristic Method". فناوری آموزش, 13, 1, 1403, 93-116. doi: 10.22061/jecei.2024.10941.751
Mahdipour, B., Zahiri, S. H., Behravan, I.. (1403). 'An Intelligent Two and Three Dimensional Path Planning, Based on a Metaheuristic Method', فناوری آموزش, 13(1), pp. 93-116. doi: 10.22061/jecei.2024.10941.751
Mahdipour, B., Zahiri, S. H., Behravan, I.. An Intelligent Two and Three Dimensional Path Planning, Based on a Metaheuristic Method. فناوری آموزش, 1403; 13(1): 93-116. doi: 10.22061/jecei.2024.10941.751

An Intelligent Two and Three Dimensional Path Planning, Based on a Metaheuristic Method

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 8، دوره 13، شماره 1، فروردین 2025، صفحه 93-116    اصل مقاله (1.59 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2024.10941.751
نویسندگان
B. Mahdipour1؛ S. H. Zahiri* 2؛ I. Behravan3
1Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
2Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
3Department of Electrical Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 05 خرداد 1403،  تاریخ بازنگری: 06 شهریور 1403،  تاریخ پذیرش: 07 شهریور 1403 
چکیده
Background and Objectives: Path planning is one of the most important topics related to the navigation of all kinds of moving vehicles such as airplanes, surface and subsurface vessels, cars, etc. Undoubtedly, in the process of making these tools more intelligent, detecting and crossing obstacles without encountering them by taking the shortest path is one of the most important goals of researchers. Significant success in this field can lead to significant progress in the use of these tools in a variety of applications such as industrial, military, transportation, commercial, etc. In this paper, a metaheuristic-based approach with the introduction of new fitness functions is presented for the problem of path planning for various types of surface and subsurface moving vehicles.
Methods: The proposed approach for path planning in this research is based on the metaheuristic methods, which makes use of a novel fitness function. Particle Swarm Optimization (PSO) is the metaheuristic method leveraged in this research but other types of metaheuristic methods can also be used in the proposed architecture for path planning.
Results: The efficiency of the proposed method, is tested on two synthetic environments for finding the best path between the predefined origin and destination for both surface and subsurface unmanned intelligent vessels. In both cases, the proposed method was able to find the best path or the closest answer to it.
Conclusion: In this paper, an efficient method for the path planning problem is presented. The proposed method is designed using Particle Swarm Optimization (PSO). In the proposed method, several effective fitness function have been defined so that the best path or one of the closest answers can be obtained by utilized metaheuristic algorithm. The results of implementing the proposed method on real and simulated geographic data show its good performance. Also, the obtained quantitative results (time elapsed, success rate, path cost, standard deviation) have been compared with other similar methods. In all of these measurements, the proposed algorithm outperforms other methods or is comparable to them.
کلیدواژه‌ها
Particle swarm optimization (PSO)؛ Path planning؛ Autonomous underwater vehicle (AUV)؛ Unmanned surface vehicle (USV)
مراجع
[1] e. Alfaro-Cid, E. W. McGookin, D. J. Murray-Smith, "Genetic algorithm optimisation of a ship navigation system," Acta Polytech., 41(4-5): 13-18, 2001.

[2] V. Kanakakis, N. Tsourveloudis, "Evolutionary path planning and navigation of autonomous underwater vehicles," in Proc. 2007 Mediterranean Conference on Control & Automation: 1-6, 2007.  

[3] K. Hao, J. Zhao, Z. Li, Y. Liu, "Dynamic path planning of underwater AUV based on an adaptive genetic algorithm," Ocean Eng., 263: 11242, 2022.

[4] M. Y. Ju, S. E. Wang, J. H. Guo, "Path planning using a hybrid evolutionary algorithm based on tree structure encoding," Sci. World J., 2014: 1-8, 2014.

[5] X. Li, S. Yu, "Three-dimensional path planning for AUVs in ocean currents environment based on an improved compression factor particle swarm optimization algorithm," Ocean Eng., 280: 114610, 2023.

[6] L.Yang, J. Qi, D. Song, J. Xiao, J. Han, Y. Xia, "survey of robot 3d path planning algorithms," J. Control Sci. Eng., 2016(7426913): 1-22, 2016.

[7] S. Singhal, S. Tanwar, Aishwarya, A. Sinha, "Ant colony optimization based routing for underwater sensor network," in Proc. 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART): 33-38, 2020.

[8] M. P. Vicmudo, E. P. Dadios, R. R. P. Vicerra, "Path planning of underwater swarm robots using genetic algorithm," 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014.

[9] H. Tang, Y. Yin, H. Shen, "A model for vessel trajectory prediction based on long short-term memory neural network," J. Mar. Eng. Technol., 21(3): 136-145, 2022.

[10] S. Singhal, S. Tanwar, Aishwarya, A. Sinha, "Ant colony optimization based routing for underwater sensor network," in Proc. 9th International Conference System Modeling and Advancement in Research Trends (SMART), 2020.

[11] H. Qin, T. Meng, Y. Cao, "Fuzzy strategy grey wolf optimizer for complex multimodal optimization problems," Sensors, 22(17): 6420, 2022

[12] M. Dinc, C. Hajiyev, "Integration of navigation systems for autonomous underwater vehicles," J. Mar. Eng. Technol., 14(1): 32-43, 2015.

[13] M. Panda, B. Das, B. Subudhi, B. Bhusan Pati, "A comprehensive review of path planning algorithms for autonomous underwater vehicles," Int. J. Autom. Comput., 17(3): 321-352, 2020.

[14] Y. Wang, "Review on greedy algorithm," Theor. Nat. Sci., 14(1): 233-239, 2023.

[15] J. C. Kinsey, R. M. Eustice, L. L. Whitcomb, "A survey of underwater vehicle navigation: Recent advances and new challenges," in Proc. IFAC Conference of Manoeuvring and Control of Marine Craft, 88: 1-12, 2016.

[16] D. Li, P. Wang, L. Du, "Path planning technologies for autonomous underwater vehicles-A review," IEEE Access, 7: 9745-9768, 2019.

[17] N. K. Yilmaz, C. Evangelinos, P. F. J. Lermusiaux, N. M. Patrikalakis, "Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming," IEEE J. Oceanic Eng., 33(4): 522-537, 2019.

[18] D. H. dos Santos, L. M. G. Goncalves, "A gain scheduling control strategy and short-term path optimization with genetic algorithm for autonomous navigation of a sailboat robot," Int. J. Adv. Rob. Syst., 6(1), 2020. 

[19] M. Y. Ju, S. E. Wang, J. H. Guo, "Path planning using a hybrid evolutionary algorithm based on tree structure encoding," Sci. World J., 2014: 1-8, 2014.

[20] I. Behravan, S. H. Zahiri, S. M. Razavi, R. Trasarti, "Clustering a big mobility dataset using an automatic swarm intelligence-based clustering method," J. Electr. Comput. Eng. Innovations, 6(2): 251-271, 2018.

[21] Y. Li, J. Zhao, Z. Chen, G. Xiong, S. Liu, "A robot path planning method based on improved genetic algorithm and improved dynamic window approach," Sustainability, 15(5): 4656, 2023.

[22] M. R. Razali, A. A. M. Faudzi, A. U. Shamsudin, S. Mohamaddan, "A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control," Front. Robot., 9, 2023.

[23] C. L. Pen, W. J. Chang, Y. H. Lin, "Fuzzy controller design approach for a ship’s dynamic path based on ais data with the takagi–sugeno fuzzy observer model," J. Mar. Sci. Eng., 11(6): 1181, 2023.

[24] M. Reda, A. Onsy, A. Y. Haikal, A. Ghanbari, "Path planning algorithms in the autonomous driving system: A comprehensive review," Rob. Auton. Syst., 174: 104630, 2024.

[25] D. An, Y. Mu, Y. Wang, et al., "Intelligent path planning technologies of underwater vehicles: A Review," J. Intell. Rob. Syst., 107(22), 2023.

[26] R. Zhou, K. Zhou, L. Wang, et al., "An improved dynamic window path planning algorithm using multi-algorithm fusion," Int. J. Control Autom. Syst., 22: 1005-1020, 2024.

[27] L. Yu, Y. Cai, X. Feng, et al., "Parallel parking path planning and tracking control based on simulated annealing algorithm," Int. J. Autom. Technol., 25: 867-880, 2024.

[28] W. Qiu, D. Zhou, W. Hui, et al., "Terrain-shape-adaptive coverage path planning with traversability analysis," J. Intell. Rob. Syst. 110(41), 2024.

[29] X. Zhai, J. Tian, J. Li, "A real-time path planning algorithm for mobile robots based on safety distance matrix and adaptive weight adjustment strategy," Int. J. Control Autom. Syst., 22: 1385-1399, 2024.

[30] B. Zhang,P. Liu,W. Liu, et al., "Search-based path planning and receding horizon based trajectory generation for quadrotor motion planning," Int. J. Control Autom. Syst., 22: 631-647, 2024.

[31] M. Reda, A. Onsy, A. Y. Haikal, A. Ghanbari, "Path planning algorithms in the autonomous driving system: A comprehensive review," Rob. Auton. Syst., 174(104630), 2024.

[32] J. Yu, Z. Chen, Z. Zhao, et al., "A path planning method for unmanned surface vessels in dynamic environment," Int. J. Control Autom. Syst., 22: 1324–1336, 2024.

[33] L. Yujie, P. Yu, S. Yixin, Z. Huajun, Z. Danhong, S. Yong, "Ship path planning based on improved particle swarm optimization," in Proc. 2018 Chinese Automation Congress (CAC), 2019.

[34] X. Wang, K. Feng, G. Wang, Q. Wang, "Local path optimization method for unmanned ship based on particle swarm acceleration calculation and dynamic optimal control," Appl. Ocean Res., 110(102588), 2021.

[35] S. Blindheim, T. A. Johansen, "Particle swarm optimization for dynamic risk-aware path following for autonomous ships," IFAC-PapersOnLine, 55(31): 70-77, 2022.

[36] H. Feng, M. J. Liu, H. Y. Xu, "Multi-target path planning for unmanned surface vessel based on adaptive hybrid particle swarm optimization," J. Huazhong Univ. Sci. Technol. (Natural Science Edition), 46(6): 59-64, 2018.

[37] W. Ma, Y. Han, H. Tang, D. Ma, H. Zheng, Y. Zhang, "Ship route planning based on intelligent mapping swarm optimization," Comput. Ind. Eng., 176(108920), 2023.

[38] D. Mu, T. Li, X. Han, Y. Fan, F. Wang, “Global path planning for unmanned surface vehicles in complex maritime environments considering environmental interference,” Ocean Eng., 310(2), 2024.     

[39] J. Pak, J. Kim, Y. Park , H. Il Son, "Field evaluation of path planning algorithms for autonomous mobile robot in smart farms," IEEE Access, 10: 60253-60266, 2022.

 

آمار
تعداد مشاهده مقاله: 568
تعداد دریافت فایل اصل مقاله: 361


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب